GTM in the Age of AI: Selling More, Differentiating Less, Delivering Value Later
By Gabbi Sherlock
AI is transforming how software companies build, market and sell. What I question is whether it is changing how they think about go-to-market.
Look across the industry and a pattern emerges: similar AI narratives, similar buyers, similar outbound plays, similar land-and-expand strategies. Even the tools used to find and engage customers are increasingly the same. We are getting better at executing GTM, but not at differentiating it.
Two pieces in the past few months frame the shift well. Scott Brinker argues that AI-driven product abundance creates a coherence problem: companies can now build faster than customers and organisations can absorb, and product marketing must become the keeper of coherence. Clay makes the case for GTM Engineering, where data, automation and engineering turn revenue operations into a faster, smarter and more scalable system.
I agree with both. But I think they point to a bigger problem sitting between them.
Software vendors are becoming better at selling the promise of value than at creating the conditions for customers to realise it.
Three Failures AI Is Exposing
01 — More sophisticated, less differentiated.
Account intelligence, intent signals, propensity models and personalised outreach are now widely accessible. When every vendor targets the same decision-makers with similar messaging and similar AI claims, the result is more noise, not more effectiveness. A personalised email is not a differentiated proposition, and a slick outbound engine doesn't fix an undifferentiated product. Real differentiation comes from distinctive customer insight, a defensible value proposition, outcomes competitors can't match and evidence to prove it.
02 — Selling the roadmap as if it were the product.
The pressure to demonstrate AI innovation is enormous, and some vendors are commercialising capabilities that are only partially functional, still in development or largely a vision. Selling a direction is legitimate; enterprise technology has always involved it. The problem is when the line between what exists, what works, what is available and what is aspirational blurs. Customers then inherit the risk. Business cases are approved against capabilities that can't yet be operationalised, sales teams are rewarded for selling futures that delivery can't support, and trust erodes when the lived experience doesn't match the pitch. Roadmap commitments start steering product priorities in place of validated customer needs. The principle should be simple: sell the vision but commercialise the reality. Be explicit about maturity, dependencies and implementation requirements. Let aspiration be strategy, not an implied deliverable.
03 — Confusing expansion with growth.
Existing customers are the easiest source of incremental revenue, so account teams are incentivised to sell more modules, more consumption, more AI add-ons. Yet the same vendors often underinvest in what makes those purchases succeed: structured onboarding, process redesign, training, change management, executive sponsorship and adoption measurement. Owning a product, deploying it, using it and deriving value from it are four different things. A larger contract is not necessarily a healthier customer, and an expansion opportunity is not necessarily a growth opportunity. Vendors are trying to sell more of something customers may not yet be using well.
What Needs to Change
From product-led launches to market-led decisions.
Start with customer needs and genuine differentiation. Just because AI makes something easy to build doesn't mean it should be commercialised.
From product marketing as messaging to product marketing as commercial strategy.
It needs a voice in what gets built, how it is positioned and priced, and whether the market is ready for it — not just a seat at the launch meeting.
From selling capability to proving value.
Make product maturity transparent and tie commercial commitments to evidence.
From land and expand to prove, adopt and grow.
Understand the need, prove the value, invest in adoption, measure realised outcomes, and only then expand. Expansion should be the result of value created, not an independent sales target.
From engineering the sales engine to engineering the customer value lifecycle.
GTM Engineering is a powerful enabler, but it optimises the mechanics of a commercial model; it cannot tell you whether the model is sound. Point it at adoption, time to value and retention, not just prospecting and pipeline.
Change the Incentives and the Metrics
None of this is accidental. These behaviours are reinforced by bookings-based compensation, ARR targets, release-count KPIs, investor expectations and functional silos. If you reward bookings but don't measure adoption, you shouldn't be surprised when bookings win.
So measure differently: deployed and actively used products rather than products sold; feature adoption and business impact rather than feature releases; expansion backed by demonstrated value rather than contract growth alone. Above all, distinguish between contracted value, deployed capability, adopted capability and realised value. They are four very different stages of success.
This also means shared accountability. Product, product marketing, sales, customer success and GTM Engineering can't keep operating as separate functions with separate scorecards. Commercial performance belongs to the CEO, CRO, CMO and CPO together.
Human Judgement, AI Execution
AI can model propensity, automate engagement, monitor usage and predict churn. It can sharpen commercial intelligence dramatically. But deciding where to compete, what to build, what to promise and what value the company is accountable for delivering are judgements that can't be delegated. AI should raise the quality of those decisions, not replace them.
The Real Advantage
The industry doesn't lack products, features or ways to reach buyers. It lacks clarity about what matters, credibility in what it promises and discipline in making sure customers get the value they were sold.
When everyone can build, personalise and automate at scale, advantage shifts to decision quality, the integrity of the proposition and sustained customer outcomes. The next edge isn't simply AI-enabled GTM. It's value-led GTM, enabled by AI.
The objective should never be to sell more software. It's to create more value through it.
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