AI adoption & automation
Move AI and automation from pilots into daily work, with an owner for every use case, controls matched to the risk, and measures that show whether it is paying off.
When this is the work
Signs AI adoption needs an operating model.
Most organizations are past the question of whether to use AI. The harder question is how to run it: who owns each use, which data it can touch, and how anyone will know it worked.
- Pilots run, but none reach production.Budget is spent learning the same lessons twice, and leadership stops expecting results.
- Teams use AI tools with no shared policy.Sensitive data and inconsistent outputs create risk no one has measured.
- No one owns the use-case pipeline.Ideas compete on who asks loudest rather than on value, risk, and readiness.
- Automation depends on one builder.When that person moves on, the workflows break and no one can repair them.
- Value is claimed but not measured.Leadership cannot tell which investments to scale and which to stop.
- The workflow was never mapped.AI is layered onto a broken process and speeds up the rework.
What it includes
What the work can include.
Every engagement is scoped in writing. We can lead an AI program, set up governance and a use-case pipeline, build automations with your team, or support an existing AI lead in a defined role.
Use-case pipeline and scoring
A single intake for AI and automation ideas, scored on value, risk, data readiness, and effort, so the strongest candidates move first.
Acceptable use policy and controls
Plain-language rules for which tools may be used, with which data, and what review each use requires, matched to its risk.
Workflow mapping before automation
The current process mapped with the people who run it, so automation removes steps instead of speeding up rework.
Pilot design and success measures
Pilots with a baseline, a decision date, and exit criteria, so each one ends in a clear decision to scale, adjust, or stop.
Automation build and ownership
Microsoft 365, Power Platform, and Copilot Studio automation built with documentation, a named owner, and a runbook for when something fails.
AI governance forum
A standing forum with defined membership and decision rights for approving, reviewing, and retiring AI uses, backed by a current inventory.
Adoption and training
Role-based guidance, champions, and support in the first weeks, so the new way of working becomes the normal one.
Value and risk reporting
A short report for leadership on use cases in production, adoption, time returned, incidents, and what to scale next.
- Measured by
- Use cases in production
- Adoption
- Time returned
- Value reported to leadership
- You keep
- A governed use-case pipeline
- An acceptable use policy
- Owners who run it
Before a pilot
What to settle before a pilot starts.
Pilots rarely stall on the model. They stall on the conditions around it. Each of these is agreed in writing before work begins.
- A workflow owner who will run the process after launch.
- A baseline measure, such as hours spent, cycle time, or error rate.
- Confirmed access to the data the use case needs, with its owner's approval.
- Controls agreed with legal, security, and risk, sized to the use.
- A decision date and the evidence that decision will rest on.
- A support model for the first weeks after launch.
How it runs
Stages and outputs.
Assess
Inventory current AI and automation use, map the priority workflows, and set a policy baseline with legal, security, and risk.
OutputUse-case inventory and policy baseline
Prioritize
Score the pipeline on value, risk, data readiness, and effort, and select the first pilots with owners and baselines.
OutputA scored pipeline and pilot charters
Pilot and build
Run pilots to their decision dates and build the automations that pass, with documentation and named owners.
OutputDecisions made and automations in production
Scale and govern
Stand up the governance forum and reporting cadence so new uses move through a known path.
OutputA pipeline, a forum, and reporting that runs
Questions
AI adoption questions.
Do you build the automations or advise on them?
Both. We can design and build Microsoft 365, Power Platform, and Copilot Studio automations with your team, or govern and advise while your IT team or a vendor builds. Either way, every automation ends with a named owner and documentation.
We work with sensitive or regulated data. Can we still use AI?
Usually, with the right controls. The acceptable use policy defines which tools can touch which data, and higher-risk uses get additional review, testing, and human oversight, consistent with the NIST AI Risk Management Framework. Your legal and security teams are part of those decisions.
Can you support a federal program working under OMB AI requirements?
We support the operating side: use-case inventories, governance forum design, risk practices for high-impact uses, and reporting. Policy interpretation stays with your agency counsel and Chief AI Officer.
How do you measure the value of AI and automation?
Each use case starts with a baseline, such as hours spent, cycle time, or error rate, and is measured against it after launch. Leadership sees results by use case, so decisions to scale or stop rest on evidence.
Where should we start with AI adoption if nothing is in place yet?
With an inventory of what is already in use, a short acceptable use policy, and two or three well-chosen pilots. The Operating Diagnostic is often the fastest way to find them.
Not sure this is the right starting point?
The Operating Diagnostic is a three-week, fixed-fee look at how the work actually runs. It tells you whether the fix is a process, a decision, a system, or all three, and gives you an action plan either way.