A useful AI roadmap starts with a problem someone is accountable for solving. Before selecting a model or agent framework, decide what a good outcome looks like and what a wrong answer could cost.
Start with one bounded workflow
Choose a task with an identifiable owner, approved inputs, and a measurable output. An internal knowledge search, document triage queue, or assisted support workflow gives you a clearer starting point than a mandate to put AI everywhere.
Evaluate before expanding access
Build a representative set of normal, difficult, and out-of-scope examples. Measure answer quality, unsupported claims, access boundaries, latency, and cost. Define when the system should abstain or ask a person to review.
Give agents narrow permissions
Separate proposing an action from executing it. Use limited credentials and explicit approval for consequential changes. Treat retrieved documents and tool responses as untrusted input, and test attempts to redirect the workflow.
Operate the whole workflow
Assign an owner, track useful task completion, and maintain a fallback. Record changes to prompts, models, tools, and knowledge sources so regressions can be investigated. Set budgets for experimentation and production separately.
Further reading
Primary references informing this perspective. Reviewed September 2026.
NIST — Generative AI Risk Management Profile (opens in a new tab)OWASP — GenAI Security Project (opens in a new tab)General guidance for planning conversations. The right approach depends on your business and technical environment.