A model can produce a convincing customer summary from a handful of records. In a real product, however, an invoice may have changed since the last CRM update, a contract amendment may be stored elsewhere, and the person requesting the summary may not have permission to see the entire account. None of these complications is likely to appear in a carefully prepared demo.
These are the issues companies need to investigate before committing to development. An AI readiness assessment helps establish whether a proposed feature is feasible and worth building. Teams can run one internally or with an outside technical partner (for example, https://sysgears.com/tech/ai-consulting/). Either way, the findings should inform product strategy while there’s still room to change plans.
What Are Users Actually Struggling to Accomplish?
Start with an existing task, preferably one people already spend considerable time on or routinely get wrong.
Take a project management platform whose users prepare weekly status reports. Managers might struggle to collect updates from different teams, overlook blocked tasks buried in comments, or dislike turning technical notes into readable client reports. What the feature needs to do depends on where the difficulty lies. Scattered updates call for bringing information together, while missed blockers call for flagging them before the report is written.
Don’t just ask users which features they’d like. Watch how they work now. Which screens do they visit? Where do they copy information from? What do they check before sending a report?
Establish a baseline for how long the task takes, which mistakes occur, and how often work gets redone. That gives you something to evaluate the improvement against, and it may reveal problems nobody noticed when the feature was first suggested.
When Ordinary Software Is the Better Choice
Some AI use cases become less compelling once broken into individual operations. Finding overdue tasks is a database query. Applying a refund limit is a business rule. Sending a standard reminder needs a template and a scheduler. None of these requires a language model.
Models become useful when the input is unstructured or the output can’t be specified through fixed rules. Examples include spotting possible blockers in free-form comments, extracting action items from meeting transcripts, or drafting an update from several sources.
A mixed approach often makes sense. If a team member writes, “We should have the integration finished by next Thursday,” a model could extract the deadline and link it to the right task. Conventional code would then validate the date, check permissions, and decide whether the record can be updated.
This matters for maintenance too. A defined rule can be tested against expected outcomes. Model interpretations need evaluation across a wider range of inputs, including ambiguous language and cases the system hasn’t seen before.
Can the Feature Trust the Available Data?
Before choosing a model, trace the information it would need.
A customer-support assistant might pull account details from a CRM, invoices from a billing platform, and past conversations from a help desk. These systems may use different customer identifiers, update at different intervals, or contain conflicting records. Improving data quality means identifying authoritative sources, resolving discrepancies, and deciding how current the information must be. A billing question may need the latest transaction status, while an internal report might tolerate overnight updates.
Retrieval-augmented generation (RAG) supplies a model with relevant documents or records at request time, which helps when answers depend on company policies or customer records. But RAG doesn’t fix problems in those sources. If it retrieves an outdated policy, the answer will be outdated too. The system must also enforce access restrictions so users can’t retrieve information they aren’t authorized to see.
Contracts may constrain processing as well. A company might be allowed to analyze support tickets internally but not send their contents to a third-party AI provider. Review vendor data-handling terms, retention settings, and privacy requirements before settling on an architecture.
How Much Responsibility Should the Feature Have?
Drafting a recommendation is very different from carrying out an action. An assistant that suggests a weekly update gives the manager something to review. One that publishes directly to clients can spread errors before anyone notices, and letting it change deadlines or reassign tasks raises the stakes further.
Design should follow from that. Generated reports may need links to source records so users can check key claims. Proposed account changes should pass validation rules and, where appropriate, require confirmation. The application must enforce these controls independently of the model’s instructions or output.
Verification has a cost. If managers must inspect every underlying task to confirm a generated report, they may spend as long reviewing it as they once spent writing it. Test the entire workflow, including corrections, approvals, and follow-up actions, not just how quickly the model produces text.
What Happens When the System Makes a Mistake?
An awkward suggested headline is inconvenient. An incorrect refund or unauthorized account change is far more serious. Teams should map possible failures and decide which can be tolerated, which need human intervention, and which must be prevented through application-level controls.
Some risks involve deliberate manipulation. A retrieved document or customer message could contain instructions meant to redirect an assistant. The OWASP Top 10 for LLM Applications covers risks including prompt injection, sensitive information disclosure, and excessive agency.
Consider a support assistant that can read order information and request changes through an API. A malicious instruction in a customer message shouldn’t be able to make it reveal another customer’s details or issue an unauthorized refund. Restricting tool permissions is a key safeguard. The assistant might check delivery status and prepare an exchange request, while refunds and address changes require separate authorization.
The application should also log relevant actions and approvals so incidents can be investigated. For features that touch real transactions, the team needs a way to disable the AI component without taking down the underlying service.
What Would a Successful Pilot Prove?
Evaluation should reflect how customers will use the finished feature. For project reporting, useful measures include preparation time, corrections needed before approval, missed blockers, and whether recipients correctly understand project status.
A report that reads well but hides unresolved issues has failed. Faster generation doesn’t save time if managers spend longer checking results.
Build evaluation cases from representative workflows, using appropriately handled data. Include incomplete records, conflicting dates, unfamiliar terminology, and situations where the right response is to ask for clarification or decline an action. Track serious errors separately, since an acceptable average score can hide occasional mistakes that would block a safe release.
The NIST AI Risk Management Framework treats measurement as an ongoing part of the AI lifecycle. Performance should be assessed under realistic conditions and monitored after deployment. A limited rollout can surface issues offline testing misses, especially when real users behave differently than the development team expected.
Who Will Handle the Work AI Creates?
AI adoption can change responsibilities for people beyond the feature’s primary users. Support staff may inherit cases the assistant can’t resolve. Customer-success teams may have to explain bad recommendations. Engineers must investigate failures and check whether updates to models, prompts, or connected systems change existing behavior. If automation shifts work from customers to internal teams, that cost belongs in the business case.
The feature may also affect users unevenly. Replacing established controls with a conversational interface can make routine operations harder for people who rely on keyboard navigation or assistive technology, and performance may vary across languages and writing styles. Test with people of different needs and familiarity levels, and consider keeping a conventional way to complete important tasks.
Can the Company Support the Feature After Launch?
An inexpensive prototype can become costly once thousands of customers use it. A single interaction may involve multiple model calls, document retrieval, validation, and retries, plus infrastructure, monitoring, and possibly human review. Estimate the cost of completing a task, not just the advertised price per token.
Usage patterns matter. An assistant an internal team calls occasionally has different economics from one generating recommendations on every page load. Response times also shape the experience, especially for users accustomed to near-instant results.
Maintenance needs a clear owner. Someone has to update evaluation cases, investigate recurring failures, monitor costs, and decide when model or system changes require more testing.
Before development expands beyond a pilot, agree on the evidence needed to proceed. For the project-reporting feature, the initial release could draft updates only from selected task records, display source links, flag missing information, and leave publication entirely to the manager. Pilot results then show whether the feature is ready for wider rollout or needs more work, while managers keep using the existing process without disruption.
That ability to change course is worth preserving. Once customers build routines around an AI feature, correcting a flawed design becomes much harder than deciding not to ship it in the first place.