Hey Megan, let's do a podcast. Great idea. What

should we talk about?

Today we're talking about the Human-Centered Adoption & Change

section of the AI Implementation Canvas.

Megan, how do we bring the people component to any AI

project that we're implementing in a nice,

organized way? Well, part of it is just

having conversation, right? The whole point of the AI Implementation Canvas is there's

14 different planning dimensions, 14 conversations we can be

having around AI.

And it's these conversations, people to people, that

are really important as we make big decisions that affect our

organizations, our performance, our people, our

customers, our community. And that's one of the things

that learning professionals do really well.

We think about the people, and we also really

rapidly onboard to a new topic; we focus on the performance of

other people, we bring experts together, we facilitate conversations — we get things

done. This now is

not just a conversation, and we're not engaging just around building learning

products. As we move into a more AI enabled world, I

want to challenge us to broaden how we look at learning and performance

in the organization, and bring in these other skill

sets, and perhaps contribute in a different way. We

can facilitate conversations around the Canvas. We can

leverage our network within the organization to seek out the right stakeholders and bring

people together to get things done. And we can leverage that

unique perspective that we have as an advocate for the learner — or the

worker, or the customer, or the community member, the constituent,

the patient — in all of our conversations. So the

conversations around the Canvas really are chunked into

four sections. There's Strategic Foundations, Technology &

Experience Infrastructure, Design & Implementation Enablers, and

let's talk about Human-Centered Adoption & Change.

AI is certainly going to be changing the way that people interact with

each other, not just how they interact with AI, because

we're adding this, kind of, this new dimension into the mix, right?

We are. And yet as we add in this new

dimension, this new teammate — sometimes it's referred to as another teammate

you might hire and give performance coaching to. But

I want to stack this on top of — in the

last, now, six years — a move toward increased work

from home, shorter job tenure, a

lot of gig work, really a fragmentation of our

social engagement in the world

with social media algorithms, a

fragmentation of the perspectives we get exposed to, and

really be thinking about how do

we maintain social engagement, human to

human, across the organization and the benefits

of that, that social engagement energy, that social construction

of what our reality

is and our processing of the world. How do we

bring all those minds together, even though it's very,

very easy to go and grab your laptop,

work at home, not talk to anybody else and get a lot

of stuff done.

You mentioned "across the organization." And so I think

many AI initiatives, you know, they're not just a single

team; they're like going broad across

an organization. And so there's got to of course be preparation

and planning on how that needs to roll out. I

would imagine that in the early days of AI that wasn't something we were thinking

a whole lot about. It was more like, drop this in, see what happens.

"Look, new cool tools!" But we need to have a conversation about that, right?

Yeah. Our change professionals among us are going to be very, very

important and continue to be important because there's more and more change. So as

we think about how people process staggering amounts

of change — not just at work, at home too, and in

their communities — how do we support people? How do we

give them a voice in the change, an ability to give feedback? How

do we help them shift their thinking, their behavior,

their workflow toward using these new

tools and using those new tools effectively and productively?

You asked the question of how do we support people? Well, of

course that makes me think about your planning dimension of AI

literacy and upskilling. Some organizations are

embracing this opportunity and thinking very

thoughtfully about how to train their workforce to use new AI tools. But

this is a consideration that — everybody's thinking about it.

Yeah. And it's interesting, right? So even in

some organizations, they still haven't rolled out AI literacy training

or AI tools. And yet for darn sure,

many people, not everybody, but many people may be using AI tools or generative

AI tools at home. They

have this mismatch of technology

environments from home to work. And how do we

make sure that everybody is operating with

basics of AI literacy? What is it? What's it good at? What's it not good

at? It? What are the risks? What are the opportunities?

A lot of times I see AI literacy training rolled out as a

compliance activity. Our friends in

compliance training are real good — make sure everybody gets something, it is

consistent, you've all got it, we track it, we make sure you've got it.

And that actually is, as much as

I'm not always wild about a — you know,

sometimes compliance feels like, "ugh, I gotta do my compliance training," right,

or it comes as it feels heavy handed — they sure do know how to

get things done. And then we get to proficiency,

which is around "how can you use this tool well?" So there's the "what

shouldn't you do?" "How does it work and what shouldn't you do?" And then "how

do you, how can you use it well? How do you use it to improve

your workflow and your work life?" And then we move

from proficiency to fluency. How

do we make AI part of the

conversation? Not that we use AI for everything; in fact, how do

we have smart conversations about when we do and don't use it, but how do

we make that a normal part of the conversation? And those considerations are —

they're both one-time

considerations like "hey, everybody needs to do this." But then because the technology changes

so frequently, it's worth circling back to it

on a regular basis and make sure we update people's skills.

When AI first came out, there was news

articles and things that I was seeing about how

there is maybe perhaps not the best

fairness or there's bias sometimes built into the models.

There's also some human protection issues

when it comes to AI. So what do we need to think

about when we think outside of our four walls of

our organization? You know, this takes

conversations around

fairness, around inclusion, around equity,

around social

well being community wide, and

levels it up and makes it that much more important. So I think what we

can be thinking about here are all the same kinds of conversations that

we've been having for the last decade

applied in this new

hyper-fast, hyper-complex environment

around AI. So I want to include all those people who helped us make those

decisions and choices before and keep them at

the table, keep them as part of the conversation, and

really be looking at all the dimensions,

both our business, our employees, our

customers, patients, students members, our community

members and family members, as we start making

big decisions based on some of this. And I

kind of have this matrix right? The more

impactful the decision and the

faster the decision gets made — both of those

are signals that say, "ah, we ought to like maybe add in

some meaningful friction, some review, some human perspective

on those as we go."

One takeaway that I think I have here, as I'm thinking about everything

we've talked about, is that when we

introduce AI into our organizations, our systems —

those are made up of people, and

systems of people are inherently kind of complex systems.

And so it's not

just a very simple, straightforward "who's using it

for what and where and when?" There's a lot of —

it's a little bit of a web, and you have to think about how that

introduction of something is going to kind of work its

way out into the web, and one thing is going to pull and affect another

thing. Kind of gets that ecosystem conversation we

had last season, huh? Yeah. Cool. Cool.

All right, Megan. I know you. One more thing. What have you got? You know,

it's interesting. I have run a number of workshops

around the Canvas as we aim to upskill learning

professionals about AI implementation.

And we intentionally save the Human-Centered Adoption & Change for

the last part of the conversation, and it's always interesting

because I was working with one group and they're like, "oh,

this is where we feel comfortable now!" And

the conversation we then had was, this

is the conversation that people who might be comfortable in the other four quadrants

are not comfortable having. This is a conversation we can lead.

We may participate and ask questions in the other domains, but

this is where we can play an active role. And

so as comfortable as we feel here and as uncomfortable as

we feel in the other three sections, we can have a little bit

of empathy about our colleagues cross functionally,

because they're feeling the same way, just about a different part of that

Canvas.

All right, Megan, how'd that go? This was fun.

What I really liked, Meg, was the opportunity to wrap up this

mini-series on the AI Implementation Canvas, and

each — people, technology and people — we've said across

each of these four that people show up in every one

of the sections, but this is really the people section. Technology

shows up in every one of the sections, and the

interplay here is really, really important.

This is Meg Fairchild and Megan Torrance, and this has

been a podcast from TorranceLearning. Tangents is the

official podcast of Torrance Learning. (As though we have an unofficial

one.) Tangents is hosted by Meg Fairchild and Megan

Torrance. It's produced by Dean Castile and Meg Fairchild,

engineered and edited by Dean Castile, with original

music also by Dean Castile. This episode was

fact checked by Meg Fairchild.