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Case study · Seneca Polytechnic

How AI became part of everyday work at Seneca

In 2024 Seneca Polytechnic, one of Canada's largest polytechnics, named Kent and Panos its two AI Thought Leaders. Over three years we led its AI adoption from the bottom up, with policy and practice moving together.

One room, then the next. Colleagues who tried it showed colleagues who hadn't.

Hundreds

of colleagues in the AI community of practice we started

4.6 / 5

average rating for our hands-on workshops

21

course sections running MyTutor, the AI tutor we helped pilot, within five months

80+

employees in a single institution-wide AI session

The method, at institution scale

The same four steps we use with every client

Experience

People used AI on their own tasks, live, in hands-on workshops across the institution.

  • Sessions for departments from marketing and events to academic schools
  • Real work on the table, not a demo
  • An institution-wide session that drew more than 80 employees
How it spread

From one room, outward

Tap a ring

One room

It started in a room

Hands-on workshops where people used AI on their own tasks, live. Not a demo, not a policy deck.

  • Departments from marketing and events to academic schools
  • People applied AI to their own work during the session
In their words

Learned to be way less fearful of AI.

Workshop participant

Walking away with practical knowledge that will assist me in my day-to-day work.

Workshop participant

Demonstration on the tools available to us and having us apply it to our work.

Workshop participant

The facilitators were very engaging, inspiring, and pushed us to think outside the box.

Workshop participant
Colleagues teaching colleagues

A community that keeps going

We started Seneca's AI community of practice so people could share what worked, ask questions and help each other. It grew from a handful of people to hundreds of colleagues. Each node is one member.

Alongside it, the AI Lab gave the community a home: a place to try ideas, get help and build early prototypes together.

Kent Peel speaking and Panos Panagiotakopoulos listening in the Seneca AI Lab
Kent and Panos in the Seneca AI Lab.
What we measured

Not just how many prototypes reached production

When the AI Lab's success measures were set, we argued it shouldn't be judged only by how many prototypes went into production. Stopping or redirecting a project for good reasons is a result too. We bring the same thinking to every client: measure what matters, not what is easy to count.

In the news

What others wrote about the work

Want the same in your organization?

We run the same four steps with companies, public bodies and leadership teams. Start with one real piece of work.