Case Study
AI Enablement Program
A professional services company moved from informal AI experimentation to a structured enablement program, assessing readiness, selecting governed pilots, and building the accountability model that made AI adoption sustainable.
Executive Lens
Read the patterns, decisions, and risks behind the work.
Relevant Services
AI Transformation / Agentic AI Workflows
Engagement detail
Context, requirements, and the controls that governed the work.
Context
A mid-market professional services company was under pressure from clients and leadership to demonstrate an AI strategy. Employees were using ChatGPT informally. Two teams had run their own experiments without informing IT or leadership. The CEO wanted a structured program but did not want to spend significant budget on something that did not produce measurable results. The starting question was: where does AI actually create value for this company, and what does it take to get there sustainably?
Requirements
Conduct an AI readiness assessment across the six key dimensions: workflow clarity, data quality, process governance, team readiness, risk profile, and measurement baseline. Identify and score 12 candidate use cases proposed by department leads. Select 3 pilots based on readiness and business impact scoring. Build a governance framework and assign accountability before any pilot launched. Define success metrics for each pilot before go-live. Report to the executive team at 30, 60, and 90 days.
Controls Applied
Use-case scoring against six readiness criteria before any pilot was approved. Only 3 of the 12 initially proposed use cases scored above the threshold for an initial pilot. The others required data quality or process improvements first. Human-in-the-loop requirement for all initial pilots: AI proposes, human approves, system executes. Named AI owner assigned to each pilot before go-live, responsible for monitoring, exceptions, and escalation. 90-day performance review cadence with defined accuracy thresholds as trigger for expanding or suspending each pilot.
Operating lessons
Patterns and decisions that apply to similar transformation work.
Starting with workflow value rather than tool selection identified 3 viable pilots from 12 proposals. The 9 that did not qualify were not bad ideas. They were premature. Getting to that answer before spending on tooling saved significant cost and avoided the credibility damage of failed pilots.
The governance framework took two weeks to build and prevented two compliance issues in the first six months. One involved customer data being submitted to an external AI service without authorization. The governance model created the review step that caught it.
Human-in-the-loop is not a temporary compromise on the path to full automation. It is the right permanent operating model for most mid-market AI workflows. The goal is to reduce the human review burden over time as accuracy is proven, not to eliminate human accountability.
The most common failure point in AI pilots is not technology. It is team adoption. The pilots with a dedicated champion who understood the business rationale and actively used the tool performed consistently better than pilots where the tool was installed and usage was expected to follow.
Measurement baselines must be established before pilots launch. Three of the 12 proposed use cases could not be piloted at all because the current-state performance was not measured. Without a baseline, there is no way to demonstrate value or justify continued investment.
Relevant services
The services most directly connected to this engagement.
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