01Critical thinking in the age of AI
AI answers sound confident whether or not they are right. We train people to verify, question, cite and decide, and to resist the pull of automation bias.
- Verification habits for AI output
- Asking for sources and checking them
- When to trust, when to challenge
02Enterprise AI use
Role-based playbooks that show people how to use AI in everyday work, and how to handle company data safely while doing it.
- Playbooks per role and area
- What may and may not be pasted into a tool
- Approved tools and accounts
03AI adoption coaching
One-to-one and cohort coaching that turns licences into habits, supported by regular office hours and a network of champions.
- 1:1 coaching for leaders and key users
- Cohort sessions on real tasks
- Open office hours
04Executive & board AI literacy
Leaders do not need to build models, but they do need to know what to ask, what to fund and what to stop.
- A shared vocabulary for the board
- Questions for any AI proposal
- Signals that a project should stop
05Change management & champions network
Adoption sticks when sponsors speak up, people have peers to learn from and good use is recognised.
- Sponsorship and communication plan
- Champions trained inside each area
- Incentives that reward good practice
06Responsible & governed AI use
Clear rules that people can actually follow: what AI may be used for, how data is classified and who is accountable.
- Usage policy in plain language
- Data classification for AI use
- Named owners and escalation paths
07Human-in-the-loop decision design
We design where people review, approve and own the outcome, so that AI speeds work up without removing accountability.
- Decision points mapped per process
- Review and approval thresholds
- Records of who decided what
08Data literacy for decision-makers
Reading a chart, a sample and a confidence level correctly is the foundation of judging any AI result.
- Reading numbers critically
- Spotting weak evidence
- Asking better questions of analysts