Business value and workflow fit
Define the business problem first. Compare potential use cases by expected value, frequency, complexity, and whether the task genuinely benefits from AI.
Practical AI adoption and readiness
AI readiness consulting that starts with your business problem
AI adoption works best when the use case, process, data, risks, and human responsibilities are understood before technology is selected. I help organizations organize those questions, prioritize potential use cases, and define realistic next steps.
What readiness covers
Evaluate value, feasibility, and risk together
A readiness review does not begin by assuming that AI should be used. It examines where the technology could help, where ordinary process improvement is a better option, and what must be true for a safe and useful test.
Data and knowledge readiness
Review whether relevant information is available, accurate, current, permissioned, and organized enough to support the proposed workflow.
Risk, privacy, and human judgement
Identify where errors matter, what information is sensitive, who can approve outputs, and when work must move to a human reviewer.
Pilot scope and measurement
Define a bounded test, a baseline, success criteria, quality checks, owners, and a decision point before committing to wider implementation.
A practical review
Questions to answer before a pilot
A useful outcome is a clearer decision, not a commitment to automate. The depth of review depends on the proposed use case and the organization's constraints.
Human accountability, privacy, security, data permissions, and a way to handle uncertain or incorrect output should be considered as part of the use case.
01
What problem are we solving?
Describe the current workflow, who is affected, and what improvement would be useful.
02
What evidence is available?
Identify data, documents, policies, examples, and baseline measures needed to assess the opportunity.
03
What could go wrong?
Consider inaccurate outputs, privacy, access, security, bias, regulatory needs, and the cost of mistakes.
04
How will we evaluate it?
Agree on quality thresholds, human review, fallback steps, ownership, and whether the pilot should stop, change, or expand.
Where AI may fit in daily operations
Depending on the business and available controls, candidate use cases might include internal knowledge search, summarizing long case histories for human review, drafting responses for agents to approve, classifying requests, or reducing repetitive documentation. These are examples to assess, not claims of an existing product or guaranteed suitability.
Assist
Support a person with retrieval, drafting, or summaries while the person remains responsible.
Improve the workflow
Clarify process steps, handoffs, data requirements, and fallback paths around an AI-assisted task.
Govern and measure
Monitor quality, errors, privacy, cost, adoption, and outcomes against an agreed baseline.
Frequently asked questions
What is an AI readiness review?
It is a structured way to consider candidate use cases, workflow and data readiness, risk, human oversight, and what would be required to run a controlled pilot.
Does AI readiness mean we must implement AI?
No. The review may conclude that a process change, better knowledge management, or a simpler tool is the better next step.
Can you promise a specific return on investment?
No. Potential value should be estimated using your data and assumptions, then tested against a defined baseline. Outcomes depend on use case fit and implementation.