The AI-Powered Go-to-Market Organization Isn’t Just a Vision Anymore

At a time when many boardrooms are still asking whether AI will disrupt software revenue, go-to-market (GTM) leaders are asking a more practical question: how can AI help us find and keep more customers?

The honest answer is that most GTM teams don’t have an AI problem. They have a decision-flow problem.

I’m seeing this across some of the world’s largest enterprises. And my clearest takeaway is that the organizations pulling ahead are not doing the same GTM motion but faster. They’re doing a fundamentally different one.

Here are five moments in the customer journey where that shift is already showing up in results.

1. Segmentation: From demographics to business signals

Most GTM organizations still segment the way they always have based on characteristics like industry, company size, geography, and more. AI changes the input. Rather than asking who a prospect is, it asks what they’re signaling right now. This includes more nuanced signals like operational pressure points, purchasing patterns, and growth indicators embedded in their business data. Reps aren’t chasing more leads; they’re chasing the right ones, and that shows up in how many of those signals turn into real opportunities.

2. Engagement: Relevance replaces volume

Cold outreach reply rates have dropped to near-historic lows across enterprise sales. The answer can’t be to send more outreach. It must be to do smarter outreach. When AI has access to full business context such as what’s happening inside an account operationally, financially, and commercially, it can help teams generate outreach grounded in what matters to a buyer at that moment. That’s not personalization at the persona level. It’s relevance at the account level. What shifts is the quality of first contact. Response rates can rise while the time it takes for the first qualified meeting shrinks because the outreach reflects what a buyer is dealing with in the moment.

3. Deal execution: Clearing the invisible friction

This is the one that most organizations underestimate. In most enterprise deals, the seller isn’t the bottleneck. The system around the seller is. Approvals, pricing sign offs, quote generation, and contract routing are where time is lost and deals slip. This isn’t because the buyer hesitates, but too often because of internal complexity. AI agents can help eliminate this friction.

For example, Amadeus, working with SAP, deployed an autonomous agent that reconciles unstructured payment data, clearing around 40,000 incorrect transactions that previously required manual intervention. That kind of autonomous resolution doesn’t just reduce cost, it changes what the buying experience feels like from the customer’s side. Deals that stalled for weeks waiting on internal processes don’t have to anymore. 

4. Post-sale: Compressing time-to-value

The handoff from sales to post-sale is historically where value gets lost. Expectations set during the sale don’t always match what a customer experiences in the first 90 days. AI makes that gap visible and actionable in real time through an “account brain.” This can be thought of as a growing repository of context and knowledge around an account, which makes handovers much easier and, most importantly, independent of any single individual. This can shift time-to-first value and 90-day adoption rate: how quickly a new customer reaches their first meaningful milestone, and whether they’re using what they bought.

5. Retention and expansion: Proactive at scale

Net revenue retention is the most durable commercial metric and it’s the one most dependent on what happens after the sale. The historical challenge is scale. AI can have a big impact here. Continuous scoring of expansion-readiness and churn risk, triggered by behavioral and operational signals, means teams act on the right accounts at the right moment not after a customer has already made up their mind.

Expansion of net revenue retention is where the largest commercial upside in most enterprise businesses lives. Both are chronically underserved when customer success is working reactively, account by account, rather than across the full base at once. The organizations getting this right haven’t simply deployed more AI tools. They’ve been deliberate about where in the customer journey AI can add real value for customers.

At SAP, we’re applying these same principles to our own GTM organization. We’re investing in a model where a single AI-powered entry point connects our sales teams to a network of specialized agents spanning planning, outreach, quoting, content, customer engagement, and more. The goal is a shared intelligence layer built around each account to give our teams more context and consistency so they can drive even more value and better outcomes for our customers at every stage of the customer journey.

So to me, the right question isn’t “Where can we implement AI?”; it’s “Where does customer value stall because information, authority, and action are separated?”

That kind of clarity is what a more intelligent go-to-market organization can already bring. And it’s only a first glance of what else will soon be possible.

Jan Gilg is global president of Customer Success & Americas and a member of the Extended Board of SAP SE.