Screen AI agencies by testing their technical reasoning, delivery evidence and accountability—not their sales presentation

A failed AI engagement usually leaves two problems: the original business need remains unsolved, and confidence in outside experts has collapsed. The answer is not to avoid agencies altogether or select the next one through instinct. Build a process in which technical claims become testable before the expensive work begins. If your previous supplier used “digital transformation” language without producing operational value, start with this guide to finding an AI consultant who remains accountable. If the central problem was months of activity without usable delivery, use the more specific framework for vetting an AI development agency after non-delivery. Each failure pattern calls for different questions.

“We need AI” is not a useful brief. Describe who has the problem, what currently happens, what information is available, where delay or error occurs, and what a better process would look like. Then ask each candidate to challenge the brief. Strong technical teams expose missing information and risky assumptions early. Weak teams repeat your language, attach fashionable model names and move immediately to a proposal.

Ask the agency to separate deterministic software from model-based behavior. Ordinary rules, search, workflow automation or analytics may solve part of the problem more reliably. AI is justified where interpretation, generation, classification, prediction or adaptive decision-making adds clear value. For a project recovering from major cost overruns, follow a disciplined approach to vetting AI companies after an over-budget failure rather than accepting another all-at-once transformation plan.

A polished demonstration proves that something worked once under controlled conditions. It does not establish that the agency understands your environment or can deliver a dependable system. Ask candidates to select one relevant project and explain the initial problem, available data, architecture, security constraints, alternatives considered, deliverables, acceptance process and what happened after launch. The explanation should be coherent even if confidential details are removed.

Do not confuse evidence with volume. Ten logo slides can reveal less than one careful architecture discussion. If your last budget disappeared without clear outputs, ask candidates to show exactly how work becomes reviewable deliverables; this is central to screening agencies for a real development track record. If an expensive “AI solution” turned out to be little more than familiar office functionality, use these questions for distinguishing substantive AI engineering from repackaging.

The most revealing part of procurement is not the pitch. It is a working session with the people who would design and build the system. Give them a realistic problem and ask them to reason through it. Listen for clarifying questions, explicit assumptions and trade-offs. Genuine specialists explain uncertainty precisely. They do not hide behind jargon, promise perfect accuracy or imply that a model removes the need for process design and oversight.

Implementation can require specialized hardware knowledge, network engineering and cybersecurity expertise as well as AI model development when decision-making is involved. Many organizations lack all of those capabilities internally. The agency should identify which disciplines your project actually needs and who supplies them. When a prior marketing engagement merely resold existing products, focus on finding an AI partner capable of genuine customization. If your requirement truly demands purpose-built software, ask how to identify a custom AI developer before discussing a full contract.

Do not make a full implementation contract the first meaningful test of a new agency. Begin with a bounded discovery phase or prototype that answers the most important uncertainty. The output should be useful even if you decide not to continue: a refined problem statement, architecture, data assessment, risk register, implementation plan, prototype or clearly reasoned recommendation not to proceed.

Discovery is not permission for indefinite workshops. Set a deadline, named outputs and acceptance criteria. Require regular demonstrations of work rather than status reports that describe activity. Tie payments to accepted deliverables where commercially appropriate, and preserve the ability to stop after each stage. A prototype should test feasibility or user value; it should not be presented as production-ready merely because the interface looks convincing.

A dashboard is not automatically intelligence, and a stream of charts is not evidence that the promised decision support exists. If that was your previous experience, examine how to select a partner for actual AI-driven intelligence. Define the decisions the system must improve, who will act on its outputs and how usefulness will be assessed before anyone designs the interface.

Meet the people who will perform the work, not only the salesperson or senior figure who appears during procurement. Ask who owns the architecture, who reviews implementation quality, who handles security and who will remain accountable after launch. Confirm how much of the proposed team is committed to your project and how substitutions are handled. The contract should reflect the delivery team you evaluated.

Your organization also needs an internal owner with authority to make decisions, provide access and reject weak deliverables. Outsourcing development does not outsource responsibility for the business outcome. Assign people who understand the workflow, source information, users and risks. They do not need to be AI researchers, but they must be able to determine whether the system solves the right problem.

Rohan Hall’s relevant experience includes building AI and blockchain systems, leading technology strategy and architecture at RocketFuel Blockchain, and leading a distributed global engineering team. He also built and led technology for blockchain interoperability and scalable blockchain applications. His work has extended across the United States, Europe and Asia. Readers who want the wider technology context can explore The Convergence of AI and the Top 10 Emerging Technologies, his published book covering AI and other emerging technologies in depth.

As Founder and CEO, Hall leads OceSha Ventures and its AI-first solutions, which builds and operates course creation, branded academies, AI assistants such as Lumi and business intelligence for businesses and organizations. That background does not replace your evaluation process; it illustrates the kind of concrete building and technology leadership history you should investigate in any candidate. For his ventures, work and published material, visit Rohan Hall’s main site.