Enterprise technology has spent decades optimizing for the close. But AI vendors are discovering that the close is no longer where revenue is won.
For AI products, the problem statement is different. Revenue is increasingly driven by consumption. Value is co-produced with customers who must modify their processes, data, and ways of working before the product can be used successfully. These dynamics change the logic of the entire go-to-market model.
For CROs, that divergence is easy to miss. Two accounts can sign similar contracts and look equally successful in the pipeline. Months later, one is moving from its first use case to its third while the other is still struggling to get the first into sustained use. Their revenue trajectories began separating long before renewal, but a commercial system organized around bookings may not see it. SaaS commercial organizations were designed to turn qualified demand into contracts. Complex AI organizations increasingly have to turn use cases into sustained consumption. The first is naturally a funnel. The second is naturally a cycle.
That shift is not uniform. Embedded copilots and task-automation features can still behave commercially much like SaaS modules. But engineered AI products, including configurable agents, workflow-transforming systems, and customized deployments, require a go-to-market reset with new organizational structures and roles. This article explains the commercial logic behind that reset and the redesign questions it raises.
Six Shifts Are Rewriting the Sale of AI Products
Buyers are not short of AI options. They are seeking greater certainty about which ones will deliver value. A new BCG CRO survey found that 51% of CROs report greater demand for customized proof of value. Buyers increasingly expect this to reflect their own data, processes, and environments, moving the burden of proof earlier in the sales process.
At the same time, AI products are evolving faster than many customers can evaluate and absorb them. That puts more weight on choosing the right problem before proving the product. Discovery has to narrow the field. Sellers need to help customers identify a use case worth pursuing, define the value at stake, and establish what would constitute sufficient proof to keep investing. How far they should narrow the field without underselling the rest of the portfolio remains an open question. In complex domains, industry credibility becomes the price of admission because that judgment depends on understanding the workflow, not simply the product.
Winning the use case increasingly means winning priority in the customer’s AI budget. Buying decisions often span functions, and some customers are funding new AI purchases by reallocating money from other vendors. Incumbent application providers and AI-native vendors can therefore find themselves competing for the same dollar. Whether those budgets remain centralized or eventually migrate back into application budgets is still unclear. Vendors have to make the use case credible enough to win the customer’s money, attention, and organizational capacity.
Appetite for open-ended strategy-first selling is fading as well. Buyers that have already been through maturity assessments and broad strategy exercises are favoring bounded, testable ways to move from discussion to execution. Time-bound bootcamps, value-discovery exercises, and pilots can provide a clearer path to a product decision, although questions remain about how far advisory work will recede or how it should be repackaged.
And the commercial burden does not end at signature. Purchased products do not reliably translate into sustained use on their own. For complex AI, some of the questions that determine value only become visible as the product meets the customer’s data, systems, workflows, and people. That is increasing expectations for continued technical engagement and value-realization support after the sale and turning post-sales into a more explicit revenue function.
Taken together, these shifts raise the commercial stakes on both sides of signature. AI product vendors must help customers choose where to place their bets, create enough evidence to keep those bets moving, and stay engaged long enough for contracted products to turn into continued consumption. (See Exhibit 1.)
The Funnel Becomes a Flywheel
A signed contract can contain very different commercial realities. One use case may be moving into production, another struggling to gain adoption, and a third ready to expand. What happens at each stage affects how much of the contract is ultimately consumed and where the next opportunity comes from.
The conventional funnel works when deployment complexity is low, workflow change is limited, and realized revenue does not depend heavily on what happens after signature. For embedded copilots and simpler task-automation products, that motion may still work well. But it starts to break down as deployment becomes engineering-intensive, customer workflows and behaviors have to change, and revenue increasingly depends on activation, usage, and expansion over time.
The pressure appears throughout the traditional funnel. Buyers may refuse to engage without credible proof, while a successful demo may show that the technology works without creating conviction that the product will work in the customer’s environment. Production exposes data, integration, governance, and process constraints that a pilot can sidestep. Gartner found that by the end of 2025 at least half of generative AI projects had been dropped after proof of concept, often because of poor data quality, inadequate risk controls, escalating costs, or unclear business value. Adoption can then stall when workflows and behaviors fail to change. By renewal, the decisive evidence has accumulated over months of usage and realized outcomes. Increasingly, signature is merely one moment in the commercial relationship rather than where the sale is won.
A flywheel is better suited to that reality because evidence and consumption continuously shape what happens next. Upstream proof builds trust in the use case. Outcome-focused pilots create a clearer path to production. Deployment becomes iterative as constraints emerge, while post-sales engagement helps turn deployment into sustained use. Usage signals reveal where the vendor should intervene, expand, or begin the next round of discovery. Several use cases can be moving through an account at once, with evidence from one altering the trajectory of another. (See Exhibit 2.)
Two consequences follow for the commercial model.
Pre-sales and post-sales carry more commercial weight. Buyer demand for outcome proof puts more substantive work into pre-sales, and that work often continues into deployment. Expansion loops back into discovery, while renewal becomes the accumulated result of deployment, adoption, and realized value. The close itself accounts for a smaller share of the work that determines commercial success.
Account economics increasingly need to be forecast by workload. Deal value alone says little about how much of an AI product will ultimately be used. Use-case pipeline, consumption velocity, and expansion potential provide a fuller view. Vendors that stop at technical delivery risk capping their own expansion potential. For complex AI products, that is pulling vendors into work that once sat with the customer or a systems integrator, from workflow integration and decision rights to change support and adoption.
The Commercial Organization Moves Beyond the Close
There is no single new operating model for every AI product. Companies need to differentiate the commercial motion according to product and deployment complexity. A standardized task-automation product may require relatively little intervention after the sale. A configurable agent or workflow-transforming product may demand deep discovery, iterative deployment, and sustained adoption support. Applying the same model across both can make simpler products unnecessarily expensive to sell, while leaving complex ones without enough support to reach sustained consumption.
That distinction has to carry through the operating model. Within a complex account, several use cases may be under way at once, with one in proof, another entering production, and a third beginning to scale. Someone has to decide which opportunity moves next, where scarce technical expertise goes, when a struggling deployment needs intervention, and whether apparent contract value is turning into consumption. Those decisions change roles, coverage, incentives, and accountability.
The account executive becomes a value architect. The AE’s job increasingly includes sequencing use cases and orchestrating the cross-functional pod needed to move them forward. As AI absorbs more sourcing, qualification, and routine outreach, the role increasingly shifts toward work that requires judgment. The strongest AEs will be distinguished by their ability to see the account as a progression of use cases, turning success in one into the opening for another.
Technical ownership spans the pre- and post-sales divide. The boundary between pre-sales and post-sales engineering is dissolving because proof, deployment, and adoption draw on the same accumulated understanding of the customer’s data and workflows. A clean handoff can discard the context needed to get a promising pilot into production. Engineers working through deployment are also often the first to spot the next value-creation opportunity. Roles such as forward-deployed engineers, deployment strategists, and AI outcome managers are emerging in response. The title matters less than ensuring continuity. Leading organizations have to decide which expertise stays with a use case, when it can step away, and how context is preserved when ownership changes.
Post-sales becomes a deliberate commercial investment. Customer success is bifurcating. Generalist coverage is being supplemented by technical and domain experts where sustained use requires deeper intervention. Professional services is becoming a strategic battleground for the same reason. Many vendors subsidize value discovery, bootcamps, and even the first pilot, with monetization coming later through scaled accelerators, center-of-excellence support, or advanced engineering. The CRO’s task is to determine where additional expertise materially improves the likelihood or pace of consumption, and therefore where scarce capacity should be dedicated, pooled, monetized, or provided by partners. Those choices will differ by segment; a specialist-heavy motion that works for the largest strategic accounts may be unaffordable beyond them.
Longer commitments make consumption harder to ignore. Some vendors are exploring longer commercial commitments, including five-year terms and extended price protection. Fewer renewal decisions do not remove the commercial risk. More of it shifts into underconsumption over the life of the contract. An account that stalls at its first use case is fundamentally different from one whose consumption compounds, even if both have made the same contractual commitment. Consumption signals therefore need to feed directly into forecasting, account reviews, and resource allocation. Compensation has to reinforce the model. AEs need incentives to grow the total customer relationship, while technical and product specialists need credit for the adoption, consumption, and expansion they materially influence. Companies that have begun making these changes are already finding where the model strains in practice. (See the sidebar, “Four Lessons from Early Client Work.”)
Four Lessons from Early Client Work
Consumption cannot be managed until it can be seen. Existing commercial systems are not built to measure AI usage reliably or make it visible to sellers. Companies need systems that can track consumption by customer and use case, monitor changes, and funnel that information directly into customer relationship management systems and the account-management workflows that sellers use. Until that capability is in place, commercial teams are effectively flying blind.
New AI products need seller conviction. Sellers need a clear reason to prioritize new AI offerings over established products. Leading organizations create that conviction by anchoring the sales story in a specific customer problem, giving sellers evidence that they can use to show measurable impact and aligning seller incentives that reward them for putting time behind the new product and growing its adoption.
Ownership of consumption cuts across traditional functional boundaries. No one function owns the flywheel, and that can create confusion about how teams should work together. A practical starting point is to map the stages from deployment through adoption; identify the capabilities needed at each stage; establish clear ownership across sales, customer success, services, and technical teams; and identify where continuity between roles is critical. That exercise creates the foundation for designing services, handoffs, and coverage as an integrated model.
The talent gap is proving as important as the operating-model gap. Organizations often find meaningful gaps between the capabilities their existing teams have and those required to sell and activate AI effectively. Companies need to identify which capabilities matter most for their new model, assess the existing workforce against them, and then make explicit choices about where to build capabilities internally versus bring in new talent.
The Hard Questions Come Next
The market is still evolving, but vendors do not need to wait for it to settle. A CRO can test the model with a simple thought experiment. Strip bookings out of the picture. If revenue reflected only what customers consumed, where would it rise, where would it disappear, and why? The answer exposes where contracted value is turning into consumption, where it is stalling, and which parts of the commercial model are helping or getting in the way.
From there, several questions become especially important.
Where does the flywheel need to apply? Which products, use cases, and customer segments require intensive discovery, proof, deployment, and adoption support, and which can still be sold efficiently through a more conventional motion? Should that distinction turn on value at stake, technical complexity, customer readiness, regulatory burden, or some combination?
What has to be proved, and when, for a use case to keep moving? What evidence moves a customer from interest to pilot, from pilot to production, and from production to wider use? Who should help create that evidence, what should the customer contribute, and which parts of that work should the vendor fund, charge for, or standardize?
Who owns progress through the cycle? Which roles need continuity from discovery through adoption and expansion? Where can ownership change without losing momentum or context? And who is ultimately accountable when contracted capacity fails to become consumption?
What should the organization learn from consumption? Which signals reveal that a deployment is accelerating, stalling, or ready to expand? How should those signals change account priorities, resource allocation, forecasting, intervention, and the timing of the next use case?
How should incentives and commercial terms reinforce the behavior the model requires? What should AEs, technical specialists, customer success teams, and other contributors be rewarded for, and over what time horizon? How should commitments, pricing, crediting, and incentives change when revenue depends as much on adoption and expansion as on the initial contract?
How much of this model can the company afford to deliver, and for whom? Where does high-touch technical and domain support generate enough incremental consumption to justify its cost? What can be productized or pooled, what needs dedicated coverage, and how should the answer change across strategic accounts, the broader enterprise base, and simpler offerings?
The playbook for selling AI products remains under development—and nobody has all the answers yet. The vendors who figure out this consumption model first will force the rest of the market to play by their rules.
