ebook-2024-ai-outlook-expert-advice-navigating-ai-economy.pdf

2024 AI Outlook
Expert Advice on Navigating the AI Economy

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6
Balancing the clock speed
dilemma with strategic value.
Todd Lohr,
Principal, KPMG LLP
9
Harnessing AI’s opportunities and
avoiding risks.
George Casey,
Principal, Data Scientist, RSM US LLP
12
Risk-tolerance and the need to reskill.
Piyush Bothra,
Field CTO, Principal Solutions Architect,
Amazon Web Services
15
Data quality, governance, and
the courage for buy-in.
Frank Schikora,
Chief Technical Officer, Roboyo
18
Taking a strategic, top-down approach to AI
(and the importance of a strong data foundation).
Piyush Kumar,
Global Head – Strategy, Strategic
Partnerships & Solutions, Wipro
21
Uncharted possibilities and the future of
working alongside AI.
Akhilesh Natani,
Managing Director and Co-founder,
Intelligent Automation, Xebia
24
Industry maturity levels and the
lesser-acknowledged risks of AI.
Brendan McElrone,
Managing Director, Deloitte Consulting LLP
3
Foreword: 4 predictions for
the future of AI.
Michael Beckley,
CTO and Co-Founder, Appian
27
Forging ahead into new markets with AI.
Hasit Trivedi,
CTO Digital Technologies and Global Head – AI,
Tech Mahindra
31
Transform your organization
with Appian.
Contents

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AI has become every organization’s top challenge—and their
top opportunity. AI is changing software from a mere tool to
an actual collaborator in the workplace and in our workflows.
This is the rapidly emerging AI economy, where organizations
will be split into two groups: those who are good at AI and
those who are bad at business.
Most experts agree that AI will not replace humans anytime
soon but instead augment us in a world of mixed autonomy.
Succeeding in this new world will not be easy. It will require
new structures to harness AI’s transformative potential while
managing its very real risks. The outcome rests on building
new methods of highly efficient human-AI collaboration. From
my perspective, few organizations are at that place yet.
While 2023 brought the world of AI to the masses, it
also stirred up a maelstrom of unanswered questions for
organizations everywhere. How can we derive practical value
from AI? What is the most cost-effective way to operationalize
it? What about data privacy?
These questions have slowed the pace of transformation for
many organizations in the AI era. The ability to become a full
AI enterprise—one that wins—depends on how you answer
these questions.
With that in mind, I’d like to share four predictions of my own.
Although the easiest way to be wrong is to make a prediction,
my predictions are rooted in my strong belief that data and
process are the two most important areas to focus if you want
your organization to become a winner in the AI economy.
Prediction 1: Data foundations will become
more critical.
Several industry experts in this outlook have mentioned
the need for a strong, foundational data architecture.
I couldn’t agree more.

ur organization to become a winner in the AI economy.
Prediction 1: Data foundations will become
more critical.
Several industry experts in this outlook have mentioned
the need for a strong, foundational data architecture.
I couldn’t agree more.
AI is nothing without data. A large language model, for
instance, reshuffles the information it’s been fed.
More data, and better data, means better answers.
And to make AI most effective, you have to feed it
your data. Yet, organizational data is often fragmented,
lying in isolated pockets, rendering them ineffective,
stagnant, and inaccessible to AI models. A data fabric
solves this problem by offering a 360-degree view of
enterprise data without migrating it from other sources.
Organizations that embrace data fabric will more easily
operationalize AI across the enterprise.
4 predictions for the future of AI.
Michael Beckley, CTO and Co-Founder, Appian
Foreword

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Prediction 2: Humans and AI will work harmoniously.
Contrary to the dystopian narratives where AI replaces
humans, the reality paints a more balanced picture for the
short-term and even the long-term. AI simply isn’t autonomous
enough to replace human judgment or expertise.
“Organizations must have a strategic
vision for mixed autonomy—in other
words, how they will use AI to augment
humans, rather than replace them.”
These narratives remind me of the worries software
developers initially had with the advent of low-code. Many
thought that low-code would eliminate developer jobs.
Instead, it made software developers far more valuable,
which leads to greater job security over the long term.
Like low-code, AI can augment human capabilities,
making employees far more valuable and accelerating
their contributions to the business. This has always been
a cornerstone of Appian’s approach: automation and AI
complements humans, not overshadows them.
AI is a partnership. AI can write, humans must edit.
AI can propose decisions, humans will decide. We will need
to route work to AI, but we’ll also have to route to other
automation technology as well as humans. This makes
sophisticated workflow and process automation critical
for turning AI into a valuable, transformative technology
that truly achieves the AI Enterprise. AI can be enormously
helpful, but it’s not a solo show—it’s part of a larger,
interactive team that helps augment humans.
Prediction 3: Businesses will need private AI.
Public AI models have captured the collective consciousness
but pressing data privacy concerns cut the honeymoon
phase short. OpenAI briefly banned ChatGPT in Italy in
early 2023 due to potential privacy issues around GDPR.1

will need private AI.
Public AI models have captured the collective consciousness
but pressing data privacy concerns cut the honeymoon
phase short. OpenAI briefly banned ChatGPT in Italy in
early 2023 due to potential privacy issues around GDPR.1
Companies across sectors have limited their usage within
the enterprise—from public sector organizations to major
banks like JPMorgan.2
While the headlines focus on these famous examples, privacy
concerns aren’t limited to chatbots. Many large public cloud
providers offer pre-packaged AI services to businesses and
organizations of all sizes. Unfortunately, these cloud providers
often train their public AI algorithms on their customers’
data. Businesses may unwittingly help the competition by
sharing this data to train algorithms used by other companies.
Plus, many of these large-scale providers aren’t transparent
about how data will be used, which can open businesses up to
potential liabilities in the event of a leak.
While some sectors can tolerate this risk and embrace AI
with open arms, they must still remain aware of the risks.
Some industries—such as public sector, life sciences, or
even financial services—simply can’t afford these risks at all.
Privacy breaches will be catastrophic. Organizations must be
strategic and careful, limiting AI usage to areas where privacy
can be better assured—or embrace vendors who emphasize a
private AI approach.
1. “ChatGPT Has a Big Privacy Problem,” Wired.  www.wired.com/story/italy-ban-chatgpt-privacy-gdpr (April 2023).
2. “JPMorgan Restricts Employee Use of ChatGPT,” CNN. www.cnn.com/2023/02/22/tech/jpmorgan-chatgpt-employees (February 2023).

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Prediction 4: Regulations will soon catch up.
If 2023 was the breakout year for AI, expect 2024 to be
the year of AI regulation. Governments have recognized
the potentially negative impacts AI could have on society
writ large—from privacy concerns to misinformation
to cybersecurity risks. In 2023, we saw the seeds of
regulations starting to take shape in the United States
and the European Union, among others.
While most of the political talk has painted broad strokes of
how we might start to address concerns, we should expect
more bills from the US Congress as well as actions and
guidelines set by various regulatory agencies. The European
Union has been hotly debating its own AI Act to curb
potential misuse of the technology (although, at the time of
publication, this was not settled law and has its opposition).3
As lawmakers sculpt these regulations, it’s unclear how
they will ultimately take shape. But watch the trend itself—
regulations will come soon and organizations will have to
adapt in kind.
Forging the AI future with confidence.
As we stand at this juncture, it’s clear that organizations
must use AI responsibly and effectively. As the CTO for an
enterprise software company, I’m part of a team using AI to
reinvent the Business Process Automation (BPA) market.
BPA software brings together enterprise data and processes.
It cuts through red tape and bureaucracy to enable everyone
to be an active participant in improving the processes
that govern our lives. The applications of AI are obvious
and exhilarating, but so are the risks and challenges for
our companies, our creators, our authors, our artists,
and our society.
In this guide, you’ll hear from industry experts across top

hat govern our lives. The applications of AI are obvious
and exhilarating, but so are the risks and challenges for
our companies, our creators, our authors, our artists,
and our society.
In this guide, you’ll hear from industry experts across top
consulting firms. They’ll echo some of the sentiments above
and share quite a few of their own. One thing is certain:
the path toward the AI Enterprise is rife with both
opportunities and challenges. We hope the following
pages will serve as a compass for you to navigate the
AI future with confidence.
3.  “EU AI Act: First Regulation on Artificial Intelligence,” European Parliament.
www.europarl.europa.eu/news/en/headlines/society/20230601STO93804/eu-ai-act-first-regulation-on-artificial-intelligence (June 2023).

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Todd Lohr, Principal, KPMG LLP
Todd Lohr is a Principal within KPMG’s Technology Enablement practice, specializing in Digital
Transformation and Enterprise Automation. He leads the Technology Consulting business and
specializes in the selection, design, and implementation of digital technologies, including
low-code, data and analytics, cloud, and AI/ML. Lohr is also a regular presenter at conferences
and guest lecturer at universities. He brings the breadth of KPMG’s 4,000+ partners and
professionals to help companies solve their most complex technology challenges. Most recently,
Lohr has focused on emerging technologies, their impact to business models, and how executives
need to lead their organizations through changes. He focuses on the ethical implications of AI and
what organizations need to do in this new realm of corporate and social responsibility.
Some career highlights include leading the implementation of conversational AI to strategically
transform customer service for a FORTUNE 10 organization, leading intelligent automation
programs at 10+ FORTUNE 100 companies, and leading enterprise automation strategies for
large technology companies focusing on RPA and cognitive solutions.
Todd Lohr earned his BBA in finance from the University of Iowa. He is also a certified business
process professional (CBPP), attained a certificate of mastery in process reengineering through
Hammer, and is a Six Sigma Green Belt.
Balancing the clock speed dilemma with strategic value.

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Q&A with Todd Lohr.
Q: How do you think AI will change the landscape
of business and how prepared are organizations for
that change?
KPMG has done a couple different surveys with executives
and found that, in general, it’s a mixed bag. But most
organizations think they’re more prepared than they really are
because they don’t fully understand AI, specifically generative
AI, and how it will affect the market.
How will it change businesses? Well, AI is not a new
phenomenon. It’s been in the works for years, if not decades,
but it’s just moving at a faster pace. So, the biggest impacts
will be around the “clock speed dilemma.” Will organizations
keep up with the exponential curve of what AI can do for their
businesses and will they be able to transform fast enough
to keep up?
“It will change every business and every
industry. Wherever you have people
working, AI will augment their work,
change what they can do, and change
the roles they play.”
Q: What about risks? What are the big risks about
charging into generative AI at the moment?
The market is focused on adopting generative AI, which
emphasizes the “here and now.” But risks occur around the
validity of the output and intellectual property (IP). Generative
AI creates content based on large sets of data, but doesn’t
really explain how it arrived at the output and whether it’s
valid. How do you show your homework, so to speak?
The bigger issue is IP protection. If you create content, how
do you protect it? And how do you ensure you’re not using
someone else’s IP?
In addition, there's algorithmic bias. The technology is only as
good as the people that trained it, the methods used, and the
underlying dataset. So how do you actually understand the

it? And how do you ensure you’re not using
someone else’s IP?
In addition, there's algorithmic bias. The technology is only as
good as the people that trained it, the methods used, and the
underlying dataset. So how do you actually understand the
biases, then develop AI that detects and mitigates them?
And on the cybersecurity side, attackers could manipulate
underlying data in an AI system, which changes the output.
This is a sophisticated attack that’s hard to detect.
Q: How else will AI shift the cybersecurity landscape?
There’s a couple ways to think about it. One is that we’re using
AI to secure environments. But also, AI makes the environment
more challenging. As much as AI can be used for good, it can
also accelerate the pace and complexity of sophisticated
cyber schemes. It both creates cyber risks and becomes a tool
to fight cyber risks at the same time.
Q: For companies that have already adopted AI,
what do you think sets the top performers apart?
They view it as a technology to solve a specific business
challenge. They don’t treat AI as a hammer looking for a nail.
A lot of organizations follow hype cycles, getting enamored
with a new technology, and want to just go apply it to their
business. But they lose sight of some important questions:
What value am I creating for shareholders? What is the
experience I'm creating for my employees? What strategic
propositions am I trying to accomplish as a business, and
how do I use this to pivot or accelerate that strategy?
Top-performing organizations stay true to their business
strategy and use AI as an accelerant.
I think a lot of organizations have gotten sideways on AI.
The first question I ask is “why?” Their answer is, “because I

or accelerate that strategy?
Top-performing organizations stay true to their business
strategy and use AI as an accelerant.
I think a lot of organizations have gotten sideways on AI.
The first question I ask is “why?” Their answer is, “because I
want to do AI.” That's not the right answer, right? That is one
of the traps.

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Q: So how do they ensure they’re doing it right?
Organizations that adopt AI quickly are ones that face more
disruptions in their business models. They know their future
business is predicated on these technologies, so they explore
it faster. Culturally, they have to allow teams to experiment
and innovate. Fail fast. These technologies are reasonably
newer, and not perfect yet. You have to have that pioneering
spirit to move into new technology areas and keep the focus
on innovation.
One of the most common questions I get asked from senior
leaders is, “tell me six of my peers in industries that have done
this.” I respond that it’s fairly new. Is there anyone who’s the
market leader in AI? Not really. A lot of organizations are trying
to figure out if they should become the market leader or follow
quickly behind one. And if so, what does that path look like?
Q: You mentioned people chasing hype and losing
sight of the value. Do you think we’ll hit a point
where people will implement AI and then wonder,
“Why did we do all this?”
AI is interesting in that it's following a number of different
hype cycles. The previous AI hype cycle was what—five, six
years ago? That was a wider automation cycle that went in
tandem with RPA and other broader automation tools.
Now, we have a new cycle that is a subset, generative AI. AI
has staying power. One challenge is that people are really
excited about gen AI, which is only a slice of AI capabilities. If
you’re thinking about your business strategically, you should
be thinking about AI more broadly, and not just generative AI.
That said, the generative AI hype cycle is based on the
democratization of large language models. If you play this

es. If
you’re thinking about your business strategically, you should
be thinking about AI more broadly, and not just generative AI.
That said, the generative AI hype cycle is based on the
democratization of large language models. If you play this
out in large swaths of time, each piece of AI likely has its own
hype cycle and trough of disillusionment. We will hit that
disillusionment because I think organizations are starting to
figure out that LLMs are interesting, but until you bring your
own data and IP to the model, it’s general knowledge. It’s not
specific enough to your business.
“AI is here to stay. I think you’re going
to continue to have intermediate waves
of this hype and disillusionment, as
with any technology. But if there's one
technology that's going to change the
course of human history in the next
decade, it's certainly AI broadly.”
Q: Any parting thoughts?
Everyone is focused on generative AI to the point they may
ignore other parts of AI. Generative AI is just one subset of
the emerging tech landscape. Someone may want to solve a
specific problem and ask how to use generative AI to do that.
Often, I’ll say, “This is how you actually solve the problem,
and you don’t need generative AI to do so.” I mean, there's
a whole host of different components of AI that kind of fit
within the overall ecosystem—advanced analytics prediction,
forecasting modules, anomaly detection… there are all sorts
of problems that can be solved with AI without focusing
exclusively on generative AI.

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George Casey, Principal, Data Scientist, RSM US LLP
George leads the Advanced Analytics practice at RSM. In this role, he advises clients on both
strategic and technology issues important to delivering value with data science. Prior to being
acquired by RSM, George was the Chief Marketing Officer and Chief Technology Officer for
Junction Solutions, a B2B technology solution company serving mid-market clients with a
focus on life sciences, supply chain, and multi-channel retail. Over his 13 years at Junction
Solutions, George had the opportunity to advise multiple clients on strategy, business intelligence
and analytics, CRM system design for both B2B and B2C clients, as well as full system
implementations of ERP solutions.
George has been published in several professional and trade journals and is a frequent seminar
speaker. He is a Microsoft Certified Trainer and has written several manuals for Microsoft on
Reporting and Business Analytics.
George holds a bachelor of science in management information systems from the University of
Illinois and a master of business administration and a master of science, predictive analytics from
Northwestern University. Additionally, he is Certified in Planning and Inventory Management
(CPIM) from the American Production and Inventory Control Society (APICS).
Harnessing AI’s opportunities and avoiding risks.

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Q&A with George Casey.
Q: How have you thought about AI over the past 5 or
10 years and how has it changed recently?
There are multiple ways of defining AI and its components.
The one I like is that AI replaces some tasks we used to think
a human was required for. Whether that’s driving a car,
reading a text message, or interpreting a picture, it all starts
with a prediction and an action. With ChatGPT, for example,
it’ll see you put in a bunch of characters, then predict your
intended meaning. And it’s doing that using natural language
processing and the ability to understand, “oh, well, this looks
like the English language and it looks like these words that
I’ve seen. And when I see them in this pattern, this is typically
what I can infer from that.”
What's more available today is the massive amounts of data
and scalable compute power. We can do these things in real
time in memory, just like if I’m driving my car, it can predict
that I’m closing too fast on the car ahead, then act and apply
the brakes. The ability to do that prediction has been around
for 50 years. But before, it would take a long time, and by the
time it finished its prediction, you would have hit the car. So,
I think that’s why we’re seeing such an uptick in this web of
disruptive technologies. We’ve seen interconnected devices
and the growth of Internet of Things devices, then massive
available datasets and compute power.
Q: Where do you think we're seeing the most impact
when it comes to AI?
Everyone sees opportunity. I’ve yet to come across an industry
that can’t take advantage of these techniques. It just depends
on the industry.
Take healthcare. I have colleagues who will say that if you

eeing the most impact
when it comes to AI?
Everyone sees opportunity. I’ve yet to come across an industry
that can’t take advantage of these techniques. It just depends
on the industry.
Take healthcare. I have colleagues who will say that if you
don’t use AI as a large healthcare practice, you’re committing
malpractice because you’re not serving the patients using
the best and greatest state-of-the-art techniques. In life
sciences, we're seeing people do massive things around
drug development and clinical trials. It’s changing the game
in clinical trials by using a simulation or model so we quickly
evaluate compounds without all of the physical testing we
used to have to do.
Industrial companies are seeing uptake on the shop floor,
in what we'd call “factory of the future” or “industrial 4.0.”
They’re moving beyond basic automation. Now we introduce
things like computer vision where we can use cameras,
video images, and data to infer things like shop floor safety,
predictive maintenance, quality, or even being able to look
at all the parts that come off of the shop floor and say,
“hey, is this a good part or a bad part?” In the old days, they
would have a sampling program with human inspection that
hopefully catches most of the problems. Now, we can catch
100% of the issues because it’s all passing by a camera that
instantly detects defects.
“I've yet to find an industry that
couldn't better leverage data to
remove uncertainty or reduce the
time it takes to make decisions.”
Q: It seems like the world is focused on generative
AI, but there are a lot of use cases around data and AI
in general. Is that fair?
I 100% agree. It’s not about the technology—all innovation
starts with a problem to be solved. When you look at

Q: It seems like the world is focused on generative
AI, but there are a lot of use cases around data and AI
in general. Is that fair?
I 100% agree. It’s not about the technology—all innovation
starts with a problem to be solved. When you look at
opportunity that way, it’s important to focus on the “why?”
Why would we do this? Why are we trying to solve a
particular problem? What's in it for us? Where is there value?
Answer those, and then we can start getting to the how.
For example, think about nonprofits and how they operate.
Their challenge is around predicting member engagement
or predicting donor engagement depending on their charter
and their structure. So, being able to better understand the
signals they get from their members through their behavior or
demographics, can they understand if the members will renew
their membership? And if they can infer or predict that they
won’t renew their membership, can the nonprofit team design
an intervention strategy?

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“Now we're talking about saving a lot
of money or creating additional value
opportunities for organizations. It starts
with understanding that they have a
problem. Then, it becomes, ‘how can
we solve it?’”
Q: What big risks should people think about
around AI?
The first one that people get concerned with is just access to
data. If I am sharing data with an entity, whether that’s the
computer or organization that controls it… I don’t necessarily
want them to have full access to that data.
The example we talked about before with healthcare is
illustrative. A lot of people can have concerns. The first risk is
that many of these systems work based on access to massive
datasets. How can you make sure you're governing that data
access appropriately to avoid misuse or misappropriation?
Another big risk is the data we use to train these models.
The underlying data may lead to bias. With AI, we may
institutionalize that bias because we base decisions off of
how we gathered the data, not necessarily what's appropriate
or representative.
So it’s important to assess whether a dataset is appropriate to
be used for a model or if it represents a specific bias that you
wouldn’t want to be pervasive. That's a risk.
A third risk is around autonomous control, where you take
humans out of the loop and the machines do more than
you originally planned for. While that’s the most publicized
worry, I think it’s a lower risk due to the controls we have
as developers and consultants when implementing these
systems. We can design around this risk. We just need to ask
the right questions, assess what data the system is exposed
to, and then decide what actions it’s allowed to take.
Q: One more question. How do you feel AI plays in

n implementing these
systems. We can design around this risk. We just need to ask
the right questions, assess what data the system is exposed
to, and then decide what actions it’s allowed to take.
Q: One more question. How do you feel AI plays in
the automation space?
You can look at companies like Appian and others that have
applied AI. You’re not necessarily creating bespoke models,
but you’re applying the technology in a low-code environment
where you enable a citizen developer to take advantage
of AI. That gives people a head start or leap ahead rather
than having to build all this from scratch. They can say,
“Hey, there’s a specific process we want to automate and
we’re going to use some AI to take what was difficult before
and help us make some of those decisions.” This approach
makes it much easier to adopt than growing systems from
the ground up.

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Piyush Bothra, Field CTO, Principal Solutions Architect,
Amazon Web Services
With over two decades of expertise in the technology realm, Piyush Bothra has a proven track
record of enabling transformative business outcomes through execution of strategic technology
initiatives. A specialist in cloud computing, he currently serves as a trusted advisor to executives
and senior leadership, offering expert guidance on cloud strategy, modern architecture, and best
practices. He is currently focused on helping customers with generative AI use cases and fostering
a vibrant AI/ML culture of innovation.
Risk-tolerance and the need to reskill.

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Q&A with Piyush Bothra.
Q: Let’s start with a broad question. How do you
think AI will change the business landscape? And
how have things changed compared to the past?
AI is already changing the landscape in several ways. AI
didn’t land on our plates yesterday or the day before. I know
that Appian has been in this business for many years and has
done a lot of innovation here as well. While AI has undergone
incremental innovation for years, the generative AI trend
has entered the public domain and broadened visibility into
AI in general.
It will have exponentially accelerated impact moving
forward in several ways. One way is business process flow
automation, like what Appian is doing. Business processes
will become more intelligent and optimized. We’ll also see
impact in areas where data will drive more business decisions
because artificial intelligence mainly relies on data to generate
insights and recommendations. We’ll see progress in that
area as well.
Another area of impact is on jobs. There’s a lot of talk about
jobs going away. The way I see it is that it will definitely
disrupt the job market where some old skills will be replaced
by AI and automation. But new skills will be required to do
existing jobs. Businesses must adapt soon to reskill their
resources and teams with the new developments.
Q: So how do businesses go about reskilling
the workforce?
To do this, it’s critical for businesses to think of AI and machine
learning as a general, top-down strategic play. If there is a
machine learning strategy or AI strategy their organization
adopts, then reskilling their human resources will obviously
be part of it. Also, give employees enough time for theoretical

learning as a general, top-down strategic play. If there is a
machine learning strategy or AI strategy their organization
adopts, then reskilling their human resources will obviously
be part of it. Also, give employees enough time for theoretical
and practical training. Many partners today provide this
kind of training. By the way, AI is a contributor to having this
training available as well.
“Start with the basics—give teams
enough time and resources to
experiment. Fail fast, adapt, and
re-experiment. That’s the best way
to learn.”
It is important to strategize about AI and ML applications as
part of operational and yearly planning for shared leadership
responsibility. This will help identify the need for more skills,
resources, and training programs. Organizations must be
willing to accept minor risks if the team fails in their own
experiments. That should be the mindset moving forward.
Q: Are you finding that organizations are more
risk-tolerant or risk-averse?
I see a mix of both. Some organizations are more risk-tolerant.
It depends on the leadership as well as the domain and
industry. If we talk about the technology or IT industries,
they’re more risk-tolerant than government or public sector,
which are more risk-averse. There are many reasons behind
this mix, like culture, regulatory environments, and availability
of skills and resources. Another factor is how much of the data
is available for building AI and machine learning applications,
and how well-governed that data is.
Take cybersecurity risks. AI systems could be targets of
hacking and manipulation. Organizations need to apply
strong cybersecurity practices to AI systems and data.
Now, technology leaders should widely recognize cyber risks

verned that data is.
Take cybersecurity risks. AI systems could be targets of
hacking and manipulation. Organizations need to apply
strong cybersecurity practices to AI systems and data.
Now, technology leaders should widely recognize cyber risks
and act proactively before launching AI applications.

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Q: What industries do you see leading in AI adoption?
The financial industry is already applying AI. We’ve seen
algorithm-driven trading for many years. I also see great
potential in areas like fraud detection. Think about the billions
of requests coming through financial systems from various
sources and places across the globe. It’s just not possible
to analyze all the requests and data flow, then still get
insights using traditional analytics. That's where using AI in
auto-detecting some of those fraud alerts comes into play.
Beyond that, we see a lot of adoption in the retail industry.
Personalized recommendations and product ads based on
our purchase history—that’s driven by AI. Healthcare is huge,
too. There’s a lot of potential to serve humans in general.
Healthcare research can leverage AI to analyze billions of
data points to get insights in a way that just isn’t possible with
human eyes or traditional analytics methods.
One favorite area of mine for application of AI is education.
I think we’re falling behind, and AI can help. AI can help take
education to all the geographic locations and communities
that might be underserved due to a lack of skilled teachers.
Also, we should think about how to change our education
system to teach kids early about AI, machine learning, and
responsible usage.
Q: What makes companies successful at using AI?
The companies that adopt machine learning and AI as part of
their product strategy or business strategy are already seeing
bottom-line and top-line growth. They’re setting themselves
up for that kind of trajectory against companies that don’t.
This gap will increase even more in the coming years. The
competitive landscape may change drastically. If the majority

eeing
bottom-line and top-line growth. They’re setting themselves
up for that kind of trajectory against companies that don’t.
This gap will increase even more in the coming years. The
competitive landscape may change drastically. If the majority
of companies don’t start thinking about it now, we may see
only a few companies running the show, which we don’t want.
Companies who are doing well pull AI in as part of their
operational planning. A few companies ask their leaders each
year how they’ll use artificial intelligence.
It’s no longer a question of whether you will use AI or machine
learning, or even why, but really, how will you apply AI and
machine learning in the business? It’s kind of mandating that
business leaders, product managers, data scientists, and
engineers get together and start thinking about how they
can improve the customer experience using machine learning
and artificial intelligence. Companies that see this as a top
strategy will succeed.
Also, it’s important to provide the right tools and data for the
teams to become successful in this journey. As we all know,
machine learning and artificial intelligence cannot run without
data. If there isn’t easy and well-governed access to data for
engineers and data scientists, then those organizations will
fall behind in terms of innovation.

appian.com   |    15
Frank Schikora, Chief Technical Officer, Roboyo
Frank has been working in the automation space for the last 14 years on a multitude of different
IT projects, from ERP implementations to large-scale infrastructure and business intelligence
efforts. Automation and freeing up time for employees has always been a driving force for him.
Since joining Roboyo, he has focused on delivering value to clients with the right mix of
technology, methodology, and optimization to solve business problems and deliver tangible
outcomes, and to do so across all the disciplines Roboyo brings to the customer, be it RPA, IDP,
low-code, process mining, or AI.
As CTO, Frank is responsible for curating the best technology partners for Roboyo.
Data quality, governance, and the courage for buy-in.

appian.com   |    16
Q&A with Frank Schikora.
Q: When it comes to AI, what do you think we need
to keep in mind as far as governance and security?
One of the main AI applications now is for large language
models (LLMs), where you ask the model something and it
generates an output. But who’s asking the questions? Are they
allowed to ask those questions? And are they really allowed
to get that answer? This is super important in terms of prompt
engineering, for example. This is where it gets interesting
for governance—you basically need to scale down what the
LLMs can do.
If you think about ChatGPT, you ask anything and it will
answer. In a business context, we don’t want that to happen.
You don’t want employees asking an LLM about the CEO’s
salary, then having it answer or even hallucinate parts of
an answer. We need to make sure that the user is allowed
to get the answer to a question and understands the data
supporting the answer. When a model gives an output, we
want it to give evidence on its data.
“We really need to focus on questions
about how we segregate data, and how
we make sure that when a question is
asked the person is allowed to receive
the answer. This is a challenge and
depends heavily on the infrastructure
lying behind everything.”
Q: Have you seen any specific industries that are
using AI well? Maybe some that’re a little further
along the maturity curve than others?
It relies a lot on data. Data is everything, including for
language models. Any industry processing and using a lot
of data can use AI effectively. Where we’ve seen it a lot
is within the financial services space or within the private
equity market.
One use case we did with Appian was to create an investment

uage models. Any industry processing and using a lot
of data can use AI effectively. Where we’ve seen it a lot
is within the financial services space or within the private
equity market.
One use case we did with Appian was to create an investment
thesis out of different inputs. Again, you must give evidence
for the outcome and ensure the person who requested it
is allowed to ask. We’re also doing conversational AI for
contact centers. For some tasks, this can almost replace
human agents.
If you have a chatbot and a good knowledge base behind
it, a language model can also help searchers find answers
faster. It’s not necessarily better—in the end it’s still a Q&A
database—but the answers are just more natural. And this
is something you can mask much better, for example, with a
large language model now than you could with a traditional
chatbot. You can have the LLM figure out the intent and
provide the answer. And now you can have the LLM create an
answer that’s coherent if you’re asking for more than one thing
and it identifies more than one search intent in this case.
Q: What makes the difference for a company that is
truly successful at using AI?
The main thing I often see missing with AI is direction
or purpose. You need buy-in and the guts to go through
with it. Yes, you can create models, but then you need
to maintain them. You need to have somebody who can
actually look after them. We’ve called this AIOps in the past.
Just because the model gives the right answers in a user
acceptance testing (UAT) environment, you still need to train
again and then validate. I think this often isn’t taken into
consideration—people think they have nine months of highly
trained and expensive people to build a model that will save

ser
acceptance testing (UAT) environment, you still need to train
again and then validate. I think this often isn’t taken into
consideration—people think they have nine months of highly
trained and expensive people to build a model that will save
them enormous amounts of money without having to look
at it again.
And again, it should not be a black box. You need to
understand the data that’s coming in and how the AI tool
gets to the output.
To put a cap on it, I’ve seen failures time and again where the
time-to-market was underestimated and the place where it
would fit within the rest of the business process just wasn’t
clear. It needs to map to clear value for the business.

appian.com   |    17
Q: What else should people know about the
future of AI?
I’m coming from delivery, so this is weird for me to hear, but
everyone is predicting that you won’t need to know things
anymore. AI will just build it for you. They think of AI as a black
box and don’t recognize the governance needed. You still need
an authority to understand what the output is and whether it’s
valid or not. We’re seeing this now time and time again.
I’ve played around a bit with generative models. I always get
a very confident answer.  I asked it how to update a certificate
in a certain application, and it was very confident about the
steps. But I think the steps were taken out of four different
versions and also two different tools that were actually used.
If I'm just trusting and don’t know what I’m doing, I would
have used the output and it wouldn’t have worked at all.
This is important—validation is such an important step.
There are SMEs for that—they can be on the business side or
in our case more on the development side, but someone needs
to understand what needs to be done.
We need to really consider this in terms of how much work can
actually be taken over by AI and not bank on just everything
within the next few years being completely AI driven. I think
it will solve a lot of problems, but there’s still specialized
knowledge required to verify and vet what is produced.

appian.com   |    18
Piyush Kumar, Global Head – Strategy,
Strategic Partnerships & Solutions, Wipro
Piyush Kumar has over 23 years of experience in the IT industry in cross-functional and global
roles. Currently, Piyush leads the Strategy, Partnerships, and Solutions function at Wipro's
Enterprise Futuring - Digital Experience business. Piyush is a digital strategist and technology
evangelist with extensive experience around emerging technologies that power customer,
employee, and partner experiences. He has a successful track record in building strong
relationships with customers and partners across different geographies and has delivered
some marquee programs around digital experience transformations in his previous roles.
Deeply passionate about the evolving landscape of technology, Piyush harbors an unyielding
fascination with gadgets and emerging tech that pushes the boundaries of innovation to
continuously enrich human experiences.
He has a bachelor’s degree in engineering from National Institute of Technology (NIT) Trichy,
India, and holds a certification of specialization in strategy from Harvard Business School
along with other certificates in strategy execution, sustainable business strategy, and
disruptive strategy.
Taking a strategic, top-down approach to AI
(and the importance of a strong data foundation).

appian.com   |    19
Q&A with Piyush Kumar.
Q: With AI being at the forefront of everyone’s mind,
how should we set our expectations?
AI is already everywhere. If you’re shopping online, for
example, you see recommendations on what you should
order. If you are on Netflix, you see suggestions. If you’re
communicating through email, there are spam filters and
auto-complete features. All of that is AI. I drive a Tesla, a
self-driving car. AI is already embedded or infused nearly
everywhere in some shape or form.
But we’re just scratching the surface. I think the potential
offered by AI to transform businesses is immense. I was
reading a paper from a top analyst firm sometime back, and
I remember they mentioned something like 30% of CIOs say
they’re already using AI, but only 10% say they’re using it
strategically, which means they’re probably doing some small
work but not using it across departments and processes. It’s
just not widely adopted yet.
ChatGPT has become a catalyst for AI. It’s important for
organizations to put AI first as part of their strategy. In the
past, organizations were selective about AI projects. Now,
you need a top-down approach to guide AI adoption across
the organization—it’s a large change the entire organization
must go through. I think organizations are preparing
themselves. ChatGPT pulled that trigger. Now everyone is
in reactive mode.
Q: You mentioned AI not being a strategic,
company-wide initiative but more tactical at this
point. What would you suggest for organizations to
use AI more strategically?
Well, when I’m talking about strategy, I mean you need to
start looking at an AI-first approach to the organization,
infusing AI into everything, where AI becomes the center of

hat would you suggest for organizations to
use AI more strategically?
Well, when I’m talking about strategy, I mean you need to
start looking at an AI-first approach to the organization,
infusing AI into everything, where AI becomes the center of
whatever you do. And at the crux of the full AI program is
data. If the data is not there, it’s garbage in, garbage out. If the
data is wrong, the AI will be wrong, too.
“Organizations must look at their whole
AI strategy first, put strong governance
in place, and look at different use cases
that can be easy wins for them in
different departments.”
Because AI is multifaceted, the technology can solve many
different problems. There’s discriminative AI, which can help
you classify or cluster things. It can classify and group things,
for example, spam email; it can group a set of news articles
or create a segment of customers. Then there’s predictive AI,
which can infer from data to make predictions and improve
your business decision-making.
Generative AI can help automate a lot of routine activities.
In the past, we used only RPA, which was rule-based.
But with AI, intelligent process automation comes into the
picture, which can use cognitive capabilities to implement a
broader range of activities. For instance, it can be used for risk
management to identify patterns and mitigating risks or in
fraud detection and remediation. AI has been applied in a few
industries like financial services and healthcare, but it’s not as
widely adopted yet as it could be.
Q: What advice do you have for organizations trying
to implement and operationalize AI?
It all boils down to the basics. You should have your data
strategy dialed in. The best organizations take a top-down

t as
widely adopted yet as it could be.
Q: What advice do you have for organizations trying
to implement and operationalize AI?
It all boils down to the basics. You should have your data
strategy dialed in. The best organizations take a top-down
approach, where the c-level buys in to the critical business
value and differentiation AI can drive. This may be easier to
achieve with the news hype and promise of generative AI in
the last nine months. Picking the right use cases to showcase
the value has always been key. Focus on early wins with
low-hanging opportunities while you build and invest toward
the bigger vision.

appian.com   |    20
But, again, you need the right data available to get the output
or results you want. How do you cleanse that data? How do
you prepare that data? How do you do feature engineering on
top of that? All that is critical for driving results and proving
the value with confidence.
The organizations that are leaders use an AI center of
excellence (CoE). This helps them set governance and drive
evangelization around what AI can do across the company. AI
is not great at everything, but AI can do quite a lot.
If businesses look at the outcomes they want, they’ll be able
to determine how data plays a crucial role in delivering that
outcome. Then, adoption becomes easier, and you get the
desired result.
Q: What’s counterintuitive to people about AI?
People need to understand that AI is not a magic box. It
can have flaws, especially in early stages. AI tools will have
instances where they hallucinate during use. This means
generative AI will make up content on its own that isn’t
based on real data. AI has a lot of shortcomings right now—
therefore, it’s important to know those shortcomings as you
design and create your own AI experiences.
I think generative AI is gaining momentum because ChatGPT
was made accessible to all. We all have the chance to interact
with it firsthand, showing people the potential use cases
and overall value in personal and professional situations.
I think people must understand that there are limitations,
especially in these early stages of generative AI. People who
acknowledge and work around those limitations do very well.
Those who don’t acknowledge the limitations end up with
chaotic implementations and get into trouble. Remember AI is
to augment human work and not to replace it. This realization

ple who
acknowledge and work around those limitations do very well.
Those who don’t acknowledge the limitations end up with
chaotic implementations and get into trouble. Remember AI is
to augment human work and not to replace it. This realization
is important to ensure that we use AI responsibly, ethically,
and transparently.
Q: Yeah, I can imagine it’s probably easier to get the
c-suite involved now, but I can also imagine a future
where people implement AI without knowing the
dangers you’re mentioning, causing people to push
back and doubt the capabilities of AI. Almost like an
overcorrection in the opposite direction. Does this
resonate?
Yes, and in fact, this is applicable to any technology. If you
win, you become a hero. If you lose, then it becomes a bigger
problem for everyone else. I think that’s why it’s critical to pick
the right business use cases from the start so that you show
successes, understand limitations well in advance, and do the
right thing for your organization.
“AI projects can fail. In fact, most AI
projects fail because they lack the right
governance and the right data.”
Another aspect, which I heard Matt Calkins from Appian
say, was private AI. Public AI is generally applicable to
almost all language models online. You have a ChatGPT
kind of interface. If someone from a bank wants to know
something and takes information and searches in something
like ChatGPT, that information becomes part of the training
set. This means that your competition can come and ask
anything about that bank, and they may end up finding
confidential information about that bank because somebody
has fed that data to train the model. Private AI is critical.
Most organizations will need to focus on private AI to
safeguard their data

appian.com   |    21
Akhilesh Natani, Managing Director and Co-founder,
Intelligent Automation, Xebia
Akhilesh Natani’s intelligent automation (IA) journey began in 2013 when he co-founded Appcino,
a prominent player in the intelligent automation arena. His leadership and dedication played a
pivotal role in elevating Appcino into a dominant force, recognized for its exceptional expertise in
the realm of intelligent automation. Subsequently, he orchestrated the successful acquisition of
Appcino by Xebia, headquartered in Atlanta, Georgia. At present, Akhilesh holds the position of
Managing Director within Appcino’s Intelligent Automation practice.
Under his guidance, the team has thrived, consistently delivering top-tier solutions to clients
spanning diverse industries. This achievement underscores Akhilesh's profound impact and
commitment to the field of intelligent automation.
Uncharted possibilities and the future of working
alongside AI.

appian.com   |    22
Q&A with Akhilesh Natani.
Q: What is AI’s role in the broader atmosphere of
automation (and by extension, productivity)?
In my opinion, AI is like a genie that can do wonders for the
organization and its people. Organizations are increasingly
turning to artificial intelligence to automate tasks done by
humans. For example, AI-powered chatbots can now answer
customer questions and resolve issues, freeing up customer
service representatives to focus on more complex tasks.
AI is also used to automate the development of custom
applications, making the process faster and cheaper. This
automation helps businesses and tech teams make software
that fits their needs perfectly. With just a few instructions,
AI can create the code for these applications, automating
much of the development process and saving a lot of time
and money.
As an associated benefit, AI is also being used to analyze data
and make decisions. For example, AI can be used to analyze
financial data to identify risks or to analyze healthcare data
to diagnose diseases. Generative AI has already been used to
design drugs for various uses within months (instead of four to
five years), offering pharma significant opportunities to reduce
both the costs and timeline of drug discovery.
As AI technology continues to develop, we are likely to
see even more ways in which AI can be used to improve
productivity. Though disruption is inevitable, I feel that this
could lead to fresh job creation. Understandably, there is
widespread concern about the impact of AI on jobs; however,
it is important to remember that AI is not a replacement
for humans. AI is a tool that could help humans to be more
productive and to do their jobs better. In the future, we are

there is
widespread concern about the impact of AI on jobs; however,
it is important to remember that AI is not a replacement
for humans. AI is a tool that could help humans to be more
productive and to do their jobs better. In the future, we are
likely to see a new breed of workers emerge, who are skilled in
working alongside AI. These workers will be in high demand,
as they will be able to leverage the power of AI to get their
jobs done smartly, delivering organizational goals faster.
Q: What are the main barriers to adoption for
organizations in terms of AI? And how do they
overcome these barriers?
Organizational barriers can hinder AI adoption. Those who
identify and remove hurdles early on will be poised for
success. The key is to identify the threats, then find solutions
that neatly define the responsibilities and preventive actions
for these threats.
Apart from the technology-related challenges that
organizations face, governance-related issues are the first
ones to address. This comes down to two points. First,
usually the projects aligned to corporate goals get more
attention from the organization, so it’s important to take
a portfolio approach to AI initiatives that maximizes the
benefits of AI while minimizing risk and ensuring alignment
with organizational goals. Second, organizations need formal
structure and accountability with a well-defined RACI matrix
for successful AI initiatives.
There are further risks beyond governance that organizations
must solve, too. First, regulatory. Organizations must be able
to continuously track and adhere to a continuously evolving
regulatory landscape. To navigate these waters, there must be
strong alignment between AI practitioners and the legal and

s
must solve, too. First, regulatory. Organizations must be able
to continuously track and adhere to a continuously evolving
regulatory landscape. To navigate these waters, there must be
strong alignment between AI practitioners and the legal and
security or risk teams to evaluate AI use cases and feasibility
with regulations in mind.
Second, organizations must deal with internal threats. Data
is critical, so teams must acknowledge that there are both
potentially malicious and benign actors within organizations
that could lead to security issues.
“Organizations must prioritize
data integrity and bolster their
organization-level security controls.”

appian.com   |    23
Third, it’s critical to align your AI strategy with your wider
technology investment strategy (such as your cloud strategy).
Make any adjustments necessary to minimize technical debt
over the long term.
Q: How do you think AI will change the landscape of
business—and how prepared are organizations for
these changes?
Organizations stand to gain significant benefits from
integrating AI into their operations, leading to both top-line
and bottom-line growth. AI enables them to streamline
processes, boost efficiency, and enhance risk management
and compliance measures.
AI can drive top-line growth by helping organizations better
understand their customers' needs and preferences. This
empowers businesses to offer personalized products and
services, leading to increased customer satisfaction and
loyalty. Additionally, AI-driven insights facilitate identifying
untapped market opportunities and emerging trends,
enabling businesses to make timely strategic decisions.
On the bottom line, AI optimizes operational efficiencies by
automating repetitive tasks, reducing errors, and maximizing
resource utilization.
AI also plays a crucial role in risk management and
compliance. By analyzing vast amounts of data and detecting
patterns, AI can identify potential risks and fraudulent
activities, enhancing organizations' ability to mitigate threats
and comply with regulations.
Some organizations have already embraced AI and
integrated it into their operations, while others are still
exploring its potential. The level of maturity depends on
factors like budget, access to AI talent, company culture, and
regulatory constraints. As one analyst claimed, only 10%
of organizations have achieved AI maturity. This underlines

re still
exploring its potential. The level of maturity depends on
factors like budget, access to AI talent, company culture, and
regulatory constraints. As one analyst claimed, only 10%
of organizations have achieved AI maturity. This underlines
the fact that the biggest challenge for organizations is the
competitive landscape. Those who successfully leverage AI
effectively differentiate themselves from the competition.
These leading organizations prioritize innovation and invest
in AI research and development, staying at the forefront of
technological advancements.

appian.com   |    24
Brendan McElrone, Managing Director, Deloitte Consulting LLP
Brendan McElrone is Managing Director, Deloitte Consulting LLP and brings nearly two decades
of deep technical experience to his current role. McElrone brings extensive subject matter
expertise in applied artificial intelligence and electronic system architectures. Prior to Deloitte,
McElrone served in VP-level engineering and AI leadership roles, leading teams of data scientists
and machine-learning experts to develop applied AI systems—including an enterprise-scale
natural language processing (NLP) project for the US Department of Defense. He has experience
with both large and smaller enterprises in prior roles at Lockheed Martin, Johns Hopkins University
Applied Physics Laboratory, and several self-started entrepreneurial endeavors. He has a
master’s degree in electrical engineering from Johns Hopkins University and an undergraduate
degree in electrical engineering from Virginia Polytechnic Institute and State University.
Industry maturity levels and the lesser-acknowledged
risks of AI.

appian.com   |    25
Q&A with Brendan McElrone.
Q: AI has had a breakout year, and it’s changing the
business landscape. How prepared do you think
organizations are for it? And where do you think it’s
making the biggest impact at the moment?
I agree AI has had a big year, and a lot has shifted with the
generative AI movement. But one thing I like to remind people
is that it is still AI, and fundamental to every AI problem is
data. When we talk about how prepared organizations are, a
driving force will be the maturity of their data ecosystem.
I think impact is going to come in several forms across the
entire spectrum. ome organizations have doubled down on
the data side of the house, which will provide longevity in this
accelerated technology evolution environment.
Q: Do you think certain industries are more mature
at the moment? In AI or on the data front?
I believe there are industries that are more mature, but those
industries may not be as forthcoming with that information.
Let's take the financial industry, for instance. I don’t have
direct insight into what happens behind the walls of those
institutions, but I would gather that because they have access
to a very large corpus of domain specific data, they have the
fuel to allow them to accelerate AI deployments.
“Broadly, it is difficult to say where
maturity levels are because
‘state-of-the-art’ seems to be
redefined weekly. I think we could
see industries accelerate maturity
seemingly overnight because of data
access. It is a fluid environment.”
Q: There’s certainly a lot of talk about the risks of AI.
What are some of the risks you see for organizations
that may go overlooked?
First is cost. Going back to data, if we’re talking petabyte

cause of data
access. It is a fluid environment.”
Q: There’s certainly a lot of talk about the risks of AI.
What are some of the risks you see for organizations
that may go overlooked?
First is cost. Going back to data, if we’re talking petabyte
scale, the cost model can quickly become astronomical. There
is a paradigm shift taking place toward smaller-sized models
trained on a larger corpus of data, and I think part of that is
hitting on cost model breakdown.
Additionally, I’d say access to infrastructure could be an
issue. Users may run into access issues due to things like
supply chain complications or demand. I often note how a
technology company in the social networking domain ordered
a billion dollars of GPUs this year.. That is just one entity in an
industry not directly related to work I specifically do. Normally
that may not be a concern but in this environment, everyone
is in competition for compute access. Will we have enough
hardware to support all metrics we’re trying to meet with
generative AI?
In addition to cost and access to infrastructure, I think more
broadly, impact. The environmental impact of all of this is a
very real thing that we can't understate. The hardware utilizes
a significant amount of power. What will be the global impact
of deploying these systems?
Q: With the emphasis on generative AI at the
moment, do you think there’s a risk of other AI
technologies falling by the wayside?
There is a land grab taking place right now. Market share
is disappearing by the minute, and I think the evolution
around generative AI is certainly taking all the headlines.
But I also feel there’s a convergence happening. The
underlying technology and infrastructure of these newer

ht now. Market share
is disappearing by the minute, and I think the evolution
around generative AI is certainly taking all the headlines.
But I also feel there’s a convergence happening. The
underlying technology and infrastructure of these newer
AI systems have similar architectures, and the imminent
evolution to multimodal models will blur the lines between
traditional AI fields.

appian.com   |    26
I believe we will need traditional AI expertise to fully leverage
the next evolution of generative AI tech. The roots of
generative AI that we know today have been around for some
time. Language models have existed for several years at this
point, but the mainstream aspect of generative AI is causing
a new wave of adoption. I look at the new tech as a very
powerful tool that can be used in a system of subsystems AI
architecture. We need to leverage other aspects of AI as we
have a client base that needs non-generative AI as well.
As Appian capabilities expand with this convergence,
customers will look to see how Appian generative AI will
further accelerate low-code development so they can get to
their ROI a lot faster. I think we’re seeing people who have
already adopted low-code and Appian who are looking for a
path to market accelerator.
Q: So, what are the gaps in the current
conversations around AI?
I think it’s being discussed, but the risks need more emphasis,
as they do for any emerging technology. Fraudulent AI use
is very real, and I think it will accelerate. Unfortunately,
sometimes it takes a significant event for the broader
mainstream to realize these risks and double down on the
fact we need to focus on them.
It’s difficult to say how we get out in front of that. The curious
engineer in me is always questioning and asking about
risks and limitations first. I firmly believe the benefits from
this technology will be staggering, but the edge cases of
potentially adversarial aspects are very real and need to be
thought out sooner rather than later.

appian.com   |    27
Hasit Trivedi, CTO Digital Technologies and Global Head – AI, Tech Mahindra
Hasit Trivedi is a seasoned expert with 25+ years of experience in the technology business and
industry recognition as a leading voice in the field of artificial intelligence. A tech evangelist
who has run businesses in a wide variety of niche technology areas like artificial intelligence,
intelligent automation, cloud, IoT, and more, Hasit has played key roles in technology innovation,
client delivery, product management, and product engineering and has experience building units
from scratch. Hasit is GPAI SME Invitee (Offshoot of G20 Summit) for AI/Data Governance, MeitY
task force member for IndiaAI, NASSCOM DeepTech Mentor as well as a member of various
industry and technology forums.
Currently, Hasit is the CTO, Digital Technologies and Global Head of AI for Tech Mahindra. He is
responsible for building technology-led businesses and platforms in the space of AI, analytics,
and intelligent automation and helping organizations to drive digital transformation. Prior to
Tech Mahindra, Hasit worked with Infosys for more than two decades. As a Global Head of AI &
Automation Services, he was responsible for building Infosys Services offering around the AI and
automation space, helping the organization incubate business in some of the niche technology
areas as well as in new geographies.
Hasit earned his bachelor’s degree in electronics and telecommunications from Devi Ahilya
Vishwavidyalaya. He is a Steering Committee member for: AI, Digital and Robotic Forum of
Confederation of Indian Industry (CII), Mentor & SME - AI & Automation: Women Wizard
Rule Tech (A Diversity and Inclusion Initiative) by NASSCOM.
Forging ahead into new markets with AI.

appian.com   |    28
Q&A with Hasit Trivedi.
Q: How do you think AI will impact the
business landscape?
AI is pervasive. It must be infused in every aspect of a
business, be it a business model, the processes that define
it, the applications that deliver it, or the infrastructure that
supports it. It’s the first time that we have a technology that
can see, read, speak, and listen like humans. So far AI has
been able to predict, recommend, detect, and converse,
and now it can create as well thanks to generative AI! This
transformative capability positions AI as a digital assistant to
humans, greatly amplifying human potential.
Generative AI will cause disruption as well as amplification.
While some jobs and functions will get disrupted significantly
due to the technology’s extreme efficiency gains, many other
roles will be amplified by deskilling complex human skills
and, thus, unlocking faster value in businesses. I will say, for
industries involved in transportation, packaging, and logistics,
there may be a reduced need for human labor, as AI and
robotics can efficiently perform these tasks.
“Like any other technology, the
adoption of generative AI varies
among companies. Some are quick to
embrace it, while others adopt a
cautious ‘wait-and-watch’ approach
to assess its actual impact on
their operations.”
Q: How should companies go about implementing
AI to get their best results?
At Tech Mahindra, we refer to the AI journey for clients as
amplifAI. Under the same term, we have created all of our
AI offerings and solutions. The term “amplifAI” conveys our
philosophy and belief that AI is pervasive, and it has incredible
power to amplify human capabilities.
We also focus on our terms “Zero Ops to Infinity Ops”

m, we have created all of our
AI offerings and solutions. The term “amplifAI” conveys our
philosophy and belief that AI is pervasive, and it has incredible
power to amplify human capabilities.
We also focus on our terms “Zero Ops to Infinity Ops”
(0 → ∞), which signifies how enterprises can derive value
from the full spectrum of AI applications. We help our clients to
aspire for Zero Ops, which refers to driving extreme efficiency
and effectiveness in business processes that enable zero
disruption, zero errors, zero touch, and zero latency. We also
assist clients to aspire for Infinity Ops to solve the problems of
the future, which are hitherto considered “un-solvable” due to
some human dependency or technology limitations.
Zero Ops is where most of the money goes today because the
savings lead to better capital expenditures (CapEx). It funds
itself, and this is what businesses prefer to do right now. But
there are ideas for Infinity Ops where the businesses must
commit CapEx to see first whether they will work or benefit
them. I think businesses need to adopt the mindset of taking
bold steps to take on new business models, products, and
experiences, which lands on the Infinity Ops side. Businesses
can spend the majority of effort, time, and energy on Zero
Ops, but some selection of bandwidth and money should go
into the Infinity Ops category. This will help businesses stand
out to customers beyond profitability and efficiency gains.
Q: I really like these concepts and their distinction.
When it comes to Infinity Ops, can you give an
example of this? How would someone enter into
new business opportunities with AI?
I would love to share an example of how at Tech Mahindra
we successfully ventured into a new revenue stream by

tion.
When it comes to Infinity Ops, can you give an
example of this? How would someone enter into
new business opportunities with AI?
I would love to share an example of how at Tech Mahindra
we successfully ventured into a new revenue stream by
leveraging innovative approaches. We were not in the
business of field services for power and utility companies.
Typically, these companies have transmission assets like
poles, towers, and wires through which electricity flows,
and there is a business process for inspecting these assets.
This inspection task was traditionally handled by specialized
field engineers from dedicated service providers, making it
seemingly inaccessible for a technology-focused company
like Tech Mahindra.

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To overcome geographical limitations and reduce the
need for field engineers to physically visit these assets,
we implemented cutting-edge technologies, drones, and
helicopters to capture image data. Instead of sending field
engineers to the assets, we started bringing the assets to our
engineers virtually, eliminating the need for travel or physical
access. Then, we integrated AI into our operations to enhance
problem detection. Initially, we relied on the expertise of field
engineers to train our AI models, hiring a select few to assist in
the process. Today, our AI systems autonomously handle the
detection tasks.
Before our intervention, our customers conducted audits
only once a year, with field inspections covering a mere
10% of their assets due to logistical challenges posed by
remote locations and difficult terrains. Now, we provide
comprehensive coverage for 100% of their assets, conducting
four inspections annually, all at a more cost-effective rate. This
exemplifies the potential of technology-driven strategies to
enter new business domains successfully.
Q: So, what do you think are the main failure points
around AI implementations right now?
The success of a project hinges on obtaining management's
alignment with the strategic plan. Ideally, this alignment
should stem from a top-down approach, unless there are
remarkable instances of effective bottom-up leadership.
Although a bottom-up approach may occasionally yield
positive results, the consensus is that a top-down strategy
tends to be more effective.
A pivotal factor in achieving success is the formation of
a cross-functional team to tackle the project. At times,
technology becomes the most important thing in projects,

lts, the consensus is that a top-down strategy
tends to be more effective.
A pivotal factor in achieving success is the formation of
a cross-functional team to tackle the project. At times,
technology becomes the most important thing in projects,
and that often leads to exclusive involvement of technical
experts: for instance, AI-driven projects where data engineers
and data scientists dominate the scene. However, this may
lead to disappointing outcomes. For example, many websites
have chatbots now, yet only a fraction truly deliver. Why?
Because they are often conceived and developed by technical
specialists rather than individuals who possess a deep
understanding of effective communication with consumers
or personas. Building chatbots is easy, but making ones that
people can use easily is hard.
Q: OK. So what’s something that people really need
to key in on? Maybe something that’s flying under
the radar about AI that needs more prominence
in discussions?
I'd like to highlight two key points. First, we avoid
acknowledging the profound influence that AI will exert on
employment. While it promises substantial transformative
effects, it is crucial to communicate this impact delicately to
avoid unsettling individuals. Although we are empathetic
and understand these concerns, it is essential to accept that
certain occupations will undergo significant changes.
The advent of AI is expected to give rise to a multitude of
new job opportunities. It is anticipated that AI will generate
approximately three to four times the number of jobs currently
in existence, mirroring the historical pattern accompanying
technological advancements. However, it is imperative to
provide support for those whose professions will be most

generate
approximately three to four times the number of jobs currently
in existence, mirroring the historical pattern accompanying
technological advancements. However, it is imperative to
provide support for those whose professions will be most
affected, facilitating their transition through reskilling,
refactoring, and the identification of new career paths.
The second point is that, while we discuss the impressive
capabilities of generative AI, we tend to ignore its potential
dangers. Ungoverned use of this technology can create turmoil
in society through fake narratives and fraudulent activities
using generative AI. For example, it might lead to crime, as
voices can be replicated or narratives altered. The speed at
which this can happen might cause significant unrest. While
this was possible in the past, it was quite resource intensive.
With current technology, these actions can be carried
out rapidly.
Humanity has shown wisdom with certain technologies. For
instance, we harnessed nuclear power, but we established
safeguards to ensure its safe usage.
“I believe that, over time, both
technology creators and regulators
will implement enough measures to
ensure responsible use. Eventually,
everything will align as it should.”

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Q: What do you think we should do about
these dangers?
Key ecosystem players need to play critical roles in AI
governance. Regulatory bodies need to play their role, and
technology creators need to bring tech interventions to ensure
the technology is safe, secure, and suitable for work, including
addressing aspects like copyrights.
Also, the people implementing this technology—companies
like ours at Tech Mahindra or any other service provider—
must recognize their own roles in this. They know how this
technology works and must safeguard it.
For example, if certain data is not to be used for a particular
purpose, they must put in technical safeguards to prevent this.
If output can be harmful or offensive, they must filter that out.
If malicious content comes out, they must block that as well.
Yet, it’s essential to remember that while AI can create issues,
it can also resolve them. It’s like an antidote. While AI can lead
to cyberattacks, it can also defend against them. Technology
can cause disruption, but it can also provide protection.

appian.com   |    31
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