An AI strategy becomes actionable when it identifies work worth changing and the conditions a system must satisfy. A list of models or use cases is not enough to make a delivery decision.
Start with one process, its owner and the evidence needed to judge an improvement. That gives architecture, security and implementation a shared problem to solve.
Map the current work
Identify where information arrives, which systems own it and where people interpret, calculate, approve or reconcile it. Include the exceptions that experienced staff handle outside the documented process.
Ask what is causing difficulty. It may be missing information, a slow handoff or a system that cannot exchange records. AI may help interpret varied material, while a conventional integration or workflow rule addresses another part of the problem.
Record a baseline with a clear measurement method. Possible measures include accepted tasks, review effort, correction rates and elapsed time. Do not invent a return on investment before understanding the current process and cost.
Define a useful boundary
Write the task’s inputs, outputs and exclusions. List the systems it may read or change. Define what happens when information is missing, sources disagree or the request exceeds its scope.
Specify which decisions remain with people. A financial preparation workflow can organise evidence and propose values without taking over the professional decision. Exact calculations and rule checks can be handled in ordinary software.
Keep the first deliverable small enough to test. A bounded system is easier to evaluate and operate than a promise to automate an entire department.
Make the data path explicit
Map source documents, prompts, retrieved content, tools, logs and model providers. Decide who controls each store and which access and retention requirements apply.
Choose hosting from the actual requirements. On-premise infrastructure, private cloud and hosted services have different trade-offs. None automatically establishes legal compliance or guarantees that data never leaves a boundary.
Review third-party terms, processing locations and subprocessors where relevant. Minimise unnecessary personal information and avoid treating pseudonymisation as proof of anonymity.
Select the technical approach after the failure is clear
A structured prompt, retrieval, fine-tuning and a model-directed agent solve different problems. Fine-tuning is not a mandatory phase of every project. Multiple agents do not establish engineering maturity by themselves.
Build a baseline and test it on representative work. If retrieval misses the right passage, investigate extraction, indexing and search before adapting the model. If a tool returns the wrong format, fix the interface contract rather than asking the model to compensate indefinitely.
Preserve the simplest approach that meets the acceptance criteria. Complexity needs a demonstrated purpose.
Define acceptance and operating evidence
Agree which test cases must pass and how results will be reviewed. Include failures, permissions, interruptions and requests the system must not fulfil.
Decide which observable events need to be recorded and who can access them. A useful record may include source identifiers, configuration versions, validation results and human approvals. It should not indiscriminately duplicate all sensitive content.
The NIST AI RMF Core is a voluntary reference for structuring risk work. It does not certify a system or determine every legal obligation.
Plan the work after launch
Name the process owner, technical owner and exception path. Establish how prompts, models, sources and integrations can change, and what testing is required before a release.
Include support, usage and review effort in the operating estimate. Monitor whether the system improves the intended process, rather than assuming that deployment completes the business case.
Ryzolv’s How We Work page describes the delivery stages. A clear scope gives your team a concrete basis for deciding what to build, what to test and when to stop or expand it.
Written by Anshul Kaushik, Managing Director at Ryzolv.
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