Notes from Women in Leadership’s AI in Action event on November 12, 2026.
AI evolving at a rapid pace, most of us asking the similar version of the same questions: how to make sense, how to keep up, what should we be using it for, where we need to draw the line
Start exploring to use in meaningful ways
- Take something off your plate, more efficient
- Where add value vs retain own judgment
- Finding work, getting found; identify opportunities
Christine Baghdassarian, Director, KPMG Canada’s Technology Strategy and Transformation practice
- How do I use AI?
- At work:
- Data: turning raw data to an exec story (messaging, delivery)
- Story: wrote a speech for delivery and relevance to audience
- Business: RFQ/RFP proposal development and delivery
- Career: improve someone’s career prospects
- Risk: using AI to help write and find contractual information
- In Life: app dev, scan relevant news, update family website, self-hosted AI retrieval augmented generation, education for kids
- KPMG AI Lab: hands-on experiences, rapid prototyping, frontier experimentation
Caroline Wakefield, The Generous AI Society, Co-founder
- Arrive with questions, leave with something to try
- Beyond the AI Brainstorm
- “Jagged frontier”: 758 consultants use AI for real client work: 12% more work, 25% faster, but 19 points less likely to get it right
- Go beyond the brainstorm: decide where it support your judgment, and where your judgment has to lead
- “Most of us use AI like a brilliant stranger”
- Knew pulling from trusting sources: stop using what it thinks, start teaching it what you know; can take some mental load when you teach it
- Example: daily brief
- Can choose the sources
- During meetings: take notes for you
- Customize into your tone / style of writing
- See patterns may not see yourself
- Comfort line: whose info, how check, what to keep for yourself, did they say yes, would put name on it, what does workplace allow
- Start with: Teach it one annoying thing
- Learn from sources you choose
- Capture a conversation: develop skills for how to follow up on meetings
Host
- NotebookLM: feed papers, summarize and turn into podcast
- Business Brain: start here, Do and don’t / guardrail, brand voice, testimonials, ; all stuff that’s final, word of law; AI is connected
- Avery: business proposals, context in folder
Rachel Krayenhoff, Ingenuity Labs, founding
- How to start automating business processes
- GenAI: take data, transform to something else
- Can interact with other platforms
- Five steps:
- Identify: choose the work: repeated, explainable, recognizable, bounded; clear start/finish, can spot success, known failure points
- Describe: Explain the work, get it out of your head, record and transcribe, speak your process naturally, dictate using voice-to-text, or type, explain the steps in writing; let AI interview you about the process, what starts the work, what records, what tools, what judgment is involved, what could go wrong
- Challenge: Improve the process, find gaps before building; what is missing, can be simplified, might fail, what stays human; improve the workflow first, then automate
- Build: make it reusable; skill, task, or instructions; skill (how work is done, resuable instructions) > trigger (when work start, on demand or schedule) > connection (what AI can access, approved info and tools)
- Test: check and refine; keep judgment human; run (try a past example), check (compare to correct result), correct (improve instructions), repeat (try a difficult case); review = QA
Discussion topics
- What is the one task that eats the most of your time, and what would it take to hand over the first piece of it?
- What are you unwilling to put into an AI tool, and what would have to be true for that to change?
- Where does your next client actually come from, and what part of finding them could you test before the end of the month?
Group discussion notes
- AI event in Vancouver (May): university students talking about AI, not just throwing personal information, pitfalls
- discussing how to use AI responsibility, social responsibility, long term effects on development
- conversations of AI has changed a lot the last year, now mainstream, kids are picking up on it but many are anti-AI
- a lot of things are shifting and changing, and AI is not an independent issue
- investigation of water and energy impact of AI tooling
- does the conversation change if the data centre is running on sustainable, and strictly benefit the local community
- AI tooling allow for saving time, for creation by people who couldn’t do it before
- huge range of awareness and privacy implications: many providers chats’ public by default
- AI providers: what is the data using for, where are they based – which privacy laws are they following
- make informed decisions
- recording devices on neighbours’ property
- how do you personalize to stand out even if you’re using AI
- hiring process: need to look at how candidates should be / allowed to use AI, how recruiters using AI
- manage based on metrics
