AI Is Scaling Your Go-to-Market. But Is It Making It Relevant? Watch Podcast!

AI has quietly, but fundamentally, reshaped how go-to-market teams operate over the past few years, compressing what once took weeks into hours and enabling a level of execution that would have been difficult to imagine not long ago. Content is generated instantly, campaigns are deployed at scale, and outreach can now be orchestrated across thousands of accounts with minimal effort.

And yet, when you look at the outcomes, something doesn’t quite add up.

Pipelines may look fuller and activity levels are higher, but conversion rates remain stubbornly low, and most outbound messages still fail to trigger meaningful engagement.

In a recent conversation with Bernard Goor, founder of Holistic Selling, a revenue acceleration framework focused on orchestrating end-to-end customer engagement, we explored a simple but critical tension that many go-to-market leaders are beginning to recognize: while AI has made it dramatically easier to scale execution, it has not necessarily made that execution more relevant.

👉 If you want to go deeper into this discussion, you can watch the full conversation here:

https://www.youtube.com/watch?v=7dwvhXM4h5g

When scale outpaces meaning

There is a tendency in today’s market to equate progress with activity, where more campaigns, more touchpoints, and more content are seen as indicators of forward momentum. AI has amplified this dynamic, lowering the cost of production and enabling teams to operate at an unprecedented level of output.

But this creates a subtle and often misleading illusion.

Because while activity increases, the underlying logic often remains unchanged, with messages still built on generic assumptions, campaigns still targeting broad segments, and personalization frequently applied as a final layer rather than embedded from the start.

This is why, in many B2B environments, outbound conversion rates still hover below 2%.

Not because teams lack tools, but because they are scaling messages that were never truly meaningful to begin with.

The misunderstanding of personalization

For years, personalization has been treated as a formatting exercise, with small adjustments such as adding a name, referencing a company, or mentioning a recent announcement creating the impression of relevance without changing how the message is perceived.

True personalization operates at a different level.

It requires understanding what is happening inside a company, why it matters now, who is affected, and how it connects to the priorities and pressures of a specific individual. It is not about making a message look tailored, but about making it feel inevitable.

Most AI models today are trained on public data and generalized language patterns, which makes them highly effective at producing fluent content but far less capable of understanding the commercial context of a specific organization or the nuanced reality of an individual decision-maker.

This is why so much AI-generated outreach, despite being technically impressive, still feels generic and easily dismissed.

Shifting the starting point

At Pangea Summit, this observation led us to rethink where go-to-market should begin, shifting the focus away from content generation and toward the commercial logic behind it.

Instead of asking what message should be sent, we begin with a more fundamental question: why should this message exist at all, for this person, at this moment?

That shift changes the dynamic.

Because once you focus on the “why”, you are pushed upstream into the structure of the commercial situation itself, where you structure the commercial situation around real triggers, outcomes, and the individual stakeholder. Only once this foundation is in place does it make sense to generate content, and when it is, the difference is immediate.

From content generation to commercial reasoning

What emerges from this approach is not simply better messaging, but a fundamentally different capability. Rather than focusing on producing more content, the emphasis shifts toward structuring the reasoning behind it, bringing together precise industry context, clearly defined sales plays, deep stakeholder and individual understanding, real-world signals indicating timing and relevance, and a compliance framework that ensures accuracy and trust.

When these elements are aligned, AI becomes far more than a productivity tool. It becomes a way to express, at scale, a well-defined and contextually grounded commercial hypothesis.

The return of human-to-human engagement

As AI becomes more pervasive, the importance of human connection does not diminish, it becomes more pronounced. When access to technology becomes universal, differentiation shifts away from the ability to generate content quickly and toward the ability to create interactions that feel relevant, timely, and worth engaging with.

This is where Human-to-Human engagement becomes central. AI can structure thinking and enable scale, but it is ultimately the human layer that builds trust, creates connection, and turns an initial interaction into a meaningful conversation.

Where the real impact happens

If you look at the traditional go-to-market funnel, it is tempting to focus on optimizing conversion at later stages, but the most significant inefficiency often sits at the very top.

Most outreach never gets a response.

Not because the offering is irrelevant, but because the message fails to connect in an environment saturated with inbound noise, where the threshold for engagement is immediate and unforgiving. Improving relevance at that first interaction changes everything downstream, leading to more conversations, better-qualified opportunities, and faster cycles.

The Takeaway

AI is no longer the differentiator, relevance is.

And relevance does not come from generating more content. It comes from structuring the right message before scaling it. Because in the end, scaling only creates advantage when the reasoning behind it is clear.

And that raises a set of questions worth revisiting before any further investment in automation.

Are we clear on why our audience should care?
Are we anchored in real business triggers?
Are we speaking to individuals, not just segments?

Because if the answer is unclear, AI will not fix the problem.

It will simply make it scale faster.

And in a world already saturated with noise, that is a risk most organizations can no longer afford.