The #1 Error with AI GTM Agents: Assuming They Can Do What Your Team Hasn’t Already Figured Out

Deploying AI agents to fix broken go-to-market motions consistently fails because these tools are force multipliers, not magicians. They excel at executing proven playbooks at scale, such as sending perfected sequences or qualifying validated leads. However, AI cannot invent an ideal customer profile or discover positioning. Treating an AI SDR like a new hire by cloning the best human representative, conducting daily manual reviews, and maintaining strict quality assurance for the first 90 days drives exceptional response rates.

Key Insights

• AI agents amplify existing processes rather than inventing new go-to-market strategies from scratch.

• Successful AI deployment requires cloning the behaviors, messaging, and objection-handling of the top-performing human representative.

• Continuous daily training and rigorous quality assurance scoring are mandatory during the initial implementation phase.

• AI outbound campaigns utilizing proven playbooks can achieve response rates significantly higher than industry averages.

Why this matters

For enterprise and private equity leaders looking to scale revenue efficiently, investing in AI automation without a solid underlying foundation leads to accelerated lead burn. Establishing a proven, high-converting human baseline is a critical prerequisite before injecting capital into AI-driven go-to-market scaling initiatives.

Read the full article HERE.

Companies Aren’t Ripping Out Business Software for AI. Here’s What They’re Doing Instead.

Large corporations are retaining their core enterprise software systems due to the complex regulatory and operational requirements they manage. Instead of completely replacing these platforms, tech leaders are leveraging AI agent-based tools to build custom applications and “vibe-code” specific customizations on top of existing infrastructure. This approach avoids costly software upgrades while transforming legacy systems into foundational data sources for new, automated AI interfaces.

Key Insights

• Core enterprise systems remain essential for their ability to handle complex compliance and geographic requirements.

• Organizations are developing internal AI agents to automate workflows, significantly reducing external subscription costs.

• Business software is transitioning from primary operational interfaces to foundational data repositories supporting AI agents.

• Smaller companies face fewer regulatory hurdles, allowing them to rapidly adopt entirely AI-coded customer relationship management systems.

Why this matters

Decision-makers must recognize that the immediate value of AI lies in enhancing, rather than replacing, robust enterprise resource planning and customer relationship systems. Strategic investments should focus on building custom agentic workflows that extract greater utility from existing data silos while actively renegotiating vendor contracts.

Read the full article HERE.

Inside ServiceNow’s $10B Go-to-Market Engine with Paul Fipps

Achieving massive enterprise scale requires a unified go-to-market motion that seamlessly integrates sales, customer success, field marketing, and partner ecosystems. This prevents customers from feeling the internal organizational structure and ensures continuity from pre-sale through post-sale implementation. Furthermore, embedding generative AI directly into workflows across thousands of employees has driven substantial annualized productivity gains, allowing the business to handle greater volume while maintaining headcount stability.

Key Insights

• Organizational complacency poses a significantly greater threat to scaling enterprise companies than external market competition.

• Customer health must be monitored through daily adoption metrics and qualitative engagement signals to proactively address churn.

• Deploying an AI control tower allows organizations to securely govern and measure the return on investment of various AI agents across multiple systems.

• Directly interacting with the community and customers uncovers unarticulated needs that drive rapid, monthly product innovation cycles.

Why this matters

Sustaining high growth rates in complex enterprise environments demands absolute alignment across all revenue-generating functions. Leadership teams must prioritize eliminating internal silos to deliver a frictionless customer experience, leveraging targeted AI integrations within established workflows to drive measurable operational efficiency and margin expansion.

Read the full article HERE.

The Private AI Model Explosion

The future of artificial intelligence revenue will be dominated by private models rather than highly publicized public ones. Organizations will use public models for baseline syntax and reasoning capabilities, deploying them as foundations alongside techniques such as retrieval-augmented generation to build secure, proprietary systems. Trust and data privacy dictate that sensitive financial, medical, and operational data will reside exclusively within specialized private models controlled directly by the respective enterprises.

Key Insights

• Private AI models are projected to capture the majority of AI-generated revenue within the next five years.

• Proprietary corporate information will double in value annually as it is converted to power internal private models.

• Public AI models will transition into foundational layers, offering reasoning capabilities while keeping sensitive customer data entirely segregated.

• The primary advantage of private models is the preservation of consumer trust regarding sensitive personal and corporate data.

Why this matters

Capitalizing on the AI revolution requires shifting focus from generic public applications to securing and refining proprietary data assets. Businesses that immediately invest in building private models will unlock substantial competitive advantages by leveraging their unique organizational knowledge to drastically improve customer retention and operational agility.

Read the full article HERE.

Final Thoughts

Thank you for reading this week’s digest. Staying ahead in the current B2B landscape means bridging the gap between emerging technologies and proven fundamental frameworks. I look forward to sharing more insights in the next edition.

Ramzi Musa

The 90/10 Rule for AI Agents, A Deep Dive: When To Replace Paid SaaS Tools With a Vibe-Coded App. And When Not To

A fundamental shift is occurring in how organizations approach their technology stacks, moving away from a total reliance on off-the-shelf SaaS. The “90/10 Rule” suggests that while 90% of business functions should still be supported by established SaaS platforms to maintain operational standards, the remaining 10% represents the unique competitive edge that should be “vibe-coded” or built internally. AI agents now allow non-engineers to create bespoke applications that solve niche problems more effectively than broad tools. By replacing generic subscriptions with owned, hyper-specialized AI agents, teams can achieve higher precision while significantly reducing recurring software costs.

Key Insights

• Organizations are shifting from being 100% SaaS-dependent to building custom AI agents for their unique competitive moats.

• “Vibe-coding” enables non-technical leaders to deploy production-ready tools in hours rather than months.

• The spend ratio is shifting dramatically toward AI agent orchestration rather than core CRM or broad seat-based licenses.

• Building internal agents for high-frequency, low-complexity tasks eliminates the “SaaS tax” of underutilized features in large platforms.

Why this matters

For Enterprise and Private Equity-backed firms, this represents a major lever for improving EBITDA and protecting margins. Replacing expensive, seat-based SaaS with owned AI agents reduces operational drag and ensures that proprietary workflows remain a protected competitive advantage rather than a generic process.

Read the full article HERE.

AI Is Rewriting the Old Rules of Google Search and SEO

The two-decade era of search engine optimization is being fundamentally rewritten as conversational AI platforms intercept the traditional buyer journey. Platforms like ChatGPT and Perplexity are increasingly providing direct answers, causing a projected 25% drop in search engine volume by 2026. Visibility now depends less on technical keyword matching and more on “social proof” and mentions within the training sets of Large Language Models. In this new reality, being cited as a trusted authority on forums and specialized review sites is more critical than ranking on the first page of traditional search results.

Key Insights

• Traditional SEO metrics, such as click-through rates from search results, are declining as AI provides answers directly to users.

• Brand visibility is now driven by citations within AI summaries rather than just indexing on Google.

• Forums like Reddit and specialized community sites have become the new “backlink” equivalent for establishing AI credibility.

• Gartner predicts a significant contraction in traditional search volume, forcing a pivot toward Generative Engine Optimization (GEO).

Why this matters

B2B decision-makers must recalibrate marketing attribution models to account for the “dark funnel” of AI discovery. In high-stakes Enterprise environments, brand authority and expert-led content are the only ways to ensure your solution is the one an autonomous agent recommends during a buyer’s research phase.

Read the full article HERE.

Organic search is fundamentally disrupted. Here’s what to do about it.

Data indicates that 73% of B2B websites experienced significant traffic losses as AI Overviews and answer engines began citing content without sending clicks back to the source. This disruption marks a shift from a “traffic-first” to an “authority-first” model. To survive, GTM teams must adopt a dual mandate: optimizing content to be cited by answer engines while simultaneously building an external brand presence that gives AI models a reason to consistently mention them. Success in 2026 requires well-structured, clear, and authoritative content that prioritizes depth of information over keyword density.

Key Insights

• Informational content is most at risk of traffic loss as AI models summarize it for the user.

• High impressions paired with low traffic suggest that your content is being cited, but your conversion strategy is failing to capture the lead.

• Answer Engine Optimization (AEO) requires structured data, FAQs, and concise definitions to be “machine-readable.”

• The focus must shift from chasing clicks to becoming the definitive source that AI platforms reference when answering buyer questions.

Why this matters

For GTM leaders, traffic is no longer a reliable growth metric. Investors and stakeholders should look for “citation share” and brand mentions within AI-driven conversational journeys to gauge market leadership and future pipeline health in a disrupted search economy.

Read the full article HERE.

Who in the C-Suite Should Own AI?

The dawn of the AI era has triggered a jurisdictional upheaval within the C-suite, as various leaders vie for control over AI initiatives. While CIOs view agentic AI as a technical infrastructure issue, COOs see an agentic workforce as an operational remit, and CFOs focus on the direct P&L impact of autonomous decision-making. This fragmented ownership creates data silos and strategic misalignment. The CEO’s role is shifting from picking a single “AI winner” to spearheading a coordinated system where each executive owns the decisions they are best positioned to make, ensuring that AI implementation is tied directly to the broader organizational map.

Key Insights

• AI ownership is currently fragmented across technical, operational, and financial functions, leading to internal conflict.

• The definition of “worker” is being challenged as AI agents take on tasks that previously fell under human resources management.

• Fragmented AI leadership leads to “shadow AI” and disconnected data permissions, exposing the firm to risk.

• Successful organizations are moving toward a coordinated governance model rather than a single point of failure within a single department.

Why this matters

Strategic clarity in AI ownership is critical for Enterprise scalability and risk management. For Private Equity firms, clear accountability for AI transformation is a prerequisite for driving value creation and ensuring that technology investments deliver measurable, cross-functional returns.

Read the full article HERE.

Final Thoughts

The transition into mid-2026 highlights a clear return to operational discipline and the need for a more technical revenue architecture. Balancing custom AI orchestration with a shift toward authority-based discovery will define the next generation of growth. I look forward to continuing this exploration of the new GTM reality with you.

Ramzi Musa

Proving the Impact of RevOps

Every process change and workflow improvement within a revenue organization must be anchored in absolute, measurable metrics to demonstrate strategic value. Instrumentation acts as the critical foundation for moving away from activity-based reporting toward tangible revenue outcomes. Leaders like Scott Sutton emphasize that RevOps has a unique advantage because its outputs are inherently measurable, provided that the right data hygiene and causal modeling are enforced before changes are implemented. By focusing on outcomes instead of just activity, RevOps earns its seat at the strategic table as a primary driver of efficiency and growth.

Key Insights

• Instrumentation is mandatory to move beyond tracking activity and toward measuring real revenue impact

• Modeling changes before implementation allows for defensible and repeatable strategic decisions

• Data hygiene must be enforced with real consequences to ensure the integrity of revenue instrumentation

• RevOps functions best when it identifies and replicates specific actions that cause revenue growth

Why this matters

For Enterprise and Private Equity stakeholders, proving the direct correlation between operational spend and revenue growth is critical. Proper instrumentation ensures that RevOps is viewed as a strategic growth driver rather than a support cost center, providing the visibility needed to optimize investments across the entire customer journey.

Read the full article HERE.

The 2026+ Sales Team: What It Actually Looks Like

The transition to AI-first sales organizations is resulting in significantly leaner teams, with the ratio of AI agent spend to CRM system spend shifting dramatically. While traditional SDR roles face extinction due to automation, technical specialists and “Super SDRs” who orchestrate agent fleets are becoming the new standard. Success in this environment involves leveraging AI for high-volume qualification while focusing human talent on complex deal navigation and technical advisory. Organizations are moving toward a model where Solutions Engineers and Implementation Specialists provide the depth of expertise that autonomous systems cannot yet replicate.

Key Insights

• AI-native companies are operating with sales teams that are significantly smaller while maintaining high productivity

• Technical roles like Solutions Engineers are increasingly becoming the primary trusted advisors in complex deals

• AI agents now handle the bulk of routine outbound and qualification tasks previously managed by human SDRs

• New hybrid roles, such as GTM Engineer,s are emerging to orchestrate and optimize AI agent fleets

Why this matters

As pipeline costs rise and margins are pressured, Enterprise leaders must adopt more specialized, technical sales motions. Restructuring the organization to prioritize technical advisors over high-volume reps provides a scalable competitive advantage and ensures that human talent is reserved for the highest-value interactions.

Read the full article HERE.

Human vs. AI Coaching: Lessons From the Front Lines of Sales Enablement

Neuroscience research demonstrates a distinct difference in how sales professionals process feedback from human coaches versus artificial intelligence. While human interaction triggers regions of the brain associated with emotional engagement and motivation, AI-delivered feedback often results in significantly higher information retention. This reveals a paradox: humans inspire action, while AI helps the brain remember specific technical details. The most effective sales enablement strategy combines human-driven trust and inspiration with the precision and consistency of AI-led coaching systems to maximize both team morale and execution.

Key Insights

• Human coaching activates brain regions linked to social connection and motivation

• Sellers retain more tactical feedback when it is delivered through AI-driven coaching systems

• Managers must evolve into interpreters and accelerators of AI-driven feedback rather than just sources of it

• Hybrid coaching models that blend data-driven insights with empathy-driven human leadership deliver the best results

Why this matters

In high-stakes Enterprise sales environments, balancing technical efficiency with human emotional intelligence is vital for long-term performance. For GTM leaders, this hybrid approach optimizes team performance by improving retention without sacrificing the motivation and culture that drive long-term success.

Read the full article HERE.

Marketing Predictions for 2026

CMOs are facing a fundamental shift as AI agents evolve into persistent digital concierges that span marketing, sales, and support functions. This evolution will collapse traditional channel strategies and force organizations to flatten structures to accommodate hybrid human-agent roles. Brand discovery is increasingly moving toward ambient devices and social-first search results, requiring a total rethink of how visibility and relevance are maintained. By 2026, a brand's success will depend on its ability to be cited by AI engines and recognized in buyers' conversational discovery journeys.

Key Insights

• AI agents are expected to collapse traditional silos between marketing, sales, and customer support

• Social content is increasingly dominating organic search visibility over traditional website-based SEO

• Over 60% of CMOs are already rethinking organizational structures to integrate hybrid human and agent roles

• Brand discovery is shifting toward ambient devices, moving the focus from transactions to research and discovery

Why this matters

Navigating the shift toward agentic commerce is essential for maintaining brand authority in an AI-saturated market. Decision-makers must prepare for a future where buyers are mediated by autonomous agents, requiring content strategies that prioritize credibility and citation frequency over simple traffic metrics.

Read the full article HERE.

Final Thoughts

The transition into 2026 highlights a clear return to operational discipline as the primary driver of revenue growth. Blending technical AI orchestration with human-centric relationship building is no longer optional; it is the foundation of a resilient and technical revenue architecture.

Ramzi Musa

Improve Your Average Sales Price

Moving pricing ownership away from the sales department is one of the most effective levers for protecting gross margins and increasing average deal values. When sales teams maintain control over pricing, individual incentives often drive a “race to the bottom” characterized by excessive discounting to secure quick wins, which ultimately devalues the product and the brand. Implementing foundational processes, such as bundling products to create more attractive offers and enforcing strict discounting rules centered on the Ideal Customer Profile, ensures that revenue growth is both sustainable and profitable.

Key Insights

• Establish pricing ownership within the finance department or an independent committee to ensure objective valuation.

• Enforce strict discounting rules and approval workflows to prevent product devaluation and erosion of brand equity.

• Prioritize outreach and sales efforts strictly toward the Ideal Customer Profile to maintain pricing integrity.

• Utilize product bundling as a strategic tool to increase deal value without significantly sacrificing margins.

Why this matters

Protecting gross margins is essential for long-term health, particularly as acquisition costs continue to fluctuate. Implementing robust pricing controls prevents brand dilution and ensures that the sales force remains focused on high-intent, high-fit opportunities that drive efficiency and profitability across the entire portfolio.

Read the full article HERE.

15 Ways Sales Has Changed This Year in the Age of AI

The landscape of B2B sales is undergoing a fundamental structural transformation, characterized by significantly smaller, high-output organizations in which AI agents handle the bulk of routine outbound and qualification tasks. Technical roles, such as Sales Engineers and Solutions Architects, are increasingly becoming the primary trusted advisors in complex deals, effectively inverting the traditional ratio of account executives to technical specialists. While AI manages the mechanical layer of high-volume engagement, human sales representatives must double down on strong emotional intelligence and relationship-building to maintain buyer trust in an automated environment.

Key Insights

• AI-native organizations are running significantly smaller sales teams that maintain or exceed the revenue output of traditional models.

• Technical Sales Engineers are evolving into the primary trusted advisors, often managing deals with minimal involvement from traditional account executives.

• Relationship-focused reps are winning over transaction-focused counterparts as AI makes mediocre, high-volume outreach less effective.

• In-person engagement remains a force multiplier, consistently delivering higher conversion rates than digital-only sales motions.

Why this matters

The transition toward an agent-assisted GTM model requires leaders to rethink their human capital strategy. Recalibrating hiring and enablement to prioritize technical problem-solving and high-EQ engagement is now mandatory to maintain a competitive advantage in an AI-saturated market.

Read the full article HERE.

AEO Changes What Content Must Do: Help Buyers Decide

Answer Engine Optimization (AEO) has exposed a significant gap in content strategies that historically focused on explaining products rather than facilitating the actual buyer decision. Decision-driving content must go beyond simple FAQs to explicitly frame comparisons, highlight trade-offs, and provide the hard evidence necessary for internal justification and consensus. This strategic shift requires marketers to move past generic positioning language and utilize real customer experiences and third-party validation to give buyers the confidence needed to finalize a selection within an AI-driven search environment.

Key Insights

• Shifting from “explainer” content to “decision-driving” content is critical for success in an AI-powered search landscape.

• Effective AEO content must explicitly address trade-offs and comparisons to help buyers justify their choices internally.

• Credibility and third-party validation are rewarded by AI systems that prioritize authoritative and structured information.

• Content must set clear boundaries about who a solution is for and who it is not for to build authentic trust.

Why this matters

In a world where buyers complete the majority of their journey through conversational AI discovery, visibility depends on being cited as a credible authority. Content that fails to help buyers actually decide will be ignored by both automated algorithms and human decision-makers.

Read the full article HERE.

Your CRM knows more about your buyers than your personas do

B2B marketing teams frequently overlook the wealth of buyer insights already residing within their internal systems, such as CRM deal notes, customer success logs, and verbatim feedback in Slack channels. Instead of investing heavily in scratch-built messaging frameworks or generic persona templates, organizations should focus on the data discipline required to extract real-world objections and success signals from existing workflows. This approach treats data-driven marketing as asking better questions of available data rather than constantly expanding the technology stack with more sophisticated tools.

Key Insights

• Marketing teams often ignore valuable verbatim customer feedback already stored in CRM notes and customer success logs.

• True data-driven marketing is often a discipline problem rather than a technology problem, requiring better use of existing data.

• Verbatim customer quotes and documented sales objections are more effective for positioning than hypothetical persona profiles.

• Cross-functional data sharing among marketing, sales, and product teams is essential for uncovering the true reasons behind buyer behavior.

Why this matters

Strategic alignment begins with accurate, internal information. Leveraging existing insights reduces operational drag caused by misaligned messaging and ensures that demand-generation efforts are grounded in the customer journey, leading to higher conversion rates and greater predictability.

Read the full article HERE.

Final Thoughts

The transition into 2026 marks a return to operational discipline as the primary driver of revenue growth. Blending technical AI orchestration with human-centric relationship building is no longer optional; it is the foundation of a resilient growth engine.

Ramzi Musa

Setting Incentives That Won’t Backfire On Your Business

Incentive structures are the most powerful drivers of behavior within a sales organization, yet they are frequently misaligned with actual business outcomes. The core principle for adequate compensation is to reward achievement, specifically, revenue generated, rather than activity volume like emails sent or meetings booked. When reps are paid on activities, they naturally optimize for quantity over quality, leading to “zombie” pipelines and wasted marketing resources. The only notable exception is the ramp-up period for new hires, where activity-based rewards can help instill foundational habits. Beyond that, a strict focus on revenue ensures that the sales team’s goals remain perfectly synchronized with the company’s growth objectives.

Key Insights

• Compensate based on achievement (revenue) rather than activity (emails, calls, meetings).

• Activity-based incentives often lead to “gaming the system” and poor lead quality.

• Use activity metrics for coaching and performance management, not for commission triggers.

• The ramp-up period for new reps is the only strategic window for activity-based rewards.

• Misaligned incentives are a primary cause of friction between sales and finance departments.

Why this matters

For GTM leaders, incentive design is a fundamental pillar of operational excellence. For both enterprise and PE-backed firms, moving away from activity-based pay protects margins and ensures the sales force focuses on high-intent opportunities. Strategic alignment starts with the comp plan; if the incentives don’t reward bottom-line growth, even the best sales process will eventually fail to deliver predictable results.

Read the full article HERE.

Why a Performance-First Mindset Will Define the Next Era of ABM

As we move deeper into 2026, the era of “brand-first, measurement-later” marketing has officially ended. Keith Turco argues that the next phase of Account-Based Marketing (ABM) is defined by a performance-first mindset that prioritizes hard outcomes over general engagement. This shift requires revenue teams to move beyond siloed campaigns and coordinate around shared objectives, using real-time intent data and AI to guide decision-making. By focusing on the buying group rather than individual leads, marketers can deliver the precision and personalization required to navigate complex enterprise cycles, ultimately tying marketing spend directly to revenue impact.

Key Insights

• ABM is evolving from a static program into a performance-driven, real-time strategy.

• Success requires complete alignment between sales and marketing on shared revenue objectives.

• Focusing on buying groups rather than individual personas leads to higher conversion in enterprise deals.

• Data and AI should be leveraged to identify in-market signals and automate personalized engagement at scale.

• Measurement must shift from top-of-funnel activity to bottom-of-funnel revenue contribution.

Why this matters

In an environment of increased scrutiny on marketing budgets, “performance-first” is the only viable path for sustainable ABM. For GTM stakeholders, this means investing in the infrastructure to track account-level progression and causal impact. Leaders who master this transition will be able to justify higher investments by demonstrating a clear, data-backed link between targeted account engagement and closed-won revenue.

Read the full article HERE.

In 2026, the Most Differentiated B2B Teams Will Use Less AI, Not More

As AI tools become ubiquitous and accessible to every organization, they are no longer a source of competitive advantage. When every company uses AI to generate content, outbound, and code, market outputs begin to converge into a synthetic consensus. Winfield Dean-Salyards contends that the true differentiator in 2026 will be the ability to think independently and sound authentically human. Differentiation will come from clarity, conviction, and the courage to make deliberate choices that machines cannot replicate. The companies that win will not be the most automated, but those that use AI as a baseline while investing heavily in unique, human-led perspectives that build trust.

Key Insights

• AI ubiquity is leading to a “commoditization of content” where everything sounds the same.

• Human judgment and independent thought are becoming the new primary competitive moats.

• Differentiation in 2026 will come from having a unique brand voice and firm strategic conviction.

• Over-reliance on AI-generated consensus risks making a brand invisible in a crowded market.

• Successful teams will use AI to improve efficiency while preserving human oversight for high-stakes decision-making.

Why this matters

For enterprise brands, the risk of “AI-washing” is real—excessive automation can erode the trust and authority required for high-value sales. Strategic leaders must identify which parts of the GTM motion require a “human moat” to remain distinct. By deliberately choosing where not to use AI, organizations can create a more resonant and trustworthy presence that stands out against a backdrop of synthetic competitors.

Read the full article HERE.

Emotional Intelligence in Sales: The Skill Top Performers Use to Close More Deals

While the modern sales stack is more technical than ever, the ability to build deep, empathetic relationships remains the hallmark of top-performing reps. High Emotional Intelligence (EQ)—comprising self-awareness, self-regulation, empathy, and social skill—allows salespeople to navigate the complex psychological landscape of B2B buying. Reps with high EQ are better at uncovering hidden objections, building genuine rapport with multiple stakeholders, and maintaining the resilience needed for long enterprise cycles. In a world where AI can handle the data and the cadence, EQ is the “human skill” that ensures a deal actually reaches the finish line by building the trust that technology cannot automate.

Key Insights

• Emotional Intelligence is a stronger predictor of sales success than technical knowledge alone.

• Empathy allows reps to understand the “buyer’s journey” from the customer’s perspective.

• High-EQ sellers are more effective at multi-threading and managing internal buying committees.

• Self-regulation helps reps remain disciplined and focused during high-pressure negotiations.

• Trust-building through active listening remains the most effective way to shorten enterprise sales cycles.

Why this matters

As AI takes over more of the administrative and analytical tasks in sales, the value of the “human touch” increases exponentially. For GTM leaders, hiring for EQ and investing in soft-skills training are critical strategic pivots. Strengthening the emotional intelligence of the sales force creates a resilient competitive advantage that is difficult for competitors—or algorithms—to replicate in complex, relationship-driven markets.

Read the full article HERE.

Final Thoughts

The insights from this week point toward a necessary rebalancing of GTM strategy: aligning systems to drive real revenue while doubling down on the human elements that create true differentiation. Whether it is through smarter incentives, performance-led ABM, or high-EQ sales engagement, the goal is to build a growth engine that is both technically precise and authentically human. I look forward to continuing this journey toward GTM excellence with you. Have a productive and successful two weeks.

Ramzi Musa

How to use AI agents for marketing

Advanced AI workflows are moving from experimental side projects to core production systems. A notable case study from SafetyCulture demonstrates how integrating tools such as Retool, ZoomInfo, and Salesforce enables AI agents to handle complex top-of-funnel engagement autonomously. These agents don’t just send templates; they fetch lead data, analyze page views for intent, cross-reference employment history, and select specific customer examples based on the prospect’s industry. This level of automated personalization led to a 3x increase in meeting booking rates and a 2x increase in opportunities created, proving that “agentic” GTM is about orchestration, not just automation.

Key Insights

• AI agents are successfully automating deep account research and multi-step outreach flows.

• Integration across the CRM, data warehouse, and sales engagement platforms is a prerequisite for success.

• High-performing teams use AI BDRs as a “warm-up act” to prepare leads for human AEs to close.

• Multi-lingual support via AI allows companies to scale into new regions without immediate local hiring.

• Prioritizing AI queries for high-fit customers is essential to managing API and compute costs.

Why this matters

For GTM leaders in both enterprise and PE-backed environments, agentic workflows represent the next frontier of operational leverage. The ability to execute hyper-personalized outreach at scale without increasing headcount significantly protects margins. Success requires a shift toward “GTM engineering,” where the focus is on building intelligent systems that feed sales reps high-intent, well-researched opportunities.

Read the full article HERE.

Zombie Pipeline: The Living-Dead Deals You Should Put in the Grave

A “zombie pipeline” consists of deals that are technically open but have no realistic path to closing, often characterized by “ghosting” prospects or a lack of clear next steps. These stagnant opportunities create a false sense of security in forecasting and consume valuable mental bandwidth for both reps and managers. The root causes often include poor qualification, a lack of urgency, or a failure to identify the actual economic buyer. Eliminating these “living-dead” deals is essential for maintaining sales velocity and ensuring that resources are focused on winnable, high-intent business.

Key Insights

• Stalled deals without a defined “next step” are the primary indicators of a zombie pipeline.

• Over-inflated pipelines lead to inaccurate forecasting and poor strategic resource allocation.

• Rigorous “Close Date” hygiene is required to identify deals that are being pushed quarter after quarter.

• A smaller, active pipeline is superior to a bloated one filled with unresponsive prospects.

• Sales leaders must foster a culture where “No” is an acceptable answer early in the cycle.

Why this matters

Predictability is the most critical metric for any GTM organization. Strategic leaders must conduct a ruthless audit of pipeline health to remove the drag from dead deals. By cleaning the CRM and enforcing strict qualification standards, teams can increase their actual win rates and provide stakeholders with a transparent, data-backed view of future revenue.

Read the full article HERE.

The future of GTM starts with causal clarity

Linear GTM models, such as the traditional MQL-to-SQL funnel, are failing to explain why buyer decisions stall or why performance collapses despite high activity. “Causal clarity” is the emerging requirement for modern revenue architecture, moving beyond simple attribution to understand the underlying drivers of growth. This approach leverages a logic layer—often visualized through the “Bow Tie” model—to orchestrate the entire customer lifecycle. By instrumenting the funnel to capture high-quality data, organizations can use AI to identify the specific interventions that predictably drive a prospect from interest to advocacy.

Key Insights

• Traditional linear funnels cannot account for the fragmented and non-linear nature of modern B2B buying.

• Causal GTM logic focuses on understanding the “why” behind deal stagnation rather than just the “what.”

• The “Bow Tie” model serves as a framework for coordinating acquisition, retention, and expansion.

• Instrumentation of all customer touchpoints is necessary to provide AI models with accurate training data.

• Orchestration across functional silos is key to delivering a consistent, predictable buyer experience.

Why this matters

Enterprise GTM complexity requires moving away from gut-driven decisions toward a coordinated, instrumented infrastructure. For stakeholders, causal clarity provides the visibility needed to optimize investments across the entire customer journey. Leaders who master this architectural shift will be able to scale more efficiently by identifying and replicating the specific actions that cause revenue growth.

Read the full article HERE.

AI Wins When It Does The GTM Work … People Won’t

The most immediate ROI for AI in go-to-market comes from executing the high-volume, high-effort tasks that humans naturally avoid or perform with mediocrity. AI agents, such as autonomous SDRs, are proving their value by booking six-figure deals on Saturday nights when human teams are offline. The reality is that low-energy outbound, boring events, and recycled content are failing harder than ever because AI has lowered the bar for average work. The winners are using AI to maintain a 100% standard of excellence in routine tasks, allowing human talent to be redeployed to high-stakes relationship building and creative strategy.

Key Insights

• AI SDRs can outperform humans by being active 24/7 and maintaining consistent output quality.

• The “lowest hanging fruit” for AI ROI is automating tasks humans often skip or rush through.

• AI succeeds by eliminating the “human mediocrity” often found in top-of-funnel activities.

• Organizations using AI in sales are reporting a 50% increase in leads and appointments.

• AI agents should be viewed as a way to run leaner while maintaining or increasing total revenue.

Why this matters

This represents a fundamental structural reset for sales and marketing organizations. Leaders must identify where human performance is inconsistent and replace those gaps with autonomous agents. For both PE and Enterprise firms, this transition is not just about cost-cutting; it is about ensuring that every potential customer interaction meets a premium standard of execution, regardless of the time or day.

Read the full article HERE.

Final Thoughts

The transition toward an agent-assisted GTM model is accelerating, demanding a dual focus on technical orchestration and pipeline hygiene. Whether it is by deploying autonomous marketing agents or ruthlessly cleaning up your “zombie” deals, the goal remains the same: building a predictable, high-velocity revenue engine. I look forward to continuing this exploration of the new GTM reality with you. See you in two weeks.

Ramzi Musa

The best growth tactics of 2025

A comprehensive study of over 130 GTM leaders reveals that the most successful growth tactics of 2025 were rooted in collapsing time-to-value and leveraging existing trust. While AI dominated the headlines, the top-performing categories were intent-based outbound, partner ecosystems, and community-driven events. Successful experiments shared a common thread: they reduced perceived buyer risk early in the journey. The data suggests that growth without a corresponding focus on retention discipline essentially creates an efficient cash-burn machine, emphasizing that the “physics” of growth remains unchanged regardless of the specific tactics deployed.

Key Insights

• Intent-based outbound and ABM accounted for 20% of the most successful growth experiments.

• Partner and ecosystem-led growth emerged as a top-three driver by “borrowing” pre-existing trust.

• High-performing teams prioritized collapsing time-to-value over simple lead volume.

• Retention discipline is now recognized as a prerequisite for any scalable growth experiment.

• Community and events continue to outperform digital-only channels by providing superior context.

Why this matters

For GTM leaders, these findings reinforce that there is no “magic” new channel that replaces fundamental execution. Success in 2026 requires a shift from chasing novelty to doubling down on intent-based signals and ecosystem partnerships. By un-siloing channels and focusing on first-value delivery, organizations can build a sustainable flywheel that compounds over time rather than relying on one-off tactical wins.

Read the full article HERE.

How to Lose the Trust of Your Sales Team

Sales organizational health depends entirely on a foundation of trust between leadership and the front line. Trust is frequently eroded through three primary failures: misaligned incentive structures, poor lead quality, and inconsistent process enforcement. Changing compensation plans mid-quarter or forcing reps to chase low-intent “vanity” leads signals a lack of strategic clarity and respect for the salesperson’s time. When leadership prioritizes short-term reporting metrics over the actual deal-making environment, they inadvertently create a culture of disengagement that directly correlates with stalled pipelines and increased turnover.

Key Insights

• Frequent changes to compensation plans are the leading cause of sales rep attrition and distrust.

• Delivering low-quality marketing leads without clear intent signals damages cross-functional alignment.

• Inconsistent enforcement of sales processes creates a “choose your own adventure” culture that kills predictability.

• Transparency regarding data integrity in the CRM is essential for maintaining rep confidence in forecasting.

• Leadership must prioritize achievement (revenue) over activity metrics (emails/calls) to retain top talent.

Why this matters

In an increasingly technical sales environment, the relationship between leadership and the sales force is your most valuable asset. For both enterprise and PE-backed firms, maintaining this trust is a prerequisite for operational excellence. Leaders must ensure that every strategic move—from comp plans to lead routing—is designed to empower the rep, not just simplify the management dashboard.

Read the full article HERE.

Growth lessons behind Lovable’s $6.6B valuation

The rapid ascent of Lovable to a $6.6B valuation within just 13 months highlights a fundamental shift in software value: moving from pure utility to empathy and emotion. As the cost of building software decreases due to AI, the competitive moat shifts from “what the tool does” to “how the tool feels” and how it integrates into the user’s community. Lovable’s success is built on a flywheel of shipping fast, building in public, and fostering deep community engagement. This approach transforms software from a static tool into a dynamic “place” where users feel heard and involved in the product’s evolution.

Key Insights

• Empathy and emotional connection are becoming primary differentiators as software utility becomes commoditized.

• “Building in public” serves as a natural distribution mechanism that builds trust before the first sale.

• The growth flywheel is powered by shipping fast, which in turn feeds community feedback loops.

• High valuations in the AI era are increasingly tied to community-led growth and authentic brand voice.

• Product-market fit is no longer a destination but a “treadmill” that requires constant iteration.

Why this matters

This paradigm shift challenges GTM leaders to rethink their brand strategy. For enterprise stakeholders, the lesson is that clinical, feature-heavy messaging is losing its effectiveness. To maintain premium valuations and customer loyalty, brands must humanize their digital presence and involve their customers in the creation process, turning users into vocal advocates within their own networks.

Read the full article HERE.

Marketers Were Blind to AI in 2025. In 2026, It’s Back to Basics

The year 2025 was defined by rapid, often unmeasured experimentation with Large Language Models (LLMs), leaving many marketers unable to track the actual ROI of their AI investments. As we enter 2026, the focus is shifting toward “LLM Fundamentals” and rigorous measurement. Early data shows that AI search engines exhibit specific biases, such as a 3x higher likelihood of choosing a product listed in the “top row” of a comparison. Success now depends on understanding how AI interprets brand descriptions and social proof, moving away from “AI magic” toward a disciplined, data-backed approach to generative engine optimization.

Key Insights

• 2025 was a year of “blind” AI adoption with significant gaps in performance measurement.

• ChatGPT and other LLMs show a strong positive bias toward products with robust social proof and reviews.

• Minor changes in word-based descriptions can impact AI citation rates by up to 25% in specific categories.

• Strategic positioning within AI-generated summaries is the new “Page 1 of Google” for 2026.

• Measuring AI-driven discovery is now a mandatory requirement for demand generation teams.

Why this matters

The novelty of AI has worn off, and GTM leaders are now being held accountable for its actual impact on the pipeline. To succeed, organizations must move beyond generic prompts and start treating AI discovery as a core, measurable channel. This means investing in the infrastructure to track citations and optimizing content specifically for the logic layers of major LLMs.

Read the full article HERE.

Final Thoughts

A clear return to operational discipline marks the transition into 2026. While the tools of growth have changed, the requirements for trust, empathy, and measurable fundamentals remain the same. The winners of this year will be those who can blend technical AI optimization with a deeply human-centric GTM approach. Happy New Year, and let’s make 2026 a year of strategic clarity.

Ramzi Musa

Top 10 SaaStr AI Predictions for 2026

The landscape of B2B sales is set for a radical transformation by 2026, with a primary shift toward significantly smaller, high-output organizations. AI-native companies are already demonstrating they can operate with sales teams that are 50% smaller than traditional models while maintaining or even increasing revenue growth. A key driver is the “inversion” of sales roles, in which technical Sales Engineers (SEs) become the primary trusted advisors on a deal. At the same time, AI agents handle the bulk of routine outbound and qualification calls. This evolution suggests that the era of the high-volume, inside-sales “mid-pack” is giving way to a more specialized, technical sales motion.

Key Insights

• AI-native B2B companies are projected to run sales teams that are 50% smaller than in 2025.

• These technical-first organizations are seeing close rates that are 50% higher than those of traditional peers.

• Sales Engineers are increasingly taking the lead on deal relationships, while AEs focus on strategic pricing and closing.

• AI agents can now autonomously generate a significant portion of new pipeline, as seen among early adopters.

Why this matters

For B2B decision-makers, this shift necessitates a fundamental rethink of organizational design. Moving away from headcount-heavy models toward technical, AI-augmented teams is no longer just an efficiency play but a core requirement for remaining competitive. Strategic leaders must prioritize technical literacy in sales roles to capitalize on the higher conversion rates and lower cost of sale offered by this new structure.

Read the full article HERE.

How To Navigate the Pipeline Crisis

The SaaS industry is currently facing a “pipeline crisis” characterized by a severe drop in customer acquisition efficiency and historic highs in CAC payback periods. Median payback periods have risen to 57 months, indicating it takes nearly 5 years to recoup the cost of acquiring a new customer. This malaise is driven by insufficient pipeline coverage and a broader web traffic crisis, where traditional organic channels are being disrupted by AI-driven search behavior. To survive, firms must move beyond standard demand generation and choose between absolute category dominance or a hyper-focus on high-intent, signal-based acquisition.

Key Insights

• Median CAC payback periods have reached 57 months, highlighting a critical decline in GTM efficiency.

• Pipeline coverage ratios are consistently falling below the 4.1x target required for predictable growth.

• The “web traffic crisis” is forcing a move away from vanity metrics toward high-intent buyer signals.

• Success in 2026 will be defined by the “Haves” (category leaders) and “Have-Nots” (those stuck in inefficient legacy funnels).

Why this matters

Navigating this crisis requires a disciplined shift from broad-reach marketing to a high-intent “inverted” funnel. For both Enterprise and PE-backed leaders, the focus must shift toward protecting margins by ruthlessly prioritizing accounts with clear purchase signals. Operational excellence in pipeline management is now the primary lever for sustainable growth in a high-CAC environment.

Read the full article HERE.

Why Old Playbooks Are Breaking and How AI Assisted GTM Actually Works

Traditional B2B playbooks are failing because they rely on manual coordination that cannot keep pace with modern buyer journeys. Dave Boyce highlights the transition from “gut-driven” to “instrumented” and now “AI-assisted” GTM systems. By leveraging a causal logic layer—often represented by the “Bow Tie” model—companies can use AI to orchestrate the entire customer lifecycle more effectively than human-led manual tasks. This approach provides the “causal clarity” needed to understand why deals stall and how to predictably drive revenue expansion.

Key Insights

• Legacy manual playbooks are breaking under the weight of fragmented buyer journeys and uncoordinated data.

• AI-assisted systems allow for a move toward automated revenue architecture and cross-functional orchestration.

• The “Bow Tie” model is evolving into an AI-coordinated system for managing both acquisition and retention.

• Success requires “instrumenting” the funnel to feed high-quality data into AI models for predictive accuracy.

Why this matters

Strategic GTM success now depends on the ability to build an intelligent, coordinated infrastructure rather than just following a static checklist. Implementing an AI-assisted architecture allows leaders to replace manual bottlenecks with scalable, data-driven workflows. This ensures a consistent experience for enterprise buyers while providing the clarity needed to optimize every stage of the revenue process.

Read the full article HERE.

An outbound playbook for 2025

Contrary to predictions of its demise, outbound remains a critical growth channel when executed through an intent-based, “un-siloed” framework. The 2025 outbound playbook emphasizes building a GTM flywheel in which hand-raisers from content, ads, and community engagement trigger outreach. While AI handles the scale of top-of-funnel identification, high-value Tier 1 accounts still require “unscalable” human tactics—such as manual gifting and highly personalized ABM—to break through the noise.

Key Insights

• Intent-based outbound is the second-highest investment priority for GTM leaders heading into the new year.

• Effective outbound requires “un-siloing” marketing channels to create a unified signal-to-outreach flywheel.

• AI should be used to handle high-volume research and task automation, not to replace the human element in Tier 1 deals.

• Combining AI workflows with human expertise is the hallmark of the most successful 2025 sales teams.

Why this matters

For GTM organizations, the challenge is balancing automated efficiency with authentic connection. Leaders should deploy AI to handle the “heavy lifting” of data processing and routine engagement while reserving human talent for high-stakes relationship-building. This hybrid approach ensures that outbound remains a high-ROI motion even as buyers become more resistant to generic outreach.

Read the full article HERE.

Final Thoughts

The end of 2025 marks a turning point, with AI-driven structural changes moving from theory to core operational reality. Whether it is through leaner sales teams, intent-based funnels, or agentic orchestration, the focus for 2026 must be on building a resilient and technical revenue architecture. I look forward to exploring these shifts further with you in the new year. Have a successful and peaceful close to the year.

Ramzi Musa

Anthropic Economic Index Report: Uneven Geographic and Enterprise AI Adoption

Anthropic’s latest Economic Index report analyzes how AI is being adopted across industries and regions, revealing a growing divide between early enterprise adopters and lagging sectors. While AI spending continues to rise globally, adoption remains highly concentrated in North America and among large tech-forward organizations. Smaller companies and emerging markets demonstrate significant interest, but they face infrastructure and talent constraints. The report finds that AI’s economic impact is already measurable in terms of productivity and revenue growth; however, the benefits are unevenly distributed, a gap that could widen without targeted investment in access and skills.

Key Insights

• AI investment grew 45% year-on-year in 2025, but 80% of spending comes from the top 15 economies.

• U.S. enterprises account for over half of measured AI productivity gains, led by tech, finance, and professional services.

• Manufacturing, education, and the public sector show the widest implementation gap despite similar interest levels.

• Enterprise AI leaders report 6.3× higher productivity growth than non-adopters, driven by workflow automation and decision support.

• Talent availability and data infrastructure remain the two most significant barriers to scaling AI beyond pilot projects.

Why this matters

The report illustrates that AI’s economic benefits are not self-equalizing. Without addressing the structural barriers to adoption, particularly in terms of data access, education, and infrastructure, organizations outside major tech centers risk falling behind. For GTM leaders, this highlights why AI enablement and talent development must be integral to the commercial strategy, not just the product roadmap.

Read the full article HERE.

Marketing Attribution is a Lie

Elena Verna argues that marketing attribution has become a false idol, a data comfort blanket that kills creativity and misleads strategy. Most teams optimize to dashboards instead of understanding human decision-making. Last-click models reward performance channels like Google while starving brand and demand-generation investments. Even more sophisticated multi-touch models often rely on assumptions rather than truth. Verna calls for a return to taste, intuition, and strategic judgment, supported by data but not enslaved to it.

Key Insights

• Attribution models explain transactions but not why buyers chose you in the first place.

• Last-click bias redirects budget to Google and other capture channels at the expense of awareness and trust.

• Self-reported “How Did You Hear About Us” data offers richer context than analytics dashboards.

• Media Mix Modeling (MMM) is a better option for large spenders seeking directional truth over false precision.

• Creativity and emotional resonance remain the unquantifiable drivers of effective marketing.

Why this matters

Marketing leaders must recognize that data can guide but cannot replace judgment. When teams confuse measurement with meaning, they optimize the visible funnel while neglecting brand equity and demand creation. Rebalancing quantitative and qualitative inputs is crucial for building sustainable growth engines that outlast algorithmic shifts.

Read the full article HERE.

Traffic Is No Longer a Reliable Growth Metric

Kyle Poyar’s analysis of Webflow’s AI search strategy reveals how traditional traffic metrics have become misleading indicators of growth. As AI platforms like ChatGPT and Perplexity reshape discovery, aggregate traffic declines while conversion rates soar from high-intent AI referrals. Webflow reports that ChatGPT-referred visits convert six times better than traffic from Google. The company now tracks visibility, comprehension, and conversion across AI channels, a new framework for growth teams operating in the answer-engine era.

Key Insights

• 10% of Webflow sign-ups now come from AI-driven discovery, up four times year-over-year.

• ChatGPT accounts for 91% of LLM referrals, with a 24% conversion rate, 6 times higher than Google.

• AI search is volatile and rankings are reshuffled with each model update, requiring continuous optimization.

• Webflow monitors “visibility, comprehension, and conversion” as core metrics for AI performance.

• Refreshing content 5 times faster with AI-driven automation yielded a 40% traffic increase on key pages.

Why this matters

In an AI-first search environment, traffic volume is a vanity metric. What matters is being understood and recommended by AI systems. B2B marketers must optimize for visibility signals and citation frequency within LLMs, treating AI discovery as a core channel on par with SEO and paid media.

Read the full article HERE.

Why Behavioral Segmentation Beats Personas for Real Personalization

Katie Templin contends that demographics and static personas oversimplify human behavior and fail to drive true personalization. Behavioral segmentation, grouping audiences by values, motivations, and contexts, reveals intent and builds trust. Using quantitative and qualitative data, brands can identify behavioral archetypes and map them to key scenarios where needs and expectations shift. This approach transforms personalization from a marketing tactic into a trust-building mechanism that humanizes digital experiences.

Key Insights

• Personas flatten human experience while behavior reveals intent and context.

• Behavioral segmentation combines values, motivations, and actions into actionable archetypes.

• Data science enables pattern recognition through factor and cluster analysis for segment creation.

• Mapping archetypes to key moments guides content, channel choice, and trust interventions.

• True personalization is shifting toward “humanization,” anticipating needs rather than targeting profiles.

Why this matters

Behavior-based segmentation enables marketers to build genuine connections and trust at scale. By understanding why people act, not just who they are, organizations can create personalization that feels empathetic and authentic, thereby gaining a competitive advantage in an AI-mediated customer journey.

Read the full article HERE.

Final Thoughts

This edition highlights how AI is redefining core marketing assumptions from attribution and metrics to personalization and economic impact. The through-line is clear: GTM leaders must measure what matters, humanize their approach, and build strategies that blend data-driven discipline with creative judgment. Those who adapt their models to how AI changes discovery, decision-making, and trust will shape the next generation of growth.

Ramzi Musa

The AI Visibility Index: Here’s Who’s Winning AI Search

Search is evolving faster than anyone predicted as AI platforms like ChatGPT and Google’s AI Mode redefine how information is discovered. Some brands are watching their organic traffic and revenue slip away, while others are optimizing for this new search channel and seeing significant gains. Semrush Enterprise built the AI Visibility Index as the definitive study into how brands perform across the world’s leading AI search engines, analyzing 2,500 real-world prompts in both ChatGPT and AI Mode across five key verticals: Business & Professional Services, Digital Technology & Software, Consumer Electronics, Fashion & Apparel, and Finance. The findings uncover a new hierarchy of visibility where mentions in AI responses, not traditional rankings, now shape awareness and purchase intent.

Key Insights

• Top Google rankings no longer guarantee placement in ChatGPT or Google AI Mode results, as AI models reward structured data, transparent pricing, and strong third-party validation.

• Being cited is not enough, as brands mentioned in AI-generated answers see exponentially higher visibility than those simply used as data sources.

• The brand effect is real, with iconic names like Patagonia and Garmin winning by owning their niche through consistent, structured visibility signals.

• User-generated content has become the new authority, as forums, reviews, and authentic user conversations drive brand visibility in AI search.

• The study offers one of the first truly evidence-based frameworks for how brands can be found, trusted, and recommended by AI across every major platform.

Why this matters

AI visibility is not accidental but engineered, and this study provides marketing leaders with a data-backed blueprint for growth by decoding how AI models decide which brands to show and what companies can do to grow their visibility in AI-powered search.

Read the full article HERE.

Sales Teams, Don’t Undervalue Face Time with Customers

Digital and virtual channels make B2B sales more efficient, but they can also make relationships brittle. While conventional wisdom says channels should line up with buying stages, with digital for awareness, virtual for efficiency, and in-person for complex negotiations, this staged model risks missing the very moments when trust is forged, insights surface, and opportunities emerge. A pharmaceutical sales rep in 2021 discovered that competitors had returned to face-to-face meetings months earlier, gaining mindshare, context, and influence that could not be replicated over Zoom. Over-relying on digital and virtual communication can produce brittle relationships where messages get read and reports get downloaded, but the emotional depth and informal give-and-take that make relationships resilient remain missing.

Key Insights

• In high-stakes B2B contexts, loyalty is fragile and hinges on trust built over multiple conversations, credibility earned through problem-solving, and a sense that the seller truly understands the customer’s business.

• In-person visits deliver outsized impact during early-stage discovery of high-value, high-ambiguity opportunities, as casual remarks and unscripted exchanges surface hidden needs.

• Post-sale expansion benefits from in-person visits that signal commitment and open doors to adjacent needs, new stakeholders, and richer strategic discussions.

• Companies like Fastenal, Microsoft, Google Cloud, Rockwell Automation, and Siemens have operationalized in-person presence through onsite programs and executive briefing centers.

• Sales leaders should re-examine channel allocation, invest in boosting discovery skills, and stay visibly present to avoid being out-presenced by competitors.

Why this matters

While digital channels are indispensable, strategic customers remember who shows up, listens, and helps them navigate ambiguity. By treating in-person time as a strategic investment rather than an afterthought, leaders give their teams the chance to strengthen trust, uncover opportunities, and turn good relationships into great ones.

Read the full article HERE.

How People Use ChatGPT

OpenAI’s economic research reveals fascinating patterns about how different user segments engage with ChatGPT across contexts, providing critical insights for B2B leaders thinking about AI adoption and customer behavior. The research analyzes actual usage data to understand who uses ChatGPT, for what purposes, and how usage patterns differ across demographics and professional contexts. The findings demonstrate that ChatGPT has become deeply integrated into professional workflows, with significant implications for how companies should think about AI-powered customer interactions, content strategy, and go-to-market approaches. Understanding these usage patterns helps GTM leaders anticipate how buyers are already using AI tools to research solutions, evaluate vendors, and make purchasing decisions, fundamentally reshaping the traditional B2B buying journey.

Key Insights

• Professional users show distinct usage patterns compared to casual users, with higher frequency of use during business hours and concentration on work-related tasks.

• ChatGPT usage spans a wide range of professional applications including research, content creation, problem-solving, and decision support across industries.

• Usage intensity varies significantly by industry sector, with technology, professional services, and knowledge work showing the highest adoption rates.

• The data reveals how AI tools are being integrated into existing workflows rather than replacing them, serving as augmentation tools for professional productivity.

• Understanding these usage patterns provides GTM teams with critical intelligence about how potential buyers are already using AI in their discovery and evaluation processes.

Why this matters

As ChatGPT and similar AI tools become embedded in how professionals work and make decisions, B2B companies must adapt their go-to-market strategies to meet buyers where they are. This means optimizing for AI discovery, understanding how AI tools influence the buying journey, and recognizing that traditional sales and marketing touchpoints are being supplemented or replaced by AI-mediated interactions.

Read the full research article HERE.

How to Turn ChatGPT into Your Best Pipeline Source

There is one GTM channel that nearly everyone seems to be investing more heavily in: AI discovery, also known as answer engine optimization or AI SEO. ChatGPT usage is reportedly up to 800 million monthly users, a material increase from 500 million at the end of March. Despite the exploding interest, there are very few tactical playbooks or case studies featuring real B2B companies seeing real results. Docebo, a publicly traded learning management system company, now has AI discovery accounting for 12.7% of their high-intent leads over the past year, achieved with just one team member dedicated to both SEO and AEO efforts. The team shared four practical takeaways: measure AI search performance through self-reported attribution, branded search traffic, and share-of-voice; recognize that late-funnel SEO is the foundation for great AEO; complete the dirty work of updating, formatting, and structuring content for LLMs; and leverage a modern tech stack to see tangible results.

Key Insights

• Brand search is becoming the only high-intent signal that matters, as LLMs make the dark funnel even darker, with prospects completing vendor shortlists without ever visiting a website.

• Docebo sees only 15% of search clicks belonging to non-branded topics, down dramatically from 35% last year, with 85% of website visitors from Google already knowing about Docebo and actively seeking it out.

• Late-funnel SEO focusing on specific money keywords representing enterprise late-funnel consideration provides the foundation for AEO success.

• Effective AEO content requires the right topic, right format with comparative content, hygiene with recent dates and author bios, optimized first 100 words, persona-pain-category structure, tables and lists, FAQs, competitor neutrality, internal linking, and customer stories.

• Docebo produces 20 high-value, on-brand SEO and AEO ready pieces per month with one dedicated person using AI workflow tools like AirOps, Xfunnel, Ahrefs, SEOTesting, and Screaming Frog.

Why this matters

Docebo’s results show that AEO is not theory but real pipeline, with AI discovery driving nearly 13% of high-intent leads, branded search accounting for 85% of clicks, and the highest share-of-voice in their category with just one dedicated team member. Their formula is not glamorous, but it works.

Read the full article HERE.

Final Thoughts

This edition highlights how AI is reshaping fundamental aspects of B2B go-to-market, from brand visibility and discovery to customer relationships and sales effectiveness. The common thread is clear: organizations that combine analytical rigor with strategic human presence will emerge stronger. Success requires treating AI-driven changes as opportunities to rethink established models rather than threats to defend against.

Ramzi Musa

How Successful Sales Teams Are Embracing Agentic AI

Agentic AI is revolutionizing B2B sales by enabling autonomous personal agents that work alongside human sales reps to identify, nurture, and close deals across channels. Unlike traditional AI tools that simply advise salespeople on next best actions, agentic AI performs tasks autonomously, from lead generation and personalized outreach to sales planning and customer retention. One B2B tech company deployed an AI-powered business development representative that autonomously managed top-of-funnel engagement, generating personalized emails based on purchase history and usage trends, which led to a 6% lift in response rates and is projected to generate $50 million in incremental annual revenue. The technology moves sales teams from “tell me what to do” to “do it for me,” fundamentally transforming workflows across the entire sales process.

Key Insights

• AI agents can autonomously handle lead scoring, personalized outreach, and qualification conversations before handing qualified leads to human sellers with full context logged in CRM.

• In post-sales operations, agentic AI-powered customer experience agents now resolve 85% of queries, including complex second-order questions, resulting in a 65% reduction in handling times.

• A North American wealth management firm using agentic AI to generate customer summaries reduced meeting prep time by over 30% and drove a 6% revenue uplift by synthesizing CRM data with external sources.

• Organizations must adopt new metrics, including conversation quality scores, drop-off rates, and agent-specific performance indicators, alongside traditional sales metrics.

• Sales roles are transforming from execution to orchestration, with account managers pivoting to relationship cultivation and business development managers focusing on agent oversight and process optimization.

Why this matters

Agentic AI represents the most significant productivity leap in sales since the dawn of CRM. Organizations that strategically position their sales functions to incorporate agentic capabilities will outpace competitors still mired in manual workflows. The real opportunity lies not in replacing people but in reshaping how humans and machines collaborate to elevate roles, increase performance, and unlock seller capacity at scale.

Read the full article HERE.

5 Interesting Learnings from Palantir at $4 Billion in ARR

Palantir’s growth to $4 billion in ARR demonstrates how AI can transform customer acquisition and operational efficiency at scale. The company’s Artificial Intelligence Platform has become their primary customer acquisition engine, with 157 deals over $1 million, 66 deals over $5 million, and 42 deals over $10 million closed in a single quarter. Their AI delivers order-of-magnitude improvements for customers, such as reducing production planning from 40 hours to 30 minutes and cutting candidate clearance time from 60 days to 12 days. Palantir has achieved a remarkable Rule of 40 score of 94%, combining 48% revenue growth with 46% adjusted operating margin, while maintaining 128% net dollar retention. The company ended the quarter with $6 billion in cash, zero debt, and 57% free cash flow margins, demonstrating exceptional capital efficiency alongside rapid growth.

Key Insights

• Palantir’s dual-engine model shows 53% YoY growth in US Government revenue and 93% YoY growth in US Commercial revenue, creating unusual resilience through cross-pollination between sectors.

• The company institutionalized delivering value in under an hour, with customers like Nebraska Medicine achieving 1,100% efficiency gains and 2,000% increases in specific operational metrics.

• Palantir’s Rule of 40 progression from 46% to 94% in just two years demonstrates how elite companies can simultaneously accelerate growth while expanding margins.

• US commercial customer count grew 64% YoY to 485 customers, with remaining deal value growing 145% YoY to $2.8 billion, showing strong land-and-expand dynamics.

• The company’s adjusted operating income jumped from $254 million to $464 million, an 83% YoY increase, while maintaining 48% revenue growth.

Why this matters

Palantir showcases how deeply embedding AI into commercial and operational use cases drives both scale and profitability when the technology delivers genuine 10x improvements in customer workflows. Their model provides valuable lessons for GTM teams on transitioning from selling software features to selling measurable business outcomes, creating a competitive advantage that compounds over time through customer expansion and operational leverage.

Read the full article HERE.

What ChatGPT Says About Your Brand and Why It Matters

ChatGPT has fundamentally transformed how buyers discover and evaluate brands, with 77% of users leveraging it as a search engine and 30% trusting it over traditional search engines including Google. The platform now engages more than 190 million people daily, processing over 2 billion queries, making it a critical channel for brand visibility and narrative control. Brands that haven’t optimized their digital footprint for AI discovery risk being defined by competitors, negative media coverage, outdated sources, or user reviews rather than directing their own narrative. Since ChatGPT pulls from readily available web content, brands with weak digital presence are excluded from responses entirely, while executive reputation closely ties to brand perception as leaders’ names often appear alongside brand mentions. Organizations can control their narrative by optimizing brand-owned assets across websites, social media, thought leadership, and executive profiles, while investing in third-party sources valued by ChatGPT to boost credibility.

Key Insights

• ChatGPT’s role has evolved from productivity tool to trusted assistant, with users increasingly relying on it for research, product comparisons, and brand evaluation throughout the buying journey.

• Content must be accurate, authoritative, consistent across platforms, and structured for AI discoverability to maintain preferred visibility in ChatGPT responses.

• Without strong narrative ownership through optimized digital assets, brands quickly become susceptible to misinformation, misrepresentations, and crisis situations they cannot control.

• Proactive and AI-inclusive reputation management helps brands identify and counter misinformation about both the company and its executive team in real time.

• As ChatGPT adoption continues to skyrocket with more complex capabilities and agentic AI on the horizon, brands need a foothold in AI platforms to influence visibility, sentiment, and reputation.

Why this matters

The shift in how audiences find information and research brands forces marketers to evolve, adopting new tactics to connect with consumers within AI itself. Brands will always need presence in traditional search and channels, but without control and influence in AI platforms, they’ll find it increasingly difficult to build and maintain trust and authority online. In this new era, brands that proactively defend, monitor, and optimize visibility will endure, while those that cede the narrative to AI platforms risk irrelevance.

Read the full article HERE.

What’s Working in GTM Right Now

Based on data from 195 GTM leaders, the 2025 State of B2B GTM report reveals that while companies average five core GTM channels plus 5.5 experimental channels, there are six dominant GTM motions with inbound leading at 23%, followed closely by outbound at 19% and account-based at 18%. The most common GTM channels are LinkedIn at 66%, SEO at 53%, and warm outbound at 48%, while the hottest experiments include intent-based outbound at 49%, AI discovery at 47%, and large conferences at 43%. Notably, 51% of respondents plan to increase investment in AI search compared to only 14% for traditional SEO, and 45% plan to increase intent-based outbound efforts. The inaugural GTM scorecard reveals intimate events, SEO, and warm outbound in the upper-right “tried-and-true” quadrant, while ChatGPT emerged as the number one most impactful GTM tool with roughly 50 write-ins, far ahead of any alternative.

Key Insights

• There was no correlation between a company’s primary GTM motion and growth rate, indicating each motion and channel can work when executed well, with even older channels showing life.

• AI fluency has become the most in-demand GTM skill, with three-in-four leaders reporting top-down pressure to adopt AI, though 53% see either no or limited impact, particularly around AI SDRs.

• Those seeing positive AI results cite three main areas: outbound including intent-driven targeting, market intelligence for competitor and account insights, and content marketing for SEO and documentation.

• Early-stage companies rely heavily on founder-led channels like intimate events, warm outbound, and founder brand, while mature companies shift to conferences, SEO, and paid ads.

• The new GTM tech stack centers on ChatGPT, with Clay, Lovable, and n8n emerging as potential next winners, pointing to the rise of technical GTM hires who build agentic workflows and custom campaigns.

Why this matters

After a year of endless GTM exploration, 2026 needs to be the year of ruthless scaling where leaders use data-driven frameworks to prioritize which proven channels deserve investment. The GTM scorecard provides a systematic approach to identify which motions deliver impact versus those that are oversaturated, helping teams move from experimentation to execution. Understanding what’s actually working across the market enables smarter resource allocation and faster growth.

Read the full article HERE.

Final Thoughts

This edition highlights how AI is fundamentally reshaping B2B go-to-market across multiple dimensions: autonomous agents transforming sales operations, visibility strategies evolving for AI-driven discovery, and GTM leaders identifying which channels and tactics actually drive results. The common thread is that organizations combining analytical rigor with willingness to adapt their models will create sustainable competitive advantages. As we move into 2026, the winners will be those who shift from endless experimentation to disciplined execution.

Ramzi Musa​​​​​​​​​​​​​​​​

AI Is Changing the Structure of Consulting Firms

The consulting industry is undergoing a fundamental structural shift as AI automates tasks traditionally handled by junior consultants. Rather than eliminating the profession, AI is reshaping it from a pyramid model with a broad base of junior talent supporting a narrow apex of senior leaders into a new “obelisk” structure. This leaner model features fewer layers, smaller teams, and greater leverage at every level, built around three core human roles: AI facilitators who design workflows, engagement architects who lead projects and interpret outputs, and client leaders who cultivate trusted executive relationships. While large firms invest in AI tools like McKinsey’s Lilli and BCG’s Deckster, a wave of AI-native boutiques, such as Unity Advisory, Monevate, and SIB, are demonstrating how to operate without the traditional analyst layer. The winners will be firms that treat AI as a reason to re-architect their delivery model from first principles rather than simply bolting new tools onto old structures.

Key Insights

• McKinsey’s AI assistant, Lilli, is used by over 72% of its workforce, reducing research and synthesis time by approximately 30%. Meanwhile, BCG and Bain deploy similar tools that automate work once handled by large junior teams.

• The consulting obelisk model features three human roles: AI facilitators, trained in the latest tools; engagement architects, who lead projects and translate insights; and client leaders, who focus on trusted executive relationships.

• AI-native boutiques, such as Unity Advisory, Monevate, and SIB, avoid the traditional pyramid structure entirely, delivering focused value with fewer people and lower overhead.

• Traditional firms face resistance to change due to their culture, economics, and talent pipelines, which are often built around the pyramid model, with systems still favoring hours billed over insights delivered.

• The shift demands new approaches to AI governance and ethics, with accountability embedded directly into team workflows rather than relying solely on centralized compliance.

Why this matters

AI isn’t making consulting obsolete; it’s fundamentally restructuring how firms deliver value. Organizations that continue relying on junior-intensive models risk becoming slower, more expensive, and less relevant. In contrast, those that evolve toward the obelisk model can emerge leaner, more expert-driven, and far more valuable to clients. The ability to balance efficiency gains with proper governance and talent development will separate winners from losers.

Read the full article HERE.

The Forecast Isn’t A Number!

Sales forecasting is fundamentally misunderstood in most organizations. The forecast isn’t a number—it’s about individual deals and whether customers will make decisions and sign purchase orders within a specific timeframe. Too often, teams base forecasts on stage-based probabilities in their CRM systems, leading to flawed logic where multiple competitors commit the same revenue to their respective forecasts, despite only one ultimately winning. This approach fails because whatever we think or wish is absolutely meaningless for a forecast. What matters is customer commitment: Will they decide to buy this quarter? The most accurate forecasting emerges from assessing three critical questions: Will they buy (driven by the urgency of change), will they select us (based on mutual commitments and past experience), and can they complete all necessary work within the timeframe? By shifting focus from stage gates to customer-driven commitments, forecasting transforms from a reporting exercise into a strategic tool that helps focus opportunity strategies, management, and execution.

Key Insights

• Standard CRM stage-based probabilities yield nonsensical forecasts, where multiple competitors commit the same revenue to their managers, but only one will ultimately win.

• Customer commitment to making a decision is the foundation of accurate forecasting, driven by understanding the urgency of change, the cost/risk of doing nothing, and whether this is their most important priority.

• Assessing whether customers will select you requires examining mutual commitments, experience with similar customers, and the type of sale, rather than relying on arbitrary stage probabilities.

• The final critical factor is whether customers can complete all required work by their deadline, taking into account sponsorship, organizational alignment, and linked dependencies.

• Forecasting based on customer commitment transforms it from reporting to a strategic tool for managing opportunity strategies and execution.

Why this matters

Most sales teams waste energy managing to arbitrary numbers rather than managing deals. By reframing forecasts around customer commitments and execution capacity, organizations can focus on what actually drives wins: helping customers navigate their buying process, managing where deals get stuck, and aligning on mutual success. This approach profoundly increases both forecast accuracy and win rates.

Read the full article HERE.

From 100M+ Free Users to $1M Enterprise Deals: The Calendly Playbook for Hybrid PLG Success

Calendly has evolved from a simple scheduling tool into one of the most successful Product-Led Growth companies of the past decade, touching over 100 million users globally. At SaaStr Annual, CEO Tope Awotona shared the hard-won lessons from building a company that now maintains a 90/10 revenue split between self-serve and sales-led motions, with customers ranging from individual contributors to million-dollar enterprise accounts. The journey required painful experimentation with balancing PLG and enterprise motions, including the mistake of over-investing in enterprise sales during the growth-at-all-costs era, which led to sales teams cannibalizing more efficient PLG revenue. Calendly spends almost zero dollars on marketing campaigns; its growth is entirely driven by user activity, making free users active marketing assets with measurable lifetime value. The company’s success stemmed from protecting the free plan at all costs, being ruthlessly analytical with holdout groups, focusing on external-facing roles as a wedge into the enterprise, and dynamically allocating product resources based on maturity and ROI analysis, rather than just revenue percentages. The companies that crack the hybrid PLG-Enterprise code are those willing to dig deep into the data and make hard decisions based on what they find, not what they hope to see.

Key Insights

• Calendly spends almost zero on marketing campaigns, with free users serving as marketing assets with measurable LTV, and the company has never removed features from the free plan since 2014.

• The company made two classic mistakes: first, under-investing in the enterprise and leaving seven-figure deals on the table, and then over-investing, resulting in sales cannibalizing PLG revenue at a higher cost.

• Their ideal customer profile focuses on external-facing roles (sales, customer success, recruiting), representing 25% of headcount, who become the wedge for enterprise expansion to seven-figure deals.

• Viral loop conversion rates naturally decline at scale, but growing absolute numbers compensate for percentage-based metric compression as you approach market saturation.

• Product resource allocation evolved from matching revenue split to being based on product maturity and diminishing returns analysis, with the pendulum swinging based on where investment creates the most value.

Why this matters

Hybrid PLG-Enterprise models are incredibly hard to execute well, and superficial metrics can mask fundamental issues with go-to-market efficiency. Calendly’s transparency about mistakes and the analytical rigor required to fix them provides a roadmap for companies building similar motions. Protecting the free plan while adding value to paid tiers, being ruthless with holdout testing, and allocating resources based on ROI rather than revenue percentages are critical success factors.

Read the full article HERE.

AI Search Is Collapsing the B2B Buyer Journey

AI-powered search tools, such as ChatGPT, Perplexity, and Google’s AI Overviews, are fundamentally transforming how B2B buyers discover and evaluate solutions. The traditional three-stage buyer journey—awareness, consideration, decision—built around keyword-based search is being replaced by conversational, context-driven discovery that delivers direct, synthesized answers without requiring users to click through multiple links. Overall, organic search is projected to drop by more than 50% by 2028, with AI-driven search already accounting for 2% to 6% of total B2B organic traffic. At the awareness stage, AI search interprets context and intent, rather than just keywords, surfacing nuanced and detailed answers to specific questions. The consideration stage sees the most disruption, with AI tools now delivering detailed comparisons, aggregated reviews, and key insights directly in results, reducing the need to visit multiple sites. At the decision stage, AI surfaces case studies, testimonials, and pricing details in conversational formats, making trust signals and transparent information more critical than ever. Success in this new landscape requires becoming a trusted, citable source through contextual authority—optimizing for conversational queries, building E-E-A-T (experience, expertise, authoritativeness, trustworthiness), using structured data effectively, and investing in AI-driven tools to understand the changing landscape.

Key Insights

• Overall, organic search is projected to decline by more than 50% by 2028; however, AI-driven search already accounts for 2% to 6% of total B2B organic traffic, fundamentally changing how buyers discover solutions.

• AI search interprets context and intent at the awareness stage, answering specific, nuanced questions rather than broad keywords, requiring content that tackles detailed use cases rather than generic topics.

• The consideration stage experiences most disruption as AI delivers detailed comparisons, reviews, and insights directly in results, making structured, AI-parseable content critical to avoid being excluded.

• Decision-stage content must be explicit, authoritative, and transparent about pricing and results, as AI surfaces validation through case studies and testimonials in conversational formats.

• Google’s E-E-A-T framework (experience, expertise, authoritativeness, trustworthiness) has become more critical as AI search engines prioritize signals of credibility to determine which sources to cite.

Why this matters

The shift from keyword-based search to conversational AI discovery represents a fundamental change in how B2B buyers find and evaluate vendors. Visibility now depends on being cited by AI engines rather than just indexed by search engines. Companies must adapt content strategies to prioritize credibility, depth, and structured information over traffic volume, focusing on becoming the trusted source that AI platforms reference when answering buyer questions.

Read the full article HERE.

Final Thoughts

This edition highlights how AI is reshaping fundamental aspects of B2B go-to-market: from the internal structure of professional services firms to how we forecast deals, scale hybrid business models, and reach buyers through search. The common thread is that organizations that adapt to these changes will emerge stronger, treating them as opportunities to rethink established models rather than threats to defend against. The winners will be those who combine analytical rigor with the courage to evolve.

Ramzi Musa

Why Some Sales Teams Are Actually Growing Alongside AI

While AI is often perceived as a threat to jobs in sales, some organizations are experiencing growth by combining AI with human-led strategies. Harvard Business Review highlights that companies integrating AI effectively are not replacing reps but augmenting them. These teams deploy AI to automate low-value tasks, freeing up time for relationship-building and generating data-driven insights that improve targeting and win rates. The result is not a leaner team but a more capable and productive one, with sales headcount in some cases even increasing

Key Insights

• AI adoption is accelerating sales productivity rather than eliminating roles.

• Teams that integrate AI with human oversight report higher revenue growth.

• Effective use cases include lead scoring, opportunity prioritization, and forecasting.

• Sales managers are redefining their skill sets to focus on data literacy and consultative selling.

• AI’s success depends on cultural adoption, not just the deployment of technology.

Why this matters

AI’s role in sales is shifting from replacement to reinforcement. Sales organizations that treat AI as a co-pilot rather than a substitute are creating stronger, more resilient teams. This perspective reframes the AI debate from cost-cutting to value creation, signaling a long-term transformation in sales strategy.

Read the full article HERE.

Enterprise Sales vs. SMB Sales: Decoding the B2B Sales

The differences between enterprise and SMB sales go far beyond deal size. UnboundB2B outlines how each requires distinct buyer engagement, sales cycles, and success strategies. Enterprise sales are long, consultative, and multi-stakeholder, often stretching 6–18 months. SMB sales emphasize agility, speed, and affordability, with deals closing in 30–90 days. Winning across both segments requires organizations to adopt a dual approach, balancing high-touch, trust-building processes with digital-first, frictionless ones.

Key Insights

• Enterprise buyers prioritize scalability, compliance, and long-term ROI.

• SMB buyers focus on speed, affordability, and immediate impact.

• Enterprise cycles demand stakeholder mapping and patient nurturing.

• SMB processes thrive on automation, transparent pricing, and self-service models.

• A “one-size-fits-all” sales strategy fails to capture both markets.

Why this matters

Sales leaders who can strike a balance between enterprise patience and SMB agility will capture a broader market share. Companies that tailor GTM strategies to segment realities are better equipped to scale sustainably across diverse customer bases.

Read the full article HERE.

The Partner Sales Divide: Why 51% Of Vendors Succeed And Others Don’t

Forrester research reveals a stark divide in partner sales performance, with only half of vendors consistently achieving their targets. The difference lies in how companies train and enable Partner Account Managers (PAMs). High performers invest in specialized training on partner economics, influence-based sales management, and joint growth planning. In contrast, underperformers often leave PAMs with only basic sales training, undermining partner success and revenue growth.

Key Insights

• 62% of firms provide only basic sales training to PAMs.

• High-performing vendors prioritize partner economics and profitability fluency.

• Effective PAMs focus on influence, coaching, and long-term strategic value.

• Joint growth planning and co-marketing are key to partner success.

• Undertrained PAMs erode partner confidence and stall revenue growth.

Why this matters

As partnerships become central to B2B growth, investing in PAM enablement is no longer optional. Companies that elevate PAMs into strategic advisors, rather than transactional managers, unlock sustained partner-driven revenue.

Read the full article HERE.

The Difference Between Search Engine Optimization and Generative AI Optimization

With the rise of AI-driven discovery, traditional SEO is giving way to Generative Engine Optimization (GEO). Social Media Today explains how GEO focuses on ensuring your content is cited in AI-generated answers rather than ranking in search results. While traditional SEO relies on keywords and backlinks, GEO emphasizes “citable chunks” of content, listicles, comparisons, and authoritative sources. The challenge is that AI-generated responses often reduce click-through rates, forcing marketers to adapt their visibility strategies.

Key Insights

• GEO aims to optimize for AI citations, not just search rankings.

• Structured, concise, and authoritative content performs best.

• Comparison tables, original research, and listicles attract AI references.

• Referral traffic is declining as AI answers reduce clicks.

• Foundations of SEO still matter, but require adaptation to conversational AI.

Why this matters

AI responses increasingly shape the buyer journey. For GTM leaders, visibility now depends on being cited by AI engines, not just indexed by search engines. Adapting strategies to GEO is critical to maintaining reach in a rapidly changing discovery ecosystem.

Read the full article HERE.

Final Thoughts

This edition highlights how AI, customer segmentation, partnerships, and discovery strategies are reshaping the GTM playbook. From redefining sales roles alongside AI to balancing enterprise and SMB approaches, from enabling partners to adapting to generative AI, the message is clear: sustainable growth requires adaptability and a willingness to rethink established models. See you in two weeks.

Ramzi Musa

1. How We Scaled OpenAI’s Sales Team from 10 to 500 People in 2 Years: The Inside Playbook from ChatGPT Enterprise’s GTM Leader Maggie Hott

Maggie Hott, GTM leader at OpenAI, shares her experience building the ChatGPT Enterprise sales organization from fewer than 10 to over 500 people in just two years. The playbook emphasizes exceptional hiring, building from scratch rather than retrofitting, and prioritizing speed and clarity. OpenAI applied a phased scaling model: foundation, systems, scale, and integration, culminating in unifying 500 people into one GTM team. Lessons include the value of “loss channels,” the importance of frontline manager development, and designing organizational structures around customers rather than legacy models.

Key Insights

• One exceptional hire is worth more than three average hires.

• Frontline managers need training as they act as “untrained therapists.”

• Loss channels drive more improvement than celebrating wins.

• Most business decisions are “two-way doors”—speed beats perfection.

• Compensation and hiring must align with mission, not just excitement.

Why this matters:

Scaling GTM in hypergrowth environments requires more than playbooks—it demands adaptability, clarity, and a culture that balances speed with learning. These lessons provide a framework for B2B leaders preparing their own organizations for rapid scale.

Read the full article HERE.

2. Bye SaaS - We Have Entered The Agentic Platform Companies Era

Forbes contributor Daniel Newman argues that traditional SaaS models are under siege. AI-native startups and hyperscalers are transforming the industry into “Agentic Platform Companies” (APCs)—adaptive, AI-driven ecosystems that deliver outcomes rather than licenses. Mid-market SaaS companies face a “big squeeze,” with net dollar retention dropping and growth slowing. The winners will reinvent themselves around AI as the core, adopt usage- or outcome-based pricing, and consolidate fragmented stacks into intelligent platforms.

Key Insights:

• SaaS mid-market players face pressure from both AI-native startups and large incumbents.

• Net dollar retention in the sector has dropped from 120% (2021) to 108% (2024).

• The BVP Nasdaq Cloud Software Index is down 10% while the broader Nasdaq rose 20%.

• AI agents, outcome-based pricing, and centralized data hubs are replacing old SaaS playbooks.

• Analysts expect one-third to one-half of SaaS firms may disappear or shrink to APIs within 36 months.

Why this matters:

The transition to APCs signals a structural shift in enterprise software. GTM leaders must rethink business models, pricing, and positioning now to avoid being left behind. For mid-tier SaaS, adaptation is not optional—it’s survival.

Read the full article HERE.

3. The Next Era of ABM is Strategy-Led and AI-Enabled

MarTech highlights how ABM has matured beyond pilot projects and vendor-driven frameworks. Experts Davis Potter and Elaine Zelby argue that ABM is no longer a subset of marketing—it is marketing. They emphasize the importance of portfolio-based account models (one-to-one, one-to-few, and one-to-many) and caution against overreliance on MQL metrics or automation without a clear strategy. AI enables scalability but must be paired with disciplined segmentation and strategic oversight .

Key Insights:

• ABM should be seen as core marketing, not a side tactic.

• The Total Account List (TAL) should act like an investment portfolio.

• Over-focus on MQLs undermines ABM’s strategic role—tie metrics to revenue and LTV.

• AI enables scalable, contextualized outreach, but risks amplifying bad practices if misused.

• Strategy and customer signals—not technology—should drive execution.

Why this matters:

ABM is evolving into a strategic GTM operating model, powered by AI but grounded in fundamentals. Companies that treat ABM as a core marketing strategy, with finance-linked outcomes, will unlock more sustainable growth than those chasing leads or tools.

Read the full article HERE.

4. 3 Ways AI Can Improve Team Meetings

Harvard Business Review outlines how AI can elevate teamwork by embedding itself before, during, and after meetings. Based on field research with 100+ managers, three models emerge: AI sets the table (preparation), AI at one seat (as a participant), and AI at every seat (individual co-thinkers). Each approach improves preparation, reflection, and collaboration. The research also highlights the role of facilitators and the importance of striking a balance between AI input and human dynamics.

Key Insights:

• Two-thirds of managers reported improved meeting outcomes when using AI.

• AI can prepare agendas, anticipate conflicts, and generate discussion prompts.

• AI can act as a “one-seat” participant, serving as a researcher, challenger, or storyteller.

• AI at every seat allows safe, individualized reflection before group consolidation.

• Facilitators remain crucial in orchestrating human-AI collaboration.

Why this matters:

Meetings consume a significant amount of organizational time, yet often fail to deliver their intended value. Using AI to improve structure, insight generation, and reflection can transform meetings from routine events into high-value collaboration moments.

Read the full article HERE.

Final Thoughts

This edition highlights the evolution of GTM across multiple fronts: scaling sales organizations at breakneck speed, adapting to new software paradigms, redefining ABM as a core strategy, and embedding AI into daily team practices. The common theme is clear: adaptability, strategy, and discipline in execution will define the winners in the next era of B2B growth.

Ramzi Musa

1. 20 Outdated Sales Practices To Retire And What To Do Instead

Forbes highlights 20 outdated sales practices that continue undermining revenue performance in 2025. From generic cold outreach and complex commission structures to over-reliance on tools and transactional selling, these approaches fall short with modern buyers. Instead, leaders are urged to embrace personalization, simplify incentives, unify sales and marketing, and focus on authentic relationships. The article compiles perspectives from industry experts, offering actionable shifts for companies aiming to stay competitive in complex markets.

Key Insights

• Success should be measured by value, not activity volume.

• Cold outreach without personalization drives low ROI.

• RevOps alignment reduces inefficiency between sales and marketing.

• Trust-building and partner-driven growth models outperform transactional selling.

• Overcomplicated funnels, demos, or commission structures erode clarity and focus.

Why this matters: Outdated habits create friction in buyer journeys and internal sales execution. Transitioning to modern, data-driven, and buyer-centric practices is essential for efficiency and long-term competitiveness.

Read the full article HERE.

2. Google Users Are Less Likely to Click on Links When an AI Summary Appears in the Results

Pew Research analyzed the impact of Google’s “AI Overviews” on user behavior. Based on browsing data from 900 U.S. adults, the study found that AI summaries significantly reduce click-through rates to publisher websites. When AI summaries appeared, users clicked on traditional results only 8% of the time compared to 15% without them. Moreover, just 1% clicked directly on links inside the AI summaries, and users were more likely to end sessions entirely. This shift signals a significant change in how digital traffic flows and how publishers must adapt.

Key Insights

• CTR drops almost 50% when AI summaries appear (8% vs. 15%).

• Only 1% of users clicked links inside summaries.

• 26% of users ended their session on AI summary pages (vs. 16% on non-summary).

• Wikipedia, YouTube, and Reddit remain the most cited sources.

• Longer, question-based queries are most likely to trigger summaries.

Why this matters: AI summaries reshape web discovery, reducing organic traffic opportunities for publishers and B2B marketers. GTM teams must rethink SEO and visibility strategies in a world where being summarized may matter more than being indexed.

Read the full article HERE.

3. How to Build a Good Basic Sales Plan for Your First SDRs

SaaStr’s Jason Lemkin outlines a practical framework for structuring SDR compensation. The plan emphasizes simplicity, transparency, and alignment with early growth goals. Recommended structures include a 60/40 base-to-variable ratio, tying bonuses to controllable KPIs (such as SQLs or qualified meetings), and implementing monthly or quarterly payouts to keep motivation high. Accelerators for overperformance, a two- to three-month ramp period, and avoiding reliance on closed revenue (unless cycles are very short) round out the approach.

Key Insights

• 60–80% base salary, 20–40% variable is a strong baseline.

• Incentives should focus on controllable metrics (SQLs, meetings booked).

• Payouts should be frequent to sustain motivation.

• Clear, one-sentence goals avoid confusion and burnout.

• Accelerators reward top performers and drive over-quota achievement.

Why this matters: A transparent and motivating SDR comp plan is foundational for early-stage or scaling teams. Aligning incentives to controllable outcomes ensures fair pay, reduces churn, and accelerates pipeline generation without overcomplicating processes.

Read the full article HERE.

4. How Interactive B2B Content Accelerates Sales Engagement and Conversion

Interactive B2B content increasingly replaces static materials like PDFs and brochures by providing personalized, two-way buyer experiences. Tools like ROI calculators, solution finders, and demo customizers allow buyers to self-qualify, explore relevant use cases, and see business value in real time. This improves lead quality and accelerates sales cycles by reducing friction and building trust earlier in the journey. Studies consistently show that interactive content generates double the engagement of static formats.

Key Insights

• At the top of the funnel, solution finders and assessments capture attention.

• In the middle, interactive tours and ROI calculators support differentiation.

• At the bottom, demo customizers help overcome last-mile objections.

• Personalized insights build credibility and stronger buyer relationships.

• Engagement levels are roughly 2x higher than passive content formats.

Why this matters: B2B buyers expect tailored, value-driven experiences from the first touchpoint. Interactive content improves engagement and directly impacts conversion by aligning solutions to real buyer needs.

Read the full article HERE.

Final Thoughts

This week’s edition highlights how GTM leaders must evolve across multiple dimensions: modernizing sales playbooks, preparing for AI-driven changes in discovery, building fair and motivating SDR structures, and adopting interactive content to engage buyers more effectively. Balancing these shifts will be essential for sustainable growth in an increasingly complex market.

Ramzi Musa

1. The Rise of B2B Influence: How Creators Are Influencing Buyers

B2B buyers are increasingly guided by creator content over traditional sales materials or ads. LinkedIn's recent research shows that creators now act as trusted advisors across the entire buying journey. With 82% of buyers reporting direct influence from creator content and nearly 80% engaging with it monthly, companies can no longer afford to ignore this shift. New formats like Thought Leader Ads and BrandLink pre-roll video placements offer scalable ways to tap into this momentum while maintaining trust and authenticity.

Key insights

• 82% of B2B buyers say creator content directly impacts decisions.

• 87% prefer credible content from industry influencers.

• Video is emerging as the most persuasive format. Video uploads on LinkedIn are up 45% YoY.

• Thought Leader Ads deliver 252% higher CTR and 48% more lead form completions.

• Buyers treat creators as the modern version of peer referrals.

Why this matters: B2B influence is no longer confined to whitepapers and analyst reports. Creator voices shape opinions, validate choices, and prompt real sales conversations. GTM teams must embrace creator-led strategies, not as vanity plays, but as credibility engines that build trust and drive pipeline.

Read the full article HERE.

2. 5 Interesting Learnings from ServiceNow at $12.5 Billion in ARR

ServiceNow is proving that scale doesn’t have to come at the cost of growth or efficiency. Despite surpassing $12.5B ARR, they’re growing at 22.5% annually while maintaining 29.5% operating margins and a 98% renewal rate. Their expansion within long-standing customer cohorts, like a 43x increase since 2010, is a blueprint for customer success-led growth. ServiceNow is also capitalizing on predictable revenue with $23.9B in total RPO and remains heavily rooted in mission-critical IT workflows while scaling globally.

Key insights

• $3.1B in quarterly revenue with 22.5% YoY growth.

• 98% renewal rates for five consecutive quarters.

• $23.9B total RPO, growing faster than current revenue.

• Customers from 2010 now spend $4.3M annually (43x expansion).

• $440K+ revenue per employee and 83% subscription gross margins.

Why this matters: ServiceNow's performance is a case study in sustainable enterprise GTM. They balance customer expansion, retention, and margin growth, while using IT as a durable GTM anchor. GTM leaders can draw inspiration from their model of category ownership and cohort monetization over time.

Read the full article HERE.

3. What 300 of the Highest Growth B2B Startups Are Actually Doing With AI

ICONIQ Growth’s 2025 State of AI report outlines how top-performing startups are using AI to drive productivity, control costs, and reframe pricing models. AI-native companies are accelerating product maturity 3.6x faster than their AI-enabled peers, while 95% of respondents are using multi-model strategies. Cost predictability, coding assistance, and hallucination prevention are dominating executive agendas. Hybrid pricing models, especially usage-based, are replacing traditional SaaS subscriptions.

Key insights

• AI-native startups outpace AI-enabled by 2x in product velocity.

• Cost is the top concern for internal AI; accuracy is key for customer-facing use cases.

• 95% use OpenAI; 54% use Anthropic and Gemini. Multi-model strategies dominate.

• Coding assistance delivers the highest productivity gain (65% rank it #1).

• 39% cite hallucinations as the #1 challenge, above cost, security, and latency.

Why this matters: AI adoption is no longer optional, but the path to impact varies by use case and architecture. GTM teams must understand the shifting economics of AI-enabled products and plan for hybrid pricing, agile development, and the organizational reallocation of resources to AI capabilities.

Read the full article HERE.

4. Gartner: 61% of B2B Buyers Prefer a Rep-Free Buying Experience

Gartner’s 2024 survey of 632 B2B buyers reveals that the majority now prefer a rep-free buying experience, conducting much of their research via digital self-service. However, buyers still seek sellers’ input when deeper contextual understanding is needed. Outreach quality is under fire; 73% of buyers actively avoid irrelevant engagement, and bad prospecting can damage brand trust. Sellers need to shift from broadcasting information to offering tailored, consultative guidance at key touchpoints.

Key insights

• 61% of buyers prefer rep-free buying overall.

• 73% actively avoid suppliers with irrelevant outreach.

• Buyers complete 3.0 activities with both reps and digital tools.

• Seller value lies in providing contextualized, not generic, information.

• Trust and relevance are more important than ever in prospecting.

Why this matters: B2B sales aren’t disappearing, they’re evolving. GTM teams must shift toward an assistive, insight-driven sales model. Rep roles aren’t obsolete; they’re becoming more strategic. Outreach must be timely, relevant, and designed around buyer context, not sales cadence.

Read the full article HERE.

Final Thoughts

This edition underlines a critical evolution in B2B go-to-market: buyers want less friction, more credibility, and better use of both tech and trust. Whether you're experimenting with creators, evolving pricing for AI-driven products, or modernizing sales engagement, now is the time to recalibrate GTM strategies around relevance and adaptability. See you in two weeks.

Ramzi Musa

1. Content Generation Outpaces Other Applications of AI for Marketers

Content generation has emerged as the top use case for AI among marketers, outpacing analytics and predictive applications. According to Demandbase and ForgeX, nearly two-thirds of marketers use AI for copywriting, particularly to support ABM efforts. Yet, only 19% of companies have a formal AI roadmap in place. The findings highlight a growing gap between experimentation and strategic maturity.

Key insights

• 66% of marketers use AI for content generation in ABM.

• Only 52% are satisfied with their AI adoption progress.

• Just 19% of companies have a formal AI roadmap.

• AI for analytics and personalization is expected to grow in the next 12–24 months.

• Lack of team readiness remains the most significant barrier to adoption.

Why this matters: While the hype often surrounds AI’s predictive capabilities, content remains king. For GTM teams, this means focusing AI investments on scalable content production, without losing sight of long-term roadmap development and enablement.

Read the full article HERE.

2. What Is the Ideal Length of a Sales Email? Insights Based on 40 Million Emails

Sales emails between 50 and 125 words yield the highest response rates, according to a Boomerang study that analyzed 40 million emails. HubSpot reaffirms this with 2024 research showing shorter emails generally perform better, especially when optimized for mobile and written at a third-grade reading level. Excessive length, emotional tone, or imagery can hurt results.

Key insights

• 75–100 words = best response rate (51%).

• Emails over 200 words see significantly lower engagement.

• Emails at a 3rd-grade reading level get 36% higher open rates.

• Personalization and clear CTAs drive conversion.

• Emails with 1–3 questions perform best.

Why this matters: For GTM and sales teams, email remains a core touchpoint. Understanding optimal length and tone isn’t just a copywriting detail; it’s a pipeline accelerator. With AI tools generating more messages, knowing what works is essential to stand out.

Read the full article HERE.

3. How to Get Cited in ChatGPT+ (and Win the New Search Game)

As generative AI changes how users discover content, being cited in tools like ChatGPT and Perplexity is emerging as a competitive advantage. This practice, dubbed Generative Engine Optimization (GEO), differs from traditional SEO. It’s about making your content credible, structured, and “pickable” by AI engines. That includes using natural-language headers, author bios, schema markup, and monitoring generative search visibility.

Key insights

• GEO is about being summarized, not just indexed.

• Add a llms.txt file to control what LLMs crawl.

• Focus on high-authority backlinks and listicle placements.

• Include structured metadata and FAQs to aid AI comprehension.

• Monitor citation presence across AI engines.

Why this matters: If your content isn’t being cited, it may not even exist in your buyer’s journey. GEO is no longer optional for GTM teams; it's the new front line of visibility in AI-assisted discovery.

Read the full article HERE.

4. In-Person Sales Generate 3x Higher Conversions

Despite the dominance of virtual selling, top CROs from Toast, Splunk, Brex, and Slice emphasize the continued value of face-to-face sales. In-person interactions consistently yield higher conversion rates, up to 3x in some cases. For field-driven models or high-trust verticals like restaurants and enterprise IT, the ROI of in-person engagement often justifies the cost.

Key insights

• Toast closes 80% of deals through field sales.

• Splunk and Brex see 3x conversion rates from in-person meetings.

• Local presence builds trust and credibility.

• Slice scaled to $100M+ ARR with a relationship-first strategy.

• Hybrid models offer the flexibility to meet buyer preferences.

Why this matters: In the age of AI and automation, the human touch still drives outcomes. For GTM leaders, it’s time to rethink where and how personal presence can be a force multiplier, especially in complex or high-value sales motions.

Read the full article HERE.

Final Thoughts

This edition reflects the balancing act GTM leaders face: leveraging AI for scale while retaining the human elements that build trust. From optimizing email outreach and content strategy to making strategic decisions about in-person engagement and AI visibility, success lies in executing both the old and the new with precision. See you in two weeks.

Ramzi Musa

1. 2025 Marketing Technology Landscape: 100X Growth and Now with AI

The 2025 edition of the Marketing Technology Landscape by ChiefMartec reveals continued expansion, now comprising 15,384 solutions, a 9% increase from last year. While older platforms consolidate or disappear, AI-native tools continue to proliferate, fueling a “hypertail” of custom software solutions. The most striking growth comes from SEO tools (+24%), contrary to the familiar narrative that claims “SEO is dead.” Meanwhile, B2B marketers increasingly anchor their stacks around CRMs, marketing automation platforms, and even custom-built environments.

Key insights

• Martech has grown 100 times since 2011, reaching 15,384 products by 2025.

• CDPs are losing centrality, with cloud data warehouses and engagement platforms gaining ground.

• Custom-built “hypertail” martech is surging via low-code/AI.

• SEO tools had the fastest category growth at +24%.

• Average B2B martech stacks expanded 2.2% last year.

Why this matters: The martech landscape is no longer about picking off-the-shelf tools; it’s about orchestrating and integrating, primarily as AI lowers barriers to building bespoke solutions. GTM teams must invest in alignment and platform strategy to avoid fragmentation.

Read the full article HERE.

2. LinkedIn’s New Guide on B2B Influencer Marketing

LinkedIn published a 22-page guide on how B2B brands can partner with creators to drive trust and purchase intent. Unlike B2C influencer marketing, B2B buyers intentionally incorporate expert content into their decision-making processes. LinkedIn emphasizes the importance of utilizing creators to enhance reputation through informed, data-driven storytelling and prioritizes video formats for increased engagement.

Key insights

• B2B buyers treat creator content as a trusted endorsement at critical buying stages.

• LinkedIn underscores video as the most effective content format for B2B engagement.

• Guide includes tactics for partnering with thought leaders and creating “Thought Leader Ads.”

• Warns to vet influencers carefully—some big names are skilled content marketers, not practitioners.

• Offers frameworks for aligning creator partnerships with specific buyer needs.

Why this matters: For B2B GTM teams, influencer marketing isn’t about broad entertainment but targeted expertise that builds credibility. Partnering with the right voices can move prospects through complex buying journeys more effectively.

Read the full article HERE.

3. 30+ Statistics to Optimize Your Sales Email Subject Lines

HubSpot’s collection of over 30 stats on email subject lines serves as a practical blueprint for boosting open and click rates. It confirms what most suspect: subject line length, personalization, and even including images all have a material impact on performance. The report also highlights AI’s accelerating role in crafting and optimizing emails.

Key insights

• The average open rate across industries in 2025 is 42.35%, with a click-through rate of 2.00%.

• Subject lines of 61-70 characters had the highest open rate (43.38%), while subject lines of 41-50 characters had the best click-through rate (17.57%).

• 45% of marketers use AI in their email campaigns; 50.7% say AI-enhanced emails outperform traditional ones.

• Automated emails generate 41% of email-driven orders, despite being only 2% of sends.

• Adding images lifts open rates to 43.12% vs. 35.79% without.

Why this matters: Small tweaks to subject lines can compound ROI, especially in saturated inboxes. For GTM leaders, leveraging AI and automation smartly turns email from a routine channel into a growth engine.

Read the full article HERE.

4. Inside Palantir’s $3.5B ARR Powerhouse

Palantir, formerly a government contractor, has evolved into an AI-driven commercial juggernaut. At $3.5B ARR and growing 39% YoY, its US commercial business rose 71% last year. The company is scaling not just revenue, but margins and customer value, with $1M+ and $10M+ deals soaring. Their Rule of 40 score reached 83%, underscoring the rare combination of growth and profitability.

Key insights

• US commercial revenue up 71% YoY, with customer count +65% and TCV +183%.

• Rule of 40 at 83%, driven by 39% growth and 44% operating margin.

• Closed 31 $10M+ deals in a single quarter.

• Free cash flow margins rose from 23% to 42% YoY.

• Net dollar retention at 124%, with manufacturing and AI deployments accelerating.

Why this matters: Palantir showcases how deeply embedding AI into commercial and operational use cases drives both scale and profitability. Their model provides valuable lessons for GTM teams on how enterprise AI adoption evolves beyond hype into sustainable growth.

Read the full article HERE.

Final Thoughts

This edition illustrates how AI continues to reshape GTM landscapes—from martech stacks and content partnerships to email campaigns and enterprise growth models. Success means adapting strategies not just to new technologies but to how they change buyer behaviors and economic fundamentals. See you in two weeks.

Ramzi Musa

1. Martech Landscape 2025: Growing, shrinking and reshaping all at once

The 2025 edition of the Marketing Technology Landscape reveals a paradox: the martech ecosystem continues to grow, now reaching 15,384 solutions, yet consolidation is accelerating, especially among older vendors. AI-native tools are proliferating, while many legacy platforms are being phased out. The average tech stack is also evolving, with B2B marketers increasingly integrating AI into existing platforms or building custom tools to enhance their operations. The rise of low-code and no-code environments has enabled a new "hypertail" of internal tools, reshaping how marketing technology (martech) is built and utilized.

Key insights

• Martech solutions have grown 9% YoY, reaching 15,384 tools

• CDPs are losing share as cloud data warehouses and engagement platforms grow

• SEO tools saw the highest growth rate (+24%) across all categories

• AI-driven tools in content and sales continue to expand, albeit more moderately than in 2024

• Custom-built martech solutions are surging, especially in B2B

Why this matters: Martech is no longer just about finding the right vendor. The balance is shifting towards integration and orchestration, mainly as AI introduces new possibilities and challenges. For GTM teams, stack discipline and cross-functional alignment are more critical than ever.

Read the full article HERE.

2. Seven Sales Compensation Plan Examples for High-Performing Teams

This comprehensive guide from Peak Sales Recruiting reviews seven sales compensation models, ranging from salary-only to draw-based and profit-bas plans. The article breaks down when to use each, how they influence behavior, and what pitfalls to avoid. With compensation being one of the most powerful levers in sales leadership, choosing the right plan is about aligning incentives with strategy, structure, and team maturity.

Key insights

• The base salary + commission model remains the most common and balanced choice

• Tiered commission plans help push overperformance and EoQ momentum

• Profit-based plans align sales with margin goals, but need transparency

• Draws are especially effective during ramp-up or new market entry

• Territory-based incentives support collaboration in team-selling environments

Why this matters: Compensation design is a strategic approach in action. It reinforces what sales leaders want their teams to prioritize—and signals how performance is measured. Misaligned plans confuse, demotivate, or worse, incentivize the wrong behavior. This guide provides a practical framework for reviewing or refining your model.

Read the full article HERE.

3. AI Startups Burn Through Cash 2x as Fast, and 10 Other Top Learnings from SVB’s Latest in Enterprise

SVB’s latest data dive into enterprise software reveals dramatic shifts in startup dynamics, especially for AI-first companies. From capital intensity and shrinking Series A graduation rates to rising revenue-per-employee benchmarks, the report outlines 10 key insights that every GTM leader, investor, or founder should track. The new AI economy is fast-moving, high-burn, and bifurcated between massive winners and the rest.

Key insights

• AI startups are burning $100M in half the time, but also scaling revenue faster

• 68% of capital now goes to $ 100M+ mega-rounds, 50% of which are AI-focused

• Series A graduation rates are at historic lows, making seed-to-A a critical gap

• Revenue per employee is nearly 2x higher in AI-native companies

• M&A activity is rising at the seed stage as exits replace up-rounds

Why this matters: AI is reshaping the landscape for capital allocation, go-to-market (GTM) velocity, and competitive advantage. For B2B teams, this means adapting expectations around growth, investment, and runway. You don’t need to be an AI company to be affected, but you do need an AI angle to stay credible.

Read the full article HERE.

4. Why AI-powered customer engagement projects fail before they start

While AI promises smarter, more personalized customer engagement, most projects fail to deliver, often before they even get off the ground. This article highlights four critical pitfalls: poor data foundations, lack of cross-functional alignment, unrealistic budgets, and a persistent talent gap. Organizations that succeed tend to start with internal AI use cases, not flashy front-end bots.

Key insights

• 55% of companies face tech stack issues before seeing AI benefits

• Data silos and quality issues cripple personalization initiatives

• Misalignment between marketing and IT is a significant hidden barrier

• Skills gaps (especially "AI translators") are a silent killer of success

• Successful teams start with behind-the-scenes use cases to build AI muscle

Why this matters: Plug-and-play AI doesn’t exist. Success in AI-driven engagement depends on internal transformation, encompassing governance, infrastructure, and collaboration. For GTM leaders, this means managing expectations and building cross-functional capability before making customer-facing promises.

Read the full article HERE.

Final Thoughts

This edition paints a clear picture: transformation is everywhere, but not always visible on the surface. Martech stacks are evolving, sales incentives are under review, AI economics are shifting fast, and engagement projects still hinge on foundational readiness. GTM leaders who align vision with execution, not just headlines, will find the edge. See you in two weeks.

Ramzi Musa

1. B2B SaaS Startups Still Spend Big on GTM

Despite pressure to operate leaner, B2B SaaS startups continue to spend aggressively on sales and marketing. According to SaaS Capital, the median startup spends 15% of revenue on sales and 10% on marketing, with high-growth startups investing even more. These levels are consistent across stages, with public SaaS companies showing even less efficiency in CAC. VC-backed companies typically spend 89% more on sales and 100% more on marketing than bootstrapped ones.

Key insights

• 15% of revenue is spent on sales (18% for fast-growing startups)

• 10% of revenue goes to marketing; this number is rising

• Public SaaS companies are often less efficient than startups

• Bootstrapped startups spend roughly half of what VC-backed companies do on GTM

• Customer success spend is trending downward

Why this matters: These benchmarks help founders and GTM leaders gauge their spending efficiency. They also serve as a caution: AI hasn’t yet replaced the need to invest heavily in growth. If anything, rising CAC and increasing market saturation demand smarter, but not necessarily cheaper, spending.

Read the full article HERE.

2. The Coming Storm in B2B Sales

Forrester forecasts a disruptive decade in B2B sales, powered by AI, buyer self-service, ecosystem complexity, and budget pressure. Traditional selling models are under pressure. To survive, sales organizations must restructure roles, upskill around automation, and rethink their go-to-market (GTM) design.

Key insights

• AI will automate routine and higher-order sales tasks

• Self-service purchasing continues to erode traditional sales engagement

• Ecosystem-driven selling (e.g., via partners) is becoming critical

• Sales orgs face a wellness and retention crisis due to rising stress and role shifts

• Sales headcount vs. tech investment trade-offs are becoming unavoidable

Why this matters: Many GTM organizations are sitting on a backlog of overdue changes. Forrester’s warning is clear: the next wave of transformation won’t be optional, and AI is not a cure-all. Smart orgs will proactively re-architect how sales, marketing, product, and success collaborate.

Read the full article HERE.

3. Navigating B2B Sales Disruption

In a companion piece to the Forrester analysis, this Forbes article frames the disruption as part of a new “supercycle” in B2B sales: a long, transformative phase reshaping every go-to-market (GTM) function. To thrive, companies must embrace buyer enablement, simplify complex sales cycles, and prioritize convergence across their go-to-market (GTM) teams.

Key insights

• AI and buyer self-service are driving massive GTM change

• Buying complexity lengthens cycles and strains seller productivity

• Route-to-market strategies are becoming a key growth lever

• VC/PE dynamics increase pressure on incumbents

• Sales, marketing, product, and CS functions are converging

Why this matters: This is a call to action: stop optimizing outdated models and start preparing for integrated, tech-enabled, and buyer-led GTM execution. Leaders who wait risk falling further behind the curve.

Read the full article HERE.

4. Gen AI: Boosting Output, Killing Motivation?

Harvard Business Review shares surprising research: while generative AI enhances short-term performance and content quality, it also reduces intrinsic motivation. It increases boredom for tasks that do not require AI support. Across four studies, motivation dipped by 11%, and boredom rose by 20% when switching from AI-supported to solo work.

Key insights

• Generative AI boosts task quality, empathy, and efficiency

• But it reduces autonomy and sense of control

• Workers report feeling bored and disengaged after using AI for tasks

• Long-term overuse of generative AI could reduce job satisfaction and growth

• Solutions: blend AI and human inputs, rotate tasks, and redesign workflows

Why this matters: GTM teams love AI for speed, but ignoring the psychological downsides could backfire. Leaders need to structure workflows that preserve creativity and ownership, not just performance. The long-term health of teams depends on it.

Read the full article HERE.

Final Thoughts

From AI trade-offs to GTM budget benchmarks, the signals are clear: transformation is here, and not only technological. Sustainable change will require alignment across teams, budgets, and expectations. See you in two weeks with more GTM insights.

Ramzi Musa