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

should we talk about?

Today we are talking about the technology and experience

infrastructure section of the AI Implementation Canvas.

I didn't mess it up. It is a mouthful.

It is so. So I know the implementation canvas really

supports a cross functional team and working together and across

boundaries. There are some sections of the canvas though that make

sense for one or another function to lead, whether

it's IT, the people, the business and so on. But

I really like how the canvas provides questions and perspective

for everyone to be involved in learning together. Yeah,

you know, this is really one of the silo-busting

features of the canvas is that everybody

comes together to talk about these topics, even though different

groups and teams within the organization might lead at different stages.

We're at a point right now in the AI

transformation space, not just within an organization, but

societally in which old structures

that used to exist are starting to become more fluid. It

becomes easier to learn about other things. It becomes

more imperative that we talk to each other across

organizations in order to get this right. So the Canvas

questions in each of these planning dimensions

help non-subject matter experts, people who are not part of that

function, engage with the subject matter experts in each area.

In the book the AI Implementation Guide, there

are series of questions that you can ask people you might want to invite

to the table or to go find if you need help in any of

these things. So that's a really key

component here in the book. I call them planning dimensions,

but really it's, it's 14 conversations to be having or

topic conversations. And so you know, here we have these 14

different sections or different planning dimensions or conversations

and we've chunked them up into four different sections on

the canvas. There's Strategic Foundations, Design and Implementation

Enablers, Human Centered Adoption and Change, and as you

noted, the

rather-difficult-to-say-all-at-once technology

and experience infrastructure. You said that fast. No

problem. No problem for you. I say everything fast. It's a lot of caffeine here,

Meg. Keep up. All right,

so we hear a lot of talk about the technical aspects of AI

implementation in organizations, but isn't that IT's role?

Like they're the technical ones? How do we think of learning leaders

and business leaders engaging in that conversation? You

know, I feel like Meg, in order to be responsible partners, we can't

just say, "oh, I don't know, it's technology!" And particularly

when we have a technology that itself is

kind of a black box. Right. It helps us

become better partners with the IT team.

It helps us become better stewards of our organization's

resources and advocates for

people who work in the organization if we participate

and seek to learn. At

the same time, I think that our ability to strategically

ask subject matter experts questions about their work — that's something

we've learned as learning design professionals, right? — helps

them articulate their positions,

what they're learning, and what they're doing to other people.

And I will just say I

have been in a number of situations in which the business

leaders in the room didn't understand the technology or its capabilities

and either didn't

know the extent to which they weren't aware or were uncomfortable

surfacing that. So we had this ability to come in as

like, "hey, I'm here for the learning team and I'm here to help. How does

this thing work?" And just know that there's probably other people around the

table who had that same question and didn't have the ability or

audacity in the moment to ask that question. Yeah. That makes me think about

how as instructional designers, we come in

and we're so used to being in that space of being

comfortable not knowing. Cause we rely on those subject matter experts

in those areas to come with the answers and tell us

what they are. Okay. That's like. That's our superpower, right? Like one of our superpowers

— we have lots of superpowers. We don't often... We live in them so often

I don't think we necessarily appreciate them. But in the organization,

we're the people who repeatedly learn new

things well enough to teach it to other people. And that's

a muscle that not everybody gets to flex. Yeah.

Yeah, that's cool.

So even though we're talking about infrastructure, there's like a people side here

to consider, right? I know in an earlier

episode, you talked about how "people" is, like, all over the Implementation

Canvas. So people need to be able to use these AI

systems and solutions in the workflows and to support the

tasks that they're doing on a daily basis. Tell

me more about how we're planning for that people side — that user experience —

here. You know, this one, Meg,

was something that emerged from the research as

I was asking people about

their stories with AI implementation in their organization.

And we experienced it ourselves, right? So

some of the research and stories that I heard from people, like "really cool

technology, but didn't account for the fact that

people may work in a certain way." So here's. Here's a story, and I actually

heard this story. This is a synthesized story. I heard

from two places. It was almost eerie. It was the same week's worth of

interviews. And I was like, "seriously, do you two people

work together?" And they don't. It was really eerie, but almost the exact same

story. You have customer service personnel and they rely

on a knowledge base, right? So in the moment, they've got a call, they

don't know what to do. They search their knowledge base. And if they can't find

an answer, then they escalate the call to a supervisor

or a more experienced person on their team. And the business's

goal is to have as few escalations — right,

escalating up to a supervisor or manager or a more experienced person — as

possible because, well, there's a couple things, right? It helps relieve

the manager, it gives agency to the call center rep, and

for the customer, they get a first call

resolution. They get somebody who knows what they're doing.

And so each of these organizations was

trying to give better tools to the service

reps so that they could solve those

problems on a first call and not have to escalate.

In both cases, this is crazy, right? In both cases,

what they did was they were moving from a system that

you could search with keywords and you had to learn the keywords, and

that was a barrier to learning and becoming fast at your job. But once you

knew that, you could like cruise through that knowledge base because you

knew the knowledge base like the back of your hand and you had the right

keywords. And they replaced that with a chatbot

assistant that required that you type like something

much more like a whole sentence into the chatbot. So you're

actually slowing down the service rep

and forcing the service rep to think about how to query

this properly in order to get a consistent answer over and over

again. And consistency of answers, by the way, is something that large language

models don't necessarily do well unless you've really throttled

them. Probably a topic for another conversation.

So what we were actually doing in these two — not we, but like these

companies — these two stories, was slowing down service reps.

And actually they didn't change

their escalation metrics. They weren't able to

improve the first call resolution because they didn't think about this

user experience component. And there are

other examples. Even on our own team, right, Your own tiny team. Early on in

the days of AI, we had like making all these GPTs and doing all this

really cool stuff. And by the way, we have a really, really fantastic set of

really well-honed GPTs with very specific instructions and best

practices, and people look at our best practices like, "wow, that's pretty cool!" Probably another

episode. But

I remember one of our

senior learning designers, Lauren, said, "yeah, but it's a pain in

the neck these. Because I constantly have to alt-tab over and copy and paste and

do all this stuff, and it's not integrated into my work. It's

yet another tool I have to go and use." Yeah.

Interesting. User experience: always

and forever important. Projects are going to live or die based on user experience.

I can imagine that there are whole new governance practices

that are newly important in the organization when it comes

to AI too, right? Yeah... And doesn't that sound so boring? I mean,

governance. Yeah, I know, like rules.

You know, I think governance, all kidding aside, governance is

becoming really, really important. It is important. I think it's a word that like five

years ago I never heard of. Yeah, I didn't think much about it.

At least not organizational governance, right? But this is.... So

let's define it. Yeah. It's how we evaluate, approve, and

monitor the various systems that an organization uses. The data

they have access to, the people who have access to them, and the impacts and

the actions that we allow those tools to do.

What this does is, in a

well-governed environment, there is a team,

a function, and a set of processes, as well as

organizational culture, that helps ensure safe,

compliant, transparent use of AI tools in a

way that the organization.... What it means is the organization has

your back when something goes wrong because they've had your front

and by upfront looking at those tools and making sure

that they are going to be doing what you expect them to do. So

sometimes people say like, "oh, I can't use that tool. It's not been approved at

work." And it's really annoying because you can't use the coolest

new tool that you find on the web because it's not been approved.

I really like knowing that before I start using a tool

or opening a browser on my, you know, on my computer that

it's going to be safe to use. I don't know about you

— we have a lot of our clients' sensitive data. It

really does behoove us to make sure we're taking really good care of that.

Yeah. So I think my takeaway here is

that when we're talking about technology, it's more than just the technology

itself. It's more than just the code or

the hardware. Totally, totally. Right. It's technology,

it's people, it's the user experience, and

the rules — the guardrails keeping us

safe. Yep. I like being safe. What's one more thing

you got for us, Megan? There's always one more thing, isn't there? There is.

Hence the well-caffeinated life. There's always one more thing.

You know, I think one of the things here is that

there are multiple roles for the learning team

in this particular section. Because not only can we

be advocating for the user

in user experience — we don't have to be a UX designer to be advocating

for the user. We build the training for this, right? And

we see these problems, I think, earlier than perhaps

other functions do because we look at things differently. Again, part of our

learning designer superpowers, right? But

also, right, so we're participating on the project, but then also, there may be

learning and communication efforts associated with governance, associated

with the user experience and how the workflows are changing.

So there's multiple roles for us here.

How'd that go? You know, I wonder if we

overplayed the difficulty or the perceived difficulty of saying

technology and experience infrastructure. There's a lot of big words, but

they're also words that we deal with all the time.

Yeah, well, and they don't end or start with the same sounds,

which is usually what trips people up. I love how you see

that. You see things I don't see. Thank you. You're welcome.

This is Meg Fairchild and Megan Torrance, and this

has been a podcast from TorranceLearning. Tangents is the

official podcast of TorranceLearning, 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.