Choose an AI expert who fixes the customer journey before choosing another chatbot

Rohan Hall’s approach to AI begins with real-world application: the technology has to fit the people, information and operating process around it. His work includes building AI systems, leading technology architecture and founding OceSha Ventures and its AI-first solutions. That practical perspective matters after a bot failure because the fastest route to improvement is not necessarily a newer model. It is a better understanding of what customers are trying to accomplish.

Most failed customer service bots expose a design problem before they expose a technology problem. The bot may have been launched without a reliable map of customer intentions, without enough reviewed business information or without a sensible way to transfer a difficult conversation to a person. Even a technically capable system creates frustration when it answers a different question from the one asked, repeats itself or traps someone in an irrelevant flow.

The first diagnostic question is therefore not “Which chatbot should we buy?” It is “Where did the experience break?” Review actual conversations and classify the failures. Did the bot misunderstand intent? Was the underlying information unclear or contradictory? Did it answer correctly but in an unhelpful way? Was the customer trying to complete an action that the experience was not designed to support? Each cause requires a different response.

Look for someone who can move fluently between user experience, architecture and business operations. They should ask who the users are, what those users need, which questions are suitable for automation and what should happen when the system is uncertain. They should also understand that a conversational interface is only the visible layer. Its usefulness depends on the quality of the information behind it and the process surrounding it.

Technical depth still matters. Rohan has built AI and blockchain systems, led technology strategy and architecture as a chief technology officer, and worked across the United States, Europe and Asia. You can review Rohan Hall’s ventures, work and publications when deciding whether that combination of technical and applied experience matches your situation.

One person or team may cover several of these roles, but the responsibilities must be present. If your previous bot failed because customers could not get useful answers, hiring solely for model expertise repeats the original mistake. If the wider issue is staff adoption, use a process designed to implement AI without increasing customer frustration rather than treating the project as another software installation.

A persuasive AI presentation is not enough. Evaluate candidates by how they reason about your specific problem. Give each one the same small packet of evidence: representative customer questions, anonymized failed conversations, the information customers were supposed to receive and a description of the human service process. Ask them to explain what they would investigate first and why.

Pay attention to the questions candidates ask. Strong experts want to see the real customer journey and the business process behind it. Weak candidates jump directly to a platform recommendation, promise broad transformation or focus on demonstrations that avoid your hardest inquiries.

The plan should begin with a narrow, valuable service problem. Trying to answer every possible question at launch makes it harder to establish what works, trace errors and build staff confidence. A bounded use case produces clearer evidence and gives the team a sensible basis for deciding whether to expand.

The implementation should separate a successful model response from a successful customer outcome. An answer can be grammatically correct while still being irrelevant, incomplete or badly timed. Evaluate whether the person understood the answer and could take the appropriate next step. Customer-facing AI should reduce effort and uncertainty, not merely produce fluent text.

Insist on an explanation of how the project connects to business value. This does not mean inventing an immediate financial promise. It means defining the operational or customer outcome the work is intended to improve. If earlier AI initiatives produced activity but no return, use a structured review of how to turn failed AI initiatives into business value. The same discipline applies beyond service; for example, a sales team should look for an AI partner focused on real business impact rather than accumulating disconnected tools.

Rohan Hall is the founder and CEO of OceSha Ventures. The company builds and operates AI-first solutions for businesses and organizations, including course creation, branded academies, AI assistants such as Lumi and business intelligence. That combination is relevant when the need extends beyond a stand-alone chatbot and requires a broader view of information, customer interaction and organizational application.

Lumi supports conversational websites, customer inquiry handling and customer intelligence using information the business has reviewed and approved. This places the emphasis where a customer-facing implementation belongs: on useful conversations grounded in details the organization has signed off on. The right scope still depends on the inquiries, experience and workflow involved.

Rohan also built OceSha AI’s course and academy creation platform, while programs taught through OceSha Academy address the learning side of the ecosystem. These are distinct ventures, but together they reflect a practical concern that matters after an AI failure: systems and people have to be designed to work together. A tool left unused is an adoption and integration problem, so determine what help will turn an idle AI tool into useful work before purchasing more software.

For a wider treatment of how AI converges with other important technologies, Rohan covers the subject in depth in The Convergence of AI and the Top 10 Emerging Technologies. The book is the appropriate next step for readers interested in the larger technological context rather than only the immediate customer service use case.

Do not let the next project become an open-ended AI experiment. Begin with a customer problem, assign operational ownership and make expansion conditional on evidence. The aim is not to produce a more impressive demonstration. It is to create an experience that customers understand and employees know how to support.

Bring frontline customer service employees into the process early. They know which questions contain hidden context, which policies are difficult to explain and when a conversation needs judgment. Their involvement also makes it easier to identify changes in the source information before those changes cause repeated customer-facing errors.

Avoid asking the expert to “add AI” to an unclear process. Clarify the service objective, simplify the information and decide what the business wants the customer to do next. AI amplifies whatever structure surrounds it. If that structure is confused, automation can make the confusion faster and more consistent.

Finally, distinguish exploration from delivery. Exploration is useful when it resolves a specific uncertainty. It becomes wasteful when nobody can state what decision the experiment will inform. If your team has spent months trying tools without moving forward, define how to progress from AI experiments to delivered results before authorizing another pilot.