Building an AI-First Go-to-Market Operating System

Executive Summary

In a recent episode of Demandbase's OnBase Podcast, host Chris Moody spoke with Yasemin Dalkilic, Head of AI at RAB2B and a former world-record freediver, about what it takes to build real AI capability inside a go-to-market organization.

Dalkilic's view is direct: B2B teams should not simply bolt AI onto existing workflows and expect transformation. They need to rethink the operating system around people, process, platform, and proof.

Her argument is not that AI should replace human strategy, creativity, or judgment. It is that AI should be intentionally embedded into the workflows, handoffs, and knowledge systems that drive revenue execution.

The practical implication for marketing, demand generation, RevOps, and ABM leaders is clear. AI becomes more valuable when it helps teams share context, interpret signals, prioritize accounts, improve handoffs, and measure what happens next.

Why Bolting AI Onto Existing Workflows Falls Short

Many B2B teams begin their AI journey by giving individuals access to tools. A marketer uses ChatGPT to draft copy. A seller uses AI to summarize a call. A RevOps leader experiments with automated analysis. A content team tests AI-assisted outlines.

Those use cases can help. But Dalkilic's view is that they are not enough.

When AI is layered on top of legacy processes, the underlying system often stays the same. The same delays remain. The same data gaps remain. The same handoff issues remain. The same disagreements between marketing and sales remain.

The organization may produce more output, but it does not necessarily produce better execution.

That is the trap. AI can make isolated tasks faster without making the go-to-market motion smarter.

For marketing leaders, this distinction matters. If campaign briefs are unclear, AI-generated content will still lack focus. If account handoffs are vague, AI-generated sales alerts may still be ignored. If CRM data is messy, AI analysis may simply summarize unreliable inputs.

Analysis: AI tends to magnify the quality of the system it enters. Strong inputs, clear workflows, and aligned teams can become more productive with AI. Weak inputs, unclear ownership, and fragmented processes may become noisier.

Instead of asking only which AI tools to buy, leaders should also ask which parts of the GTM system need to be redesigned so AI can actually improve performance.

AI Belongs in the Handoffs

One of the most useful ideas from Dalkilic's perspective is that AI should sit in the handoffs.

In B2B revenue organizations, handoffs are where many things break. Marketing identifies an account, but sales does not understand why it matters. Sales speaks with a prospect, but the insight never makes it back into campaign strategy. Customer conversations reveal market pain points, but content teams do not see them.

These are not only individual productivity problems. They are coordination problems.

AI can help when it is used to capture, interpret, summarize, and distribute context across transition points:

• Marketing hands prioritized accounts to sales with clear context about behavior, intent, and recommended messaging.
• Sales calls are summarized into reusable insights for demand generation, ABM, and product marketing.
• Customer feedback is organized into themes that inform campaigns, positioning, and enablement.
• Campaign performance insights are translated into next steps for media, content, and sales teams.
• Intent signals are connected to CRM history, account fit, buying-group behavior, and sales ownership.

Pull quote: The highest-value AI use cases often live between teams, not inside one person's task list.

That is where AI can create leverage. Not by replacing the team, but by improving how the team works together.

Building a Common Brain Across the GTM Organization

Dalkilic also describes AI as a way to create a shared knowledge layer across the organization. This common brain concept is one of the most compelling ideas in the conversation.

In many B2B organizations, knowledge is scattered. Strategy lives in slide decks. Sales insights live in call recordings. Customer language lives in transcripts. Campaign decisions live in project management tools. Performance learnings live in dashboards. Account intelligence lives in ABM platforms. CRM data lives somewhere else entirely.

The result is fragmentation. Teams make decisions from different sources of truth. Marketing builds campaigns based on one view of the customer. Sales prioritizes accounts based on another. Leadership reviews performance through yet another lens.

AI can help consolidate this knowledge if the organization designs for it. A common brain might draw from:

• Customer call transcripts
• Sales notes
• Campaign briefs
• ABM account plans
• CRM records
• Intent data
• Website engagement
• Email performance
• Paid media results
• Customer success feedback

This does not mean dumping every piece of data into an AI tool and hoping insight appears. It means creating a governed knowledge system where AI can help teams retrieve context, identify patterns, summarize themes, and support decision-making.

For ABM practitioners, this matters because traditional ABM often starts with static account lists. But buying behavior changes. Stakeholders engage at different times. Intent signals shift. Content consumption changes. Sales conversations reveal new objections.

Dalkilic's view suggests that AI can make ABM more dynamic by helping teams interpret behavioral signals and buying-group activity instead of relying only on fixed lists.

The Four P Framework for AI Adoption

Dalkilic's Four P framework offers a simple way to think about sustainable AI transformation: People, Process, Platform, and Proof.

Each element matters. If one is missing, adoption becomes fragile.

People: AI adoption is not just a technology rollout. It is a behavior change. Dalkilic emphasizes AI fluency, meaning the ability of employees to understand how to use AI effectively, where it fits, and how to apply it responsibly.

Her agency introduced 15-minute weekly AI sessions to improve adoption and culture. That detail matters because it shows enablement does not need to be heavy to be useful. Consistent, practical exposure can build confidence over time.

Process: The process layer is where AI becomes operational. This includes the handoffs, approvals, triggers, summaries, workflows, and feedback loops that move work across the GTM organization.

Platform: The right AI tools should support integration, data flow, governance, collaboration, and scale. For B2B teams, this often means thinking carefully about how AI connects to CRM, marketing automation, ABM platforms, content systems, analytics tools, and internal knowledge bases.

Proof: Dalkilic points to employee adoption and fluency as important indicators. AI transformation does not become real just because a company buys tools. People have to use them. Teams have to trust them. Workflows have to improve. Decision-making has to get better.

Analysis: Proof should include both behavior and business impact. Adoption, confidence, and improved handoffs are useful early signs. Over time, leaders should connect those changes to campaign velocity, sales follow-up quality, account progression, meetings, opportunities, and pipeline influence.

Lessons from Freediving

Before her work in AI and B2B, Yasemin Dalkilic was a world-record-holding freediver. In the podcast, that background becomes more than a personal detail. It becomes a useful analogy for transformation.

Freediving requires preparation, discipline, calm under pressure, and trust in fundamentals. A diver cannot panic at depth. They have to understand the system, control their response, and execute under stress.

Dalkilic connects that mindset to AI transformation. Big organizational changes can feel overwhelming. AI introduces uncertainty. Teams may worry about skills, roles, quality, governance, and speed. Leaders may feel pressure to move quickly while still making responsible decisions.

The freediving lesson is to break the challenge into manageable parts. Focus on fundamentals. Build capability step by step. Do not confuse motion with progress. Trust the process.

Pull quote: Sustainable AI transformation comes from discipline and intentional design, not quick fixes.

That idea is especially important now, when AI hype can push organizations toward scattered experimentation. Experimentation is useful. But without discipline, it does not become an operating advantage.

The Risk of Average

One of the most memorable ideas from Dalkilic is her warning that "machine learning in general is the art of averaging."

It is a short quote, but it raises an important strategic issue.

AI can help raise the floor. It can make average work better. It can help teams move faster, summarize more clearly, and produce usable drafts. But if organizations use AI blindly, they may also flatten the distinctiveness of their thinking, messaging, and market point of view.

That is the commoditization risk.

If every company uses similar prompts, similar models, and similar workflows against similar inputs, outputs may begin to sound and feel the same.

This is why human judgment remains essential. AI can support research, synthesis, summarization, and workflow execution. But leaders still need to define the strategy. Teams still need to understand customers. Subject matter experts still need to shape the point of view.

Analysis: AI should raise the floor, not lower the ceiling. The goal is to reduce low-value friction so people can spend more time on higher-value judgment.

How This Applies to Your Organization

If you are a marketing, demand generation, RevOps, or ABM leader, the practical takeaway is simple: do not start with AI tools alone. Start with the operating system.

First, identify where GTM handoffs are breaking. Look at the points where work moves between marketing and sales, sales and customer success, campaign strategy and execution, intent data and account prioritization, customer insight and content, or reporting and decision-making.

Second, audit whether you have a shared knowledge layer. If customer insights, sales notes, campaign briefs, and account intelligence are scattered across systems, your team may need a common brain before it needs another point solution.

Third, create a repeatable training rhythm. AI fluency does not happen through one kickoff meeting. Consider lightweight recurring sessions, internal examples, prompt libraries, office hours, or departmental champions.

Fourth, connect AI to execution systems. AI is more valuable when it supports real workflows inside CRM, marketing automation, ABM platforms, call recording tools, analytics, and project systems.

Finally, define proof early. Do not wait six months to decide whether AI is working. Define early indicators of adoption and workflow improvement, then connect those indicators to commercial outcomes over time.

Key Takeaways

• AI should be designed into the GTM operating system, not simply added to existing tasks.
• The highest-value AI use cases often happen at handoff points between marketing, sales, RevOps, and customer success.
• Dalkilic's common brain concept points to a shared knowledge layer built from customer insights, briefs, transcripts, and account data.
• The Four P framework, People, Process, Platform, and Proof, provides a practical structure for AI adoption.
• AI fluency requires ongoing training, internal champions, and repeated practical use.
• AI can support ABM by helping teams interpret behavioral signals and buying-group activity.
• Human judgment is still essential because AI can average outputs and reduce differentiation if used blindly.

Conclusion

The conversation between Chris Moody and Yasemin Dalkilic is valuable because it moves beyond the usual AI conversation.

It is not about chasing the newest tool. It is not about replacing people. It is not about adding AI to every task for the sake of it.

It is about redesigning the go-to-market operating system so AI can improve how teams work, share knowledge, interpret signals, and act on opportunity.

Dalkilic's perspective is especially relevant for B2B organizations investing in ABM, demand generation, marketing operations, and revenue intelligence. These teams already operate across complex systems, long buying cycles, and multi-stakeholder decisions.

The companies that benefit most will likely be the ones that combine AI with disciplined process design, cross-functional fluency, strong data foundations, and human judgment.

AI may be powerful, but the operating system still matters.