AI does not have a demand problem. It has a trust problem.
McKinsey found that 88% of organizations use AI in at least one business function, yet only about one-third have started scaling it.
Builders need to make useful products easier to believe. Buyers need a better way to separate evidence from a polished demo.
Part 1: For AI sellers and builders
Buyers need three things from you: a clear result, relevant proof and manageable risk.
Start with one job. “An intelligent automation platform” says little. “Cuts the time needed to prepare a first support reply from eight minutes to two” gives the buyer something to judge. Measure the baseline, state the improvement and explain the conditions behind it.
This matters because buyers increasingly research without speaking to sales. G2 reported that two-thirds prefer to contact sales only after doing their own research. Nine in ten AI power users would pay more, but only when the vendor demonstrates clear value or productivity gains.
Publish a short evidence record for every important claim. Include the task, sample, baseline, metric, result, product version and test date. Show failures as well as successes. NIST recommends testing AI under conditions similar to its intended use and documenting the test sets, metrics and tools.
Make the first purchase easy to reverse. Offer a small pilot with one workflow, fixed success criteria, a spending limit and a clear way to export data. In a survey of 100 CIOs, a16z found that leaders expected LLM budgets to grow by roughly 75%. More than 90% were testing third-party customer support applications, but many remained wary of unclear outcome pricing and unpredictable costs.
Explain data retention, permissions, logs and which actions need human approval. OWASP lists prompt injection, sensitive information disclosure and excessive agency among the major LLM application risks.
Finally, make the facts easy to find. G2’s 2026 research found that 51% of B2B buyers start software research with an AI chatbot more often than Google. Sixty-nine percent said those tools led them to choose a different vendor than expected, and 33% bought from a brand they had not known before.
Part 2: For AI buyers
Do not begin with “we need AI.” Begin with a costly, frequent problem.
Record how the work happens today: time, cost, volume, error rate and consequence of failure. Decide what improvement would justify a purchase. If success cannot be defined, the product cannot be judged fairly.
Write an acceptance test before watching demos. Give each product the same normal, edge and failure cases using representative data. Measure accepted outputs, corrections, latency and full cost, not just the first response.
Inspect access and control. Ask what the system can read, store and send. Find out whether your data is used for training, how long it is retained, which tools the agent can use and whether actions are logged and reversible. A writing assistant and an agent that can issue refunds should not face the same standard.
Use a scorecard. A useful starting point is 25% business fit, 20% evidence, 20% security and privacy, 15% integration and operations, 10% total cost, and 10% support and governance. Raise the threshold when failure could affect money, employment, health or rights.
Buying is the start of evaluation, not the end. Assign an owner, monitor quality and cost, record incidents and retest after meaningful changes. In Europe, transparency requirements for certain AI systems begin applying on 2 August 2026, so regulatory readiness belongs in the buying conversation.
Achrima is building an evidence-first marketplace for AI agents, applications, automations, APIs and services. Builders can show what their products actually do, while buyers can search and compare with more clarity. The aim is simple: make credible AI easier to sell and safer to buy. https://achrima.com/early-access
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