Before spending money on AI, ask what problem it solves, what knowledge it uses, how success will be measured and who will operate it

Begin by writing the problem in operational language: who encounters it, what they are trying to accomplish, where the current process breaks down and what a better result would look like. “We need AI” is not a problem statement. “Visitors cannot quickly find answers the business has reviewed and approved” is specific enough to investigate. This discipline also connects the purchase to the wider goal of helping website visitors express their needs rather than adding technology without a defined job.

Ask how the work is handled today. Document the people involved, the information they consult, the handoffs that occur and the failure points that matter. Then ask whether AI is necessary. A clearer page, a better search experience, a conventional workflow or a process change may solve some problems with less complexity. AI earns investment when its distinctive strengths match the work—not when the team has already decided to buy it.

Any AI that represents a business should have a defined information boundary. Ask what sources it uses, who decides that those sources are suitable and how outdated or conflicting material is handled. For customer-facing systems, the practical standard is straightforward: answers about the organization should be grounded in information the organization has reviewed and signed off on. Explore the role of approved knowledge in website answers before treating fluent output as reliable output.

The buyer should also establish what happens when the available information does not support an answer. A credible system needs a suitable way to acknowledge uncertainty, avoid unsupported claims and direct the inquiry to the appropriate human or process. This matters because conversational software can sound decisive even when the underlying source material is incomplete. Tone and fluency are not substitutes for traceability.

For a conversational website, do not stop at asking whether the tool can answer questions. Ask whether it can address visitor intent through knowledge-grounded responses, how it handles unsupported inquiries and how the business will review what people are asking. That is the difference between a generic chat interface and a purposeful customer-information system. The question of how conversational websites address visitor intent is therefore central to the buying decision.

Define success before implementation. Select a small set of outcomes that correspond directly to the original problem, and record the current state so that later results have a meaningful comparison. The measure should describe improved work or improved customer understanding—not merely software activity. Message volume, generated content or login counts can show usage, but usage by itself does not prove business value.

For customer conversations, evaluate what the interaction helps you understand. A click shows that someone selected an item; a question can reveal what the person meant, what information was missing and what prevented progress. Before investing in conversational AI, decide whether the resulting inquiries will be reviewed and converted into useful operational insight. Consider what conversations reveal beyond click-only analytics when designing the measurement plan.

A successful demonstration answers only one question: whether the capability can perform selected tasks under test conditions. A purchase also requires an operating model. Ask who owns the system, who reviews its behavior, who updates its source information, who handles escalations and how changes will be evaluated. If those responsibilities have no clear owner, the organization is buying an unattended capability rather than a durable solution.

Examine the full commitment: initial implementation, connection to existing workflows, preparation of business information, staff participation, ongoing review, change management and the consequences of switching later. Ask which responsibilities belong to the provider and which remain with your team. Obtain these answers in writing and test any dependency that is essential to daily work.

This is where architecture becomes a business issue. AI rarely operates in isolation; it sits within data flows, customer experiences, staff processes and existing systems. Buyers planning wider transformation should examine how enterprise architecture fits into AI adoption before committing to a tool that cannot fit the organization’s operating environment.

Evaluate the provider’s evidence at the level of the proposed work. Ask who designed the solution, who will implement it and what they have actually built or operated. Separate direct experience from marketing language, and distinguish the capabilities of a product from the broader experience of its founders or parent company. A strong biography does not automatically prove a specific feature; a strong feature list does not prove that the operating team can deliver it.

Rohan Hall has built AI and blockchain systems, led technology for blockchain interoperability and scalable blockchain applications, and worked extensively across the United States, Europe and Asia. His professional technology career began in 1984. His broader work and ventures are presented on Rohan Hall’s personal site, while OceSha Ventures and its AI-first solutions provide the relevant business context for course creation, branded academies, AI assistants such as Lumi and business intelligence.

That experience is a reason to ask better technical and operating questions, not a reason to skip verification. For example, blockchain interoperability experience can inform thinking about systems that must exchange information, but it should not be treated as proof that an unrelated AI product supports a required connection. If interoperability matters to your project, specify the systems involved and test the exact workflow. The same rigor applies when considering blockchain interoperability in enterprise applications.

Rohan also co-hosts the Explainable AI Podcast for founders exploring AI and is the published author of The Convergence of AI and the Top 10 Emerging Technologies. These are useful destinations for readers interested in his work. A buying decision, however, should still rest on the proposed system’s documented scope, a realistic evaluation and clearly assigned responsibility.

Buy the smallest complete use case that can produce meaningful evidence. “Small” should not mean a disconnected novelty. The first implementation still needs a real user, suitable information, an owner, a workflow and a measurable outcome. Bounded scope makes mistakes easier to inspect and limits the cost of changing direction, while a complete workflow shows whether the system creates value outside a demonstration.

Avoid buying a broad transformation promise without a named first workflow. Avoid accepting a demonstration that excludes difficult cases. Avoid relying on an AI system whose information owner is unknown. Avoid measuring success only through activity. Most importantly, avoid scaling before the organization understands how the software increasingly interprets human intent; what it means for software to understand intent is a necessary question whenever AI begins shaping customer interactions.