AI
AI Innovation & Pilots
Try new ideas without betting the budget on them. Hands-on workshops and short, measured pilots test an AI use against your real work, then scale it or stop it based on evidence rather than enthusiasm.
Innovation with a stop button
Small experiments, clear measures, honest decisions
AI changes quickly, and the only reliable way to know whether something helps your organization is to try it on your own work. The risk is not experimenting; it is experimenting without a way to decide. Pilots that have no measure of success and no owner neither reach production nor get shut down, and they quietly accumulate cost and data exposure.
Our innovation work is built around one discipline: every pilot starts with an agreed question, a baseline, a small group of real users, a fixed time box, and a decision at the end. The environment is secured before anyone starts, so trying something new never means pasting sensitive data into an unapproved tool.
Workshops sit alongside pilots. They give leaders and staff enough hands-on experience to spot good opportunities themselves, which is where the best ideas usually come from.
Scope
What innovation work includes
Ways to learn fast without losing control of data or budget.
Leadership and staff workshops
Hands-on sessions on your own kinds of tasks, covering capabilities, limits, safe use, and how to spot opportunities.
Idea intake and ranking
A simple way for staff to propose uses of AI, with each idea scored by value, effort, and data risk.
Time-boxed pilots
A small group tests one use on real work for a fixed period, against a baseline and a measure agreed up front.
Prototypes
Working proofs of concept for integrations or automations, built in a secured environment before any production commitment.
Evaluation reports
Plain results against the measure, what users said, what it would cost to scale, and a clear recommendation.
Scale-up planning
For pilots that succeed, the plan for rollout, integration, training, and support.
Our approach
How a pilot runs
Question, baseline, trial, decision.
- 01
Frame
Choose the task, the question, the users, and the measure of success, and confirm the data is permitted.
- 02
Baseline
Record how the task performs today so improvement can be measured rather than assumed.
- 03
Trial
Run the pilot on real work in a secured environment for the agreed period.
- 04
Decide
Compare against the baseline and choose: scale it through integration, adjust and retest, or stop.
Business outcomes
What changes
AI adoption driven by evidence from your own work.
Less risk per idea
Each experiment is small, time-boxed, and run on a secured environment.
Decisions on evidence
Scale-or-stop calls are based on measured results against a baseline.
Ideas from the people doing the work
Workshops and idea intake surface the opportunities leaders rarely see.
No orphaned experiments
Every pilot ends with a decision, and successful ones get an owner and support.
Fit
Who this is for
- Organizations that want to explore AI without a large upfront commitment
- Leadership teams that want hands-on understanding before setting AI strategy
- Departments with a specific idea they want tested properly
- Public agencies and contractors that need experiments to respect data rules
When it may not be the right fit
We would rather tell you up front than sell you something that will not help.
- Residents seeking help with personal AI tools, which we do not provide
- Projects that need a guaranteed outcome before any testing
Pilot questions
Trying AI the careful way
What makes a good first AI pilot?
A task that happens often, takes real time, has a clear right answer or quality bar, and uses data you are permitted to use with the chosen service. Something a small group does every week is better than something impressive that happens twice a year.
How do you measure whether a pilot worked?
We agree the measure before the pilot starts: time per task, error rate, turnaround, or the share of work that still needs rework. We record a baseline, run the pilot with real users on real work, and compare. If the measure is not met, stopping is a successful outcome, because it saved you a full rollout.
What happens to a pilot that works?
It moves into integration: permissions and data boundaries are finalized, it is rolled out to the wider group, documented, and supported as part of your managed environment. Nothing successful is left running as an unowned experiment.
Can you run an AI workshop for our leadership or staff?
Yes. Workshops are hands-on sessions on your own kinds of tasks: what current AI tools do well, where they fail, what data must never be entered, and how to spot the work in your own organization that is worth piloting. We hold them on site across the CSRA or remotely.
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Related services
Security Awareness Training
Help staff recognize the risks that come with new tools and new kinds of phishing.
Learn moreTest one idea properly
Bring an idea, or the task your team dreads most. We will help you frame a pilot that answers whether AI helps, with your data kept safe throughout.