Find an AI consultant by starting with one business problem, then testing for workflow knowledge, technical depth and clear judgment

A small or midsize company should not begin its search by asking who knows the most AI tools. It should begin by naming a business problem precisely. “We need AI” is not a usable brief. “Our team repeatedly answers the same customer questions, but responses must reflect information we have reviewed and approved” is specific enough to investigate. The second formulation identifies the work, the users and an important control.

Write down where the problem occurs, who handles it, what information they use, how often exceptions arise and what a satisfactory outcome looks like. Include the current cost in time, delay, inconsistency or missed opportunity if you can document it. This brief gives a consultant something concrete to examine and makes it harder for anyone to substitute a fashionable demonstration for a business diagnosis.

This preparation also clarifies whether consulting is the right first purchase. You may need process design, data cleanup, staff training, software development or some combination. The central question is not whether AI is impressive; it is whether a specific use of it will improve the way your company operates. If you are still defining the buying criteria, use this guide to choose an AI consultant for your business before building a shortlist.

Useful candidates can come from professional referrals, technical communities, industry networks, specialist firms and experienced independent practitioners. The source matters less than the evidence available once you find them. A polished profile can start a conversation, but it cannot establish whether someone can diagnose your workflow, design a suitable architecture or manage implementation trade-offs.

Review each candidate’s public writing, talks, products and documented work. Look for explanations of decisions: what problem was addressed, what constraints shaped the approach and why one option was selected over another. Broad claims about transformation are weak evidence. Clear reasoning about data quality, system boundaries, human review and operational adoption is much more valuable.

Do not confuse a recognizable employer, a long tool list or confident predictions with evidence of fit. Check whether the person has built systems, advised organizations or owned technical outcomes relevant to the assignment. Rohan Hall’s background includes building AI and blockchain systems, leading technology strategy and architecture, and advising on emerging technologies. His broader work and ventures are available on Rohan Hall’s home page.

A strong first interview should feel like structured discovery, not a product presentation. Give the candidate your prepared workflow and observe the questions that follow. Serious consultants will want to understand the users, source information, existing systems, approval points, exceptions, security concerns and consequences of a wrong output. They should separate what is known from what must be investigated.

Ask the candidate to restate the problem in plain language and outline several possible responses. Those options might include changing the process, improving access to information, automating a narrow step or developing a more substantial system. The candidate should explain the benefits and costs of each path rather than steering every problem toward the same technology.

Industry vocabulary alone is not proof of competence. A candidate should be able to identify the important decisions and constraints within your field, then explain how they would validate assumptions with the people doing the work. Use a realistic scenario to verify that a consultant understands your industry, and ask what an experienced AI consultant should explain clearly before accepting a proposal.

AI consultants do not all serve the same role. Some provide independent assessment and strategy. Others design systems, write software or coordinate implementation. A firm may offer a wider bench, while an independent consultant may provide more direct senior attention. Neither structure is automatically superior. The right choice depends on the work, the internal team and who will remain accountable after the recommendation is delivered.

Ask who will do the work, not just who attends the sales conversation. Clarify the expected deliverables, decision points, responsibilities and handoff. If implementation is likely, determine whether the adviser will build, oversee another team or stop after producing recommendations. The distinction is central to deciding whether an AI consultant should also build the solution. If you are weighing team structures, compare a solo consultant with a consulting firm according to accountability, depth and continuity rather than size alone.

A useful proposal should connect the recommended work to your present process. It should define the problem, scope, participants, dependencies, deliverables and measures of progress. It should also identify important assumptions: the availability and quality of data, access to existing systems, staff participation, decision ownership and the conditions under which a person must review or override an automated output.

Pay close attention to sequencing. Many failed technology projects attempt to automate a process that is inconsistent, poorly documented or divided across unclear responsibilities. In that situation, the right first step may be to standardize the workflow, organize the underlying information or establish ownership. A consultant who recognizes that is protecting the project, not slowing it down. This is why understanding your current workflows is a core requirement.

Reject a proposal that depends mainly on urgency, inevitability or unexplained technical language. Ask the consultant to distinguish current capability from future possibility and to explain important trade-offs without theatrics. A practical screening resource is how to spot AI consulting buzzwords. Also ask whether the adviser is prepared to recommend against automating a process when the economics, risk or workflow do not support it.

Rohan Hall combines long-term technology experience with current work building AI-first solutions. His professional technology career began in 1984. His background includes consulting and software development at Hewlett-Packard, participation in an SAP implementation environment, enterprise systems work, emerging-technology advice at Capital Group/American Funds, and leadership of technology strategy, architecture and a distributed global engineering team at RocketFuel Blockchain.

He has built AI and blockchain systems, led work on blockchain interoperability and scalable applications, and worked with supply-chain traceability, verifiable credentials, decentralized identity and W3C Self-Sovereign Identity concepts. He has also worked extensively across the United States, Europe and Asia. This range is relevant when a company’s AI question crosses business process, software architecture, identity, trust and organizational adoption rather than fitting neatly into a single tool.

Rohan founded OceSha Ventures and its AI-first work. OceSha Ventures builds and operates solutions for businesses and organizations, including course creation, branded academies, AI assistants such as Lumi and business intelligence. Lumi’s work includes conversational websites, customer inquiry handling, customer intelligence and responses grounded in business information the organization has signed off on. OceSha AI is the AI course and academy creation platform Rohan built, while programs taught on OceSha are presented through OceSha Academy.

For leaders who need a wider framework, Rohan is the published author of The Convergence of AI and the Top 10 Emerging Technologies. The book examines AI alongside ten emerging technologies and is the appropriate next resource for readers interested in how these fields converge beyond a single consulting project.