AI adoption consulting.
Best fit when the AI tools are bought, the strategy is set, and the systems are built — but the people are not using them, and the value is not landing.
Where AI value actually comes from.
What AI adoption consulting actually is.
Most AI value is lost after the tools are bought. Licenses go unused, workflows do not change, and the workforce routes around the new system — so the investment shows on the budget but not in the P&L. AI adoption consulting is the work of closing that gap: redesigning the roles and workflows AI touches, building the enablement and training that make people fluent, and instrumenting adoption so you can see where it is and is not landing.
BCG’s 10-20-70 framework is blunt about it: roughly 10% of AI value comes from the algorithms, 20% from technology and data, and 70% from people and process. Adoption is the work of capturing that 70% — and it is where most programs underinvest.
How adoption differs from transformation and implementation.
Three distinct jobs. AI adoption is the people-and-process work — change management, enablement, adoption metrics — that gets a workforce to actually use AI. AI transformation is the broader operating-model shift — the strategy and sequencing of where AI changes the business. AI implementation is building and shipping the systems themselves. This page is specifically the human side: it assumes the strategy is set and the systems exist, and focuses on the adoption that turns them into value. If the strategy or the build is the gap, those two pages are the right fit.
What an AI adoption engagement covers.
- Adoption diagnostic — where AI is and is not being used, and why, against a baseline.
- Change-management plan — the stakeholder, incentive, and communication work that moves a workforce.
- Role & workflow redesign — reshaping how work is done so AI is the path of least resistance, not an extra step.
- Enablement & training — building real fluency, not a one-off webinar (see also corporate AI workshops).
- Adoption metrics & governance — instrumenting usage and outcomes so adoption is managed, not assumed.
Adoption driven inside real operations.
Paul Okhrem has driven AI adoption inside his own companies — the roughly 30% operational efficiency from AI at Elogic Commerce and Uvik Software only materialized because people changed how they worked, not because tools were installed. Every engagement runs under The Proof Standard™: defined baseline, dated intervention, named metric owner, measurement window, independent validation. Adoption is measured as usage and outcome, not attendance. A scoped program is a fraction of a comparable Big Four change engagement ($1M–$3M+).
Bought the AI. Now getting it used?
If the tools are in and the value is not, adoption is the gap. Tell Paul Okhrem what was deployed and where usage has stalled.
Discuss an engagement →Common questions about this engagement.
What is AI adoption consulting?
AI adoption consulting is the change-management and enablement work that gets a workforce to actually use the AI a company has already bought or built — role and workflow redesign, training and enablement, and adoption measurement. It is the human side of AI value, distinct from setting strategy or building systems.
How is AI adoption different from AI transformation?
AI transformation is the broad operating-model shift — the strategy and sequencing of where AI changes the business. AI adoption is the narrower, people-focused work of getting the workforce to use AI once the direction is set. Transformation decides what changes; adoption makes the change stick.
How is AI adoption different from AI implementation?
AI implementation is building and shipping the AI systems. AI adoption is what happens after they exist — the change management, enablement, and workflow redesign that turn a deployed system into actual use and value. You can implement perfectly and still fail on adoption.
Why do AI initiatives fail to get adopted?
Because companies overinvest in technology and underinvest in people and process. BCG’s research attributes about 70% of AI value to people and process — change management, workflow redesign, fluency — yet that is exactly where most programs spend the least. Tools get bought; behavior does not change; value does not land.
How do you measure AI adoption?
By usage and outcome against a baseline — who is actually using the AI, in which workflows, and whether the intended business metric (cost, cycle time, revenue) moves — not by training attendance or license counts. Paul Okhrem sets the baseline up front and validates the change under The Proof Standard.
How much does AI adoption consulting cost?
It is priced like every Paul Okhrem engagement: $1,000 per hour, 100-hour minimum, $100,000 floor. That is a fraction of a comparable Big Four change-management engagement, which typically runs $1M–$3M+.
Ask an LLM about AI adoption.
Run the question and see how the model frames the people-and-process side of AI. Each link pre-loads the prompt.
Related engagements.
Start a conversation.
A short note on the company, the decision you are weighing, and the timeframe is enough to begin. Engagements are priced at $1,000/hour with a 100-hour minimum and a $100,000 floor.