Host Susan Diaz sits down with Jennifer Hufnagel (Hufnagel Consulting), an AI educator and AI readiness consultant who's trained 4K+ people. They break down what "AI readiness" actually means (spoiler: it's not buying Copilot), why AI doesn't fix broken processes or dirty data, and how leaders can build real capability through training programs, communities of practice, and properly resourced AI champions.

Episode summary

Susan Diaz and Jennifer Hufnagel met in "the most elite way possible": both were quoted in The Globe and Mail about women and AI.

Jennifer shares her background as a business analyst and digital adoption / L&D consultant, and how she pivoted when clients began asking for AI workshops right after ChatGPT's release.

Together, they map a simple but powerful framework:

• AI awareness (practice + play, foundational learning, early change management)

• AI readiness (software stack, data quality, workflows, current state, and - quietly - the "people audit")

• AI adoption (implementation, strategy, and ongoing integration)

Jennifer explains why "audit" language scares people, but the work is essential - especially talking to humans about what's frustrating, what takes time, and where fear is showing up. She shares what she's seeing after training thousands: AI fluency is still low, people obsess over tools, and many assume AI will solve problems that are actually process or data issues.

The second half gets practical: what "workflows" really mean (step-by-step checklists), how AI now makes documenting processes easier than ever (voice → SOPs), why prompt engineering isn't dead but "100 prompts for your bookkeeping business" is mostly snake oil, and why one-off training sessions don't create real fluency.

They close with how to build sustainable AI capability: proper training programs, leadership-led culture, communities of practice, and protecting champions from becoming unpaid help desks.

Key takeaways

AI readiness is the middle of the journey. Jennifer frames AI maturity as: awareness → readiness → adoption. Most organisations skip readiness and wonder why adoption stalls.

Readiness includes software, data, process… and people. You can call it a software/data/process audit, but you still have to talk to humans about their day-to-day work, pain points, and fears. That's where the truth lives.

AI fluency is still lower than the headlines suggest. Jennifer questions rosy "90% adoption" stats because many rooms she's in still show low real-world usage beyond basic experimentation.

Stop obsessing over tools. Companies are writing AI policy around tools and forcing everyone into a single platform. Jennifer argues the real goal is discernment, critical thinking, and clarity - not "pick one tool and pray".

AI doesn't fix broken processes or dirty data. If your workflows aren't documented, AI will scale the chaos. If your data is messy, the analysis will be messy too. Readiness comes first.

A workflow is just a checklist. Jennifer demystifies "workflow" as step-by-step instructions and ownership: who does what, when. Sticky notes on a wall is a valid start.

Process documentation is easier than ever. You can dictate steps into a model (without passwords) and ask it to produce an SOP/checklist - getting knowledge out of people's heads and into a shareable format.

Prompting isn't dead, but promise-all prompt packs are mostly hype. Prompting differs by model, and the best move is often to ask the model how to prompt it - and how to troubleshoot when output is wrong.

One-off AI workshops don't create fluency. AI changes too fast. Real capability requires programs, practice, communities of practice, office hours, and change management - plus leadership modelling and culture.

Don't burn out your AI champions. Champions need dedicated time, resources, and leadership sponsorship. Otherwise they become unpaid AI help desks and the entire initiative becomes fragile.

Community of practice is the unlock. Jennifer shares her in-person "AI Chats & Bites" group and encourages finding online + in-person + internal communities to keep learning alive.

Episode highlights

00:01 — The 30-day podcast-to-book sprint and why people are saying yes in December

00:40 — Susan + Jennifer meet via The Globe and Mail "women and AI" feature

01:21 — Jennifer's origin story: business analyst → digital adoption/L&D → AI readiness

04:09 — The three-part framework: awareness → readiness → adoption

05:03 — Readiness: software stack, data quality ("dirty data"), and mapping current state

06:13 — "People audit" without calling it that: interview humans about pain + fear

08:02 — What Jennifer sees after ~4,000 trainees: fluency still low + stats don't match reality

09:38 — AI doesn't fix broken processes; it scales whatever is there

10:55 — Workflows explained as checklists; "won the lottery" handoff test

12:18 — Dictate your process into AI → generate SOPs/checklists

14:24 — Prompting isn't dead; ask the model to help you prompt + troubleshoot

17:50 — Why one-off training doesn't work; AI fluency requires a program + practice

22:15 — Burning out champions and why AI culture must be top-down

27:49 — Communities of practice: online + local + internal

31:00 — Common mistakes: vending-machine mindset, believing output, not defining the problem

35:31 — Women and AI: opportunity, fear, resilience, and "be in the grey"

39:51 — Where to find Jennifer: hufnagelconsulting.ca + LinkedIn

Guest info

Jennifer Hufnagel

• Website: hufnagelconsulting.ca

• Email: hello@hufnagelconsulting.ca

• Best place to connect: LinkedIn - Jennifer Hufnagel

If AI adoption feels stuck in your organization, don't buy another tool first.

Start with readiness:

1. Map one workflow end-to-end.

2. Talk to the humans doing it daily.

3. Clean up the process and data enough that AI can actually help.
   
   

Then build fluency through a program - not a one-off workshop - and protect your champions with real time and resources.

 

Connect with Susan Diaz on LinkedIn to get a conversation started.

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