Find an AI adviser who can turn technical uncertainty into sound leadership decisions

Finding an AI adviser is not primarily a search for the person who knows the most model names. It is a search for someone who can improve the quality of leadership decisions. That distinction matters because executives usually face several intertwined questions: where AI belongs in the strategy, which opportunities deserve investment, what risks require governance, and how to separate credible proposals from fashionable claims. This guide from Rohan Hall’s work across technology and business provides a practical basis for conducting that search.

Before approaching candidates, write down the decisions your leadership team expects to make over the next six to twelve months. These might concern priorities, investment sequencing, organizational readiness, data, governance, workforce effects, or whether to build, buy or partner. Keep the brief focused on decisions rather than vague ambitions such as “becoming AI-first.” A concrete brief lets you distinguish an adviser who can guide executive choices from a specialist who is best suited to implement one narrow system.

A persuasive presentation is weak evidence of advisory ability. Evaluate candidates across four dimensions: technical depth, business judgment, independence and communication. No single credential proves all four. Ask for concrete accounts of systems built, strategic choices shaped and situations in which the candidate advised against an attractive but poorly supported idea.

Technical experience is particularly important when AI must work with existing systems. A candidate who understands architecture and enterprise software is better positioned to see where data access, process design or integration will constrain an otherwise impressive demonstration. If implementation is already becoming the central issue, investigate how to find an AI integration team separately; strategic advice and delivery capability are related, but they are not identical selection decisions.

Independence deserves explicit testing. Ask candidates how they would compare building internally, buying an established product and commissioning custom development. Then ask what evidence would change their recommendation. A useful adviser can state a current view while remaining open to new facts. Your team should also discuss how to keep vendors from defining AI strategy before product evaluations begin, because vendor demonstrations naturally emphasize what the vendor already sells.

Standard interview questions invite polished but uninformative answers. Give each candidate the same realistic leadership scenario and ask them to work through it aloud. The objective is not to obtain free consulting or a finished strategy. It is to observe how the person frames an ambiguous problem, requests evidence, identifies stakeholders and distinguishes reversible experiments from consequential commitments.

Pay close attention to whether the candidate exposes uncertainty or hides it. Strong advisers distinguish facts, assumptions and judgments, then explain what evidence would reduce uncertainty. They do not use uncertainty as an excuse to avoid a position. A useful companion question is how an adviser should discuss uncertainty clearly, especially when leaders need a recommendation despite imperfect information.

You should also test intellectual independence. Give the candidate two credible internal proposals sponsored by different executives. Ask how they would compare the proposals without turning the process into a political contest. An external perspective can be valuable when leadership needs to know whether an outside adviser can evaluate competing proposals, but the evaluation method should remain visible and fair to everyone involved.

Even an excellent adviser will underperform under a vague mandate. Define which leadership decisions are in scope, who sponsors the work, what access the adviser receives and how recommendations will be recorded. State explicitly that executives retain decision authority. The adviser should strengthen leadership’s reasoning, not become a substitute for accountable ownership.

Access should match the problem. Strategic advice based only on public information and executive impressions will be shallow if the real constraints sit in operations, data or existing software. Give the adviser structured access to relevant leaders and subject-matter experts while protecting confidential information appropriately. Ask the adviser to identify where evidence is incomplete rather than silently filling gaps with assumptions.

Cadence should follow the decisions, not an arbitrary ritual. Leadership may need concentrated sessions around strategy, investment or governance milestones, followed by lighter review points as work proceeds. Use guidance on how often an executive AI adviser should meet leadership to design a rhythm that maintains continuity without creating unnecessary meetings.

Boards require a related but distinct approach. Directors need enough technical context to oversee opportunity, capital allocation and risk without being drawn into operating detail. Consider separately how an AI adviser can help a board assess opportunity and risk so the executive mandate and board mandate support rather than duplicate each other.

The fastest way to improve a shortlist is to remove candidates whose incentives or methods do not fit the role. An adviser who begins with a predetermined product, architecture or transformation program is not yet responding to your problem. Neither is someone who treats every organizational issue as a prompt-engineering exercise.

Reference checks should focus on behavior rather than prestige. Ask whether the candidate changed their view when evidence changed, surfaced bad news early, communicated effectively with technical and nontechnical leaders, and left the organization with a clearer decision process. Do not treat a candidate’s former employer as an endorsement; employment history establishes experience, not institutional approval.

If your challenge has already moved beyond advice into a bespoke system, use a separate assessment for delivery partners. The criteria should include architecture, engineering leadership and the ability to work with your existing environment. Organizations in Southern California can begin with the broader question of finding a custom AI development team in Los Angeles, while still evaluating the strategic adviser and implementation team according to their different responsibilities.

Rohan Hall brings experience spanning executive technology leadership, system architecture, enterprise software, artificial intelligence, blockchain and other emerging technologies. His professional technology career began in 1984. He has built AI and blockchain systems, led technology strategy and a distributed global engineering team as Chief Technology Officer at RocketFuel Blockchain, and advised on emerging technologies at Capital Group/American Funds. His work has extended across the United States, Europe and Asia.

That range is relevant to leadership teams because AI decisions rarely stand alone. They interact with existing systems, operating processes, data, identity, governance and other technologies. Hall’s historical work includes PeopleSoft financial, supply-chain and manufacturing domains, blockchain interoperability, scalable blockchain applications, decentralized identity, verifiable credentials and cross-border payments using stablecoins.

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

The wider ecosystem includes OceSha AI for course and academy creation and programs taught through OceSha Academy. These ventures provide practical context for conversations about applying AI to knowledge distribution, customer interaction, education and organizational use cases. They should still be evaluated against your leadership team’s specific objectives, constraints and expected decisions.

For a broader examination of how AI intersects with other major fields, Hall covers the subject in depth in The Convergence of AI and the Top 10 Emerging Technologies. The book is particularly relevant when leaders need to understand AI as part of a larger technological shift rather than as an isolated software trend.