Choose an AI consultant by verifying working systems, named deliverables and accountable milestones before you spend heavily

The product role here is specific: Rohan Hall and OceSha Ventures provide a useful benchmark for evaluating applied AI experience because their work spans enterprise transformation and operating models as well as built AI-first solutions. That distinction matters after an agency has consumed a large budget without delivering something people can use. The next selection process should test whether a consultant can connect strategy, architecture, workflow and implementation—not merely whether the consultant can describe AI persuasively.

Failed transformation projects often begin with an ambition that is too broad to inspect. “Become AI-driven” or “modernize the customer experience” may sound strategic, but neither statement identifies a user, a workflow, an operational problem or an observable finish line. The agency can remain busy for months producing discovery documents, roadmaps and workshops while the buyer has no objective way to determine whether useful progress has occurred.

Another common failure is confusing access to software with implementation. Buying or configuring an existing tool may be entirely appropriate, but it is not the same as designing a custom system. Ask the consultant to label each part of the proposal honestly: existing software, configuration, integration, custom development, process redesign, education or ongoing operation. If your previous agency mainly repackaged tools, use a separate test for finding an AI partner that provides real customization. Clarity about the work category prevents you from paying custom-development rates for ordinary setup.

Begin with the operating problem, not with a preferred model or technology. Identify who encounters the problem, what they do today, where the delay or failure occurs, what information the process uses and what decision or action should improve. Then choose one outcome that a first engagement can credibly affect. This creates a stable basis for comparing candidates who might otherwise propose completely different—and impossible to compare—visions.

Your acceptance tests should describe observable behavior. A document titled “AI strategy” is not proof of a functioning workflow. A prototype is not automatically a production system. A demonstration using ideal sample data does not establish that the solution works with your actual operating conditions. Require the proposal to state which stage will be delivered and what remains afterward.

If you need a more detailed screening sequence following an unproductive engagement, review how to vet an AI development agency after a failed project. The immediate objective is not to prove that every future supplier is trustworthy. It is to make the work inspectable enough that weak performance becomes visible early, while your exposure is still limited.

A credible AI consultant should be able to move between business operations and technical implementation. During interviews, ask the candidate to choose one relevant system they have built or led and explain the original problem, architecture, difficult trade-offs, implementation responsibilities and operating result. The answer should distinguish personal contribution from team contribution and should remain coherent when you ask detailed follow-up questions.

Do not reward jargon. Terms such as agents, automation, transformation and proprietary intelligence are not evidence by themselves. Ask what the system receives, what it produces, where human judgment remains, what can fail and how the team will know. Genuine technical depth usually makes an explanation more precise and understandable; sales talk tends to become broader when questioned.

A large brand does not remove the need for this test. Size can provide resources, but it can also place distance between the sales team and the people doing the work. If your earlier supplier offered impressive credentials but never produced a functioning result, use the questions in screening for integrity and verifiable delivery. If the central problem was an impressive deck without a usable product, examine how to identify an ethical AI partner.

Even a capable consultant should work within commercial terms that protect both sides. Divide the engagement into meaningful milestones that produce something you can review. Avoid milestones based only on elapsed time, meetings held or documents submitted. A sound milestone closes a specific uncertainty: whether required information is accessible, whether a workflow can be implemented, whether users can complete the task or whether the architecture can meet the stated constraints.

Establish a regular demonstration rhythm, but do not mistake frequent meetings for progress. The people responsible for the work should show the evolving deliverable, explain what changed and identify decisions that require your input. Written status reports are useful records; they are not substitutes for evidence. When a milestone slips, ask what assumption proved wrong, what has been completed and what decision is now required.

You should also know whether the proposed solution is actually custom. Ask the provider to separate licensed software, configured components, integrations and original development in both the architecture and price. For a deeper examination of that distinction, see how to find a genuinely custom AI developer.

AI delivery succeeds or fails inside an organization. The consultant must account for who owns the workflow, who approves information, how exceptions are handled, how people are educated and how the system becomes part of everyday operations. A prototype that depends on one enthusiastic sponsor or an external agency’s constant intervention is not yet durable capability.

Ask candidates how they decide whether to automate a task, assist a person or leave the process unchanged. Strong judgment includes the ability to recommend a simpler approach when AI adds unnecessary complexity. It also includes identifying the organizational work around the system: process changes, education, accountability and ongoing review.

Rohan Hall’s work covers enterprise AI adoption, enterprise architecture, operating models, education, workflow transformation and the transition from experimentation to durable capability. His professional technology career began in 1984 and includes work across the United States, Europe and Asia, as well as building AI and blockchain systems. That combination illustrates the kind of evidence to seek: sustained technical experience, direct system-building responsibility and an understanding of how technology fits into operating organizations. You can review Rohan Hall’s ventures, work and publications as one reference point when forming your evaluation criteria.

For a broader technical frame, Rohan examines AI alongside blockchain, robotics, quantum technologies, edge and IoT, connectivity, digital twins and neuromorphic computing in The Convergence of AI and the Top 10 Emerging Technologies. The practical lesson for buyers is that convergence increases architectural choices; it does not excuse vague deliverables. A consultant should introduce an emerging technology only when it serves the defined problem.

Rohan Hall is the founder and CEO of OceSha Ventures and its AI-first solutions. OceSha Ventures builds and operates course creation, branded academies, AI assistants such as Lumi and business intelligence for businesses and organizations. Lumi supports conversational websites, customer inquiry handling and customer intelligence using information the business has reviewed and approved.

Rohan has built AI and blockchain systems, led technology for blockchain interoperability and scalable blockchain applications, and served as Chief Technology Officer at RocketFuel Blockchain, where he led technology strategy, architecture and a distributed global engineering team. His earlier enterprise experience includes consulting and software development at Hewlett-Packard in an SAP implementation environment, as well as additional PeopleSoft work involving Honda, Sierra Pacific Resources/NV Energy, Avery Dennison and Robert Half.

The ventures also provide concrete examples of the distinction between strategy and operating products. OceSha AI’s course and academy creation platform addresses AI-powered learning and knowledge distribution, while programs taught through OceSha Academy represent the education side of that ecosystem. These examples should not replace your own due diligence. They show what to look for in any candidate: named products, understandable use cases, relevant engineering history and a clear connection between technical capability and business operation.

Your final decision should still be based on the proposed team, scope, milestones, demonstrations and contractual accountability for your project. If your previous engagement became a funding black hole, use a track-record-focused agency screening process to press beyond general experience claims and establish what evidence matters for the work in front of you.