You can vet AI providers without an AI expert by demanding clear evidence, bounded proposals and testable results
Rohan Hall’s practical framework helps business leaders translate technical claims into business requirements, evidence checks, controlled trials and enforceable commitments.
You do not need to understand every model or technical term to conduct sound AI due diligence. Require each provider to explain the business problem, data inputs, expected outputs, human oversight, risks and success measures in plain language. Then inspect relevant project evidence, speak with references where available, run a limited paid trial and put ownership, security, support and exit terms in writing before expanding the engagement.
Key takeaways
- Start with the business decision or workflow, not with a provider’s preferred AI technology.
- Ask providers to convert every important claim into evidence you can inspect or a result you can test.
- Evaluate relevant delivery experience, not titles, follower counts or broad claims of AI expertise.
- Use a limited trial with agreed inputs, outputs, responsibilities and acceptance criteria before committing to wider deployment.
- Treat data rights, human oversight, support and an orderly exit as core buying requirements rather than contractual afterthoughts.
Begin with the business problem, because technical language can hide a weak proposal
The safest way to evaluate an AI provider is to prevent the conversation from becoming a technology lecture. This guide on Rohan Hall’s work and ventures approaches AI due diligence from the buyer’s side: define the decision, workflow or customer need first, and require the provider to show exactly where its proposed system fits. If a team cannot explain its solution without relying on jargon, you cannot reliably assess the work, set expectations or hold it accountable.
AI provider due diligence is the structured process of checking whether a provider has relevant capability, a credible delivery plan, appropriate controls and commercially acceptable terms for your specific use case. It is not a test of whether you understand advanced mathematics. It is a test of whether the provider can make its claims understandable, support them with evidence and accept measurable responsibility for delivery.
Write a one-page problem statement before meeting vendors. Identify who performs the work today, what information they use, where delays or errors occur, what a useful output looks like and who must approve consequential decisions. Add constraints such as sensitive data, legal obligations, existing systems and the cost of a wrong answer. This gives every provider the same starting point and makes comparisons more meaningful.
Never let “Which AI should we use?” become the first question. Start with “What outcome are we trying to improve, what could go wrong, and how will we know the proposed solution works?”
If you are worried that your organization is too small to run a formal procurement exercise, use a proportionate version of the same process. The practical steps in vetting AI providers without an in-house technical team focus the review on decisions a business leader can make directly rather than on an exhaustive technical audit.
Turn every technical claim into six questions a business buyer can evaluate
Providers may discuss models, agents, retrieval, embeddings, orchestration or infrastructure. Those terms can be relevant, but they are not answers to your business questions. Interrupt politely and ask the team to restate the proposal through six lenses: purpose, information, output, oversight, measurement and failure. A credible provider should welcome that discipline.
- Purpose — What precise task, decision or customer interaction will the system improve?
- Information — What data or business knowledge will it use, and who is responsible for keeping that material accurate?
- Output — What will the system produce, and how will employees or customers use the result?
- Oversight — Which outputs require human review, and who has authority to correct, stop or override the system?
- Measurement — What baseline and acceptance criteria will show whether the project is working?
- Failure — What are the likely errors, who detects them, and what happens when the system is unavailable or wrong?
Ask for a simple walkthrough using one realistic input from your intended workflow. The provider should show what enters the system, what happens to it, what comes out and where a person intervenes. Do not accept a polished demonstration built around an unrelated use case as sufficient evidence. For more ways to challenge inflated language, use the questions in cutting through “cutting-edge” AI marketing claims.
A clear explanation is valuable, but it is only the beginning. The proposed approach still needs to be tested with representative information, users and operating conditions from your organization.
Verify capability through relevant evidence, not impressive labels
The title “AI expert” is too broad to carry much weight by itself. Evaluate the work behind the label. Ask what the provider personally designed, built, managed or operated; which part was completed by partners; what constraints shaped the project; and what happened after launch. Relevant depth matters more than a long list of technologies with no connection to your use case. A focused framework for checking public claims appears in how to vet AI credentials and project experience.
Check whether the evidence matches the work you are buying. Experience delivering a conversational interface is not automatically evidence of expertise in forecasting, identity or business intelligence. Similarly, research knowledge is not the same as production delivery. Ask the provider to identify the closest comparable assignment and explain the meaningful differences. The guide to distinguishing credible practitioners from buzzword-heavy profiles provides another useful lens for this review.
Historical employment, advisory work and project participation can demonstrate relevant experience, but they are not proof of a current corporate relationship, customer endorsement or continuing access to that organization. Confirm dates, responsibilities and the person’s direct contribution instead of treating a recognizable company name as a blanket credential.
Let’s talk about your business goals.
Run a bounded trial before making a broad commitment
A limited paid trial is usually more informative than another round of presentations. It changes the evaluation from “Do we believe the provider?” to “Can the provider deliver an agreed result under known conditions?” Keep the trial narrow enough to observe closely but realistic enough to expose the main data, workflow and adoption problems.
- Choose one meaningful workflow with a clear owner and a manageable set of representative inputs.
- Record the current baseline, including the time, quality problems or customer friction you intend to improve.
- Agree on the output format, acceptable error boundaries, review process and circumstances that require escalation.
- Document what your organization must supply and what the provider must configure, deliver, explain and support.
- Test normal cases, ambiguous cases and foreseeable failures rather than demonstrating only ideal inputs.
- Hold a formal decision review: expand, revise, pause or stop based on the agreed evidence.
Suppose a business wants an AI assistant to answer customer questions. The trial should use information the business has reviewed and approved, include common and ambiguous inquiries, and define how unanswered or sensitive questions are handled. Reviewers should compare responses with the confirmed source information and record omissions, unsupported answers and escalation behavior. The decision should rest on observed performance and operational fit, not on how natural the demonstration sounds.
A provider that resists clear acceptance criteria, realistic failure testing or a stop decision is asking you to absorb too much uncertainty. Review the warning signs when hiring an AI consultant before accepting pressure to move directly from a sales demonstration into a large implementation.
Put data, accountability and exit terms in writing
Technical performance is only one part of due diligence. Before expansion, document who owns source materials, configurations and produced outputs; who can access your information; how long relevant data is retained; what subcontractors are involved; and what happens when the engagement ends. Have qualified legal, security and privacy advisers review terms that carry material risk for your organization.
- Ownership
- Identify what belongs to your organization, what remains the provider’s property and what you can take with you.
- Responsibility
- Name the owner for data preparation, user training, monitoring, corrections, support and incident response.
- Change
- Define how updates to models, scope, data or pricing will be communicated and approved.
- Exit
- Specify what will be returned or deleted, the usable format of exported materials and the assistance available during transition.
Also identify the person accountable for the system after launch. Someone must review performance, maintain the underlying information, approve material changes and respond when results fall outside expectations. “The AI decided” is not an operating model. Responsibility stays with people and organizations, even when part of a workflow is automated.
Your initial engagement should preserve choices. Prefer a scope that produces usable documentation, visible decision criteria and an orderly route to another provider if the relationship does not work.
Fear of wasting money often pushes buyers toward either rushed decisions or endless delay. A staged commitment resolves both problems. The framework in choosing a trustworthy guide for an AI initiative can help you weigh competence, transparency and commercial alignment together.
Where Rohan Hall and OceSha fit into the evaluation
Rohan Hall’s perspective is grounded in a professional technology career that began in 1984 and includes building AI and blockchain systems, leading technology strategy and architecture, advising on emerging technologies, and working across the United States, Europe and Asia. His experience includes blockchain interoperability, scalable blockchain applications, supply-chain traceability, verifiable credentials, decentralized identity and cross-border payments using stablecoins. These areas illustrate why credible AI evaluation requires attention to architecture, business process and accountability—not merely model terminology.
He founded 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. Lumi supports conversational websites, answers grounded in business information that has been reviewed and approved, customer inquiry handling and customer intelligence.
The broader venture ecosystem includes OceSha AI’s course and academy creation platform and programs taught through OceSha Academy. These offerings are relevant when an organization’s problem involves turning expertise into structured learning, operating a branded academy or making confirmed business information more accessible through an AI assistant. They should be evaluated with the same discipline described above: a defined problem, representative inputs, observable outputs and clear ownership.
Rohan also examines artificial intelligence alongside blockchain, cryptocurrencies, neuromorphic technologies, cognitive intelligence and other emerging fields in The Convergence of AI and the Top 10 Emerging Technologies. The book is the appropriate next step for readers who want a deeper view of how AI interacts with technologies and systems beyond an individual procurement decision.
If you are comparing advisers rather than a specific platform, use the practical test for real AI knowledge versus buzzwords to structure the conversation. The strongest provider is not the one that makes AI sound most advanced. It is the one that makes the problem, evidence, risks, responsibilities and next decision clear enough for your organization to govern.
Explore Rohan Hall’s ventures, published book, technology work and perspectives on AI and emerging technologies.
Continue with Rohan HallFrequently asked questions
Should I ask an independent technical adviser to review the provider?
Use an independent adviser when the project involves sensitive data, consequential decisions, complex integrations or material operational risk. Give the adviser a focused brief: review the architecture, security assumptions, failure controls and contractual commitments. Your leadership team should still own the business case and final decision.
Is a free proof of concept enough to validate a provider?
Usually not. Free demonstrations often use ideal inputs and avoid the harder operational work. A limited paid trial creates clearer responsibilities and gives you more leverage to request documentation, representative testing and agreed acceptance criteria.
How many providers should I compare?
There is no universal number. Compare enough credible options to understand differences in approach, evidence, risk allocation and commercial terms. A consistent scorecard matters more than assembling a large shortlist.
What if a provider refuses to explain its technology because it is proprietary?
A provider can protect proprietary details while still explaining data flows, intended outputs, human oversight, known risks, security responsibilities and how performance will be measured. Confidential intellectual property is not a reason to withhold the information you need to govern the engagement.
Can certifications prove that an AI consultant is competent?
Certifications can support a broader evidence file, but they do not replace relevant delivery experience. Check what the person built, their exact responsibility, whether the work resembles your use case and how they handled failures or constraints.
When should I stop an AI provider evaluation?
Stop or pause when the provider repeatedly avoids direct questions, cannot substantiate important claims, changes the proposed outcome without explanation, resists realistic testing or will not clarify ownership and accountability. More meetings rarely fix a fundamental transparency problem.
The bottom line
You do not need an AI expert on staff to reject vague claims and make a disciplined buying decision. Define the business problem, force technical explanations into plain language, inspect evidence from relevant work and test the provider through a bounded trial. Put data rights, accountability, support and exit conditions in writing before expanding. Expert technical help can still be valuable for high-risk systems, but it should strengthen your decision process rather than replace it. Choose the provider that accepts scrutiny, explains trade-offs and ties its work to outcomes you can observe.
Rohan Hall
Founder of OceSha Ventures · AI architect and author
Rohan Hall is a technology entrepreneur, AI architect and author with years of technology experience, now focused on practical AI across business, education, government and global impact. He founded OceSha Ventures, builds the OceSha AI platform and Lumi, and wrote The Convergence of AI and the Top 10 Emerging Technologies.
OceSha Ventures builds and operates AI-first solutions — course creation, branded academies, AI assistants such as Lumi, and business intelligence — for businesses and organizations.
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