Spending is shifting toward providers who can prove value under production conditions.
Worldwide end-user spending on AI models and platforms is projected to reach $64 billion in 2026, up 63.4% from $39 billion the year before, according to Gartner. Gartner analyst Arunasree Cheparthi attributes that growth to a shift in what buyers are screening for: spending is moving toward providers that can demonstrate clear value across cost, latency, performance, and reliability, rather than providers with the strongest demonstration in a controlled setting.
That distinction, between what a vendor can show and what a vendor can prove under production conditions, is the central problem in enterprise AI evaluation right now.
Why Demo Performance Is a Poor Predictor
A demo is built to succeed. The vendor selects the data, controls the environment, and presents the system at its best, so a demo answers a narrower question than most buyers think. It shows what the system can do under ideal conditions. It does not show how the system performs against an enterprise’s actual data quality, usage volume, or edge cases—the conditions that determine whether a deployment holds up once it leaves the pilot.
KPMG’s research on enterprise AI agent strategy identifies a related risk that compounds this problem at scale: when AI tool adoption is driven vendor by vendor, function by function, without centralized evaluation, the result is tool sprawl, inconsistent controls, rising complexity and cost, without a corresponding increase in enterprise-level outcomes. Each tool may have performed well in its own evaluation. However, the enterprise as a whole ends up managing a fragmented set of platforms that were never evaluated against each other or against a shared standard.
What Gartner’s Data Suggests Buyers Should Screen For
The four criteria Gartner identifies, cost, latency, performance, and reliability, share a common property: none of them can be verified from a vendor’s own presentation. Cost requires modeling total spend at production volume, not pilot volume, since AI pricing is typically usage-based and the unit economics that look favorable in a small pilot often change at scale. Latency and performance require testing against the enterprise’s own data and workflows, not the vendor’s benchmark dataset. Reliability requires observing the system over a sustained period, since a system that performs well in a two-week trial can behave differently once it is handling a wider range of inputs over months.
None of these four can be assessed inside a single demo meeting. Each requires a structured evaluation period, run against the buyer’s own conditions, before a contract decision, which is the gap between the current default evaluation process, largely based on presentations and reference calls, and the process Gartner’s data suggests the market is actually shifting toward.
Evaluating for the Enterprise, Not Just the Use Case
KPMG’s tool sprawl finding points to a second dimension most vendor evaluations miss: how a new platform fits alongside what the enterprise has already deployed. A tool evaluated purely on its own merits, in isolation from the rest of the AI stack, can pass every individual test and still add net complexity if it duplicates a capability another platform already provides, or if it cannot be governed under the same controls as the rest of the environment. Centralized evaluation, weighing a new platform against the existing stack rather than against its own feature list alone, is what KPMG’s research identifies as the difference between AI investment that compounds and AI investment that fragments.
For enterprises evaluating a new AI platform or vendor, the deciding question is whether the system’s cost, latency, performance, and reliability hold up under the buyer’s own production conditions, and whether adding it strengthens or fragments the AI environment the enterprise is already responsible for governing.
