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Welcome back to Sustainability 
Forward. 

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I'm your host, Rishi. 
With me, as always, is my Co 

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host Carmine. 
How are you, Carmine? 

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Good Rishi, welcome to be back. 
That's right, Carmine. 

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Usually on our podcast, we talk 
about sustainability in terms 

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of, you know, energy systems. 
We talk about infrastructure, 

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capital, what's the long term 
impact of all of this? 

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But one of the constraints that 
we usually do not talk about is 

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organizational capacity. 
We may have touched up on this 

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topic in the past, but we don't 
discuss it at length. 

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Of course, we've, you know, 
talked about the fact that 

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energy demand, demand is rising 
quite a bit and systems are 

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getting more and more complex 
with the focus on energy 

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transition, focus on safety, 
reliability. 

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And yet it feels like most 
organizations are being asked to

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deliver all of this with the 
same teams, more or less the 

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same kind of structure and the 
same ways of working. 

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Having sort of read some of the 
things in and around this topic,

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I thought today we'll ask one 
simple question and that is how 

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do you scale output? 
How do you improve decision 

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quality and reliability without 
simply adding more people, 

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right? 
That's the central thing that we

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want to explore in the in this 
episode. 

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And to do that and help us 
understand the nuances of this, 

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we have a special guest. 
You please introduce him a 

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minute. 
Yes. 

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So we're talking today about the
topic and the section of 

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technology and people, and we 
had the pleasure to have Subhot 

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Kumar. 
And Subhot is the founder and 

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CEO of Brisk AI and alumnus of 
Howard Business School. 

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He brings over 18 years of 
global experience across the 

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energy and technology sectors, 
with roles spanning operations, 

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strategy and product leadership.
Other organizations such as 

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Shell, BP, the Company, and Dell
Technologies. 

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This work focuses on helping 
enterprises translate data and 

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AI into measurable business 
impact. 

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Subot is the author of Agentic 
AI for Leaders, where the 

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explorer leaders can build AI 
fluency, redesign work, and 

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scale agentic AI from 
experimentation to enterprise 

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adoption. 
This perspective bridges deep 

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industry context with modern AI 
capabilities, helping leaders 

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think and act transformationally
as they transition towards AI 

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native operating model. 
Subo has also written a book 

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recently, the title release 
Agentic AI for Leaders, about 

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which we will hear more in this 
episode. 

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So Subot, welcome to 
Sustainability Forward. 

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Thank you. 
Thanks Carmine and Rishi for 

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inviting me to this podcast. 
Looking forward to a discussion 

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on this topic. 
Yeah, no, absolutely. 

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And for a Full disclosure, I 
must say that Subot and I go a 

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long back when we started our 
careers in in an energy company.

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And so today's discussion is 
kind of also reflection of what 

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has happened since the time we 
started working in the energy 

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industry. 
Obviously, a lot has changed and

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in the context of AI and things 
that have evolved in the energy 

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industry, today's discussion is 
going to be, I think a very 

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fruitful 1. 
So Swarton, the first thing that

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I that I was thinking about is, 
you know, as I mentioned in the 

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introduction, the industry seems
to have become more complex 

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because of things that have 
happened within the industry as 

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well as as market forces. 
Is the complexity and the 

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cognitive load that the industry
faces today, is it more 

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substantial or bigger than the 
workforce size that companies 

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that companies have? 
Can can you help us unpack that 

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a little bit? 
Absolutely, Rashid. 

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So the way I'm seeing the 
industry and as I work with the 

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leaders in these industries both
on upstream and downstream, one 

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of the things is becoming 
clearer is it's not only the 

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headcount issue that the 
industry is facing. 

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Like you rightly said, it's all 
about the complexity and the 

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cognitive load that the existing
people, whether they are in 

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engineering operating roles that
they have to deal with. 

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So look around globally, right, 
Our asset bases are aging and 

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they have been retrofitted with 
additional layers of digital 

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sensors systems and the 
regulations have gone more 

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stricter. 
So there are more compliance 

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requirements and then you add to
it another layer of energy 

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transition, right. 
So there are these new type of 

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assets and technologies getting 
added in the whole value chain. 

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So that's what I see as 
increasing that cognitive load 

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and the need to preserve the 
context. 

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So, so that's what I see. 
And indeed, like one of the 

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operations leaders that I was 
talking to, he made a very 

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interesting point. 
He said in 90% of the cases when

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something goes down in the 
plant, they said we are very 

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sure that this issue has come in
the past, but they just don't 

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know when it happened and how it
was fixed, right. 

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So in this industry, I mean, 
there are some unique aspects, 

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right? 
Work happens in shifts and the 

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problems are episodic. 
So they are, they may not be 

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regular. 
So it's all about how do you 

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build that context, reserve that
context and surface it at the 

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right time so that people don't 
have to deal with finding the 

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information, but rather quickly 
with judgement and act on it. 

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Yeah, I, I remember about when 
we were starting off in one of 

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the earlier kind of foundational
courses about process 

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engineering, someone said that 
you'll not only encounter these 

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problems again in your career, 
you'll also encounter the same 

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solutions again. 
It's just that you may not 

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remember that this solution was 
done by somebody. 

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So I think that is, that's 
probably, you know, it was 

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probably the same thing that was
reflected many years ago that we

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are talking about probably now 
at A at a much bigger, bigger 

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scale. 
Yeah. 

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Yeah, I would say so. 
And I mean, one additional force

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that I am seeing, right, that 
leaders, especially in Northern 

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America like US, Canada, and I 
would love to hear your context 

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from Europe, is the expertise 
retiring faster than the new 

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talent joining the industry, 
right? 

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So even if it's not that there 
are less people in the industry,

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it's about leaving, losing that 
institutional knowledge. 

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So I think that's becoming a 
bigger issue. 

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And this is where technology 
could help. 

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So there is lot of talk here and
there about what AI can bring. 

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But before entering more into 
details, probably is helpful to 

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baseline with you AI tools from 
Argentic AI or let's say other 

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AI types, how they differ in 
terms of automation or a 

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copilot, how they can be a tool 
or a teammate. 

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Can you help us to understand a 
little bit how they can play? 

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I can help us here. 
Absolutely common. 

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And like that's a great point. 
And I think the time at which we

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are talking about is, is really 
transformational. 

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Like if if you look into some of
the recent news like the SAS 

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companies stocks have been under
a huge downfall with one news 

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coming from Entropic that they 
launched their core work 

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platform. 
So like you rightly said, so 

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there are tools that are coming 
up, but the bigger thing is the 

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way in which Agenti Ki has 
evolved is that it's no longer 

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about adopting another tool. 
It's really thinking of it as 

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your teammate, right? 
So it's a teammate that can 

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actually own parts of the work 
that can augment the existing 

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human talent so that you can do 
a lot more with the same 

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workforce, right? 
So in the past, our industry in 

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the past 1520 years, we adopted 
lots of machine learning based 

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solutions, be it on the 
operations, on the maintenance 

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side of things. 
But any of those solutions would

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eventually give you some 
recommendations and then the 

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humans will make further 
judgments, act on those. 

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And in many cases any solution 
that you develop is customized 

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to a certain plan to a 
customized to a certain 

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scenario. 
But now with generative AI that 

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scaling has been achieved, that 
the same trained model can be 

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taken and applied to many 
different problems and many 

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different contexts with a 
reasonable level of certainty. 

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So that's what is making this 
generative AI powered solutions 

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as your teammates than just 
another tool. 

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And so both is this volume play,
is an efficiency play, is it a 

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quality play For all the above, 
how we should think about it? 

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I would think all of the above, 
but most importantly it's about 

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the expertise really bringing 
more expertise that 

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organizations that as humans we 
have for our rescue, for our 

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available to, to help us in our 
daily jobs, right? 

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Because efficiency play, I would
say is more about automation. 

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So even in the traditional 
automation, when we have a 

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reasonable understanding of the 
steps that it needs to be taken,

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that level of automation has 
been in existence for maybe over

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a couple of decades now. 
I think it's about that 

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intelligence clear, which to me 
is really exciting. 

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So is IT support incorrect to 
think of what is coming into the

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industry as the next level of 
automation? 

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That's probably the wrong 
benchmark, right? 

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It's not like we are going from 
one level of automation to a 

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much higher level of automation 
with these new tools. 

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We are actually talking about 
intelligence that's going to 

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recommend that's what to do and 
not necessarily just automate 

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something that we already know 
what the steps are. 

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See the way they see actually 
thinking of it as an automation 

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kind of sets the wrong bar to to
A to a large extent, because 

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when you think of automation, 
you like, we tend to think of a 

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near perfection, right? 
So here we automated it. 

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So given any of these inputs, 
this is output. 

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But in an agentic AI context, I 
think the more we think of it as

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a team member, like a new team 
member, it may not be giving the

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right answers on day one, but we
need to remember that AI based 

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systems can learn much faster 
than lot of the human humans and

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they only tend to become better 
and better over time, right? 

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So that's where I tend to think 
that it's not about automation, 

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it's more about intelligence 
that works alongside the humans.

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Makes sense. 
One of the things that I was 

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thinking of in this context is. 
I mean some of the use cases 

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come to mind very quickly and 
there may be are multiple other 

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use cases that we do not 
typically think about in the 

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context of AI in energy. 
Can you help us understand some 

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of the categories of these use 
cases and maybe also give us 

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some examples which can help us 
understand this better? 

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Sure. 
So see there are two kind of use

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cases that I am saying, one of 
one is kind of these low hanging

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fruits which are already in 
practice, other are more 

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aspirational. 
So if I think of the low hanging

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fruits, those are the use cases 
which helps you to reduce 

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cognitive load and preserve 
context. 

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So think of A use case that we 
were working with one of the 

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clients into maintenance 
planning. 

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So having an AI powered 
maintenance planning agent that 

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drafts of work orders based on 
your inspection data by going 

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into your standard operating 
procedures, equipment manuals. 

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So that the human planners don't
have to spend time in the 

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paperwork, but rather they spend
more time in simply prioritizing

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the tasks and not becoming a 
bottleneck for your technicians 

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on site, right. 
So those are the kind of use 

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cases and another example being 
in shift handovers, AI playing a

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role as a connective tissue so 
that the team members are not 

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not spending time on 
rediscovering the same problems.

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So these are the more kind of 
practical use cases that I'm 

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already seeing companies 
experimenting with. 

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And then there are more 
aspirational use cases. 

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The kind of use cases are like 
one comes to my mind is related 

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to subsurface, let's say 
reservoir planning, thinking of 

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deciding where to drill the next
wells. 

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So in the current context, 
engineers spend months of time 

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in running these simulations and
then deciding where to drill 

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that next well. 
So those are the use cases where

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again there are experimentation 
happening on how we use these 

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large language models or large 
visual models to to help 

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facilitate that. 
But those are fairly complex 

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problems given the underlying 
physics involved. 

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So those are the ones I would 
say will materialize probably 

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with more kind of capabilities 
on the modeling side. 

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Yeah. 
And so I've had some familiarity

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with these, with these issues. 
And I think there are two 

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aspects. 1 is that from 
subsurface information linked to

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the reservoir? 
So #1 you get a huge volume of 

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data. 
And that has always been true. 

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So a huge volume of data needs 
to be processed and it gets 

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collected, right? 
And some surface locations talk 

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to each other. 
So if you know something about 

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one part of subsurface, it'll 
tell you possibly something else

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about another part of the same 
structure, right? 

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So with time, AI will improve, 
but it can probably bring 

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improvements in this, in this 
understanding in the processing 

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of this large quality quantity 
of data. 

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I also think there's a second 
aspect, which is that drilling 

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one well incorrectly, 
particularly in an offshore 

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context, can lead to significant
loss of money, right? 

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And I think that's why coming up
with this pinpointed location 

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has always been a big issue for 
all exploration production 

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companies. 
And I think that's where with 

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time better, better use of AI 
can probably help us pinpoint, 

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pinpoint accurately where the 
next well needs to be drilled. 

240
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Yeah. 
Any other sort of left field 

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crazy cases that you have seen 
in as as examples of, you know, 

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potential use cases? 
I think use cases are plenty, if

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I had to think of it, like you 
rightly said, Rishi, it's all 

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about that tolerance for error 
and what's at stake, right? 

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So there are use cases which are
either economically too, too 

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risky to perceive or it could be
a little safety. 

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So that's what I think use 
cases, you pick any function, 

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any rule, and that's where like 
like in the book that I have 

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written, right? 
So I have come with a framework 

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for leaders and for team members
to literally go through 

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systematically and identify what
are the use cases that they can 

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have. 
I think the real next step is 

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about really prioritizing and 
deciding what are you 

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comfortable with having AI to do
versus what you continue to own.

255
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So I think that that's the real 
decision to be made. 

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It's about, let's go a little 
bit deeper here, also connecting

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00:17:52,480 --> 00:17:56,760
what you said before about 
thinking AI as a teammate, 

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00:17:58,040 --> 00:18:04,640
because this will underlines 
also a, a level of trust when 

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thinking AI, the technology as a
colleague, a level of safety. 

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So how we can still make this 
happening, keeping in mind the 

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human interaction on how we can 
control it? 

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00:18:25,520 --> 00:18:29,720
I think that's, that's a, that's
a really, I mean, good point, 

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00:18:29,840 --> 00:18:32,520
Parvine. 
I think the answers we will kind

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00:18:32,520 --> 00:18:35,480
of find over time in the grand 
scheme of things. 

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But I would say the key here is 
to start with use cases as we 

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call it, like human in the loop 
kind of use cases. 

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So that see AI based solutions, 
you need to assume that they are

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going to make some mistakes. 
They will not always be 

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00:18:54,720 --> 00:18:58,360
accurate, right? 
So the key here is how do you 

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design the work or the use cases
so that it's not right away 

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hitting the end point, right? 
Let's say if it's a customer 

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facing solution, like if I pick 
another example from another 

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industry like Air Canada, they 
had launched an AI powered chat 

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00:19:14,640 --> 00:19:18,840
bot and this chat bot ended up 
in, in one of the customer 

275
00:19:18,840 --> 00:19:24,080
instructions, ended up promising
a refund to the customer in a 

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00:19:24,080 --> 00:19:27,920
very famous case that went even 
to the courts here, which was 

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00:19:27,920 --> 00:19:31,800
against the policy, right? 
But I wonder the company, 

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00:19:31,920 --> 00:19:34,040
company had to live with that 
because they could not really 

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00:19:34,040 --> 00:19:37,000
pass on the accountability to 
the AI that hey, it's on the AI 

280
00:19:37,000 --> 00:19:40,040
who said that? 
I wonder what would have 

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00:19:40,040 --> 00:19:44,640
happened if the agent would have
upgraded the person to 1st 

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00:19:44,640 --> 00:19:51,320
class. 
You know, but but it's an it's 

283
00:19:51,320 --> 00:19:56,920
an interesting issue. 
I think the point about trusting

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an AI teammate, I think that 
probably is 1 One thing that's 

285
00:20:01,360 --> 00:20:05,400
probably has a long way to go 
from from now. 

286
00:20:07,720 --> 00:20:09,480
At least, not really. 
Out of dress. 

287
00:20:09,480 --> 00:20:13,920
Maybe. 
So it's about fear, it's about 

288
00:20:14,120 --> 00:20:18,960
OK, probably some part of the 
work is speaking taken by 

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00:20:18,960 --> 00:20:21,280
somebody else, kind of 
automated. 

290
00:20:21,280 --> 00:20:24,560
So there are all this kind of 
the dynamic that we need to 

291
00:20:24,720 --> 00:20:29,120
learn to live with, I think. 
Yeah, yeah, it's the. 

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00:20:29,120 --> 00:20:31,680
Fear of even job loss to some 
extent. 

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00:20:32,040 --> 00:20:37,400
And today, interestingly, one of
my ex colleagues, he, he, he 

294
00:20:37,400 --> 00:20:39,920
just sent me a text right after 
reading the book and he made 

295
00:20:39,920 --> 00:20:41,480
some really interesting 
observations. 

296
00:20:41,480 --> 00:20:45,960
He's like, OK, AI as a team 
member, but how do I think about

297
00:20:45,960 --> 00:20:50,320
social interactions? 
Then it's he's like, OK, maybe I

298
00:20:50,320 --> 00:20:52,720
will do some work. 
I will do some work, We will 

299
00:20:52,720 --> 00:20:55,840
collaborate. 
But I go to office for work 

300
00:20:55,840 --> 00:20:58,200
because I also want to interact 
with people, right? 

301
00:20:58,440 --> 00:21:02,360
So I would say there, there are 
some issues that are going to 

302
00:21:02,360 --> 00:21:05,400
come up as we adopt the 
technology more and more. 

303
00:21:05,880 --> 00:21:09,480
So at least it's good to kind of
think through these potential 

304
00:21:09,480 --> 00:21:13,240
issues and then slowly the 
solutions will start shaping. 

305
00:21:14,240 --> 00:21:16,440
Yeah. 
Maybe 10 years later I'll be 

306
00:21:16,440 --> 00:21:20,960
doing a podcast with with an AI 
teammate probably even. 

307
00:21:21,080 --> 00:21:27,640
Sooner, yeah. 
Now, what I wanted to ask you 

308
00:21:27,840 --> 00:21:33,440
about having read, you know, 
some parts of your book, which 

309
00:21:33,440 --> 00:21:39,480
is appropriately titled Agentic 
AI for Leaders, is how should or

310
00:21:39,480 --> 00:21:42,480
what should leaders be doing 
differently today? 

311
00:21:43,240 --> 00:21:47,840
Whether it comes to redesigning 
the work, you know, building AI 

312
00:21:47,840 --> 00:21:51,360
fluency within their 
organizations or even 

313
00:21:51,360 --> 00:21:54,520
encouraging safe 
experimentation, right? 

314
00:21:55,000 --> 00:21:59,680
Where should they start? 
Is there sort of a good sequence

315
00:21:59,680 --> 00:22:03,800
of things to do today that will 
set them up for success for the 

316
00:22:03,800 --> 00:22:08,160
future? 
I would say Rishi, like from the

317
00:22:08,160 --> 00:22:11,720
experience that I have had after
interacting with multiple 

318
00:22:11,720 --> 00:22:16,040
companies, I see one of the key 
starting points has to be 

319
00:22:16,760 --> 00:22:21,320
building a, a fluency for the 
functional teams. 

320
00:22:21,680 --> 00:22:25,640
Because a lot of the companies 
that I interact with, people are

321
00:22:26,040 --> 00:22:31,960
still thinking of agent AI in 
the form of automation, right? 

322
00:22:31,960 --> 00:22:36,600
So, and sometimes they are even 
linking a technology tool. 

323
00:22:36,880 --> 00:22:40,400
The failure of the technology 
tool, let's say without naming, 

324
00:22:40,400 --> 00:22:44,760
say one of the copilots in a 
workplace gave them wrong 

325
00:22:44,760 --> 00:22:48,280
answer. 
And people would very soon 

326
00:22:48,360 --> 00:22:52,200
extrapolated that, hey, agent AI
is still not, hasn't still 

327
00:22:52,200 --> 00:22:55,080
reached that level, right? 
So that's where I think that 

328
00:22:55,080 --> 00:22:59,000
fluency is the foundational 
stone for people to understand 

329
00:22:59,000 --> 00:23:03,080
what agent AI generated AI are 
capable of, what they are not. 

330
00:23:03,640 --> 00:23:06,000
Because end of the day, 
functional teams have to play 

331
00:23:06,000 --> 00:23:08,360
much bigger role in this 
transition. 

332
00:23:08,680 --> 00:23:12,680
So it's less of a technology 
part, more of an operating model

333
00:23:12,680 --> 00:23:15,280
redesign that leaders need to 
think about, right? 

334
00:23:15,760 --> 00:23:17,720
So that's what I think the 
starting point is building a 

335
00:23:17,800 --> 00:23:21,960
fluency. 
And as teams start to leverage 

336
00:23:22,040 --> 00:23:27,400
AI, they start to experiment. 
Leaders have to start thinking 

337
00:23:27,400 --> 00:23:30,040
about this operating model 
redesign. 

338
00:23:30,040 --> 00:23:33,280
What I mean by that is what are 
the new roles and 

339
00:23:33,280 --> 00:23:35,840
responsibilities? 
What, what are we comfortable 

340
00:23:35,840 --> 00:23:39,760
with AI owning versus human 
zoning, right? 

341
00:23:40,040 --> 00:23:42,400
What are the right guardrails 
that we put in place? 

342
00:23:42,760 --> 00:23:45,280
What are the right control 
mechanisms we put in place, 

343
00:23:45,600 --> 00:23:47,880
right? 
So let's say if there are 

344
00:23:47,960 --> 00:23:53,080
autonomous cars on the roads, 
are you able to switch them off 

345
00:23:53,080 --> 00:23:56,960
when you need to, right? 
So and if anything goes wrong, 

346
00:23:56,960 --> 00:23:59,480
who takes accountability? 
So designing that whole 

347
00:23:59,480 --> 00:24:03,960
operating model is crucial next 
step once you build that air 

348
00:24:03,960 --> 00:24:09,000
fluency and start experimenting.
Yeah, so. 

349
00:24:09,560 --> 00:24:12,440
Let's. 
Take that on on the book on this

350
00:24:12,440 --> 00:24:15,280
new book you just wrote. 
So a gentic AI for leaders. 

351
00:24:17,160 --> 00:24:21,200
Why did you choose to write this
book and what do you think are 

352
00:24:21,200 --> 00:24:25,240
some of the key messages and 
blind spot that we should be 

353
00:24:25,240 --> 00:24:27,680
aware? 
Yeah. 

354
00:24:27,680 --> 00:24:33,160
So my core motivation for this 
Carmen has been based on the 

355
00:24:33,160 --> 00:24:37,000
consistent pattern that I saw 
while interacting with the 

356
00:24:37,000 --> 00:24:40,720
leaders from from across 
industries. 

357
00:24:41,360 --> 00:24:47,440
And that's been that there's a 
huge curiosity and excitement 

358
00:24:47,440 --> 00:24:51,480
about the technology that 
leaders have, but they don't 

359
00:24:51,480 --> 00:24:54,560
know where to start, right? 
So and that's what we are 

360
00:24:54,560 --> 00:24:56,600
seeing, right. 
The advancements in a a 

361
00:24:56,680 --> 00:25:00,120
technologies have been 
humongous, but the real world 

362
00:25:00,120 --> 00:25:05,440
applications are still very few.
So that was my core motivation. 

363
00:25:05,640 --> 00:25:12,760
How do we create the right set 
of information like that source 

364
00:25:12,760 --> 00:25:16,800
of how do how does it become the
right source of information so 

365
00:25:16,800 --> 00:25:20,520
that the function teams leaders 
can understand what this AI 

366
00:25:20,520 --> 00:25:24,040
technology is all about? 
And secondly, how do they go 

367
00:25:24,040 --> 00:25:27,080
about systematically discovering
use cases in their own teams 

368
00:25:27,080 --> 00:25:31,520
because that's where they're 
struggling that what all can 

369
00:25:31,520 --> 00:25:34,320
they really do with AI and where
should they start? 

370
00:25:34,320 --> 00:25:36,360
So that's been the core 
motivation. 

371
00:25:38,120 --> 00:25:41,000
Sorry, I forgot Carmina, what 
was the other part of the 

372
00:25:41,000 --> 00:25:43,120
question? 
I know it was probably and if 

373
00:25:43,120 --> 00:25:45,720
you I. 
Know that's very helpful it's 

374
00:25:46,560 --> 00:25:51,040
but the question was there is 
any blind spot that you're doing

375
00:25:51,040 --> 00:25:55,560
your research that you saw and 
you recommend the leader to look

376
00:25:55,560 --> 00:26:00,040
at things that for example we 
are underestimating, 

377
00:26:00,040 --> 00:26:02,600
overlooking. 
Absolutely. 

378
00:26:02,600 --> 00:26:06,680
I think one of the biggest blind
spots I see is lot of the 

379
00:26:06,680 --> 00:26:11,840
leaders are still treating AI as
another technology deployment 

380
00:26:12,200 --> 00:26:15,880
and hence they're leaving it 
largely to their IT teams or to 

381
00:26:15,880 --> 00:26:19,360
the technology vendors to come 
and pitch it to them, right? 

382
00:26:19,360 --> 00:26:22,240
And that's where you see, right,
MIT came with some statistics 

383
00:26:22,240 --> 00:26:26,760
like 95% of the pilots, early 
pilots have failed, right. 

384
00:26:27,040 --> 00:26:30,480
And one of the key reasons is 
when you are going after those 

385
00:26:30,480 --> 00:26:34,720
shiny demos by technology 
vendors, right, you are not 

386
00:26:34,720 --> 00:26:37,760
necessarily picking the right 
use cases for your functions, 

387
00:26:38,560 --> 00:26:40,040
right? 
So I think that's one of the key

388
00:26:40,280 --> 00:26:44,880
kind of blind spots that it's 
not just another technology to 

389
00:26:44,880 --> 00:26:48,200
be adopted, it's a real world 
operating model change. 

390
00:26:48,560 --> 00:26:52,720
So leaders need to proactively 
think through what parts of 

391
00:26:52,720 --> 00:26:57,800
their work could be reshaped and
assigned to some of the AI 

392
00:26:57,800 --> 00:27:01,920
agents, while still keeping the 
overall control on the decision 

393
00:27:01,920 --> 00:27:06,680
making. 
But I'm. 

394
00:27:06,680 --> 00:27:11,760
Going to ask you to do a little 
bit of crystal ball gazing and, 

395
00:27:11,760 --> 00:27:15,720
and, and tell us when do you 
think the energy industry in 

396
00:27:15,720 --> 00:27:19,960
particular will see a 
breakthrough in terms of 

397
00:27:19,960 --> 00:27:25,320
adoption of of AI? 
Are we talking about 10 years or

398
00:27:25,320 --> 00:27:29,360
is it coming in 18 months? 
You know, is there sort of, you 

399
00:27:29,360 --> 00:27:31,600
know, having done all this 
research for your book and 

400
00:27:31,640 --> 00:27:34,520
having worked with these 
organizations, do you have a 

401
00:27:34,520 --> 00:27:39,840
sense of when that time is? 
I would say somewhere in between

402
00:27:40,360 --> 00:27:44,640
Rishi, when we can see 
meaningful adoption of agentic 

403
00:27:44,640 --> 00:27:48,280
AI, I would, my guess would be 
like maybe four to five years 

404
00:27:48,280 --> 00:27:51,560
time frame because I already see
things in motion. 

405
00:27:51,560 --> 00:27:54,960
So some of the low hanging 
fruits like we talked about 

406
00:27:54,960 --> 00:27:59,200
those context preserving use 
cases, right or reducing the 

407
00:27:59,200 --> 00:28:02,520
cognitive loads. 
I see some of the tools for 

408
00:28:02,520 --> 00:28:06,280
those use cases are already 
being used and I think that's 

409
00:28:06,280 --> 00:28:07,880
the right kind of starting 
point. 

410
00:28:08,480 --> 00:28:12,400
So probably four to five years. 
And which is, which is OK for 

411
00:28:12,400 --> 00:28:14,320
this sector, right? 
Because unlike many other 

412
00:28:14,320 --> 00:28:18,400
sectors like SAS or digital 
companies, I think the stakes 

413
00:28:18,400 --> 00:28:22,280
are high here. 
So it's better to kind of take 

414
00:28:22,280 --> 00:28:25,880
more judicious decisions, yeah. 
Now it is. 

415
00:28:26,960 --> 00:28:30,840
And it's not an industry that's 
known for a rapid pace. 

416
00:28:30,840 --> 00:28:34,920
So five to six years probably 
will be a good achievement. 

417
00:28:35,760 --> 00:28:41,600
So we're getting sort of to the,
to, to the end of this episode. 

418
00:28:42,280 --> 00:28:45,840
And when, when we, when I 
started thinking about this 

419
00:28:45,840 --> 00:28:50,080
episode, I actually wasn't 
thinking of this as a 

420
00:28:50,080 --> 00:28:56,080
conversation on AI. 
The, the keywords that were in 

421
00:28:56,080 --> 00:28:59,080
my mind were around capacity, 
trust, you know, how 

422
00:28:59,080 --> 00:29:02,280
organizations actually get work 
done in this new context. 

423
00:29:02,640 --> 00:29:06,160
Those are the things that I had 
in mind and on the podcast 

424
00:29:06,160 --> 00:29:08,600
Carmen, you'll obviously 
remember is that we we've talked

425
00:29:08,760 --> 00:29:13,040
about building systems and 
scaling them over time. 

426
00:29:13,440 --> 00:29:17,360
I think with some of the things 
that Sabor has described, it 

427
00:29:17,360 --> 00:29:20,200
seems like. 
With AI. 

428
00:29:20,840 --> 00:29:25,080
And you know, with the help of 
AI as well as with AI in the at 

429
00:29:25,080 --> 00:29:28,400
the centre, there are probably 
some systems that need to be 

430
00:29:28,840 --> 00:29:32,560
developed, not just thinking 
about AI as a tool as as Subodh 

431
00:29:32,560 --> 00:29:36,640
was saying earlier, I think that
that phase is coming for this, 

432
00:29:36,840 --> 00:29:40,880
for this industry as well for 
our business. 

433
00:29:40,880 --> 00:29:43,720
I just want to highlight again 
that Subodh has written this 

434
00:29:44,440 --> 00:29:47,120
amazing book called Agentic AI 
for Leaders. 

435
00:29:47,400 --> 00:29:51,680
It's available on Amazon. 
We'll also live leave a link to 

436
00:29:51,680 --> 00:29:54,000
this book in the podcast 
description. 

437
00:29:54,000 --> 00:29:57,720
So those of who you're 
interested in this topic, 

438
00:29:57,720 --> 00:30:01,080
please, please feel free to buy 
a copy of the book. 

439
00:30:01,440 --> 00:30:05,240
So both thanks for joining us 
and sharing your perspective on 

440
00:30:06,120 --> 00:30:11,160
on this subject. 
Again, great conversation and of

441
00:30:11,160 --> 00:30:15,080
course, a great book as well. 
Thank you guys. 

442
00:30:15,080 --> 00:30:17,360
I really appreciate you guys 
inviting. 

443
00:30:17,800 --> 00:30:20,600
Thank you so much All right and 
to. 

444
00:30:20,600 --> 00:30:23,560
Our listeners around the world, 
thanks for tuning into 

445
00:30:23,560 --> 00:30:27,400
Sustainability Forward. 
Make sure to subscribe to the 

446
00:30:27,400 --> 00:30:31,160
podcast on any of the platforms 
that you listen to podcasts on. 

447
00:30:31,840 --> 00:30:34,840
We'll, of course, continue to 
keep interesting episodes coming

448
00:30:34,840 --> 00:30:37,200
to you. 
Carmine, thank you again for 

449
00:30:37,440 --> 00:30:39,800
joining me. 
Thank you, Richie, and. 

450
00:30:39,800 --> 00:30:44,120
I hope I will not be substituted
soon by energetic podcaster 

451
00:30:44,120 --> 00:30:48,000
soon, so I'll invite. 
I'll invite you to podcast 10 

452
00:30:48,000 --> 00:30:52,320
years down the line for sure. 
All right, take care. 

453
00:30:52,760 --> 00:30:53,200
Thank you.