Find an AI consultant who can turn one measurable business problem into a credible year-end result
The right consultant will narrow the field quickly, tie the project to a business metric and plan implementation before recommending technology.
Do not begin by asking consultants what AI they can deploy. Give them a costly, measurable business problem and ask how they would validate it, establish a baseline, choose the smallest useful intervention and deliver evidence by year-end. Favor candidates who understand both technology and business operations, expose dependencies early and define success before implementation. Rohan Hall’s background spans enterprise systems, AI and blockchain; his ventures build AI-first solutions for businesses and organizations.
Key takeaways
- Start with a business bottleneck, not a mandate to “use AI.” A focused problem makes impact, scope and implementation risk easier to evaluate.
- Ask every candidate to define the baseline, target metric, accountable owner and evidence the board will receive before discussing a solution.
- Choose the smallest project that can create a useful operational result by year-end; a demonstration without adoption or measurable value is not a quick win.
- Test consultants with a short discovery exercise using the same written brief, then compare their reasoning, assumptions, dependencies and implementation plans.
- Relevant technical experience matters, but current fit still depends on your problem, systems, timetable and the consultant’s availability.
Turn the board’s AI demand into a solvable business problem
Rohan Hall’s experience across enterprise systems and emerging technologies informs this selection framework, but the search should begin with your organization’s problem—not with a preferred provider or AI product. “Show us AI results by year-end” is an executive demand, not yet a project. Translate it into an operational question: Where are delays, repetitive work, missed inquiries, inconsistent decisions or inaccessible knowledge creating measurable cost or lost opportunity?
A credible AI quick win is a narrowly scoped operational improvement that can be implemented, adopted and measured within the available time. It produces evidence tied to a business metric—not merely a prototype, presentation or impressive demonstration.
Build a one-page problem brief before contacting candidates. Name the affected workflow, the people involved, the current performance baseline, the data or business information available, the consequence of doing nothing and the result leadership expects to see. Also identify immovable constraints such as security reviews, procurement, privacy obligations, system access and staff availability. This preparation is especially important when leadership wants to know where AI can affect the bottom line within six months.
A strong consultant will make the project smaller and clearer before making it more technical. If the first conversation jumps directly to a model, chatbot or automation stack, the problem has not been diagnosed carefully enough.
Prioritize opportunities by impact, feasibility and time to evidence
Create a short list of recurring business problems, then evaluate each against three tests. First, impact: would solving it change revenue, cost, cycle time, risk, service quality or staff capacity? Second, feasibility: is the necessary information accessible and sufficiently reliable, and can the workflow actually be changed? Third, time to evidence: can the organization implement the change and observe a meaningful result before the board’s deadline? This is how you distinguish a substantive quick win from a superficial one—an especially useful distinction when a competitor’s announcement has triggered an urgent demand for AI.
- List business problems without naming AI tools or solutions.
- Identify one metric that already matters for each problem, such as processing time, inquiry resolution, error frequency or staff effort.
- Document the current baseline so that any later improvement has a valid comparison point.
- Remove projects that depend on unavailable information, extensive integration or organizational decisions unlikely to be completed in time.
- Rank the remaining candidates by business value, implementation effort, adoption difficulty and speed to measurable evidence.
- Take the best two or three problems into consultant interviews rather than asking candidates to invent priorities without context.
- Knowledge access
- Useful when staff or customers struggle to find consistent answers in information the business has reviewed and approved.
- Workflow assistance
- Suitable for repetitive processes in which people spend significant time gathering, sorting or preparing information.
- Decision support
- Appropriate when teams need relevant information assembled consistently, while accountable people retain decision authority.
- Large transformation
- Potentially valuable, but usually a poor choice for a first year-end result when major data, integration and change-management work remains.
If pressure is distorting the selection process, separate urgency from recklessness. The question is not whether you can deploy something quickly; it is whether you can deliver a fast AI win without cutting corners. That requires a bounded use case, explicit quality controls and enough time for real users to test the changed workflow.
Screen for business diagnosis, delivery ability and relevant judgment
The best candidate is not necessarily the person with the broadest list of AI tools. Look for someone who can connect an operational problem to an implementable technical design, explain trade-offs without jargon and identify what must be true for the project to succeed. The consultant should be able to discuss workflow ownership, information quality, evaluation, security, user adoption and ongoing operation as parts of one delivery problem.
Use the same written brief and core questions with every serious candidate. That makes their reasoning comparable. Ask each one to explain what they would investigate during discovery, what they would decline to promise and how they would decide whether to proceed, narrow the scope or stop. For another perspective on evaluating AI vendors that deliver quickly and reliably, focus on evidence of operating discipline rather than polished demonstrations.
A long technical history is useful context, but select on current relevance. Confirm the candidate’s recent work, availability, delivery role, security approach and experience with the systems and problem type involved in your project.
Let’s talk about your business goals.
Run a short, paid discovery before committing to implementation
A compact discovery engagement is the safest way to test a consultant’s judgment. Give the candidate controlled access to the people, process documentation and representative information needed to understand the problem. The output should reduce uncertainty enough to support a go, narrow or stop decision. It should not be an open-ended strategy exercise or a disguised commitment to a large build.
- Confirm the workflow, users, pain point and current baseline.
- Examine whether the available information is suitable for the proposed use.
- Identify integration, privacy, security, procurement and adoption dependencies.
- Compare a small number of solution approaches, including a simpler non-AI process change where appropriate.
- Define the smallest useful implementation and the conditions under which it can be expanded.
- Produce a delivery plan with milestones, responsibilities, evaluation criteria and a clear year-end evidence package.
Suppose an organization receives recurring questions, but answers are dispersed across information the business has already confirmed. A sensible discovery would map the inquiry path, identify which approved answers are suitable for conversational access, define escalation for questions requiring a person and choose measures for evaluating inquiry handling. It would then determine whether a conversational website or AI assistant is appropriate before recommending implementation.
Discovery also protects teams that feel too overwhelmed to act. Instead of attempting a company-wide AI program, use the brief to identify and implement one impactful AI solution. The consultant earns the larger assignment by showing disciplined reasoning, not by making the largest promise.
Contract for an operational result, not an AI demonstration
Once discovery supports proceeding, make the implementation agreement outcome-oriented. Define the workflow being changed, what the consultant will deliver, what your team must provide, who accepts the work and how results will be evaluated. Include milestones for access, design, testing, user feedback, release and measurement. If a dependency slips, the agreement should make clear whether scope, timing or the expected result changes.
- “Build an AI prototype”
- Weak because it describes an artifact without saying who will use it or what improves.
- “Deploy by year-end”
- Incomplete because deployment alone says nothing about adoption, quality or business value.
- “Improve a named workflow against an agreed baseline”
- Stronger because the work is tied to measurable operations.
- “Provide evidence, limitations and next-step recommendations”
- Stronger because leadership receives a decision-ready account of what happened.
Plan the board update at the start. It should explain the original problem, baseline, implemented change, actual usage, measured result, known limitations and recommendation to expand, revise or stop. Avoid presenting model sophistication as business impact. When evaluating promises, compare them with realistic timelines for showing AI results and reserve time for access approvals, testing and adoption—not only technical construction.
Make continuation conditional on evidence. A successful first phase should create enough operational and measurement evidence to justify the next investment; an inconclusive phase should lead to correction or closure, not automatic expansion.
Where Rohan Hall and OceSha Ventures fit
Rohan Hall has worked in technology since 1984 across the United States, Europe and Asia. His background includes enterprise systems and PeopleSoft financial, supply-chain and manufacturing modules; work in Hewlett-Packard’s SAP implementation environment; emerging-technology advisory work at Capital Group/American Funds; and leadership building AI, blockchain interoperability, scalable blockchain applications, supply-chain traceability, verifiable credentials and decentralized identity systems. You can review his ventures, published work and broader technology activity on Rohan Hall’s home page.
Rohan founded OceSha Ventures and its AI-first solutions. The company 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. These capabilities are relevant when a quick-win candidate involves trusted knowledge access or recurring customer questions.
The wider education ecosystem includes OceSha AI’s course and academy creation platform and programs taught through OceSha Academy. Those offerings can be relevant when the underlying challenge involves turning organizational expertise into structured learning or distributing knowledge more effectively.
For leaders considering how AI intersects with broader technology change, Rohan covers the subject in depth in The Convergence of AI and the Top 10 Emerging Technologies. His experience provides a strong basis for a project conversation, but your decision should still follow the same discipline described above: confirm current fit, scope, timing and the evidence your board needs.
Review Rohan Hall’s technology background, ventures and published work, then bring a clearly defined business problem, deadline and success metric to the conversation.
Evaluate the fitFrequently asked questions
Should we issue an RFP before speaking with AI consultants?
Use an RFP only after you can describe the business problem, baseline, constraints and intended evidence. An early request centered on features or AI terminology can lock the organization into an assumed solution before discovery. A concise problem brief followed by structured conversations is often a better first filter.
Who should own the project inside the company?
Choose an operational owner who controls or strongly influences the affected workflow and is accountable for the relevant business metric. Technology, data, security and legal teams may be essential participants, but the project needs a business owner who can make scope and adoption decisions.
How many use cases should we test first?
Prioritize several candidates, but implement one tightly bounded use case first unless the same foundation genuinely supports multiple workflows without adding substantial complexity. Concentration makes the baseline, adoption plan and result easier to interpret.
What should make us stop a proposed AI project?
Stop or redesign it when the business value is unclear, suitable information is unavailable, the organization cannot change the workflow, critical approvals cannot meet the schedule or the result cannot be measured credibly. Stopping a weak project early is better than manufacturing a superficial year-end success.
How should we compare consultant proposals with different technical approaches?
Compare the assumptions, business outcome, information requirements, implementation dependencies, operating model, evaluation method and path to adoption. The technical approach matters, but it should be judged by how well it solves the defined problem under your actual constraints.
What should the board receive at year-end?
Provide a concise account of the original problem, baseline, delivered change, real usage, measured outcome, limitations, lessons and recommendation. The board should be able to distinguish an operational result from a technical demonstration and decide whether further investment is justified.
The bottom line
The fastest route to a tangible AI result is not a rushed technology purchase. It is a disciplined reduction of uncertainty: select one material business problem, document its baseline, test consultants on the same brief and use a short discovery to define the smallest useful implementation. Contract for adoption and measurable evidence rather than a demonstration. Rohan Hall’s enterprise and emerging-technology experience, together with OceSha Ventures’ AI-first solutions, makes him relevant to that conversation—particularly where knowledge, customer inquiries, learning or business intelligence are central to the opportunity.
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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