Hi Sesh, thanks for joining us.
When it comes to GenAI,
There are tech challenges that business leaders face.
What are they?
I see many tech challenges.
Let me just start with three of them.
And and there are others.
I think the first is just data quality and availability.
A lot of GenAI
is driven by underlying data quality, availability, how you
actually go to the right sources of data, how you develop the
right pipelines, how you implement the right data
governance.
These are things that have to be put in place to be able to
extract insight.
I think that's one, and that's a real area of focus.
I think a second thing is there are so many different failure
modes when you implement
GenAI solutions.
Hallucinations, prompt injection and jailbreaking, degeneration
of models, catastrophic forgetting of models, privacy
violations, these are all things that have to be put in place.
LLM bias, fair use of of LLMS, all of these start to become
challenges that you have to overcome when you're deploying
at-scale solutions.
That's the second thing that I see.
The third thing is just human capital and scarcity of talent.
We are clearly seeing most organizations having to upskill
or reskill their people to be able to put these people against
GenAI work.
46% of people, based on the survey that we did recently, in
these organizations on average need to be upscaled over the
next three years.
Then there are of course a broader set of challenges like
legacy integrations, we talked about security.
There is a lot that needs to be done around the explainability
or interpretability of the results that these models give you
So there are a host of other things that need to be overcome.
But the nice thing is we are starting to find solutions.
We're able to actually address these issues and get solutions
to scale.
And are there opportunities, tech-driven opportunities, that
business leaders are overlooking, I imagine?
We did a recent survey and we surveyed 1400 executives.
The big thing that came out of that is there's only one in ten
executives that are actually pioneers.
Nine out of ten are actually waiting and watching, to the
first question that you asked.
And maybe the way I answer the question is, what are the things
that the pioneers are doing well?
The first thing is they're massively ambitious.
So when they think about investing in GenAI
for top line and productivity, their ambition is 1.3 to 1.4
times higher than what we see [in] folks who are not ready to invest.
I think the second is around upskilling their talent.
There are close to, you know, 60% of these executives who
think that their senior leadership is not ready to
actually capitalize
on GenAI solutions.
So upscaling is a massive opportunity.
A third one is really understanding the cost base of
these GenAI
solutions, what is the cost to actually implement the
solutions, direct costs and indirect costs and really
understanding the detail of what these costs are going to be over time.
That's the third one.
I think the fourth is around partnerships and ecosystems.
Many of these pioneers are investing in really deep
partnerships, building out their ecosystems.
And then the last one I would say is when you develop these
GenAI solutions, you have to do it with responsible AI principles.
We still see only, you know about 17% of organizations have
responsible AI on the CEO's agenda.
So these are the things that the winners are doing.
I think the observers need to learn from this and start to
really focus on these five things.
It's a really overwhelming issue.
Sesh, thank you for breaking it down for us.
Thank you for having me.
This is great.
Good talking to you.