Before You Buy the Bot: Align Strategy and Requirements to Operational Reality

Selecting an AI vendor can be challenging, especially if you do not have an overarching AI strategy. Even if you pick AI tools, they may not lead immediately to productivity gains.

The value of implementing an AI solution, will only materialize when business requirements, workflow reality, governance, and success measures line up.

We have seen a familiar pattern in the insurance and pension industries – and it starts before a single line of code is written

• Pressure: Board wants some type of status update on use of AI
• Interest: Teams want to be allowed to use AI tools
• Disparity: On the other hand, many staff do not know how to use AI at all
• Problem: A prior AI solution did not produce successful outcomes
• Why: Outputs did not translate into clear action; signals were contested; adoption stagnated; value never materialized

We have sat in those rooms. The lesson was never "AI does not work." The lesson was that requirements need to be aligned to the operational realities of the organization before a solution is ever selected. If this resonates, there are still opportunities to create a culture of AI at your organization that leads to better performance across teams and better member experiences.

Here is a practical approach to align requirements to an AI solution before signing, scaling, or renewing.

Start with what problem do you want to solve. Define the high volume and high-risk pain points. Document minimal viable product requirements and success criteria. Do not skip the data preparation step.

Translate requirements into measurable outcomes. Metrics must map to operations. For example, a manager can speed up the analysis time by converting lengthy participant and member files into organized summaries that allow staff to take the next best action faster.

Confirm data readiness with the vendor’s actual ingestion model. Vendor demos assume clean, labeled data. Reality includes inconsistent notes, missing fields, shifting codes. Require a data assessment that mirrors production, plus a plan for ongoing monitoring.

Test for explainability that matches your organization.

• “Explainable” means different things across roles. Front-line users need rationale that supports a next step. Leaders need defensible reporting. Risk teams need traceability. Procurement needs clarity on what is guaranteed and what is probabilistic.

Formalize the deployment process. Do not just hand an AI solution to your teams. Guide them to how the tool fits within your organization’s workflow, surface the benefits it will provide, and properly train your staff on them.

Validate governance, not just security. Policies matter, but so do roles, permissions, model update cadence, and ensuring there is a “human in the loop” with the ability to make changes.

AI value is not a vendor promise. It is an operational contract between requirements and reality. The best outcomes come from treating vendor selection as requirements engineering, plus change management, plus measurement design.

Have Questions? We can help.

Linea Solutions has been providing strategic guidance to our clients for over 27 years. In that time, we have diagnosed failed AI deployments, rebuilt requirements processes, and helped clients move from stalled pilots to sustained adoption. We would be happy to meet with you virtually to discuss what type of assessment would be ideal for your organization. If you have questions about the best way to improve your organizational efficiency, contact us to see how we can help.

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