September 22, 2025 • 36 mins
In this very special episode of The DEX Show, we welcome back one of the world’s most influential voices on digital transformation and the future of AI leadership: Charlene Li.
Charlene is a bestselling author and trailblazing thinker who has helped leaders navigate disruption for over two decades. She returns to the show for an unmissable conversation on the realities of AI Transformation—and what it means for organizations, leaders, and employees at every level.
Part of our AI Transformation Series marking the launch of AI Drive, this conversation goes well beyond hype.
Learn more about AI Drive here
Book your tickets for Nexthink Experience (Boston or London) here: https://nexthink.com/experience
Get the latest edition of the Gartner Magic Quadrant here (https://nexthink.com/gartner-magic-quadrant-dex)
Charlene is a bestselling author and trailblazing thinker who has helped leaders navigate disruption for over two decades. She returns to the show for an unmissable conversation on the realities of AI Transformation—and what it means for organizations, leaders, and employees at every level.
Part of our AI Transformation Series marking the launch of AI Drive, this conversation goes well beyond hype.
Learn more about AI Drive here
Book your tickets for Nexthink Experience (Boston or London) here: https://nexthink.com/experience
Get the latest edition of the Gartner Magic Quadrant here (https://nexthink.com/gartner-magic-quadrant-dex)
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Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Speaker 1 (00:01):
Welcome it, change Makers to the Deck Show with Tim
Flower and Tom McGraw. Let's get into it.
Speaker 2 (00:09):
Hello, change Makers, Welcome back to the Deck Show. I'm
flying solo today. It's Tom McGraw and I've got I've
got an interesting mission to fly everybody, because we've had
repeat guests on the Deck Show before and we're delighted
today to welcome back for the second time Charlie Lee.
She's a business author, speaker, analyst, and thought leader known
(00:32):
for her work on digital transformation, the future of work
and what we want to talk to her about today
AI strategy. Welcome back to the show, Charlie. How are
you good.
Speaker 3 (00:43):
I'm happy to be here.
Speaker 2 (00:45):
And what I was going to say I was going
to qualify it, Charlie, is that while we have had
repeat guests, not so many, you know, it always means
a great deal when we want somebody back on. We've
never done it quite like this. So the first time
you're on the show, Tim was flying solo and Tim
did they interview? Now it's me. So although you're the
second time guest, I've never actually met you before, so
(01:05):
it's it's almost like a completely new guest for me.
Speaker 4 (01:08):
At least well this is this is gonta be interesting then,
but you've used you know, the other episodes, so you
know what I said, I'm.
Speaker 2 (01:14):
Not going to ask you exactly the same questions now,
I'm not. I'm not. We we actually know you're part
of a sort of special series of shows where we're
really delving into AI transformation. And particularly because you know,
sponsor of the show, Next Think is just released its
AI Drive, which gives it teams a kind of unprecedented
(01:35):
real visibility into AI usage of their organization. So so
many things to talk about in and around that. But
first of all, so it's been about a year since
you since you came on a show with Tim and
the people who were able to listen. How have you been.
What have you been up to lately? Has the last year.
Speaker 4 (01:53):
Been It's been good. We've been my co arth Bearcatcher
Whilsh and I have been putting the finishing touches on
our book, a long awaited book called Winning with Ai.
And I've been working with a lot of people with
speaking workshops and advisory to just hear what they're doing
and helping them create an AI roadmap that creates value.
(02:14):
So it's been an interesting year. It's lots of changes, happy,
lots of.
Speaker 2 (02:18):
Changes, right. I mean, it's fair to say, it's fair
to say that AI is changing all of our working lives,
in our lives, and I was interested to ask how
has your own regular use of AI evolved over the
last twelve months.
Speaker 4 (02:33):
One of the things I've done is created a lot
of custom GPTs. They're sometimes in GPT their projects and
Claude their gems and Google. They all have the pros
and cons and I have everything from obviously, one that's
helping me write the book has all my interviews and
past writings, so it kind of knows all of my content.
It's sort of my second brain. But I also have
(02:55):
one for adventure travel, so I've given it my past
itinerary sounderstands what I like. I have one that helps
me cook, another one that helps me with gardening, nutrition, bookkeeping,
because I can't stand bookkeeping, like how do I do
this versus that? And I think it's just very helpful
to have sort of these customized versions of whatever it
(03:19):
is that I do on a routine basis. I have
also been doing some bibe coding along the way, just
to experiment my fingers in it. I don't know any
of the technology. It is fantastic, but it definitely helps
if you know some coating and knows like what does
it mean to commit to a GitHub? I still don't
quite understand what that is, but I know it's something
(03:41):
that you have to do, So it's one of those
things that I'm experimenting with.
Speaker 2 (03:46):
Very cool selection of all of those things, what would
you find it hardest to live without? Do you think?
Speaker 4 (03:51):
Oh, definitely all of my customs gputs because I rely
on them, my bookkeeping one in particular, because when I
have an issue going and using the traditional Google searches,
this doesn't work because it doesn't understand me. And sometimes
I'm asking the same question again and or a different
variation on it, and it has that memory so it
(04:13):
understands my situation and my circumstances. So that has been
a lifesaver.
Speaker 2 (04:19):
Last question of this front, Charlie, are there any are
there any ways in which you're using these different and
these different custom what you call from.
Speaker 3 (04:29):
Custom one custom GPTs custom GPTs.
Speaker 2 (04:32):
Are there any ways that you're using these custom GPTs
in such a way as to mitigate some kind of
side effect they might have on your on your creativity
or your imagination, because these are all quite imaginative and
creative well many of them, not the bookkeeping, but many
of them are quite imaginative and creative areas to use
them in. And are are there ways you need to
moderate or navigate it which which bring the best out
(04:55):
of you and make sure the technology doesn't kind of
overwhelm what you do. That's your approach to that.
Speaker 4 (05:00):
Well again, people who ask me all the times like
does it help you or hurt you, especially for things
like my creative writing, And one of the things I
do is I don't use it to just write. I
use it to help me brainstorm for it actually adds
to my creativity because it's kind of like having another
version of myself to brainstorm with. And there aren't very
(05:21):
many people who understand the context of things I have
to explain to them. They don't quite underget it. This
thing just knows everything about me, and frankly, it remembers
more of the stuff that I've written than I have
in the past, So it'll bring up things that I've
said or done and talked about make connections with things
that I may not have remembered. So I do think
(05:44):
of it as a second brain that I can careery.
And the way I use any of these checkbots is
when I put in a query at the end, I
ask before you start asking me any clarifying questions, because
it turns it from an answer machine into a conversation.
And that creativity and that that expansiveness comes from the
(06:07):
conversation that I have with it.
Speaker 2 (06:09):
Oh that's a cool prompt. I've never heard like that
a lot. It is amazing, right, like, uh, these these
these are applications. They know us as well as anybody
could ever know us, right, right, you know, it's astonished
thing I mean, And do you have any do you
have any misgivings about that level of transparency and intimacy?
Speaker 3 (06:30):
I do. I'll give you a little exercise.
Speaker 4 (06:33):
Someone sent me a little thing that said ask especially
use the one that you use the most. So I
tend to use chatchy Bet because it's sort of all
around decent.
Speaker 3 (06:43):
Not the best at everything, but it's all around decent.
So it's my sort of default.
Speaker 4 (06:48):
And I asked it tell me everything you know about me,
which is an interesting exercise anyways. And then the second
prompt is go deeper. And the third prompt is do
a full cycle analysis of me. And then the fourth
prompt is write a letter.
Speaker 3 (07:06):
To my soul.
Speaker 2 (07:08):
Oh my yarness.
Speaker 3 (07:09):
What I was so curious, like, what's it going to say?
Speaker 4 (07:15):
And it was incredibly moving. I mean, these things are
built to do that. I mean, if it's going to
write to your soul, it's going to speak. It understands
what moves you. And again it helps if you do
it across multiple domains of your life. And I'm not
to say that it has it knows everything about me,
(07:35):
but it's kind of like when you go see a
fortune teller or you get your palm red. It's kind
of an interesting exercise. You don't take it too seriously.
You take it with a grain of salt. But it
was incredibly moving. And I sent this to some friends
and they go, this, This brought me to tears.
Speaker 3 (07:53):
So I.
Speaker 4 (07:55):
Look at it again with a big grain of sult.
It doesn't know anything. It doesn't true really know me, right,
it has a lot of information for me about me.
It could extraperate a lot of understanding and pull together
things in a very interesting way. And some parts of
it truly resonated with me. I'm like, how did it
(08:16):
figure that out? So somehow we did it just again
with those prompts. It was set up in the right way.
Speaker 2 (08:24):
I keep saying, one more question in this direction, you know,
or more, just one more something? How how how far
are we from the first AI centric religion? I know
that that sounds like a left field question, but I
mean you you know, people, what did you just cite
that you cited? People go into mystics, People go into
(08:47):
people with you know, like effectively little religious pilgrimages, right like,
you know, and and then you're getting a resonance and
a sense of comprehension which is both self comprehension, which
is mysterious moving meaningful. You know, like, how are people
going to react to that? At scale? Some people are
(09:07):
going to go very very far with that connection, right.
Speaker 4 (09:10):
Well, I want to draw a difference between religion, which
is an organized uh or a place where beliefs and
rituals and traditions, sacraments, the that's of religion. There's a
practice to it, there's a ritual to it, and spirituality,
which is diving deeper into this whole mystical world. And
(09:32):
I don't know if you want to call it spirituality,
but I do believe AS want to help us understand
ourselves better.
Speaker 3 (09:39):
In the world of.
Speaker 4 (09:41):
Knowledge where AI can know everything, the area where it
doesn't really know is us, like what's inside of this head?
Speaker 3 (09:50):
How do people think?
Speaker 4 (09:52):
And if it can help us understand each other better
and help us smooth the communications that we have. Just
look at within organizations it communicating with HR two groups
that really need to work together very closely when it
comes to transformation. They speak completely different languages, or a
(10:13):
startup and a VC speak to completely different languages with
common goals, but they have to do some sort of
subtle translation.
Speaker 3 (10:21):
Or let's just take even our relationships.
Speaker 4 (10:24):
I have said so many times in my relationships, with
friendships and with partners, I say something I asked them
repeated and it comes back as something completely different. I'm like,
what am I doing that they can't understand me? So
I think that AI can help us understand ourselves and
each other better, and if that is an opportunity, I
think to be able to use these tools to help
(10:46):
us understand what is it that I don't understand about myself,
so that I can be better at whatever it is
that I want to accomplish well, I think.
Speaker 2 (10:55):
This has been an amazing way to label foundations of
the rest of the conversation because I've we've got to
the heart of some of the more extreme and interesting
use cases and potentialities of this technology, and now we
have to imagine what that means for our workplaces and
for our professional lives, and for our careers. But specifically
the first of us, specifically for the workplace, and something
(11:16):
you've written about extensively is the concept of AI leadership,
and you touched upon it with Tim and your last appearance,
But maybe just recap to begin with what the core
principles in your view of good AI leadership looks like
in this era.
Speaker 4 (11:31):
Well, I think good AI leadership starts with just good leadership.
And one of the things that's interesting about leadership is
that it becomes ultimately incredibly important when things are uncertain,
when they're unpredictable, because if things are not changing, it's
sort of just maintaining their status quo. That's where managers
(11:53):
are really good at. But when change is happening and
changes required, that's when you need leadership. And leaders what
they do really well is they create structure, They create
a purpose and a why, an underlying why do we
have to go do this change? We don't want to
it's hard. And they create the strategy, the roadmap, and
(12:18):
they also create accountability, and they're the ones who establish
credibility and a relationship so that people will trust them.
And I have always believed that with leadership. Classic leadership
has always been a relationship between people who aspire to
create change and the people who are inspired to follow them,
(12:41):
and that has been the truth for a millennia.
Speaker 3 (12:45):
So nothing changes around that now.
Speaker 4 (12:49):
Yeah, And the thing when it comes to AI leadership
is that that has to come forward even more and
people forget that. And this is a moment where I
think people are not standing in leadership. They see that,
they know that AI is important, they may or may
not be doing it. They're not AI fluet for the
(13:09):
most part, and without that fluency, it's very difficult to
lead a change. They're not showing up as leaders. They're
not saying this is the direction we need to go in.
That's all go in that direction, and how can I
support you through this change? So again I think great
(13:33):
AI leadersharps. Leadership starts with a mandate that says this
is why we have to change. We see some CEOs
now from places like Shopify, from Amazon. It's mostly technology
companies because they're kind of more on it. But even
like the CEO of Walmart has said, this is the
future of what we're going to do, and what you
(13:54):
see Walmart doing is rolling it out to all of
their associates and stores and giving them access to very
powerful AI tools in the existing applications that they use.
So if this is something that again there's leadership happening,
but also it's not just enough to talk about it,
you actually have to follow up with the actions.
Speaker 2 (14:15):
So interesting and just on that, I just sat an
interesting post just yesterday is who should be leading AI
transformation in an organization? Is it the CIO, the CEO,
even throughout the CMO, which I you know even you
are its cause not completely preposterous, right, I don't imagine
you're going to name one, But what are your thoughts
on that question of who in the existing structures should
(14:39):
be could be taking the leader?
Speaker 4 (14:41):
It's the person who can understand the technology and how
it works, and they don't have to be a technologist
and the person who understands your business strategy because in
the end AI has to be in service of your strategy.
It is not about technology, it is about how it
can be use as a strategic initiative. So at Moderna,
(15:04):
the pharmaceutical company that does mRNA co vaccines, for example,
AI lives with the CHRO HR. In fact, they took
their technical team and put it under the HR team
reporting to the CHRO because they realized that this transformation
(15:25):
was going to be human led. It is about transforming
that people who are going to use the technology, so
that people come first and the technology comes second. The
technology is in service of the people who will execute
a strategy. So that is that's a big outlier. I
would say that is not the norm. A lot of
(15:45):
people by default give it to the IT and I
would say, really think about this and say, is your
IT leader, the person who's going to be driving this,
do they understand your business strategy?
Speaker 3 (15:57):
Are they focused.
Speaker 4 (15:59):
On ex cuting and supporting business or are they focused
on primarily technology and procurement And that's not going to
work A I would tie a very slow and painful
death if you give it to a person who just
doesn't connect the technology to the business strategy.
Speaker 2 (16:18):
I think that let's say that as an incentive for
our listeners who are in it, and I T leadership
who are being given these responsibilities to take responsibility for
that dimension, right like, because a lot of it, you know,
and and and so so you know that becomes that
becomes what you just said becomes an incentive for it.
(16:41):
If it's going to be their responsibility in an organization, A,
it's a huge responsibility, and B it might require a
reorientation of their perspective. Would you say, right, how would
you how would you turn that advice around to it?
Leaders who are being asked to affect this change.
Speaker 4 (16:58):
They should be constantly asking, there's one question, what is
our business strategy and how can AI support it? What
are the biggest challenges we have and our business strategy
the biggest problems, the biggest opportunities, and how can AI
address them. We typically have three, let's say, typical business
(17:20):
objectives that we have. It's hard to keep check more
than three, and any use case. And I'd like to
say use cases are not a strategy. So when you
have a whole long list of AI use cases, that's
not a strategy. Strategy is this is? These are our
business objectives, These are our top three. This is how
(17:42):
AI is going to support that, and you can talk
about that until the cows come home. So everybody goes, so,
what's our AI strategy going to be while we're using
AR to support these three business strategies which you all
know and are supportive of, we all agree on that is,
you don't have to rethink that.
Speaker 3 (18:00):
And this is why we're doing it now. This is
what we're doing.
Speaker 4 (18:05):
Here's a rogue map of what we're going to do,
and this is how we're going to actually implement it.
These are the supports, these are the the training we
should definitely come and talk about in a second. And
this is the change we're going to have. And it's
not necessarily going to be impossible, but it is going
(18:26):
to be changed, and we need to be prepared for this. Again,
that's leadership, it's strategy. It's about having a concerted focus,
and focus says you have to choose what you will
do and what you won't do. And right now there's
not a lot of choices being made. It's just a
(18:46):
lot of people trying a lot of different things.
Speaker 3 (18:50):
I like what your sponsor does.
Speaker 4 (18:51):
It's looking at how AI is actually being used in
the organization because that can give you information about where
the value is being found. And if you're pilots, if
you're an endless pilot purgatory, that's not a good sign.
It's an indicator that you do not have a strategy
of how to use AI. And so I say, stop
(19:12):
doing the pilots, really focus on doing the top strategic
AI initiatives that are going to support your strategy and
put all your eggs in those baskets, because you're not
going to do visibility studies. You're not going to do pilots.
You're going to say this is really important to us,
We're going to invest, we're going to figure out the technology.
(19:34):
We're going to connect this to outcomes for our customers
and impact every single quarter. And that's what a coherent
strategy in roadmap looks like when it comes to AI.
Speaker 2 (19:44):
And I mean, if it leaders can get that kind
of level of visibility into how AI and IT are
actually being used across for organizations. What do you think
would surprise the board or management of the C suite
most about what it? What it would tell.
Speaker 4 (20:00):
Them, Oh, that there's so much happening. We call it
shadow AI sort of like shadow it anybody with a
browser can use it. They don't need the permission of it.
They don't need anyone's permission to go and start using
these things. And I think they would be surprised by
how much value is actually being created. People send to
(20:23):
me like, oh, I see a little of efficiency here
and there, but is there true value being created? Go
and look at how people actually are using this. They
wouldn't be using it, but wasn't creating value. So its
value is absolutely being created. You don't know exactly how.
How I can tell is when they're given access to
(20:45):
these tools. If you try to take it away from them,
they go with my cold dead fingers on it when
you take it away, So like, no, I need this
to do my job. There's value there, but I don't
know if you can measure it in specific ROI, which
is why I think ROI is the worst measure you
(21:06):
could try to be looking at. It's the one question
what's the roy of AI? It's kind of like saying,
so what can AI do? It could do so many things,
but is it creating value that matters to you? So
ask instead of what can AI do? Ask what can
AI do for us to help us? Achieve our biggest
(21:29):
strategic objectives and what.
Speaker 2 (21:31):
Kind of more meaningful metrics or user patterns should we
be looking for in place if something as reductive as ROI.
Speaker 4 (21:40):
Well, again, you already have business metrics, you have objectives
and metrics associated with that that tells you whether you're
moving towards your objectives better, faster, cheaper. And I would
add another metric, which is safer. And so we talked
about safety, security, privacy all the time. When it comes
(22:03):
to AI, it could potentially help you do those because
it can highlight the things that may be going wrong. Again,
it's a security list too, but it can also tell you, hey,
this thing may not be aligned because it can give
it very clear objectives. This is our objectives, this is
our strategic objective. Highlight anything that doesn't help us get
to that point. Highlight anything that could be standing in
(22:27):
the way or could be jeopardizing it. So again, AI
is very objective driven, and if you tell it this
is our objective, this is our stage real objective.
Speaker 3 (22:37):
How do we get there? How do we use AI
to help us get there?
Speaker 4 (22:42):
So I kind of think it's meta that use AI
to figure out how to use AI.
Speaker 3 (22:47):
So may as well use the tools available to like.
Speaker 2 (22:50):
Macro prompting in a way. Yeah, exactly. Yeah, what about
the question of like productivity metric and specifically and maybe
a difference between the qualitative and the quantitative, right, which
is something that I feel is you know, when you
talk about that employee lead AI transformation, you know that
(23:13):
for me can be reduced to that contrast between qualitative
and quantitative. You know, we all know that AI could
reel off a library worth of pretty bad books for
us and with you know, a series of prompts. But
that's not what we're after, is it. We're after work
which cuts through, which retains personality and originality. And that's
(23:36):
is that an area where you feel the employee has
to be in the driver's seat, most most importantly, most significantly.
Speaker 4 (23:44):
Well, there's a disconnect because employees know what the problems
are with their job, like these are the bottlenecks, these
are the things that are pain and to do do
and but there's a disconnect between them knowing what the
problem is and how use AI to solve it. So again,
I think employees are incredibly smart. People generally are smart.
(24:07):
They will not do things that are not productive for them. Okay,
this is this does not include procrastination, so that's something
completely different. But this is again about us understanding that
if something is going to help us, if you're given
a tool to help us do our jobs better, we're
going to use it. And so I can tell you like, oh,
(24:29):
I can do this a lot better, the results are better.
I don't have to I can be more creative and
come up with better solutions. But you give them the tool,
and they also have to give them the training, and
the training comes in two flavors.
Speaker 3 (24:45):
They're sort of top.
Speaker 4 (24:46):
Down, centralized, very organized. It's great for understanding what AI does,
and it's great for responsible and ethical use of AI.
But when it comes to that additional level of fluid
and where you can apply AI to your job until
your work, that requires much more of a decentralized approach.
(25:08):
It's about pure learning and support. It's about cross fertilization
across departments. Like we learned this, this could apply to
you too as well. And then the fourth area of
fluency is can you use and talk about how to
share this? Again, that sharing, the teaching of it, the
collaboration that happens is that fluency there to be able
(25:32):
to talk about and share and learn collectively too as well.
So those are types of individual fluency, but also organizational
fluency with AI that's really important to develop. And I'll
put a line in the sand here. I believe that
organizations should be focused on getting their AI, getting their
most important workforce, especially the knowledge based workforce. Everyone fluent
(25:57):
with AI in the next six months. There's no excuse.
We know this is the future. So invest the money,
the time, and it's both to get people to be fluent,
get the technology in place where everybody can get access
to these powerful models. You can do it at fairly
(26:19):
low costs if you go and get an enterprise license
use against some of the managed services like Amazon Bedrock
to be able to get access to this. It's fairly
turnkey and can be a lot less expensive, and frankly,
it brings that shadow AI into the light so that
(26:40):
you can truly talk about it and advance the use
of AI in your organization, because there are some risks.
Speaker 2 (26:46):
With shadow AI as well. Right, it's not just to this,
you know, there is. Maybe you could phrase it frame
it as a duty of care on an organizational level,
maybe a duty of self protection and security. You know
it needs to be about transparency.
Speaker 4 (27:02):
Right absolutely, I feel like people should declared AI amnesty.
It's like, okay, if you're using it, we forgive you,
but you have to come forward and here's the timeframe.
Speaker 2 (27:16):
And with my prompt into to submit, that made me
do it.
Speaker 4 (27:25):
No exactly, it's just like again, that's the only way
you can bring all the people out from the edges,
and you have to give them amnesty and and so
they won't get in trouble because I'm not going to
talk about everybody because I've been doing this rogue.
Speaker 3 (27:40):
Okay, Okay, we know this has been happening.
Speaker 4 (27:42):
We have the data, we already know that you're using this,
so just come forward, do this in the appropriate way,
and and say, look, we may have to shut it
down because it has these risks, or we can do
it in this way. But it's it's about having this
really thoughtful conversation.
Speaker 2 (28:01):
It's about AI. How do I religions earlier, Charlie, maybe
we should call it my absolution A right way to
frame it. So, where have you seen real world organizations
supporting this process of transformation in imagine ty if effective ways?
(28:21):
And I guess what advice would you give to leaders
looking to support their employees with different temperaments or attitudes
towards the extreme novelty and power of this technology.
Speaker 4 (28:33):
Yeah, we've worked with this and interviewed this company that's
a very large call center and they implemented AI and
they saw huge productivity gains. But it wasn't about replacing people.
It was really about taking the bottom twenty five percent,
for example, and bring them up to the level.
Speaker 3 (28:53):
Of their top performers.
Speaker 4 (28:55):
And it was really focused on helping people do their
best work. And so there are productivity games because of it,
and so they can absolutely anticipate that their numbers will
go down over time. But they were saying that we're
just going to manage that through attrition. And we know
that some jobs have changed. We know that jobs are
(29:15):
going to change. Some jobs are just going to go away.
Somebody gave me the example of reviewing checks, like somebody
actually used to look at checks and key them into
a system. And now with AI and computer vision, those
jobs are just going to go away. There's no way
of just it's saying that it's not going to be there.
(29:37):
So you need a small number of people to look
at the exceptions and this is a reality and we
need to prepare ourselves as an organization for that. And
again this has been the case for decades now with
technology coming in. This is not new and unique to AI,
and so having the responsibility to take care of your
(30:00):
people I think is a really important leadership trade here
to be able to say change is coming.
Speaker 3 (30:07):
We'll do our.
Speaker 4 (30:07):
Best to upskill you, to reskal you, but overall, there
may be some jobs that just simply aren't going to
be here. So we can't guarantee your job, we can't
guarantee your role is going to be here, but we
can do our best to prepare you for this new
future because you understand our organization. You're a valuable worker,
(30:28):
because you've been coming here and you know us. We
know you to take them possible.
Speaker 2 (30:33):
We'd love to keep you very cool.
Speaker 4 (30:36):
And I think it's irresponsible of companies to give two
week notices. It's like saying you just don't have your
app together first as saying we can see this change happening,
we're going to prepare for it.
Speaker 3 (30:48):
There's a long runway. These are the ways we can
do this.
Speaker 2 (30:55):
I became interested in recent we actually had some of
the show was talking about study about how they'd showed
for excessive use or use of AI can sometimes sometimes
have detrimental effects on creativity. And you know, and I
couldn't I could imagine this being true in some circumstances, right.
(31:16):
Could you imagine a future where there's mitigation of the
use of AI or control the fuse of AI within
organizations where they're looking at specific tasks and they're maybe
providing prompts above the AI app saying you know, look,
maybe step away, maybe carve out some more more autonomy
in this process. Similarly, with you see stories charline about
(31:41):
AI addiction, AI over use, say, unhealthy attachments to it.
These these are all you know, There's going to be
so many known unknowns that come about from a new
powerful technology such as this that could have implications for
HR departments, for leaderships. In my position, what are you
forced in this direction?
Speaker 4 (32:02):
Well, again, this is where I think education helps tremendously,
because there was just an article in the New York
Times talking about how somebody became very delusional about their
ability to do math because AI is kind of like
telling you, yeah, you're a math genius. And then when
I actually put the formulas and everything into another AI.
(32:25):
It's that, oh no, this is bunk, like he goes,
why didn't I think to do that?
Speaker 3 (32:29):
Like?
Speaker 4 (32:29):
How did I get sucked into this? And again, one
of the best practices is critical thinking. We cannot let
go of critical thinking because AI is just going to
tell us exactly what it is that we want to hear.
It's designed to do that for beautiful words. Now we
can talk about designing AI so that the objectives are
(32:51):
not just to answer the things that you want, but
to also look at a body of knowledge that's considered
quote right, whatever that means. Again, if we can't even
agree on what is right, because there are multiple versions of.
Speaker 3 (33:03):
That, it is very hard for AI to do that.
Speaker 4 (33:05):
So I go back to an organization defining what does
right look like? And I'll give you an example of fairness.
We talk about bias being built into AI. There is
bias in everything that we do because we are human
and AI was built by humans. We come and show
up with our biases based on our experiences. They're not
(33:26):
good and bad necessarily, but there are biases, and so
understanding what fairness means to an organization because there is
no universal definition of fairness, What does fairness mean? What
does unbiased mean? And then building that into your AI
and to the way that it delivers results is one
way to mitigate those inavertent points that you don't want
(33:51):
to have happening. So I do believe that teaching people
about critical thinking, how to use AI, understand that it
can hallucinate, Understand that you can fall into these straps,
that it's just going to keep you the answer, so
you have to ask it and say, you know, take
the other perspective, challenge me on these ideas, use a
(34:14):
different engine to check the results of another engine. So
these are best practices that you can learn. But right now,
you know something like seventy eight percent of employees see
the future about being AI and they know it's going
to impact their jobs and they want training, and only
thirty one percent say they're getting the training that they need.
(34:35):
It's a huge gap, and then disconnect. Organizations are just
not investing in the training that people need in order
to be successful with AI.
Speaker 2 (34:44):
Fascinating Sholing, I'm soorry. I'm so glad we had to
have you back on because I had to have a
chance to talk to you. It's not fair. It wasn't
fair that any team go to do it. So that
was absolutely scintillating conversation. Where can people find you in
your work?
Speaker 4 (35:00):
And find me at my website Charlie Lee dot com.
This is my name. I am also very active on LinkedIn.
It's a great place to connect with me. So hope
to connect with and many of you. Please write me.
I tell people connect with me, write me. I read
everything and almost nobody does so, but I'm very sucere
about business.
Speaker 2 (35:18):
This is all right to you tomorrow, okay, very correact
and I hear you. I hear you're in You're in
the UK presently where I am?
Speaker 3 (35:25):
Yes, I'm right in the middle of London right now.
Speaker 2 (35:27):
Yeah, I'm in London too. So yeah. Nice to connect
on this in the same city, even if virtually, and
a pleasure to meet and talk to you.
Speaker 3 (35:35):
Thank you so much for having me.
Speaker 2 (35:36):
Thank you, Shenny. Thank you.
Speaker 1 (35:38):
To make sure that you never miss an episode, subscribe
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podcast player, and if you're listening on Apple podcasts, make
sure to leave a rating of the show. Just have
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you'd like to learn more about how next Thing can
help me improve your digital employee experience, head over to
next think dot com. Thank you so much for listening.
(36:00):
Until next time, M
Welcome it, change Makers to the Deck Show with Tim
Flower and Tom McGraw. Let's get into it.
Speaker 2 (00:09):
Hello, change Makers, Welcome back to the Deck Show. I'm
flying solo today. It's Tom McGraw and I've got I've
got an interesting mission to fly everybody, because we've had
repeat guests on the Deck Show before and we're delighted
today to welcome back for the second time Charlie Lee.
She's a business author, speaker, analyst, and thought leader known
(00:32):
for her work on digital transformation, the future of work
and what we want to talk to her about today
AI strategy. Welcome back to the show, Charlie. How are
you good.
Speaker 3 (00:43):
I'm happy to be here.
Speaker 2 (00:45):
And what I was going to say I was going
to qualify it, Charlie, is that while we have had
repeat guests, not so many, you know, it always means
a great deal when we want somebody back on. We've
never done it quite like this. So the first time
you're on the show, Tim was flying solo and Tim
did they interview? Now it's me. So although you're the
second time guest, I've never actually met you before, so
(01:05):
it's it's almost like a completely new guest for me.
Speaker 4 (01:08):
At least well this is this is gonta be interesting then,
but you've used you know, the other episodes, so you
know what I said, I'm.
Speaker 2 (01:14):
Not going to ask you exactly the same questions now,
I'm not. I'm not. We we actually know you're part
of a sort of special series of shows where we're
really delving into AI transformation. And particularly because you know,
sponsor of the show, Next Think is just released its
AI Drive, which gives it teams a kind of unprecedented
(01:35):
real visibility into AI usage of their organization. So so
many things to talk about in and around that. But
first of all, so it's been about a year since
you since you came on a show with Tim and
the people who were able to listen. How have you been.
What have you been up to lately? Has the last year.
Speaker 4 (01:53):
Been It's been good. We've been my co arth Bearcatcher
Whilsh and I have been putting the finishing touches on
our book, a long awaited book called Winning with Ai.
And I've been working with a lot of people with
speaking workshops and advisory to just hear what they're doing
and helping them create an AI roadmap that creates value.
(02:14):
So it's been an interesting year. It's lots of changes, happy,
lots of.
Speaker 2 (02:18):
Changes, right. I mean, it's fair to say, it's fair
to say that AI is changing all of our working lives,
in our lives, and I was interested to ask how
has your own regular use of AI evolved over the
last twelve months.
Speaker 4 (02:33):
One of the things I've done is created a lot
of custom GPTs. They're sometimes in GPT their projects and
Claude their gems and Google. They all have the pros
and cons and I have everything from obviously, one that's
helping me write the book has all my interviews and
past writings, so it kind of knows all of my content.
It's sort of my second brain. But I also have
(02:55):
one for adventure travel, so I've given it my past
itinerary sounderstands what I like. I have one that helps
me cook, another one that helps me with gardening, nutrition, bookkeeping,
because I can't stand bookkeeping, like how do I do
this versus that? And I think it's just very helpful
to have sort of these customized versions of whatever it
(03:19):
is that I do on a routine basis. I have
also been doing some bibe coding along the way, just
to experiment my fingers in it. I don't know any
of the technology. It is fantastic, but it definitely helps
if you know some coating and knows like what does
it mean to commit to a GitHub? I still don't
quite understand what that is, but I know it's something
(03:41):
that you have to do, So it's one of those
things that I'm experimenting with.
Speaker 2 (03:46):
Very cool selection of all of those things, what would
you find it hardest to live without? Do you think?
Speaker 4 (03:51):
Oh, definitely all of my customs gputs because I rely
on them, my bookkeeping one in particular, because when I
have an issue going and using the traditional Google searches,
this doesn't work because it doesn't understand me. And sometimes
I'm asking the same question again and or a different
variation on it, and it has that memory so it
(04:13):
understands my situation and my circumstances. So that has been
a lifesaver.
Speaker 2 (04:19):
Last question of this front, Charlie, are there any are
there any ways in which you're using these different and
these different custom what you call from.
Speaker 3 (04:29):
Custom one custom GPTs custom GPTs.
Speaker 2 (04:32):
Are there any ways that you're using these custom GPTs
in such a way as to mitigate some kind of
side effect they might have on your on your creativity
or your imagination, because these are all quite imaginative and
creative well many of them, not the bookkeeping, but many
of them are quite imaginative and creative areas to use
them in. And are are there ways you need to
moderate or navigate it which which bring the best out
(04:55):
of you and make sure the technology doesn't kind of
overwhelm what you do. That's your approach to that.
Speaker 4 (05:00):
Well again, people who ask me all the times like
does it help you or hurt you, especially for things
like my creative writing, And one of the things I
do is I don't use it to just write. I
use it to help me brainstorm for it actually adds
to my creativity because it's kind of like having another
version of myself to brainstorm with. And there aren't very
(05:21):
many people who understand the context of things I have
to explain to them. They don't quite underget it. This
thing just knows everything about me, and frankly, it remembers
more of the stuff that I've written than I have
in the past, So it'll bring up things that I've
said or done and talked about make connections with things
that I may not have remembered. So I do think
(05:44):
of it as a second brain that I can careery.
And the way I use any of these checkbots is
when I put in a query at the end, I
ask before you start asking me any clarifying questions, because
it turns it from an answer machine into a conversation.
And that creativity and that that expansiveness comes from the
(06:07):
conversation that I have with it.
Speaker 2 (06:09):
Oh that's a cool prompt. I've never heard like that
a lot. It is amazing, right, like, uh, these these
these are applications. They know us as well as anybody
could ever know us, right, right, you know, it's astonished
thing I mean, And do you have any do you
have any misgivings about that level of transparency and intimacy?
Speaker 3 (06:30):
I do. I'll give you a little exercise.
Speaker 4 (06:33):
Someone sent me a little thing that said ask especially
use the one that you use the most. So I
tend to use chatchy Bet because it's sort of all
around decent.
Speaker 3 (06:43):
Not the best at everything, but it's all around decent.
So it's my sort of default.
Speaker 4 (06:48):
And I asked it tell me everything you know about me,
which is an interesting exercise anyways. And then the second
prompt is go deeper. And the third prompt is do
a full cycle analysis of me. And then the fourth
prompt is write a letter.
Speaker 3 (07:06):
To my soul.
Speaker 2 (07:08):
Oh my yarness.
Speaker 3 (07:09):
What I was so curious, like, what's it going to say?
Speaker 4 (07:15):
And it was incredibly moving. I mean, these things are
built to do that. I mean, if it's going to
write to your soul, it's going to speak. It understands
what moves you. And again it helps if you do
it across multiple domains of your life. And I'm not
to say that it has it knows everything about me,
(07:35):
but it's kind of like when you go see a
fortune teller or you get your palm red. It's kind
of an interesting exercise. You don't take it too seriously.
You take it with a grain of salt. But it
was incredibly moving. And I sent this to some friends
and they go, this, This brought me to tears.
Speaker 3 (07:53):
So I.
Speaker 4 (07:55):
Look at it again with a big grain of sult.
It doesn't know anything. It doesn't true really know me, right,
it has a lot of information for me about me.
It could extraperate a lot of understanding and pull together
things in a very interesting way. And some parts of
it truly resonated with me. I'm like, how did it
(08:16):
figure that out? So somehow we did it just again
with those prompts. It was set up in the right way.
Speaker 2 (08:24):
I keep saying, one more question in this direction, you know,
or more, just one more something? How how how far
are we from the first AI centric religion? I know
that that sounds like a left field question, but I
mean you you know, people, what did you just cite
that you cited? People go into mystics, People go into
(08:47):
people with you know, like effectively little religious pilgrimages, right like,
you know, and and then you're getting a resonance and
a sense of comprehension which is both self comprehension, which
is mysterious moving meaningful. You know, like, how are people
going to react to that? At scale? Some people are
(09:07):
going to go very very far with that connection, right.
Speaker 4 (09:10):
Well, I want to draw a difference between religion, which
is an organized uh or a place where beliefs and
rituals and traditions, sacraments, the that's of religion. There's a
practice to it, there's a ritual to it, and spirituality,
which is diving deeper into this whole mystical world. And
(09:32):
I don't know if you want to call it spirituality,
but I do believe AS want to help us understand
ourselves better.
Speaker 3 (09:39):
In the world of.
Speaker 4 (09:41):
Knowledge where AI can know everything, the area where it
doesn't really know is us, like what's inside of this head?
Speaker 3 (09:50):
How do people think?
Speaker 4 (09:52):
And if it can help us understand each other better
and help us smooth the communications that we have. Just
look at within organizations it communicating with HR two groups
that really need to work together very closely when it
comes to transformation. They speak completely different languages, or a
(10:13):
startup and a VC speak to completely different languages with
common goals, but they have to do some sort of
subtle translation.
Speaker 3 (10:21):
Or let's just take even our relationships.
Speaker 4 (10:24):
I have said so many times in my relationships, with
friendships and with partners, I say something I asked them
repeated and it comes back as something completely different. I'm like,
what am I doing that they can't understand me? So
I think that AI can help us understand ourselves and
each other better, and if that is an opportunity, I
think to be able to use these tools to help
(10:46):
us understand what is it that I don't understand about myself,
so that I can be better at whatever it is
that I want to accomplish well, I think.
Speaker 2 (10:55):
This has been an amazing way to label foundations of
the rest of the conversation because I've we've got to
the heart of some of the more extreme and interesting
use cases and potentialities of this technology, and now we
have to imagine what that means for our workplaces and
for our professional lives, and for our careers. But specifically
the first of us, specifically for the workplace, and something
(11:16):
you've written about extensively is the concept of AI leadership,
and you touched upon it with Tim and your last appearance,
But maybe just recap to begin with what the core
principles in your view of good AI leadership looks like
in this era.
Speaker 4 (11:31):
Well, I think good AI leadership starts with just good leadership.
And one of the things that's interesting about leadership is
that it becomes ultimately incredibly important when things are uncertain,
when they're unpredictable, because if things are not changing, it's
sort of just maintaining their status quo. That's where managers
(11:53):
are really good at. But when change is happening and
changes required, that's when you need leadership. And leaders what
they do really well is they create structure, They create
a purpose and a why, an underlying why do we
have to go do this change? We don't want to
it's hard. And they create the strategy, the roadmap, and
(12:18):
they also create accountability, and they're the ones who establish
credibility and a relationship so that people will trust them.
And I have always believed that with leadership. Classic leadership
has always been a relationship between people who aspire to
create change and the people who are inspired to follow them,
(12:41):
and that has been the truth for a millennia.
Speaker 3 (12:45):
So nothing changes around that now.
Speaker 4 (12:49):
Yeah, And the thing when it comes to AI leadership
is that that has to come forward even more and
people forget that. And this is a moment where I
think people are not standing in leadership. They see that,
they know that AI is important, they may or may
not be doing it. They're not AI fluet for the
(13:09):
most part, and without that fluency, it's very difficult to
lead a change. They're not showing up as leaders. They're
not saying this is the direction we need to go in.
That's all go in that direction, and how can I
support you through this change? So again I think great
(13:33):
AI leadersharps. Leadership starts with a mandate that says this
is why we have to change. We see some CEOs
now from places like Shopify, from Amazon. It's mostly technology
companies because they're kind of more on it. But even
like the CEO of Walmart has said, this is the
future of what we're going to do, and what you
(13:54):
see Walmart doing is rolling it out to all of
their associates and stores and giving them access to very
powerful AI tools in the existing applications that they use.
So if this is something that again there's leadership happening,
but also it's not just enough to talk about it,
you actually have to follow up with the actions.
Speaker 2 (14:15):
So interesting and just on that, I just sat an
interesting post just yesterday is who should be leading AI
transformation in an organization? Is it the CIO, the CEO,
even throughout the CMO, which I you know even you
are its cause not completely preposterous, right, I don't imagine
you're going to name one, But what are your thoughts
on that question of who in the existing structures should
(14:39):
be could be taking the leader?
Speaker 4 (14:41):
It's the person who can understand the technology and how
it works, and they don't have to be a technologist
and the person who understands your business strategy because in
the end AI has to be in service of your strategy.
It is not about technology, it is about how it
can be use as a strategic initiative. So at Moderna,
(15:04):
the pharmaceutical company that does mRNA co vaccines, for example,
AI lives with the CHRO HR. In fact, they took
their technical team and put it under the HR team
reporting to the CHRO because they realized that this transformation
(15:25):
was going to be human led. It is about transforming
that people who are going to use the technology, so
that people come first and the technology comes second. The
technology is in service of the people who will execute
a strategy. So that is that's a big outlier. I
would say that is not the norm. A lot of
(15:45):
people by default give it to the IT and I
would say, really think about this and say, is your
IT leader, the person who's going to be driving this,
do they understand your business strategy?
Speaker 3 (15:57):
Are they focused.
Speaker 4 (15:59):
On ex cuting and supporting business or are they focused
on primarily technology and procurement And that's not going to
work A I would tie a very slow and painful
death if you give it to a person who just
doesn't connect the technology to the business strategy.
Speaker 2 (16:18):
I think that let's say that as an incentive for
our listeners who are in it, and I T leadership
who are being given these responsibilities to take responsibility for
that dimension, right like, because a lot of it, you know,
and and and so so you know that becomes that
becomes what you just said becomes an incentive for it.
(16:41):
If it's going to be their responsibility in an organization, A,
it's a huge responsibility, and B it might require a
reorientation of their perspective. Would you say, right, how would
you how would you turn that advice around to it?
Leaders who are being asked to affect this change.
Speaker 4 (16:58):
They should be constantly asking, there's one question, what is
our business strategy and how can AI support it? What
are the biggest challenges we have and our business strategy
the biggest problems, the biggest opportunities, and how can AI
address them. We typically have three, let's say, typical business
(17:20):
objectives that we have. It's hard to keep check more
than three, and any use case. And I'd like to
say use cases are not a strategy. So when you
have a whole long list of AI use cases, that's
not a strategy. Strategy is this is? These are our
business objectives, These are our top three. This is how
(17:42):
AI is going to support that, and you can talk
about that until the cows come home. So everybody goes, so,
what's our AI strategy going to be while we're using
AR to support these three business strategies which you all
know and are supportive of, we all agree on that is,
you don't have to rethink that.
Speaker 3 (18:00):
And this is why we're doing it now. This is
what we're doing.
Speaker 4 (18:05):
Here's a rogue map of what we're going to do,
and this is how we're going to actually implement it.
These are the supports, these are the the training we
should definitely come and talk about in a second. And
this is the change we're going to have. And it's
not necessarily going to be impossible, but it is going
(18:26):
to be changed, and we need to be prepared for this. Again,
that's leadership, it's strategy. It's about having a concerted focus,
and focus says you have to choose what you will
do and what you won't do. And right now there's
not a lot of choices being made. It's just a
(18:46):
lot of people trying a lot of different things.
Speaker 3 (18:50):
I like what your sponsor does.
Speaker 4 (18:51):
It's looking at how AI is actually being used in
the organization because that can give you information about where
the value is being found. And if you're pilots, if
you're an endless pilot purgatory, that's not a good sign.
It's an indicator that you do not have a strategy
of how to use AI. And so I say, stop
(19:12):
doing the pilots, really focus on doing the top strategic
AI initiatives that are going to support your strategy and
put all your eggs in those baskets, because you're not
going to do visibility studies. You're not going to do pilots.
You're going to say this is really important to us,
We're going to invest, we're going to figure out the technology.
(19:34):
We're going to connect this to outcomes for our customers
and impact every single quarter. And that's what a coherent
strategy in roadmap looks like when it comes to AI.
Speaker 2 (19:44):
And I mean, if it leaders can get that kind
of level of visibility into how AI and IT are
actually being used across for organizations. What do you think
would surprise the board or management of the C suite
most about what it? What it would tell.
Speaker 4 (20:00):
Them, Oh, that there's so much happening. We call it
shadow AI sort of like shadow it anybody with a
browser can use it. They don't need the permission of it.
They don't need anyone's permission to go and start using
these things. And I think they would be surprised by
how much value is actually being created. People send to
(20:23):
me like, oh, I see a little of efficiency here
and there, but is there true value being created? Go
and look at how people actually are using this. They
wouldn't be using it, but wasn't creating value. So its
value is absolutely being created. You don't know exactly how.
How I can tell is when they're given access to
(20:45):
these tools. If you try to take it away from them,
they go with my cold dead fingers on it when
you take it away, So like, no, I need this
to do my job. There's value there, but I don't
know if you can measure it in specific ROI, which
is why I think ROI is the worst measure you
(21:06):
could try to be looking at. It's the one question
what's the roy of AI? It's kind of like saying,
so what can AI do? It could do so many things,
but is it creating value that matters to you? So
ask instead of what can AI do? Ask what can
AI do for us to help us? Achieve our biggest
(21:29):
strategic objectives and what.
Speaker 2 (21:31):
Kind of more meaningful metrics or user patterns should we
be looking for in place if something as reductive as ROI.
Speaker 4 (21:40):
Well, again, you already have business metrics, you have objectives
and metrics associated with that that tells you whether you're
moving towards your objectives better, faster, cheaper. And I would
add another metric, which is safer. And so we talked
about safety, security, privacy all the time. When it comes
(22:03):
to AI, it could potentially help you do those because
it can highlight the things that may be going wrong. Again,
it's a security list too, but it can also tell you, hey,
this thing may not be aligned because it can give
it very clear objectives. This is our objectives, this is
our strategic objective. Highlight anything that doesn't help us get
to that point. Highlight anything that could be standing in
(22:27):
the way or could be jeopardizing it. So again, AI
is very objective driven, and if you tell it this
is our objective, this is our stage real objective.
Speaker 3 (22:37):
How do we get there? How do we use AI
to help us get there?
Speaker 4 (22:42):
So I kind of think it's meta that use AI
to figure out how to use AI.
Speaker 3 (22:47):
So may as well use the tools available to like.
Speaker 2 (22:50):
Macro prompting in a way. Yeah, exactly. Yeah, what about
the question of like productivity metric and specifically and maybe
a difference between the qualitative and the quantitative, right, which
is something that I feel is you know, when you
talk about that employee lead AI transformation, you know that
(23:13):
for me can be reduced to that contrast between qualitative
and quantitative. You know, we all know that AI could
reel off a library worth of pretty bad books for
us and with you know, a series of prompts. But
that's not what we're after, is it. We're after work
which cuts through, which retains personality and originality. And that's
(23:36):
is that an area where you feel the employee has
to be in the driver's seat, most most importantly, most significantly.
Speaker 4 (23:44):
Well, there's a disconnect because employees know what the problems
are with their job, like these are the bottlenecks, these
are the things that are pain and to do do
and but there's a disconnect between them knowing what the
problem is and how use AI to solve it. So again,
I think employees are incredibly smart. People generally are smart.
(24:07):
They will not do things that are not productive for them. Okay,
this is this does not include procrastination, so that's something
completely different. But this is again about us understanding that
if something is going to help us, if you're given
a tool to help us do our jobs better, we're
going to use it. And so I can tell you like, oh,
(24:29):
I can do this a lot better, the results are better.
I don't have to I can be more creative and
come up with better solutions. But you give them the tool,
and they also have to give them the training, and
the training comes in two flavors.
Speaker 3 (24:45):
They're sort of top.
Speaker 4 (24:46):
Down, centralized, very organized. It's great for understanding what AI does,
and it's great for responsible and ethical use of AI.
But when it comes to that additional level of fluid
and where you can apply AI to your job until
your work, that requires much more of a decentralized approach.
(25:08):
It's about pure learning and support. It's about cross fertilization
across departments. Like we learned this, this could apply to
you too as well. And then the fourth area of
fluency is can you use and talk about how to
share this? Again, that sharing, the teaching of it, the
collaboration that happens is that fluency there to be able
(25:32):
to talk about and share and learn collectively too as well.
So those are types of individual fluency, but also organizational
fluency with AI that's really important to develop. And I'll
put a line in the sand here. I believe that
organizations should be focused on getting their AI, getting their
most important workforce, especially the knowledge based workforce. Everyone fluent
(25:57):
with AI in the next six months. There's no excuse.
We know this is the future. So invest the money,
the time, and it's both to get people to be fluent,
get the technology in place where everybody can get access
to these powerful models. You can do it at fairly
(26:19):
low costs if you go and get an enterprise license
use against some of the managed services like Amazon Bedrock
to be able to get access to this. It's fairly
turnkey and can be a lot less expensive, and frankly,
it brings that shadow AI into the light so that
(26:40):
you can truly talk about it and advance the use
of AI in your organization, because there are some risks.
Speaker 2 (26:46):
With shadow AI as well. Right, it's not just to this,
you know, there is. Maybe you could phrase it frame
it as a duty of care on an organizational level,
maybe a duty of self protection and security. You know
it needs to be about transparency.
Speaker 4 (27:02):
Right absolutely, I feel like people should declared AI amnesty.
It's like, okay, if you're using it, we forgive you,
but you have to come forward and here's the timeframe.
Speaker 2 (27:16):
And with my prompt into to submit, that made me
do it.
Speaker 4 (27:25):
No exactly, it's just like again, that's the only way
you can bring all the people out from the edges,
and you have to give them amnesty and and so
they won't get in trouble because I'm not going to
talk about everybody because I've been doing this rogue.
Speaker 3 (27:40):
Okay, Okay, we know this has been happening.
Speaker 4 (27:42):
We have the data, we already know that you're using this,
so just come forward, do this in the appropriate way,
and and say, look, we may have to shut it
down because it has these risks, or we can do
it in this way. But it's it's about having this
really thoughtful conversation.
Speaker 2 (28:01):
It's about AI. How do I religions earlier, Charlie, maybe
we should call it my absolution A right way to
frame it. So, where have you seen real world organizations
supporting this process of transformation in imagine ty if effective ways?
(28:21):
And I guess what advice would you give to leaders
looking to support their employees with different temperaments or attitudes
towards the extreme novelty and power of this technology.
Speaker 4 (28:33):
Yeah, we've worked with this and interviewed this company that's
a very large call center and they implemented AI and
they saw huge productivity gains. But it wasn't about replacing people.
It was really about taking the bottom twenty five percent,
for example, and bring them up to the level.
Speaker 3 (28:53):
Of their top performers.
Speaker 4 (28:55):
And it was really focused on helping people do their
best work. And so there are productivity games because of it,
and so they can absolutely anticipate that their numbers will
go down over time. But they were saying that we're
just going to manage that through attrition. And we know
that some jobs have changed. We know that jobs are
(29:15):
going to change. Some jobs are just going to go away.
Somebody gave me the example of reviewing checks, like somebody
actually used to look at checks and key them into
a system. And now with AI and computer vision, those
jobs are just going to go away. There's no way
of just it's saying that it's not going to be there.
(29:37):
So you need a small number of people to look
at the exceptions and this is a reality and we
need to prepare ourselves as an organization for that. And
again this has been the case for decades now with
technology coming in. This is not new and unique to AI,
and so having the responsibility to take care of your
(30:00):
people I think is a really important leadership trade here
to be able to say change is coming.
Speaker 3 (30:07):
We'll do our.
Speaker 4 (30:07):
Best to upskill you, to reskal you, but overall, there
may be some jobs that just simply aren't going to
be here. So we can't guarantee your job, we can't
guarantee your role is going to be here, but we
can do our best to prepare you for this new
future because you understand our organization. You're a valuable worker,
(30:28):
because you've been coming here and you know us. We
know you to take them possible.
Speaker 2 (30:33):
We'd love to keep you very cool.
Speaker 4 (30:36):
And I think it's irresponsible of companies to give two
week notices. It's like saying you just don't have your
app together first as saying we can see this change happening,
we're going to prepare for it.
Speaker 3 (30:48):
There's a long runway. These are the ways we can
do this.
Speaker 2 (30:55):
I became interested in recent we actually had some of
the show was talking about study about how they'd showed
for excessive use or use of AI can sometimes sometimes
have detrimental effects on creativity. And you know, and I
couldn't I could imagine this being true in some circumstances, right.
(31:16):
Could you imagine a future where there's mitigation of the
use of AI or control the fuse of AI within
organizations where they're looking at specific tasks and they're maybe
providing prompts above the AI app saying you know, look,
maybe step away, maybe carve out some more more autonomy
in this process. Similarly, with you see stories charline about
(31:41):
AI addiction, AI over use, say, unhealthy attachments to it.
These these are all you know, There's going to be
so many known unknowns that come about from a new
powerful technology such as this that could have implications for
HR departments, for leaderships. In my position, what are you
forced in this direction?
Speaker 4 (32:02):
Well, again, this is where I think education helps tremendously,
because there was just an article in the New York
Times talking about how somebody became very delusional about their
ability to do math because AI is kind of like
telling you, yeah, you're a math genius. And then when
I actually put the formulas and everything into another AI.
(32:25):
It's that, oh no, this is bunk, like he goes,
why didn't I think to do that?
Speaker 3 (32:29):
Like?
Speaker 4 (32:29):
How did I get sucked into this? And again, one
of the best practices is critical thinking. We cannot let
go of critical thinking because AI is just going to
tell us exactly what it is that we want to hear.
It's designed to do that for beautiful words. Now we
can talk about designing AI so that the objectives are
(32:51):
not just to answer the things that you want, but
to also look at a body of knowledge that's considered
quote right, whatever that means. Again, if we can't even
agree on what is right, because there are multiple versions of.
Speaker 3 (33:03):
That, it is very hard for AI to do that.
Speaker 4 (33:05):
So I go back to an organization defining what does
right look like? And I'll give you an example of fairness.
We talk about bias being built into AI. There is
bias in everything that we do because we are human
and AI was built by humans. We come and show
up with our biases based on our experiences. They're not
(33:26):
good and bad necessarily, but there are biases, and so
understanding what fairness means to an organization because there is
no universal definition of fairness, What does fairness mean? What
does unbiased mean? And then building that into your AI
and to the way that it delivers results is one
way to mitigate those inavertent points that you don't want
(33:51):
to have happening. So I do believe that teaching people
about critical thinking, how to use AI, understand that it
can hallucinate, Understand that you can fall into these straps,
that it's just going to keep you the answer, so
you have to ask it and say, you know, take
the other perspective, challenge me on these ideas, use a
(34:14):
different engine to check the results of another engine. So
these are best practices that you can learn. But right now,
you know something like seventy eight percent of employees see
the future about being AI and they know it's going
to impact their jobs and they want training, and only
thirty one percent say they're getting the training that they need.
(34:35):
It's a huge gap, and then disconnect. Organizations are just
not investing in the training that people need in order
to be successful with AI.
Speaker 2 (34:44):
Fascinating Sholing, I'm soorry. I'm so glad we had to
have you back on because I had to have a
chance to talk to you. It's not fair. It wasn't
fair that any team go to do it. So that
was absolutely scintillating conversation. Where can people find you in
your work?
Speaker 4 (35:00):
And find me at my website Charlie Lee dot com.
This is my name. I am also very active on LinkedIn.
It's a great place to connect with me. So hope
to connect with and many of you. Please write me.
I tell people connect with me, write me. I read
everything and almost nobody does so, but I'm very sucere
about business.
Speaker 2 (35:18):
This is all right to you tomorrow, okay, very correact
and I hear you. I hear you're in You're in
the UK presently where I am?
Speaker 3 (35:25):
Yes, I'm right in the middle of London right now.
Speaker 2 (35:27):
Yeah, I'm in London too. So yeah. Nice to connect
on this in the same city, even if virtually, and
a pleasure to meet and talk to you.
Speaker 3 (35:35):
Thank you so much for having me.
Speaker 2 (35:36):
Thank you, Shenny. Thank you.
Speaker 1 (35:38):
To make sure that you never miss an episode, subscribe
to the show in Apple Podcasts, Spotify for your favorite
podcast player, and if you're listening on Apple podcasts, make
sure to leave a rating of the show. Just have
the number of stars you think the podcast deserves. If
you'd like to learn more about how next Thing can
help me improve your digital employee experience, head over to
next think dot com. Thank you so much for listening.
(36:00):
Until next time, M
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