What's going on, everyone? Today we are talking AI hype
versus real business value with Manish Dasur.
He's working at PwC. He's been in the data and AI
game for over 20 years, helping clients over 100 different
clients navigate the AI disruption field and also
evaluate and deliver business value through the strategic
adoption and application of data AI and now agentic AI efforts.
Huge shout out to PwC for supporting this episode.
Let's get into it. I'll tell you what I was
building real fast because I think this is in line with what
we're going to talk about right now as far as value and looking
at costs. I built a little widget that can
track the spend, my API spend across different providers and I
gamified it a little bit. So every X amount of money you
get to choose if it's $20 increments or $100 or $1000, it
will go cha ching. And so that, you know, you just
spent some cash and you want to make sure that you have eyes on
that. A lot of times we don't even
realize how much we're spending, especially if it's going through
the API. And next thing you know, maybe
it's your boss or your boss's boss that's trying to drill down
and say, hey, what's going on here?
What are you getting from this? Yeah, Yeah, I couldn't.
I couldn't agree more. I mean, look, I'm glad you're
tracking it. I think it's something that's
also surprising a few organizations, right?
Like all of the AI programs they launched, they were all
predicated on a ROIROY equation that they, they got themselves
aligned to and approved internally.
And that ROI equation starts to really get some pressure and
stress if you're not careful about the compute cost and the
tokenization costs and all those things that can add up.
So I, I've got a lot of clients right now thinking about how do
I apply fin OPS to that discipline?
And to your point, they haven't gotten quite to the Vegas to
Ching sound, but certainly are thinking about in the same way.
How do you really mitigate that cost?
Yeah, I've heard the term tokenomics become more and more
popular and it's really just looking at your spend and
recognizing where's the value from it.
I was talking actually at length last week with a bunch of fin
OPS folks on how they're classifying it and how they're
deciding what is valuable spend versus what is a little bit
supervaluous. Yeah.
I think that you have some cool thoughts on where and how to
justify your different initiatives with AI.
And so maybe we can even just like start with what is getting
value from AI look like in your opinion?
Yeah. Well, let's first, let's first
set a target, right? When I think about clients who
are doing this, well, I think they're reporting about like a
30% improvement on value, right? So if they were, for example, if
they were measuring code production or software
development, life cycle production for their teams, what
they're reporting is, hey, previous to giving my team a
series of AI tools to help them through SDLC, prior to that, we
were seeing about 100 Sprint points come out of that Sprint,
right? So I gave my team a task.
They're going to go develop a bunch of code, and in the past
they're kind of averaging 100 Sprint points of output per
Sprint. Now I've enabled them with a
series of tools, arming them to accelerate to the software
development life cycle. These tools are often writing
user stories, They're often writing test scenarios in cases.
Obviously they're doing code assistant code generation and
debugging and things like that. And what they're kind of
reporting back is now that team is able to get through 130
points per Sprint, right? So they're kind of measuring it
that way. They're measuring the output
that way. If in the past it was 100
points, now it's 130 points. So that's one example of what
good looks like, right? It's kind of a 30% efficiency
gain. If I think about some other
clients, they're applying AI to do some, some more aggressive
workflows in finance or HR and supply chain, also reporting
about a 30% automation efficiency in those cases.
So when we think about how do organizations get value from AI,
let's first start with what do we mean, what is value?
And I think 30% roughly is a good target for organizations to
think about when they think about value from AI, from
automation and what it can drive.
Now, how to get to that value is always the challenge, right?
And, and there's a few points I would share, Dmitri 1, focus on
the workflow, not the use case right?
In, in, in client examples that I get to work with clients that
have clients that describe their AI initiative.
As you know, we're targeting this pilot case here and this
proof of concept here and we're working on this use case here.
Often times I feel like they don't see the end to end the
EBITDA value that they would want to see.
Whereas when clients are saying, hey, look, I'm going to focus on
a workflow, I'm going to embed AI into that workflow.
And if the workflow previously took me 20 steps and 100 humans,
now it'll take me 5 steps, 5 AI agents and 15 humans.
And that delta is the value in EBITDA value that they can take
home. So if you think about it like
I've got an accounts payable workflow or I've got an accounts
receivable workflow, and I'm going to embed AI into that
workflow, right? So I know in step one, a human's
going to do that a Step 2, and AI agent's going to do that Step
3, the invoice is going to get created.
So when I start applying AI to the actual workflow and I can
look at the workflow end to end and compare what my old one was
to what my new one was, that's how I'm seeing clients really
get EBITDA value, not disconnected use cases,
connected workflow redesign with AI generating the value.
So that would be kind of .1. So let's not get into isolated
tools. Let's think about a connected
workflow and how AI really changed that workflow all.
Right. I want to double click on the
idea. They're real fast about how you
were saying that 30% increase. And one thing that I've heard
folks talk about is how they will look back a year ago and
recognize certain big pushes that they've had to do and what
productivity looked like around there, how much they code they
were shipping, whatever these metrics were that they were
tracking. And they can then compare that
to now. And it's, it's not necessarily
apples to apples, but it's a much better way of deciding,
hey, how much are we spending on AI versus how much more are we
producing? Yeah.
And then you can see that in a way that isn't just asking your
employees to tell me, how much more value do you think AI is
providing to you? How much more productive are
you? That self reporting metric I
think is a little bit biased. And I'm always weary when I see
those. But I just want, I wanted to
talk about that. And there's so many other things
on this workflow piece that I want to dive into, but I know
you were going to say something else.
Well, I mean, look, I think your point's a good one.
In fact, every transformation initiative that I've seen really
be successful is always challenged to prove value
ongoing basis, right. I want to every transformation,
if within the first quarter you want to be able to start to
demonstrate value, you want to be able to update or increase
that value quarter over quarter. To your point, it's got to be
very, very quantifiable, right? So the other maybe point I'll
add to how do organizations make sure they get value?
What I've seen a lot is organizations will seek to prove
the ROI and they'll set up a champion challenger kind of
test, right? So as they roll out AI
automation, they'll have a control group working in the old
process and they'll have a test group working in the new AI
automated process. And at least for a couple of
cycles, they'll compare the outputs, they'll compare the
results. Depending on the use case, the
comparison might be different, but they'll compare the results
so they can get themselves to a quantifiable value statement and
then find a way to tweak it, improve it over time, right?
So I think that's super important to, to have that
measure of whether you're measuring cost or speed or
revenue uplift, whatever it is that you're measuring, but have
that champion challenger kind of mindset set up that way.
To your point, you're not guessing it, you're not talking
about it. You're actually demonstrating
with some numbers. And that, I think is a is a
winning formula for organizations.
Yeah. You know, I saw that and I think
these days there is no software engineer that I know that
doesn't want to use some kind of a coding agent.
So it's not necessarily what are we going to do AI versus no AI
with especially software engineering and even product
development, that type of thing, those internal type tools when
we're dealing with software. I think the really difficult
part here is you've got some software engineers that are
burning a ton and getting a ton done and then you've got other
software engineers, AKA me, who are burning a ton and not
getting a lot done. And so how do you distinguish
between that efficient burning of tokens versus the non
efficient ones? Yeah.
No, it's a great point. I think we're going to talk
about a little bit of the the fin OPS angle as well.
And look, I don't think a lot of organizations have found that
answer quite yet, right? I do think a lot of
organizations have been pushing AI everywhere, AI for everyone,
right? And, and while that's fantastic
and the right thing to do, the, the cost of that can be quite
high, right? But I do think now organizations
are starting to shift a little bit.
They're thinking about, do I use the right model for the right
use case? I don't have to use the same
model for all use cases. So I can kind of right size the
model to the use case I'm driving that can help me with
some cost. I can do workflow engineering or
prompt engineering in a way that can help me manage my cost.
Right. So those, and then of course,
I've got the fin OPS angle talking about, to your point,
the cha ching thing that you were describing kind of every
time I use, every time I use that compute, use that token,
I've got a measurement that way. And like I said, I, I think all
of that has to be baked into the ROI equation of the program of
the transformation that we're seeking to drive.
But I will tell you overall, I do think organizations are
reporting good benefits. 30% benefits are hard to ignore when
you think about the size of these organizations.
If you can drive a significant reduction in operating cost or a
significant growth in revenue generation, then it does at the
large AI benefits will exceed the value associated to it.
Yeah, so you gave us the point #1 which was these workflows and
trying to figure out where you're plugging AI into.
I wanted to mention that I have a theory that the reason you saw
so many successful use cases with support chat bots and
support agents. Yeah, was because that is a
field where there's very clear SO.
PS And because of that, it's all mapped out.
It's easier to create these workflows like you're talking
about and go end to end. And if you really need to, you
can escalate up to a human when there's something that will
stump the agent or it's not in the books.
But as a friend of mine told me, especially in call centers, you
routinely get folks who are very new at their jobs and all
they're doing is looking through the SO.
PS Yeah. And they're doing a poor job at
that, and an agent can do it much better.
So that idea of like having the workflows defined and then being
able to plug in the agents or the LMS where you can makes a
ton of sense to me. I look and customer care is an
excellent example. So let's double click on that
for a second because I got to tell you a fun story as as as
important it is as it is to architect it into the workflow.
And I agree with you, customer care is a fantastic example for
that. It is still freaking hard.
And let me tell you, let me give you a couple of things that in
my opinion, Stump, I had a, I had an opportunity to work with
a large telecommunications company and they were, they were
seeking to do exactly what you described, 80 percent, 75% of
the calls coming into my call center.
I want an AI agent to be able to handle them and only take it
into a human when I need to for that remaining 2025%.
So that's the, that's the, that's the attempt, that's the
workflow automation. So I, I would say the two
hardest challenges we found in that work was 1.
As you know, when you start with a model, it's relatively
generic. You have to give it an context
and training to make it specific and useful in the workflow that
we're talking about, right? So for example, I've got a
generic customer call centre type of model.
It needs to be trained in my company's policies, inventory
plans, things like that, so that when a customer calls, I've got
relevant information to give it. So when you think about training
a model like that, a customer could be calling about anything.
They could be calling about my invoice, my trip, my phone,
what's wrong with it, etcetera. So the context, the amount of
training you have to give that model is quite unique.
And some of that training comes from previous call logs that the
customer care reps have written down and written down.
So of course we're going to feed that into the model, right?
But the model also needs to know about when Dimitri calls in.
Let's let's use an example. Dimitri calls in and says, hey,
I'm travelling through Asia next month.
Mr. cell phone provider, would you plead at please add roaming
to my plan, right? So I can go around Asia and use
my phone. Now for an AI agent to be able
to handle that call, first it needs to know who Dimitri is.
So it's going to need a way to connect into this.
I'll call it ACDPA customer data platform, customer 360, whatever
terminology you use that so the, so the model knows who Dimitri
is. How many lines does he have?
Has he paid his bill on time? What phone does he have?
Does he, how often does he travel?
What plan does he have today? Right.
So it needs to know who Dimitri is.
Then once it knows who Dimitri is, it's going to ask you some
clarifying questions. What countries are you traveling
to? What dates are you going to be
traveling in? Based on the answers you give
it, it needs to update the algorithm and go into the
inventory of plans I could offer you and select the one that we
think is the right one for you, Right?
Then I've got to make that offer to Dimitri on the phone.
Dimitri is going to say, yeah, OK, I like that plan, give me
that one. He's going to confirm the cost
is, I don't know, an extra $10 a month, and he's going to say
good at it for that month. Then the model needs to have the
authority to go back into your invoice and confirm that you are
now going to pay 10 more, charge your credit card and update your
plan. That's quite a complicated
transaction. It needs to connect to your
billing system is likely written in one like 1 platform.
Your, your, your planning system is likely in another platform.
Your customer system is likely in another platform.
So there's a bunch of API connectivity, a bunch of
integration that you've got to solve for bunch of context and
training. You've got a software.
So it, you know, for those kinds of things, it can be quite
complicated. But that's why I think having
the right governance approach, right structure approach and and
a plan to attack that workflow end to end is super critical.
Don't forget you if I'm calling you on the phone, you have to be
doing this with very low latency.
That's right. That's right.
And you remember like the models of the past, they were taking
voice, going to text, generating a response in text and bringing
it back to voice, which created a lot of latency, right?
But now in the new world, we can go voice to voice.
We don't have to go to voice to text to text to back to voice.
So we can go to voice to voice with the latency is certainly
starting to get better. But I still think training
models, contextualizing models and then applying all the
integration you need with all the enterprise corporate systems
that models need to pull from and interact with it in order to
satisfy the customer query is quite a tall order, right?
And that's why I think organizations need a a practical
approach to solving that problem.
Yeah. So there we go.
That's the workflows on point number one.
Yeah, you had more points and I remember I cut you off.
So it keep it rocking on the different pieces that you want
to hit on. Well, the last thing I look,
we've talked about focus on workflows.
We've talked about, you know, use the champion challenger
model to really to really prove out the value.
The Third Point I would just tell you is I do think it's very
important for leaders to build the culture of innovation right
at the, the only places where I've really seen AI be adopted
is when leaders are rewarding innovation and pushing the
organization to reimagine how they do it.
It's a hard thing to do, right? People have been running these
workflows for two decades in their jobs, in their roles, and
now we're saying, hey, that workflow is going to change.
So if you can in a, in a leadership way, in an
organizational way, find a way to reward a culture of
innovation, reward a culture of let's try something new.
Let's adopt something new. I think that's super critical to
your success as well. And I'll just tell you, man,
like I think our, we did some surveys on this stuff.
I think we saw like 88% of companies say they're doing AI,
but only 33% of companies say they're scaling AI, right?
So I still think there's quite a barrier here.
And as organizations go from I have 10 agents to soon I'll have
100 to soon I'll have 1000, right?
These are the kinds of things that are going to help them get
there. Have you seen this idea of
rewarding AI and creating that culture of innovation, being
successful across organizations, especially the larger
organizations that I can imagine?
You have folks that are set in their ways and they've been
doing something for a while, and it's very hard to change.
Yeah. It's a combination of things, I
think, right? But I do think I see
organizations seeking to reward, promote those who are adopting
innovation. I've even started to see
organizations who are measuring how often you use AI, right?
If you're a regular consumer, you're doing your job.
Organizations are starting to measure how often do you prompt
your, your local LLM or your local authentic solution?
How effectively are you actually using AI?
And that's becoming one of the criterias for for your
evaluation, for your performance.
And I think adopting a culture of innovation and and a culture
that pushes the boundary I think is a good thing to do.
Yeah, I've seen that at certain companies that can remain
unnamed. But it's also almost like the
double edged sword is that it becomes toxic and then you have
token maxing or just prompting to prompt.
Yeah. And if you only are going off of
that, then it can be dangerous too.
So it it's like everything. It's quite nuanced in that
regard. Yeah.
Absolutely, absolutely. But I think, you know, if you've
got I, I think the best thing you can do as a leader is
embrace to change yourself, right?
Be the example that you're seeking for your team to be use
AI in your everyday use it for the right use cases.
Be mindful of how much you're spending on it, but also, you
know, don't hesitate to drive top line growth or bottom line
growth with AI and be the best example of that.
And if you can do that as a leader, then hopefully the rest
of the organization can and use that and follow.
Yeah, bring them with you. And yeah, where have you seen it
Feels like you. This is a nice segue into where
you can fail and where you've seen these efforts fall flat.
Those who get it right versus those who stumble right is
always a good question to kind of think about it.
And it helps clients think about this too, because then they
avoid those same mistakes that others have failed at.
The first one I would tell you is the concept of AI everywhere.
I don't think is the right concept for most clients to
think about it. When you think about AI
everywhere, you end up incurring a lot of cost for not a lot of
value. So I the first kind of point of
guidance I would give clients there is you don't want to think
about it like AI everywhere. Of course we want AI to be
pervasive. That's not what of course that
we, we want that right. But we also want AI to be very
focused on those high value use cases that you think are going
to be game changing for your organization.
You also might want to get AI focus and start with some low
risk use cases that won't be super sensitive to the market or
to your brand. So I think you the first, the
first point of guidance I would share is don't think about
applying AI everywhere. Instead think about it as I've
got this fantastic tool. Of course, eventually it will be
pervasive in my organization. But if I'm starting out or if
I'm in those stages, going from 10 implementations to 100
implementations, I want to be strategic about where I use it.
I want to be prescriptive about where I use it.
I want to measure the value that it creates, right?
So the first thing I would tell you is it's not AI everywhere.
It's AI applied and utilized in those workflows, in those use
cases that are going to generate the biggest bang from my buck,
right? So that's the first point, maybe
I would share. Second point I would share is,
look, you have to watch out for the compute costs, right?
I think, I think consumption driven cost models sometimes
great challenges. I think it's very, very
important that organizations have the appropriate governance
set up. So they're, so they're too,
they've got some oversight and it's not a surprise.
The surprise is what I worry about, right?
A lot of clients have been enamoured with what AI can do.
They haven't really focused on what do the cost look like as it
starts to scale. And often they can get
surprised, right. So I think there you've got a,
first of all, you've got a architect for efficiency one,
it's not one model that fits all use cases, right?
So it's very important that you think about what am I trying to
do? And I select the right model for
that. If I'm doing image creation, I
might pick a certain model. If I'm doing deep thinking, I
might pick a different model. If I'm doing PowerPoint
creation, then I might do a different model, right?
So using the right model for the right use case will help clients
mitigate cost. 2 the prompt engineering discipline is, is
one that I, you know, I have to tell you is going to become
increasingly important. The type of prompt you put into
a model can generate different types of cost as well.
So prompt engineering and making sure we've got the model
architected, the workflow design architected to minimize token
usage, I think is, is the second thing that I would kind of think
about there. And then lastly, like you, you
started the conversation with fin OPS, right?
But having a financial way of tracking cost per workflow and
enforcing that budget and forcing that governance, I would
certainly say is a is a key thing for clients to do.
So yeah, what I hit on, I hit on, I hit on not AI everywhere.
The cost model is super important.
And maybe the last point I'll still share here is as AI become
smarter, as the reasoning capability of these models
becomes better, we can apply them to more complex use cases.
And as the autonomy gets better, as they're able to execute
actions on their own behalf better, now I can have them make
complex decisions and execute actions against those complex
decisions. But what I also see happening as
those two factors are improving is clients are seeking to use AI
in more sensitive use cases. I want an AI, we talked about
customer care. I want an AI agent handling my
customer care call. Well, that's very important
because your brand, your policies all need to be is how
your AI agent needs to behave in a way that's consistent with
your brand, in a way that's consistent with your policy, so
your customer has a consistent experience.
I want an AI agent approving purchase orders.
And now in this case, I've got an AI agent making a financial
decision on your behalf and approving purchase orders.
And money is actually going out the door based on a decision
that an AI agent has made. So as I think organizations seek
to use AI in that way, seek to put AI in more sensitive use
cases, really having a robust framework and methodology for
testing accuracy, for testing hallucination, for testing
responsible behavior, right, for essentially auditing agentic
behavior is going to become super important for our clients.
That's something at PwC we're pretty excited about because
obviously we do a lot of risk and auditing and, and you know,
we'd like to extend that kind of thinking to your AI world as
well, right? And I think in the workforce of
tomorrow, if I imagine it to be humans and AI working together,
we've got to be able to really review, validate, audit that
work product to make sure it's consistent with what we're
doing. And as we gain more trust there,
that'll be the unlock of allowing AI to participate and
really make decisions in sensitive use cases.
And just like, just like the cost point, I think the testing
point, the responsibility point is still an area that a lot of
our clients haven't explored just because it's relatively
new. And I think that will be a key
unlock for the for the industry as well.
Have you thought through how that could potentially look we?
Absolutely. We're starting to build
capabilities now that can test, audit, validate results that are
coming out of an LLM model. As you know, that's not easy to
do. In the past, systems we designed
was one input has a series of finite outputs, Let's say it has
five. I can test for those outputs
relatively simple. I can put the input in, I can
validate that I got the one of the five outputs, and if I did,
then the test succeeds. Much, much harder to do in the
agentic world. In the LLM world, one input
could have million different outputs.
So we are now building frameworks and testing
methodologies that essentially use AI to test AI.
But to be able to do that at scale, that's really the only
way to do it, right. So yes, we are absolutely
starting to starting to build frameworks and capabilities that
we can take the clients and offer them some help in being
able to test and validate AI results.
I do agree with you. However, like today, when you go
buy a car, there's AJD power associate certification.
So you know you're buying a car that you can trust.
I do agree with you the, the I don't know what the answer will
be yet. I don't think the marketer
industry has has aligned on what the answer will be yet.
But I do agree, I think clients and organizations are going to
want a way to essentially certify or some, some sort of
validate that this agent is good to go.
I can use it in a sensitive use case.
I can put it in front of my customers.
I've got a way to mitigate the risk to my brand and to my
policy. And I certainly think that once
once the industry has aligned on the right solution, that'll
allow us to take a leap forward. In the meantime, I think some of
the frameworks in the agentic work that we're doing is exactly
how class should be thinking about that answer.
Yeah, I really wonder how it would look and what it would
shape out to be. I've joked around about how you
potentially would have verified MCP servers, but it's not
necessarily. What's so weird about this is
that you can't quite say like, well, maybe you can.
Companies are Sock 2 certified, right?
And that means that they've done a bunch of stuff and they've
gotten that checklist of things and they've passed their Sock 2
certifications. And there's more stringent
versions of that and less stringent potentially every
agent that's out there. You can ask what you can just
like have a sock 2 type certification for the agents
that are out in the wild, but it feels much harder to police.
And it also feels like that might not be the right
abstraction. So I'm a little bit at a loss
for what and how you would audit it.
What I do know is that the idea of just evaling the output, like
you were saying, that's not going to get you anywhere.
Yeah. And I, you know, I, I, I
completely agree. I think we, I think the answer
to exactly how it's going to be done is still being formulated.
And it, and it is very nuanced, though it's still very use case
dependent, right? But I do think responsible AI,
trusting AI is still, it's still quite a challenge, right?
When I speak with organizations and we're talking about a
complex use case, I usually get a mix of opinion of executives
on the table. There are some executives that
are like, yeah, good to go, love it.
Why wouldn't we do it? Let's go right.
There are other executives who are focused on the financial
picture and they can't ignore the benefits that AI can drive.
But then there's always a, you know, executive or two that has
a higher trust quotient. That feels like we've got to be
able to demonstrate the the clarity, the accuracy, the
compliance in order to move forward.
And I think that's going to be an important lock for the
industry. Absolutely.
Another piece that I want to hit on that you mentioned and I tend
to ponder about quite a bit is in these workflow type
scenarios, how some of the optimization efforts can be just
figuring out where to have that human sit in the loop, if at
all. Because maybe the ultimate
optimization is that you can let the agent just go wild and
depending on the use case, like you were saying, if it is this
low stakes kind of high value use case, well, let it run wild
and see if it works. But then if an, if a human's
getting pinged consistently about something that an agent is
doing, is the value really there?
I don't know. I guess if, if I don't have to
do it, I'm going to be happy to have a computer do it.
I'm not doing math by hand, right, even if it so I do
understand that. But the whole idea, if you look
at a workflow and you have these graphs and you have these nodes
and you're thinking, well, an agent can take over this process
and then we're going to have a human in here when it comes to
approving invoices or anything that has to do with money.
What happens with me, I don't know if you've had this happen,
is that when I'm asked to verify stuff that the agent will throw
at me, I just like automatically say, yeah, except let's see what
happens type thing. And so I'm worried that that
type of a habit will get instilled in us as we start
trusting the AI more and more. And next thing you know, we
don't even look at it and we're there as a box that you have to
check. Well, I mean, I will tell you
it's an evolving scale as, as the way I see it, most clients
who are implementing use cases, implementing workflows with AI
today certainly are having a human in the loop, right?
Especially for, for the ones that I would classify anything
as medium or high sensitivity, they're certainly keeping a
human in the loop, but it's also with an evolving scale, right?
The intention is these models get smarter, they learn over
time, they get more effective over time.
So the intention is that, hey, when you first start with that
automation, maybe you have the human look at 50% of what the
models performing, right? And as you start to gain more
trust, as you start to gain more confidence that the matter model
is behaving accurately, and as the model becomes smarter and
more efficient at what it's trying to do, maybe you can get
that 50% down to 20%, down to 10%, right?
And that's how you kind of move up the chain eventually.
I do think just like when you punch in a number to your
calculator today, you trust the answer.
You're not double checking that work today, right?
But eventually, I do think we get there.
But I would advise clients and organizations to think about
that as an evolving scale for the if you've got a sensitive
use case, for a medium sensitive use case, you want to keep a
human in the loop. You want to validate that you're
getting the right answer. And once you've really start to
prove yourself that over a few quarters, then you can start to
reduce the amount of human in the loop that you need.
OK. So I wanted to also talk to you
about this idea of now you have AI working within your company,
you're having success. You can do more with less is
what we're hearing a lot of folks say.
And I was literally just talking to a friend this morning about
how I don't buy into the idea of, oh, we're all going to be
able to do more with less. That means that we're going to
not need as many people to work because if we have all of this
free cash flow that's being spit off now, yeah, we're assuming
that companies are not going to reinvest that into their
companies and either hire more people or potentially lower
prices. Yeah, right.
I agree with you. I think our research indicates
the same, right? We don't we, we certainly see, I
think more than 75% of jobs will require reskilling or
upskilling, but that doesn't mean those jobs are going away
or that hiring will stop. It just means they get reskilled
and upskilled, right? So we certainly see the trend
heading that way. Look, I think about it as the
future of the workforce is humans plus agents working
together, right? The benefits are too great to
ignore. I think we will see that.
But largely I, I, my hypothesis is more closely aligned to
yours. I think the job reduction, the
job displacement is relatively modest at this point.
I think the job reskilling, upskilling is much more
relevant. But I always tell folks that,
look, this might be a controversial thing to say, but
I will, I will say that if you're in a job function,
regardless of kind of what your function is, if you're an
accountant or if you're a salesperson or if you're a
supply chain person. And if you, if, if you're, if
you're kind of at the bottom 25% of other accountants, near the
bottom 25% of other sales leaders, then you probably will
be challenged with AI. Because if you're, if you're, if
you're a low performer in that way, you're likely doing a
series of mundane tasks that AI can replace.
But if you're in the top 50% of those roles, if you're the top
50% of accountants, if you're in the top 50% of sales leaders,
then AI is going to assist you in being able to do a lot more.
So I kind of think about it. As for those who are good at
their jobs, they're going to think about AI as a massive tool
set that they've just gone and now they can go from being good
to great at their jobs and and that will allow them to create
more value. Organizations will hire more for
those skills. We might see a little bit of
rotation from back office to front office, just like you're
talking about. As the back office gets
automated, I can dedicate more time to to revenue course.
So yeah, I think we'll see all of those.
But I think if I'm a, if I'm a person in the labor force, I
always say this to, I don't think AI is going to take your
job, but I think another human that's using AI, if you're not
using AI might take your job, right?
So I kind of the way I kind of think about it as whatever your
function is, you need to be thinking about how can I do more
in my role? How can I do more in my
function, what with using AI? And that's going to not mean
more hours. That's not going to mean more
work. It's actually might mean less
hours and less work, but it's going to mean more output.
And that's how I think. That's how I think humans should
be thinking about that. Yeah, in every job, I have yet
to come across a job that doesn't have just an absurd
amount of rote work that I would happily automate away any day of
the week. And I guess I'm very privileged
to be talking about like this kind of blue collar work.
It's a much different story than if we're or blue collar is a
white collar. I'm getting confused here on
basically I'm sitting behind a keyboard and doing it.
So I'm much more privileged to be able to sit behind a keyboard
and do my work in that way. And I know that there's a lot of
clicking around that I can automate away.
So I will say that I am fully on board with a ton of rote work
that can just get out of what I need to do and allow me to focus
on what is important. And the real key here is this
discernment of knowing what's important and knowing where I
can add that extra value. But I still, I still will like
say this until I'm blue in the face that what I see with
companies is that they're not going to just say, all right,
cool, we now can do the work of 100 people with 50 people.
We're only going to have 50 people now, right?
And then just continue to do the work of 100 people.
No, what I'm seeing is they're going to take 100 people, use
the 100 people, but just have the output now of double that.
So now they have maybe the output of 200 people.
And it's a, it's one of those ones.
Actually, I was just seeing a friend sent me something on one
of these big corporations that did exactly that, where they
realized, oh, because of AI, we are now going to not need these
positions. But instead of this trope that
you're hearing thrown around a lot of, all right, we got rid of
20% of our workforce because we're able to do more with less.
And AI is the reason for these firings.
They said no. What we're going to do is we're
going to purposefully reskill these folks like you're talking
about. Yeah.
I mean, I it's spot on, spot on, right.
Like I, I think about it as AI is going to reset your operating
model, that's how you should think about as an organization.
It is going to reset your operating model where as we
automate the workflows, that means the operating model
changes. And when the operating model
changes, that means people's jobs change, not that those jobs
go away. Those jobs change.
The operating model has changed. I might need less people doing
one function, but I need more people doing a different
function. And that other function creates
more value. And the people that I have in
that function can use a bunch of tools for the automated mundane
tasks, right? So I, I completely agree.
I think you've got to think about the operating model is
changing. I'm essentially now an
organization that has humans and agents working together.
And when I think about that equation, when I think about
that peanut butter and Jelly sandwich, the operating model of
that should be different than the operating model today.
And that will that will create the job change the job
reskilling that I think we're going to see in the market.
Yeah, I was just on a podcast a few weeks ago and the title of
the podcast was we're all managers now, because we've all,
we now have these, the agent that we can go and spin off and
if we manage them correctly, hopefully it makes our lives 10
times easier and we're still able to get everything done and
more Exactly, exactly right. So I mean anything that I should
be asking you that I haven't? No, I think, I think we've hit
out some. I think we've hit on the right
topics, right. I I guess I would just part with
transformation. Over experimentation, right?
I think the first topic we talked about how to get really
good value from AI, the best examples of that, the most value
I'm seeing clients generate is when they're applying it to an
end to end workflow transformation.
Not an experiment here, not a use case here, and a POC there.
So that I would kind of wrap us up, but that would be point
number one point #2 I would say governance, cost governance,
risk governance, all those those governance is, is a key lever of
success, right? So and it's separating leaders
from laggards, right? So if you've got the right
governance set up, fin op set up it, then then I think you're
going to have some success. And then, you know, lastly, I
think mindset matters, especially early on, a lot of
clients are seeking to make a shift.
They feel like it's going to be an organizational shift and
operating model shift. And it is, I think it's very
important for leaders, but all the way up and down the
corporate work pyramid to really adopt this mindset of of course
we're going to innovate, Of course we're going to relearn.
You know, I think the, the, this is the age of continuous
learning, right? If you went to school one year
ago or if you went to school 20 years ago, all good.
But this is the age of continuous learning.
Everyone, whether you're 20 years in the game or one years
in the game, is going to need to find a way to continue to learn
new capabilities, new tools, new work automations that are
coming. Being able to learn those
things, apply those things to my job and and make my work output
even stronger and better. That's the secret to success
now. And the beauty is that you can
use AI to learn about AI. And how to better.
Yeah, Yeah. Look, I joke around about this
up. It has never been easier to
learn. This is the age of information
and the past. People had to go travel and and
walk to the top of a mountain to sit down with a guru and learn
something. Then people had to pay thousands
of thousands of dollars to attend a university to go learn
something. It has never been easier than
today to learn, right. There's so much information
available so it's never been easier to learn, but the onus is
on all of us to always be learning and always be applying
and if we can do that then then we're going to be successful in
the workforce. All right, Manish, I appreciate
you man. So great talking to you, so much
fun. Thank you for having me.