Process redesign
We map how work is done today and redesign the steps around AI, so it removes effort instead of adding a step.
AI Adoption and Training
AI only pays off when people change how they work.
We help your teams use AI well in their daily work. That means redesigning the processes around it, training staff and leaders for their own roles, writing a usage policy people can follow and tracking adoption and value so you know it is working.
The problem
Why AI rollouts so often fail to stick.
01
Tools bought, habits unchanged
Licences are rolled out to everyone. A few enthusiasts use them, most people try once and go back to the old way.
02
Generic training
A one-hour session on prompting does not help a claims handler or a finance analyst use AI in their actual job.
03
No clear rules
Staff do not know what data they can paste into which tool, so some avoid AI entirely and others take risks.
04
Value nobody can see
Leadership asks what the AI spend has delivered, and the only answer is a count of logins.
What we deliver
The people side of AI, done properly.
Adoption work alongside the technology, planned with your managers, HR and the teams who will use it.
Change management
A plan for each affected team: what changes, why, who supports them and how concerns are heard.
Role-based training
Hands-on sessions built on real tasks for each role, from front-line staff to analysts and engineers.
Leadership sessions
Short, practical sessions for leaders on what AI can and cannot do, how to judge proposals and how to manage the risk.
AI usage policy
A clear policy on approved tools, data handling, review of AI output and accountability, written for staff to follow.
Champions and value tracking
A network of champions in each team, plus tracking of adoption and the business metric each use case should move.
How we deliver
From launch to lasting habit.
Adoption is planned before launch and measured long after it.
Always included
- One business metric agreed upfront and reported every week
- A senior squad of two to four people, with a demo every week
- Evaluations, guardrails and security built in from day one
- All code, models and documentation owned by you once invoices are settled
Typical tools
01
Weeks 1 to 2
Understand
We learn how each team works today and what worries them about AI.
- Interviews and shadowing with affected teams
- Current tools, data rules and policies reviewed
- Adoption and value measures agreed
02
Weeks 2 to 4
Redesign and prepare
We redesign the work and get people ready before launch.
- New process maps agreed with team leads
- AI usage policy drafted with legal and security
- Champions chosen and briefed
03
Weeks 4 to 8
Train and launch
Role-based training timed with the rollout.
- Hands-on sessions per role
- Leadership sessions
- Guides and office hours for the first weeks
04
Ongoing
Track and reinforce
We measure use and value, and act on what we find.
- Adoption and value reports for leadership
- Refresher sessions where usage drops
- Process and policy updates as tools change
Where we apply it
Industries we know well.
Further reading
From the blog.
Why AI Projects Fail Between Pilot and Production, and How to Fix It
Most AI pilots stall before production for predictable reasons. Here is how to spot them early and design pilots that are built to ship.
AI Readiness Assessment: A Practical Checklist for Leaders
A scored AI readiness assessment checklist covering strategy, data, technology, people and governance, with a simple rubric and guidance on what to fix first.
FAQ
Questions we hear a lot.
How do we get employees to actually use AI tools?
Make AI part of the process, not an extra step. People adopt it when it is built into the tools they already use, when training uses their own tasks and when their manager expects it. We redesign the workflow, train by role, appoint champions in each team and track usage so you can see where it is not landing and fix it.
What should an AI usage policy include?
Which tools are approved, what data may and may not go into them, when AI output must be reviewed by a person, who is accountable for decisions made with AI and how to report a problem. It should be short enough for staff to read and specific enough to act on. We write it with your legal, security and HR teams so it fits your rules, including the DPDP Act or EU AI Act where they apply.
Do you train leadership teams as well as staff?
Yes. Leaders need different things: a clear view of what AI can and cannot do, how to judge proposals and vendors, how to set metrics and how to manage the risk. Their sessions are short, practical and built around decisions they actually face.
How do you measure AI adoption and value?
We track two things. Adoption: who uses the AI, how often and where they override or ignore it. Value: the business metric each use case was meant to move, compared with the baseline we recorded before launch. Leadership gets both in a regular report, not just a count of licences.
Can you train our teams on tools you did not build?
Yes. We train teams on the tools you already use, such as Microsoft Copilot, ChatGPT Enterprise or Gemini, as well as on systems we build. We are platform agnostic, so the advice is about what works for your people, not about selling a particular tool.
AI implementation services
Every part of implementation, covered.
AI Readiness Assessment
Use cases ranked, data and systems reviewed, and a costed roadmap in about two weeks.
AI Pilot to Production
One use case taken from proof of concept to a live system people use, in about eight weeks.
AI Integration Services
AI connected to your ERP, CRM, core systems and product, with permissions and audit built in.
Managed AI Operations
AI in production kept accurate, safe and affordable, fully managed or co-managed.
Our services
AI strategy, GenAI and agents, data platforms, analytics, MLOps and transformation.
Talk to a founder about AI Adoption and Training.
Tell us where you are. We will come back within one business day with a point of view, not a sales deck.