Today on Episode #799 of CXO Talk, we're discussing
enterprise A I the Leadership Lessons.
We're speaking with Sunil Sennen, Head of Data Analytics
and a I for Infosys. I've been with the company for
over 22 years working with customers on their digital and
AI led transformation journeys. For the industry leaders you
know across the globe, Tell us about your role.
You have a really interesting title, Global Head of Data
Analytics and AI. I work very closely with our
clients, CXOS and really, you know, helping them understand.
How they can look at data and AI or their transformation and
really sift through, you know, all the hype, to then convert
this into a meaningful blueprint that delivers value, right?
That delivers on the promise of data and AI, not just for their
enterprise, but also for the society that they touch in.
And also, you know, really creating what we call data
economy around the enterprises, which is a very meaningful way
in which you can create value for all stakeholders and bring
in a network of entities and partners, citizens and consumers
together to then create net new value.
And that's something that data and AI holds for nations,
societies and communities. So Neil, you're speaking with so
many different companies of varying sizes.
What do people tell you about AI?
Everybody is curious. Everybody is interested in AI.
Everybody knows they have to do something.
But can you with a broad brushstroke, describe the
general state of the market as you're seeing it?
This is the conversation and then the.
In the boardrooms that our customers are having, clearly
there is a very, very heightened interest in learning about AI
and what it means for enterprises.
But I think the key questions that our customers have is how
do I translate the potential of AI for my business?
And you know how I can reimagine my business, how I can reimagine
my business models, what it means for my products and
services, what it means for my customers and other stakeholders
whom I serve, and most importantly, how do I go about
it right? You know there isn't a Big Bang
approach to AI. And it's something that touches
the roots of the organization. There are cultural aspects of
things, there are processes and obviously there is impact on
people that needs to be well understood and articulated for
how it will amplify the potential for people.
You know within the organization and outside, but how do you
translate this really into an execution blueprint so that you
can deliver on the value that data and I promised for the
organization. So these are, you know some of
the key questions and I would say there are more questions
than answers in their mind and that's why they are reaching out
to us and we are engaging them on figuring this for them as
well as the industry in which they operate.
You made an interesting comment just now.
You said that there is no Big Bang approach to AI and for
folks who may be younger that have not been through large ERP
projects, in quick summary, Big Bang means do it all at once.
Take the company live is 1 huge expensive long project and
you're saying Sunil that AI is different.
Can you elaborate on that? We live in a world where there
is a continuous delivery of new capabilities that allows.
Not only the enterprise to learn as to how to operate newer
systems such as these, but also the the users, the customers,
the consumers to really embrace that and and there is a
continuous feedback that then makes this system evolve.
But if I if I have to break it down into two or three elements
as to why AI systems are like this compared to, you know, the
Erps and others that you spoke about.
First, there is a clear adoption, you know problem,
right? Which is the trust deficit in
terms of what AI systems would tell you versus what the tribal
knowledge is? I think it takes certain
experience, you know work for the systems as well As for the
humans who are interacting or using such systems to then build
the trust and the way of utilizing such capabilities for
amplifying the potential, getting that productivity that
is needed, making that impact on the business and the customers.
But underlying problems are also that of data quality.
How do you govern such systems? How do you make sure that the
system is operating on ethical considerations that are very
important for the society and also making that larger impact?
Converting this a I effort into something that can deliver good
for the society and for, you know, everyone who's going to
interact with it, I think it takes certain.
Amount of maturity in order for these systems to be tuned and
really looking at you know how this is working with the
ecosystem and then you put this at scale which is very, very
important. You know you can't get the value
out of these systems by only limiting it to small Poc's or
experimentations that are important to get started.
But I think the end goal is to then scale it at the enterprise
level and that is why the journey goes through the quick.
Iterations and what we call digital is to then take this to
business functions, operate it at an ecosystem level and so on
with these large traditional software projects.
They were highly technology based, but still they had impact
across the company. If you were doing an ERP system
for example, with these AI systems, there still is impact
across the company as you were just describing, but it's very
different. So how are these AI systems
different from traditional enterprise software?
The enterprise softwares gave a new way of or an automated way
of executing right, which is how you could run a process at a
global scale. You could standardize a process
even though there were specific customizations for how
individual reasons needed to cater to local compliance laws
and so on, but the idea was to bring an automated system and
industrialization and standardization of that process.
What we are talking about in terms of AI is to bring the
cognitive capabilities into a system that would interact with
humans and the other systems at large.
And this has to learn from the data that exists in the
ecosystem and within the enterprise and as you can
imagine. If you have a bias, let's say
existing in the existing data, AI would amplify that.
And that's something that would completely distort at minimum
and give out inaccurate decisions.
But also it would then not be fair.
It's not going to be free. It's not something that would
drive, you know, or even meet the ethical considerations with
which we all operate. And hence the AI systems need to
be governed and need to be looked at differently from that
full automation journey to then say how am I tuning this?
What business problems am I solving and am I solving it in
the ways that are acceptable to the enterprise standards and
also to the societal standards? Would it be correct to say that
with these AI projects that they have, they retain the elements
of traditional software, but now you have these layers that did
not exist before, such as learning from the data as you
were just describing? At some level, I think it goes
beyond that, Michael, in my view.
When you start to look into the trust deficit aspect of things
and how do you bridge that trust between air systems and the
tribal knowledge, you know the the story starts to diverge from
that of Erp's and other rollouts that we've done.
But there's also an aspect of culture.
You know, the the culture of data, the culture of insights
driven or data-driven decision making.
Is a journey. And you know, as as you would
imagine in large enterprises, it is not only an individual who
has to get on to a system like this and really understand how
to work with the system, but it's also the groups of people,
teams, not necessarily in one department, but also cutting
across other departments and oftentimes even across other
companies. And how do you bring an
ecosystem? To that level of understanding
and having that expertise to say how you leverage data in AI and
solve problems together, this is where this journey starts to
diverge and look at how the adoption and the utility of AI
for different business functions would emerge.
The other thing is most of what we're going to do in AI is to
reimagine the model. You're going to see things that
we have not seen before. And in that sense, it's a great
opportunity for organizations to differentiate, to create that
discontinuous growth potential and also create new models
etcetera. But on the other hand, it's also
something that needs to be imagined, tested, experimented
and then put into place. And hence, you know, pivoting AI
on what it means for business solving problems is where the
starting point is. AI cannot be done for the sake
of AI. It's not a system that you're
looking to put in place because that's the system that's your
end goal. End goal is essentially driving
transformation for business. Getting that outcome that you
know the enterprise is emphasizing not for itself or
not only for itself but also for you know the, the entities that
interacts with and the and the stakeholders that it is serving.
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We really have great shows coming up and we have a very
interesting question from Arsalan Khan.
Arsalan is a regular listener. He asked wonderful questions and
thank you Arsalan for that. And he says when thinking about
the ethics in a I Are there ethical standards that
organizations can follow? If not, then who decides what is
as ethical? How do you make sure that your
competitor's AI is ethical? That they're not cheating and
putting yourself at a disadvantage?
Any thoughts on this? It's a really thorny it's a
thorny topic. You can see this as an
evolution. Of standards and regulations
that that you have begun to see, but it's also more that are
going to come in. But the, you know, ground level,
if you distillate down to two or three things that you know
companies can look at. One is you know you have
standards around privacy for example.
And it's a very, very important consideration to see how you
build that relationship. With with your consumers and and
partners and employees you know who will have their stakes into
the data that you're processing. Really making sure that you have
the permissions or the consents necessary for you to utilize the
data or store the data or process it for the purpose that
you're stating and for how long you want to do.
That is is known and it's something that has been defined
in many regulations both within the states as well as across
Europe and other geographies even though there is more that's
that's coming in on that you know aspect as well.
Most corporates operate with values and standards that
they're known for, and that's a good guardrail as well.
Most organizations, and successful ones at that, have
looked at the societal values, and you know how they have
created more value for everybody, not only their own
consumers, but also others who operate in the societies in
which they operate. Those guardrails apply to AI as
well. And that's something that's
known. Most importantly, I think it is
also to anticipate and see what kind of you know, regulations
you're going to see in the industry, you know around the
impact of AI on people. In ways that you know would
benefit them if done right, but it would also create negative
impact in the society. Anticipating some of those
preparing for that journey and making sure that you're doing
the right things from that aspect would put you on the
right side of the laws and regulations when they do come
into effect, and we know that they will.
And I think those organizations and enterprises would find
success far more than the ones who don't.
And I think beyond this, there are companies that are working
together to lay down ethical standards that can be referred
to. We are working on some of these
as well. We do help our customers adopt.
Some of these processes and standards as we build those
systems, how do you take care of biases for example, there are
ways to do this and we do incorporate those frameworks
into every project and every AI driven initiative that we take
up for our customers. Marketing, for example, is one
of the most common area where AI has been applied and we have
without exception always held trust, ethics, privacy,
compliance and security standards.
To each one of those are our projects and those customers
have gone about benefiting from the use of AI and share that
value with the consumers that they serve.
So there are frameworks that you could adopt while working.
On the air projects have another great question from Twitter.
This is from Kayla Aragonas and Kayla says what are the biggest
opportunities that you predict AI will yield for enterprises,
Sunil. For the enterprises, it's going
to drive, in our view, 3 theaters of value creation.
You know, AI is going to accelerate growth.
For enterprises, this is by way of identifying newer markets,
newer segments, newer needs that they can serve or serve those
needs in a different way, which is far more valuable for
customers or to even figure their play in the industry or
across industry value chains. You know one of the things that
we always, you know discuss with our customers and and guide them
on is that the physical products.
Don't transcend industry boundary, but when you think
about data, it does and that means tremendous opportunity and
potential for looking at newer ways to create these new
data-driven, air driven products and services.
You could be a medical device manufacturer, for example, one
of our clients and using data that the medical devices in this
particular case for diabetes, they were able to really help
the other parts of the value chain that interacts with those
very patients. It could be you know, hospitals
which are in the same value chain and how do you turn the
the bane of the industry which is post facto that you know
anything that happens is post facto the the sugar event and
using data and AI to predict those events.
You could then turn this into a pre facto, which is, you know,
really working proactively to help the well-being of those
diabetes patients, but also going across other parts of the
industries that touch. Diabetes patients could be
consumer products on one side, the physical lifestyle products
that can increase the activity levels of these very patients
and we all know that has an impact the food industry.
You know, as a big recipient of all this data and how you could
use this for stitching an ecosystem that improves not only
the well-being for these patients, but also in general
for society. So you know, figuring
accelerated growth is 1 big theater of value creation,
analog efficiencies at scale, you know, you could now really
push those economic frontiers to do things at cost that are far
lesser if done right. And drive more efficiencies into
your operation processes in your field operations and how you
operate your business globally. But most importantly also
building connected ecosystems, the kind that I was talking
about both in the medical device industry as well as in general
where you are creating an economy around you through new
data and air driven products and new business models is a
tremendous opportunity and the network effects.
Of such data and AI, you know, products, services can create
immense value, unprecedented value in the industry.
And this is what we have, you know, embraced in our imposis
Topaz. Offering that we launched, it's
a services brand that brings together all of what we have to
offer as in process and the network of partners that we've
switched together. The solution investments that
we're making to help drive on these three objectives for our
customers. Given the differences between AI
projects and traditional enterprise software projects
that you were describing earlier, what are the conditions
that need to be in place in order to get started in the
right way? In other words, what are the
factors at the beginning that will drive downstream success?
AI should not be done for the sake of AI, and what that
essentially means is to envisage and envision what AI means for
the business and the industry in which you know the company
operates. Really thinking about the
fundamentals of what makes AI successful to deliver those
objectives is the very next thing you know data?
Is it in place? Is it accessible?
Is it available? Does it have the quality that
you could trust in? And if there are specific AI
projects on the horizon, you could even start to look into
whether this is the data that you want to base your AI systems
on. The other thing that I would say
is preparing for the journey, you know it's it's oftentimes we
see enterprises seeing a lot of surprises as they start.
You know for example there are many Poc's that don't see the
day, you know the light of the day because the the impact or
the cultural change or the you know, the enablement of people
who will be working on such systems is often not thought
about and the even costs are not, you know, properly
understood or risk mitigations. Contingency planning to see how
you govern such an AI system are not thought through and hence
you know, doing this as a tech first project which is just a
cool shiny technology that has been used often remains in in
that very center as well rather than really bringing it to the
business. So thinking and preparing for
the journey, you know you you should have and should look at,
you know how AI can change the business, but really breaking it
down into smaller blueprints with defined very specific
objectives and bring an ecosystem together to to really
work on, you know, such a thing. The other thing that I would say
is to take a responsible AI design, which is to say that the
ethics, trust, security, compliance, privacy cannot be an
afterthought. It needs to be baked in right at
the front. Even as you communicate, you
know what AI is for your business, that you lay down some
of those principles so all stakeholders know what it is
that AI is seeking to do or for the business, how they can
engage and what are the fundamentals and the
underpinnings of such a system that you would operate.
Arsalan Khan comes back on Twitter and he says.
Organizations want to do something useful with a I but
still struggle with shadow IT. And so he wants to know how
using a I affects the organizational culture and makes
a I more of an enabler rather than an obstructor.
And I think this gets to the dimension of culture and
organizational change. Sunil that you were a leading
alluding to earlier, Absolutely. And I think it's a shift in the
way in which we view AI. AI is not to displace but
essentially to amplify the potential it for example, and
this is something that we have embraced at Infosys as well,
using AI to improve productivity in software engineering, life
cycles in the way in which we test our systems or the systems
that we build for our customers, how we ensure data standards or
data privacy across all our projects and so on.
There are multiple ways in which you would look at AI and what
this does is to shift the work value chain where humans and
software engineers in this case would then shift to more
complex, more value adding activities and you would have
yeah, really amplify the productivity of people by
running many things autonomously.
The same would happen on the business front as well.
And you know we need to look at AI as a way to change or
reimagine the business processes or business functions or
business models and embrace this to design those new systems in
the way in which we need to do. So I think the thing that I was
saying earlier, yeah, for the sake of AI would not envisage
all of this. And I think if we put the right
foundation and envision the future from a business lens
perspective, I think it tends to clearly communicate the purpose
of their AI project and also bring the various teams that
need to come together it business and find those
champions who can then lead the way to create those systems at
scale. As you talk with senior business
leaders and. With boards, to what extent do
you think that there is an understanding of the complex
impact that a I will have on their organization?
Because even when you talk about a I amplifying the benefit
rather than displacing humans, the reality is is that there is
going to be job displacement. As well.
So it's very complex. And so again, to what extent do
boards and senior business leaders recognize the depth of
complexity on their organizations?
I think there's a great appreciation for the complexity
that exists, but I think I would say that understanding that
complexity and in what are the ways in which you could manage
that complexity and turn this into a positive cycle.
It is where the effort and the focus is shifting and that's why
we are helping our customers really understand how do you
bring those aspects into the things.
For example, we take a responsible layer, design by
design for example, that brings that thought process up front in
the process, so that you're putting the right underpinnings
these systems as you build it, rather than letting it be an
afterthought. That can be a nightmare for the
organization and similarly, when you are envisaging your business
blueprints, the thought process on why you're doing it and how
you want to actually do this, how you're going to bring things
together in order to execute on. This is a conversation that we
have upfront that prepares the organization to then run such
systems at scale. And there are several examples
of this where we have changed existing models, they put new
models in place as well bringing new processes and new entities
together to do things that were not done before.
So let me take a few examples of food and beverage company.
We helped them build the AI core that help them pivot to a more
off store model to serve their customers and integrate digital
partners seamlessly while taking care of privacy and compliance
and some of those other aspects. It became the core about the
company whereby they were able to then plug in new partners as
they evolved this model and very successfully, you know continue
to have that consumer loyalty and in fact built new loyalties
on the digital channels which was something that you know they
were able to take advantage of. Similarly for a national
railroad company, they were envisaging a new ecosystem that
you know they they could create that would improve the yield and
the throughput of the value chain.
And this included not only the other partners, the you know the
first mid mile partners, but also their competitors who could
be part of this ecosystem whereby the entire industry is
able to increase the throughput, the economic throughput, but
also shift their position from being a commodity provider which
is capacity in this case to a value added player right, where
you could look at the end to end business outcomes for their
customers and be able to orchestrate it in a very complex
web of partners who can then dynamically come together and so
on. So there are a number of
examples where we have delivered these systems at scale and have
worked through the underpinnings of making sure that we're doing
this right. We're bringing those micro
change management principles which allows the organizations
to scale this and then bring the teams together through those
learnings. We have a really interesting
question from LinkedIn. This is from Mike Prest who is
Chief Information Officer at a private equity investment group,
and he says the following. He says as new adversarial A I
agents are introduced without ethical limitations to penetrate
enterprise systems. Technology leaders often
struggle in balancing optimization and innovation
within their organizations. And here's his question.
What would you say to leaders who are under pressure to
develop a I and the consequences of acting too fast or too slow?
And I would just add to that as I speak with business leaders,
one of the challenges they face which I think is very much along
these these lines is. There's an expectation that they
will make these investments, but yet it's a shifting, it's all a
shifting ground. And so how do you, how do you,
how do you invest in something that you know you need to invest
in, but you don't know exactly what you're investing in because
it's all changing? I'll take this in two parts.
You know, one is how do you balance the need for moving with
speed, but also keeping that purpose and the responsible
design in consideration. I think the first thing is to be
able to clearly articulate what division is and what is it that
you're trying to achieve and having that translate into a
blueprint because that brings the appreciation for what the
what are the design and ethical considerations that need to go
into this. That would also make all the
teams involved in this ready for dealing with that particular
challenge and hence not make decisions that might not meet
those considerations. So I think that articulation is
very important. The 2nd is to look at not only
addressing this on a case to case basis with with each
enterprise with each project, but also to develop not only
standards that your projects can look at but also you're building
your platforms in ways that it has those underpinnings.
In fact for one of the global retailers, we looked at building
a privacy first data platform and what that essentially did
was that in this particular case when they were engaging their
partners, they were engaging various different projects, AI
teams internally. Each team did not have to you
know deal with the complexity as in you know as if they were
doing it for the first time. The lesson learned and the best
practice were best practices were baked into the platform.
So for example, we used AI to discover privacy sensitive
information which was very useful for every AI project as
they were coming out and trying to leverage the data that
existed there. We had automated workflows for
privacy sensitive information that was not properly masked.
So it was not left to the decision of each project to see
what should they be doing. The workflow has baked in rules
and and the actors to whom such an approval should go to and
that allowed the organization to kind of scale this while
protecting the underpinnings that are so very important for
doing this. And I think those things can
meet the need for speed on the business side because they want
to move faster. But you also have a way to
ensure that you're not violating the privacy considerations or
not meeting the compliance standards or the ethical
standards. So those are a few
considerations to keep in mind and work with partners that can
build an ecosystem across people, process and technology.
Now you just spoke about the retention of lessons and
incorporating lessons that are learned into new projects, and
we have a question on exactly that topic from Twitter.
Elizabeth Shaw asks how do you take lessons learned from prior
AI implementation engagements and use them to support support
new client engagements. So this is where this becomes an
evolving, you know, practice. There are there are a few ways
in which we do this. One is we maintain blueprints
that are available to our practitioners globally and this
has all the updated standards best practices.
You know the lesson learned in these standards.
But more importantly, we bring a community of practitioners
together wherein they share the learnings, they share their
experiences and and look at ways in which they've dealt with some
of those challenges. Many of our customers look to
understand, you know, how these things are taken care of.
Obviously the confidentiality of each project is maintained.
It's only the ways in which we are dealing with some of these
challenges that get discussed in the community of, you know,
practitioners. We bake this into our solutions.
So any solution that is used by the practitioners globally have
these standards baked in as well.
And of course for our customers and we are engaging on these
projects, we lead with data strategists who are able to
engage with the business stakeholders, the Cxos and
envision the blueprint or the business potential.
Like we say, the biggest problem to solve in this industry is to
find the right problem to solve. And that's where our data
strategists come in and they are well versed with the standards
and you know the compliance laws.
We guide many of our customers on privacy standards or you
know, looking at remediations that are necessary in their
systems to operate such systems at scale.
So when you are doing this, any kind of such projects building
that ecosystem wherein you can push this into multiple vehicles
that your teams would use for implementing such projects
become important. So best practices industries or
the standards document putting this in their training systems
so that anybody who's getting enabled on this is well aware
of, you know, those standards that need to be invited and then
the projects that they will execute.
And also, you know, making that available through, you know,
data privacy office or the compliance office is also a very
meaningful way so that people know who to go for getting that
guidance. And this office can really take
on the initiative to make everybody aware, enable them,
engage them, become a resource when necessary to to guide those
teams as well. On the topic of teams, what
would you say is the the team composition that an organization
needs to look for? It's clearly a business first
approach to this where you're looking at the business teams
really coming together along with the IT or the technology
teams to deliver this. But they are like we're saying
the considerations of the responsible by design.
So you would definitely have a play of your data privacy or
leads, your compliance leads, you know, can audit the project
or give blueprints upfront for what the projects need to comply
with and similarly looking at the other considerations of data
security etc. To bring those experts.
So it's essentially, you know, our tribe so to speak that
brings together these skills and in an agile passion compose
these teams to to address the skills required for delivering
on that project. Like I was saying earlier, it's
it's a dynamic composition because you would take on, you
know, the business, you know needs in an agile passion and
hence building that tribe wherein you're able to pull
these resources and create the part necessary for for
addressing on this. So it's it's a a more of a cross
functional team that you would have to work on your.
We have a very interesting question again from Arsalan
Khan. And, he says should given the
emphasis on AI systems today, to what extent should organizations
be focused on AI? Tools and projects, as opposed
to traditional business and digital transformation projects
using traditional enterprise software.
Over the past few years, we saw businesses embracing digital
businesses embracing cloud. In fact, the businesses that
embrace cloud were able to respond to events like Pandemic
way better than those who did not.
So they've invested in cloud, they've invested in digital.
And getting to AI is the very next logical step where you're
able to then amplify the outcomes that you can get
through this. So it's kind of a continuum on
that particular chain, but it also leverages the investments
that businesses have made in the digital and data thus far.
It then enables them to get quicker ROI through AI projects,
through AI initiatives and that's you know very important
consideration even as you look at scaling the AI initiatives to
enterprise level, the underpinnings that you have in
your digital and data would allow you to scale it at that
level. Simple example is that if you
are using generative AI for enabling users or consumers to
ask questions and get answers, you would want to have the right
level of authorizations built in.
For example, let's say I'm a non finance person and I shouldn't
be seeing certain numbers. You want to make sure that the
generator via system does not give out the information that
I'm not supposed to see. And those things are well baked
in into the digital and data foundations that most
enterprises have, you know laid and that can be scaled to the
newer systems as you're doing this.
So I think this is a continuum that you build on.
It allows you to get ROI quicker, ROI on the investments
that you've already made, allows you to scale at the enterprise
level and with the right considerations put in place of
responsible by design, you can operate with confidence as well.
So it's kind of an initiative, but there is an aspect of
experimentation that has to take place with AI.
That's a very important aspect of how you will figure or learn
new opportunities that business can really take advantage of and
how ready or what kind of data do you have, what kind of data
quality you have to really solve.
Some of those problems will come through the experimentation
punnel, but when you're scaling it, it's gonna go back to some
of the foundations that have been put in place.
So you're kind of getting from digital cloud to now AI?
That experimentation process or do you find that organizations?
Are having trouble with that, or does it seem to go pretty
smoothly for folks that are very process bound?
I would imagine that this experimentation is just a very
different way of thinking the. Enterprise is to think about
setting up that experimentation ecosystem.
We guide our customers and we do a number of engagements for our
customers. We were thinking through the
experimentation. That is not wasteful but is
productive and there is a way to think about it.
How do you funnel ideas into the experimentation zone?
There are ways to do this, you know, through design thinking on
one side where you're exploring with business what problems can
be solved and in what ways can that be solved.
You could use data to nudge and recommend what areas you could
look at. You know, for example data could
tell you a trade promotion of certain kind, could improve, you
know the sales for other teams and could become the idea for
you to experiment on. Or it could be the business
teams coming out with newer ideas that they would like to
look at because they are hearing those problems in the field or
they are experiencing certain bottlenecks in which you know
the business is experiencing you know problems.
But how do you then run this through the idea funnel to
scenario certain things You could simulate to better
understand those things, convert those into real Poc's and you
know the the small experimentation projects.
But really putting those measure measurements in place whereby
you are able to evaluate what the experimentation is, telling
the business in terms of what it can get.
And then connecting that into, you know, how you can scale
successful ideas when when they need to be pushed to that.
But more importantly, also feeding those experimentations
back into the funnel. So that next time when the
business is looking to do an experiment that has already been
conducted by somebody else, one could discover that and use that
to then see whether there's a need to do this.
So I think there's a a clear experimentation design that one
could adopt, making sure that the whole experimentation cycle
is serving the need to innovate at speed, but also gives you the
basis on which you could scale those ideas and make this a very
productive cycle for yourself. With that, I have to say a huge
thank you to Sunil Sennen. From Infosys for taking the time
to be with us. Sunil, thank you for being here.
I really, really appreciate your time and your expertise.
Thank you so much for having me on your show and it was great
talking with you. And thank you to everybody who
watched, and especially to those folks who asked such great
questions. I always say this, but you guys
are an amazing audience. You're so smart.
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