The AI Execution Gap: Why B2B Teams Are Stuck Between Strategy and Action

Panel Insights | Why AI adoption is moving faster than GTM readiness – and what it really takes to close the gap.
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Every board is asking about AI. Every leadership team has a point of view. Every 2026 conference agenda seems built around it.

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But ask senior GTM leaders, behind closed doors, whether their teams are ready to turn experimentation into execution, and the answer is more complicated than the headlines suggest.

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Across two executive panel discussions held in different parts of the world, we asked exactly that question. What came back was candid, consistent, and worth paying attention to.

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The message was clear: AI adoption is accelerating. GTM readiness is not keeping pace.

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The Gap Nobody Talks About Publicly

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There's a version of the AI conversation that happens on stage, in press releases, and on LinkedIn. Teams are experimenting. Pilots are running. Efficiency gains are being reported. In fact, McKinsey & Company in their report on the State of AI found 39% of B2B marketers are piloting advanced agentic AI systems.

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Then there's the conversation that happens in the room.

At our recent executive panel events, we brought together senior marketing and GTM leaders for exactly that second kind of conversation. What emerged was striking. Not because participants were behind, but because even the most advanced teams were honest about the distance between where they are and where they need to be.

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"Everybody up here sounds like we know 100% what we're doing, but we actually don't. We're faking it till we're making it and learning as we go. Just like everybody else is."

             -       Melissa Guiterrez, Senior Director - Demand Generation, CouchBase.

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That kind of honesty is rare. And it's exactly the startingpoint this conversation needs.

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Adoption Is Outpacing Readiness

AI adoption in B2B marketing has moved quickly, but the move from pilot to performance is proving harder. Gartner predicted that at least 30% of GenAI projects would be abandoned after proof of concept - driven by poor data quality, inadequate risk controls, escalating costs, and unclear business value. Seperately, it forecasts that through 2026 organizations will abandon 60% of AI projects unsupported by AI-ready data.

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The bottleneck is rarely the technology alone. More often, it sits in the foundation beneath it.

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That is exactly what we heard across both panel discussions. Teams are adding AI capabilities on top of GTM infrastructure that was never designed for them: CRM data accumulated over a decade or more, siloed systems with no universal identifier, fragmented buyer signals, and marketing and sales teams operating from different versions of reality.

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"Only because of that cleanup - which was 20 years of bad infrastructure - are we able to do what we do now. The shiny object is fine, but if the shiny object doesn't work, it sort of defeats the purpose."

           -    Hannah Greenberg, CEO, Eleven Software

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Two separate panelists, in two different cities, independently offered the same observation: garbage in, garbage out. The convergence is telling.

Why Pilots Succeed and Operationalization Fails

There is a meaningful difference between a successful AI pilot and a sustainable AI-powered GTM motion.

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Pilots are bounded – specific use case, motivated team, management attention. The conditions are ideal.

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Operationalization is different.

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It requires AI to work across functions, at scale, with imperfect data, inconsistent adoption, competing priorities, and without a dedicated team watching every step. That is where the execution gap opens.

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We saw this pattern across both discussions. Teams generate early wins: faster copy, better segmentation, more efficient lead scoring, stronger campaign analysis. Then momentum slows. Adoption becomes inconsistent. People revert to familiar tools and workflows.

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Melissa Gutierrez was candid about what this looked like in practice at Couchbase – describing how a promising AI initiative lost momentum not because of the technology, but because the team didn’t have the capacity to properly enable the wider marketing organisation around it. People reverted to familiar tools. Progress stalled. It’s a pattern that surfaced across both panels more than once.

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This is not a failure of ambition. It is a failure of sequencing.

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Moving AI from being an experiment to infrastructure requires the same organisational discipline as any serious capability build. The technology may move fast. The change management does not.

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The Prerequisites That Actually Matter

Across both panels, three conditions kept recurring among teams genuinely moving from AI experimentation to AI-enabled GTM execution.

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1. Data quality before AI capability.

‍Foundational hygiene – clean records, unified identifiers, consistent taxonomy – is the unglamorous prerequisite that everything else depends on.

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For B2B GTM teams, this matters because AI is only as useful as the data and signals it can act on. If the account view is fragmented, if buying-group members are missing, or if engagement signals are not connected back to the right context, AI will amplify the gaps rather than close them.

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2. Workflow redesign, not workflow automation.

‍The teams making the most progress are not using AI to do the same things faster. They are asking different questions. Daisy Harris, VP Market Innovation at Trilliad, put it sharply: "Speed in the service of better thinking, not speed for speed's sake." Automating a broken process at scalemakes it worse, not better.

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Automating a broken process at scale does not create transformation. It creates a faster version of the same problem.

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The more valuable question is not, "How can AI make this workflow faster?"

It is, "Is this still the right workflow for the way buyers behave now?"

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3. Role-based implementation, not blanket adoption.

Blanket AI mandates produce uneven results. The more effective approach is persona-specific, use-case-driven enablement tied to the workflows that matter for each function.

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A demand generation leader, a content strategist, a revenue operations team, a sales development team, and a field marketing team do not need the same AI playbook. They need use cases that connect directly to how they create value.

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This is where enablement becomes critical. AI adoption cannot sit as a side project inside one team. It needs ownership, context, governance, training, and reinforcement.

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The Difference Between Experimenting and Executing

There is a useful litmus test for where any team sits on this spectrum.

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Experimenting teams ask:

"What can we try with AI?"

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Executing teams ask:

‍"Which workflows break without AI?"

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That distinction matters. Experimentation is useful. It builds confidence, surfaces use cases and creates early momentum. But experimentation alone does not create the advantage that comes when AI becomes embedded in the way teams identify accounts, understand buying groups, personalise journeys, prioritise follow-up, and act on real engagement signals.

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For GTM leaders, the priority is not simply to add more AI tools into the stack. It is to understand where the current operating model is not ready for AI yet: the data that cannot be trusted, the workflows that are not clearly defined, the buyer signals that are not visible, and the teams that have not been enabled to use AI in context.

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What This Means for B2B GTM Leaders

The organisations that will build a durable advantage are not necessarily the ones adopting AI fastest. They are the ones building the foundations that allow AI to work properly.

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That means looking beyond the toolset and asking harder operational questions:

·      Do we trust the data AI is using?

·      Can we see the full buying group,or only the most visible lead?

·      Are our content journeys givingus meaningful engagement signals?

·      Do sales and marketing have thesame view of account readiness?

·      Have we redesigned the workflow,or simply automated the old one?

·      Have teams been enabled by role,use case, and business outcome?

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The execution gap is real. It is also closeable.

But closing it requires leaders to treat AI operationalisation as a strategic capability build, not a technology deployment.

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From AI Adoption to GTM Execution

AI is everywhere. Execution is what separates experimentation from advantage.

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The next phase of B2B marketing will not be defined by who has the most tools, the most pilots, or the boldest AI roadmap. It will be defined by who can connect clean data, buyer intelligence, content engagement, workflow design, and human judgment into a GTM motion that scales.

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That is the real AI execution gap.

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And it is the gap senior GTM leaders need to close now.

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Watch the Full Panel Discussions

Hear senior B2B marketing and GTM leaders from Silicon Valley and London discuss what is working, what has failed, and what they are prioritising now.

Ready to move from AI experimentation to GTM execution?

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Speak to the ProspectBase team about how cleaner data, richer engagement signals, buying-group visibility and AI-enabled content journeys can help your team close the gap.

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