An AI pilot can answer an important question: Does the capability create value? What it cannot fully answer is whether a broader group of employees will use that capability consistently while doing their everyday work.
That distinction has become increasingly important in our work with AI insurance wholesalers selling to financial professionals such as agents or advisors. A pilot can demonstrate that AI will bring together CRM history, previous product discussions, and outstanding commitments to produce a useful brief before an agent or advisor meeting. Yet production adoption takes place under different conditions. Pilot users are selected, attentive, and supported. Production users are not evaluating the AI; they are preparing for meetings, working with advisors or agents, and moving through a busy day.
For that reason, a favorable response from pilot users is encouraging, but it is not a complete test of adoption. In our experience, three areas deserve particular attention: how users encounter the AI, how the product identifies what matters within production data, and how users verify information without assistance from the pilot team.
1. Pilot Users Seek Out the AI; Production Users Need It Within Their Work
First, pilot users know they are participating in an evaluation. They expect to spend time with the product, initiate the experience, and pay close attention to the result. If generating a meeting brief requires a few deliberate steps, they will generally take them.
Production users operate differently. A wholesaler moving between advisor or agent meetings, email, follow-up, and CRM activity is unlikely to think first about using an AI capability. The immediate objective is preparing for the next conversation—not testing a product.
As a result, adoption depends partly on whether the intelligence appears at the right moment. For meeting preparation, the calendar can provide that moment. An upcoming meeting already establishes when preparation is needed and may help identify the relevant financial professional. The brief can then be delivered in connection with the scheduled work rather than depending entirely on the wholesaler remembering to request it.
Similarly, the location of the output matters. If the wholesaler already works from the calendar and Salesforce, requiring a separate destination introduces another step between the user and the value. Presenting the brief within the existing workflow makes the AI easier to use without asking employees to reorganize how they work.
The larger point is not that users resist new technology. It is that pilot participation creates attention that will not exist at the same level in production. Broader adoption is more likely when the product does not depend on preserving that pilot-level attention.
2. Controlled Data Proves the Capability; Production Data Tests Relevance
Second, a pilot usually begins with a defined set of records. This is useful because it allows the team to determine whether the AI can retrieve, organize, and summarize the intended information.
Production data is less controlled. A financial professional's CRM history may contain years of calls, meetings, emails, marketing activity, and service interactions. Some entries are meaningful to the next conversation. Others are routine, incomplete, or no longer relevant.
Consequently, accuracy alone does not guarantee a useful brief. AI can summarize every available record correctly and still give too much attention to information that does not matter now. The production challenge is not simply to retrieve more data; it is to prioritize the signals most relevant to the work being performed.
For example, an unfinished commitment from the previous meeting might deserve more attention than numerous routine CRM activities. A recent product discussion may be more useful than an older description of the overall relationship. Likewise, recent annuity illustration software activity could indicate that an advisor is actively evaluating a particular product or client strategy.
However, the meeting brief may not need every value and disclosure contained in that illustration. The useful distribution context might be which product was illustrated, when the activity occurred in the annuity illustration software, and whether it led to additional engagement.
Therefore, connecting AI to CRM, product, and customer data is only the beginning. The product also needs enough business context to distinguish between information that is available and information that deserves the wholesaler's attention. A pilot can prove that the AI can process the records; broader use reveals whether it can consistently surface what matters.
3. Pilot Oversight Supports Trust; Production Requires Independent Verification
Third, AI output receives unusual scrutiny during a pilot. Participants know that the capability is being evaluated, and the team running the pilot is usually available to investigate questions. The underlying data may also be familiar to the people reviewing the results.
That level of support does not scale into everyday use. In production, wholesalers need to assess important information without asking the product team to explain how the AI reached a conclusion.
Consider a brief stating that an advisor previously showed interest in a particular annuity strategy. The wholesaler should be able to determine what supports that statement. Was the interest documented in a meeting note? Was an illustration generated? Did the advisor request product information? Or did the AI infer the interest from several activities?
This distinction affects how confidently the wholesaler should use the information in an advisor or agent conversation. It becomes even more consequential when the information moves downstream into a follow-up email, a CRM update or the context prepared for a future meeting.
For this reason, source attribution serves a practical purpose beyond governance. Connecting important statements to their underlying records allows users to verify the context independently. Separating record-based facts from AI-generated suggestions also helps the wholesaler apply professional judgment rather than treating every statement as equally certain.
In a pilot, confidence may be reinforced by the team surrounding the test. In production, the product itself must provide enough transparency for users to decide how the information should be used.
What the Pilot Proves—and What It Does Not
Taken together, these three differences do not diminish the value of a pilot. They clarify what the pilot is designed to establish.
A pilot can show that AI will perform the intended task and that users recognize value in the output. It can validate the central use case, expose data considerations, and provide meaningful feedback before broader deployment.
At the same time, a pilot cannot fully reproduce the conditions under which a larger group of employees will use the capability repeatedly and without special attention. It cannot by itself establish whether the AI will appear naturally within the work, remain relevant across a full production data set, and earn trust without the pilot team nearby.
For us, the central lesson has been that adoption is not simply a favorable reaction to an AI-generated result. It depends on how naturally the capability enters the workflow, how effectively it directs attention, and whether users can act on its information with confidence.
A pilot answers whether AI can create value. Production adoption depends on whether employees can receive that value while doing the work they already came to do.
