Welcome back everybody.
Ready to dive into more AI goodness.
Today, it's all about generative AI
and how it's really shaking things up
in the business world.
Yeah, it's getting real now.
Definitely.
And we've got two really interesting reports
to kind of unpack and look at.
We've got the Deloitte state of generative AI
in the enterprise report and then some insights
from this MIT Technology Review Insights report
called the Great Acceleration,
CIO Perspectives on Generative AI.
And the interesting thing here is that
we're past the hype phase.
AI is not just this shiny new toy anymore.
It's like companies are really,
they're starting to demand results, right?
They're looking for how is this actually gonna
help my bottom line?
How's this gonna, you know?
It's like the honeymoon's over, right?
Exactly, yeah, the honeymoon's over and it's like, okay.
Show me the money.
Show me the money, yeah, exactly.
And both of these reports are kind of hinting
that while there's a ton of enthusiasm,
obviously tons of investment going into this area
actually scaling up those AI projects,
it's proving to be a real challenge.
Yeah, it's that gap between the potential
and the actual rubber hitting the road, so to speak.
Exactly, and the Deloitte report actually found
that a whopping 67% of companies are increasing
their investments in generative AI.
But here's the catch, almost 70% haven't even been able
to move 30% of their AI experiments into full production.
So like everyone's excited about it,
but then actually getting it to work, you know?
Yeah, we can get real.
At scale.
Yeah.
That's the tough part.
Yeah, that's a hurdle.
And I think the MIT report gives us some good perspective
here because it seems that even before we had this whole
explosion of generative AI, a lot of businesses
were already facing hurdles when it came to integrating AI,
any kind of AI to their core operations
and generative AI just kind of like amplified
those challenges.
So it's not just a generative AI problem,
it's an AI problem.
Yeah, it's a bigger AI adoption problem,
I think that's right.
And what is it they're saying?
The reports are saying data is a big part of that?
Yeah, the lack of the right kind of data,
I think that's a huge piece of it.
And the Deloitte report actually mentions
that something like 75% of organizations
have increased their investments in data lifecycle
management specifically to support
their generative AI initiatives.
OK, so data lifecycle management now,
for those of us who aren't data scientists,
break that down, what does that even mean?
Yeah, sure.
So think of it as the entire journey of data.
Within a company, it's everything
from how you collect it, how you store it, how you clean it,
how you analyze it, and then ultimately how you use it
to actually make decisions.
So it's not just about having a lot of data,
it's about having the right processes and systems
to manage that data well.
Yeah, exactly.
And this is where it gets really tricky for a lot of companies,
because these generative AI models,
they're just incredibly data hungry.
They need high quality, well-organized data
to actually function properly.
It's like trying to bake a cake without the right ingredients
or knowing how to use them properly.
You just end up with a mess.
Yeah, that's a great analogy.
It is like that.
And the consequences can be pretty significant too.
I mean, the Deloitte report found that over half of the companies,
they surveyed 55% admitted to actually avoiding certain AI
projects altogether just because of all these data roadblocks.
Yeah, that's a big number.
So data is a big bottleneck.
But then what about the risks of AI?
Right, we hear about algorithmic bias, data privacy, all of that.
Yeah, that's a key point.
And it's something that both reports address,
specifically the need for really strong governance frameworks
to help mitigate those risks.
OK, well, let's unpack that a little bit.
Because what does governance, when we're talking about AI,
what does that actually mean?
Yeah, so think of it as this set of rules, processes,
guidelines that companies put in place to make sure
that their AI systems are developed and used responsibly.
And that includes things like protecting data privacy,
preventing algorithmic bias, and really making sure
that those AI systems are aligned with the company's values
and ethical principles.
So it's not just making sure that the AI works,
it's making sure it's working in an ethical way.
That's right.
And it gets interesting here because there's often this gap
between recognizing that you need governance
and actually feeling like you're prepared to do it.
Like, OK, I know this is important.
Yeah, exactly.
And the Delay Report found that only 23% of companies
felt like they were highly prepared to kind of deal
with the governance challenges.
So there's that disconnect.
We know we should be doing it, but how do we do it?
Exactly.
And this is where I think the MIT Report is really valuable
because it highlights how important it
is to have clear leadership and this culture of responsible AI
within an organization.
So it's not just about the policies.
It's about having the right people and the right mindset.
Yeah, that's right.
And that requires a real shift in things,
not just within the tech teams, but at every level
of the organization.
It sounds like a lot of companies still have some work to do.
Yeah, they do, but it's also a huge opportunity, right?
The companies that can figure out
how to balance that power of AI with the need
for responsible ethical development,
those are the ones that are going to thrive in this new era.
That's a great point.
It seems like the future of AI success
isn't just about having the coolest algorithms.
It's about having the right approach to governance
and ethics and data management.
Yeah, it's all of those things together.
It's a whole package.
Exactly.
It is a whole package.
And it's those factors, I think, that are really
going to determine which companies can truly
harness this transformative power of AI.
OK, this is a great place to kind of pause and reflect
for a second.
We've uncovered some really interesting insights, I think,
about the state of AI adoption.
Yeah, we've got a lot to think about.
In the enterprise.
So let's take a quick break and we'll come back and explore
some of these ideas even further.
Welcome back.
We were talking before the break about that tension
between the excitement for AI and then the very real challenges
companies are facing in terms of implementing it responsibly
at scale.
Yeah, and one of the biggest things
is just the sheer cost and complexity
of actually developing and deploying
these large language models that are really
the engine behind a lot of these generative AI tools.
And the MIT report highlighted this, right?
You talked about just the enormous resources
required to train these massive models.
It's insane.
It's not something that everyone can just jump into, right?
No, not at all.
I mean, we're talking about millions of dollars
in computing power and energy consumption
just to get these models up and running.
And even then, there's no guarantee
that they're going to perform exactly as expected.
So for companies that don't have those kinds of resources,
what are their options?
Are there other ways to get into this game?
Yeah, I mean, the MIT report seems
to suggest that maybe smaller, more specialized AI models
could be a viable option.
Interesting.
Yeah, so the thinking there is that instead
of trying to create this one gigantic model that
can do everything, you focus on building
smaller, more targeted models that are specifically designed
for particular tasks or industries.
So it's like having a team of specialists
instead of one generalist who's trying to do everything.
Exactly, yeah.
It's a great analogy.
And actually, these smaller, more focused models,
they offer a number of advantages.
For one thing, they're a lot cheaper and faster to train,
which makes them much more accessible to a wider
range of companies.
That makes sense.
But are these smaller models really powerful enough?
I mean, can they actually compete with the big guys
like ChatGPT and all that?
Well, you might be surprised the report actually
highlights a really interesting example.
This model called Dali, which was developed by Databricks.
It's an open source model that was trained for less than $30.
Wait, less than $30?
Yeah, less than $30.
And it's showing some pretty impressive capabilities
that actually rival some of the bigger, more expensive models
out there.
That's amazing, especially when you compare it
to the millions we were talking about before.
Yeah, it's crazy.
And that's why these open source models like Dali
are generating so much excitement.
They have this potential to really democratize access
to AI so that businesses of all sizes
can start experimenting with it and benefiting from it.
But going the open source route, you're
kind of giving up some control.
Like companies are very protective of their data,
their intellectual property.
Yeah, that's a valid concern.
And it's something that both reports kind of acknowledge.
There's definitely a trade-off to consider
between using these pre-trained models
that you get from third party providers
versus building your own right.
Because with a third party model,
you might have concerns about data security
limited customization options or even potential biases that
are baked into the model itself.
So building your own gives you more control,
but it's more expensive and it's more complex.
Exactly.
It's a tough decision for companies to make.
And as the MIT report emphasizes,
even if you do decide to build your own model,
you still need the right infrastructure to support it.
You need powerful hardware specialized software
and a team of skilled engineers to kind of manage it all.
So it's not just about the AI.
It's about building this whole ecosystem to support it.
It really is.
And both reports talk about these things,
kind of data lake houses as being a key part
of that infrastructure.
OK, data lake houses.
What are those?
So think of it as like the centralized hub
for all of a company's data.
It's like a supercharged data warehouse
that can handle both structured data,
like sales figures, customer demographics,
and unstructured data, like, you know,
tech documents, images, videos.
So it's a more modern way to manage all of the different types
of data that companies are collecting.
Yeah, exactly.
And that flexibility is really crucial when it comes to AI
because these models, they need to be able to access and analyze
a wide variety of data to learn and improve.
So it really seems like data is the foundation for all of this.
Yeah.
I mean, without the right data, even the best AI model
is just going to flounder.
Oh, absolutely.
I mean, you can have the most brilliant AI algorithm
in the world.
But if you don't have the right data to feed it,
you're not going to get very far.
And it's not just about having the data.
It's about being able to clean it, organize it,
and make sure it's accurate.
Yeah, if you put garbage in, you're
going to get a garbage out.
Exactly.
Garbage in, garbage out.
And that kind of brings us to another important aspect
that both reports touch on, which is the human element
of all of this.
That's a big one.
Yeah, because there's a lot of anxiety out there
about AI and automation.
People are worried about jobs being displaced.
Yeah, for sure.
What are the reports saying about that?
Well, I think both reports, they acknowledge those concerns,
but they offer, I think, a more nuanced perspective.
They're suggesting that instead of viewing AI
as this job killer, we should think of it
as a tool that can actually augment human capabilities.
So it's about freeing us from these tedious tasks
and allowing us to focus on higher value work that
requires creativity and critical thinking
and emotional intelligence.
So not AI replacing humans, but AI working alongside humans.
Yeah, exactly.
Making us more efficient and effective.
And the experts that were interviewed for the MIT
report, they really emphasize this point.
They argue that AI is not about replacing human judgment
or expertise.
It's about providing us with the tools and insights we need
to make better decisions and solve complex problems.
OK, so that's a more optimistic view.
Yeah, for sure.
And can you give me an example of what
that might look like in practice?
Sure, so let's say you're in the health care industry.
AI could be incredibly helpful in analyzing medical images,
identifying patterns that maybe a human eye might miss,
but it's never going to replace the expertise of a trained
radiologist who can interpret those images in the context
of a patient's history and symptoms.
So the AI is like an assistant in a way.
Exactly.
It's providing information that can help the doctor make
a more informed decision.
Yeah, that's right.
And this idea of AI as a co-pilot rather than a replacement,
that's a theme that runs through both reports.
I mean, for instance, in the creative industry,
AI can help designers generate new ideas
and automate repetitive tasks, but it
can't replicate the human spark of creativity that's
so essential for truly innovative work.
So it's about finding that balance between the human
and the machine.
Yeah, exactly.
Leveraging the strengths of each to get the best possible
outcome, and that requires a shift in mindset,
both within organizations and in society as a whole.
We need to start thinking about AI not as a threat,
but as a tool that can help us solve some of the world's
most pressing challenges.
It's about embracing the potential,
but also recognizing the limitations.
Exactly, and making sure that humans stay
at the center of the equation.
Right at the heart of it.
And that brings us back to this crucial issue of governance
and risk management.
Yeah, we can't forget about that.
Right, because we talked earlier about the need
for these clear governance frameworks
to make sure AI is being developed and used responsibly.
But what does that actually look like?
Yeah, I mean, both reports emphasize
several key elements.
First, there needs to be clear accountability
within organizations.
Somebody needs to be responsible for making sure
that AI systems are developed and used in a way that
aligns with the company's values and ethical principles.
So someone to steer the ship.
Yeah, someone at the helm.
And that person needs to have the authority
to make decisions and enforce policies related to AI.
And the second key element is transparency.
Companies need to be open about how they're using AI,
what data they're collecting, and how they're protecting
that data.
Transparency builds trust.
It does.
It helps to alleviate some of those concerns
that people have about AI being used in ways
that are kind of opaque or potentially harmful.
Right, so it's about being upfront about it.
Yeah, and finally, there needs to be a robust process
for actually identifying and mitigating
the risks associated with AI.
And that includes things like conducting
thorough ethical reviews of AI systems before they're deployed,
monitoring their performance over time,
and having clear protocols in place
for dealing with any unintended consequences or biases that
might emerge.
So it's about being proactive rather than reactive.
Exactly, anticipate potential problems
and have planned to address them before they become major issues.
And this sounds like an area where a lot of companies
are still trying to catch up.
Yeah, you're right.
The Deloitte report found that while most companies recognize
the need for this robust AI governance,
very few feel adequately prepared to actually implement it.
They know it's important, but they don't know how.
Figuring out how to do it is the next step.
Exactly, there's a lot of work to be done
in terms of developing the right frameworks and the tools
and the expertise to really manage AI responsibly.
And we can't forget about the evolving regulatory landscape,
too.
The MIT report mentions the EU's AI Act, which
came into effect earlier this year.
And there are likely to be more regulations
as governments around the world grapple
with all the implications of AI.
Right, so companies need to stay informed about all of that.
They do.
They need to stay informed.
And they need to be prepared to adapt.
You know, to all the changes.
Exactly.
It sounds like navigating this AI world is a real balancing act.
It is.
You've got all this amazing potential,
but then all of these risks that you have to consider.
Yeah, you hit the nail on the head.
It's a balancing act that requires
a thoughtful and proactive approach.
Companies can't just sort of rush into this head first
without thinking about the potential consequences.
And that's a great place to pause.
Now we've covered a lot of ground in this deep dive.
We're back and ready to kind of wrap things up.
We've been talking about generative AI in the enterprise.
And before the break, we were really
focused on that critical need for companies
to establish those strong governance frameworks
as they're adopting AI.
Yeah, it's one thing to say, you know,
responsible AI is important.
But how do you actually put it into practice?
Exactly.
How do you actually make it happen?
Yeah, and the Deloitte Report gives us
some pretty practical guidance here.
OK.
So they really stress the importance
of getting buy-in and oversight from the very top.
You know, the board, the C-suite,
this can't just be like an IT initiative that's off in a silo.
It needs to be a strategic priority
that everyone's on board with.
So AI needs to be aligned with the company's overall goals
and values.
Yeah, exactly.
And there needs to be that sense of ownership
and accountability at every level.
The report also suggests establishing
these cross-functional teams to kind of lead the charge when
it comes to identifying and mitigating the risks associated
with AI.
And this is really make sure you're
getting diverse perspectives and that you're
thinking about the ethical and societal implications.
Right, because AI touches so many parts of a business,
you need a team with a lot of different expertise
to really assess that impact.
Yeah, absolutely.
And this team should be empowered to proactively identify
and address potential risks, not just react to problems
after they've already happened.
Right, so proactive, not reactive.
Exactly.
And another key recommendation from the Deloitte Report
is to actually appoint a single executive
to oversee all AI-related risks.
So you have that one person who's accountable.
Yeah, AI risk office.
Yeah, exactly.
Someone who's specifically responsible for keeping
an eye on the regulations and making sure
the company is compliant.
Having that dedicated person could be a really good way
to streamline things and make sure that those governance
policies are actually happening.
Yeah, I think so.
And the MIT report adds another layer to this conversation.
They emphasize that governance isn't just
about having the right rules and regulations on paper.
It's also about building this culture of responsible AI
within the organization.
Right, it's about education and awareness,
making sure employees understand the ethical issues
and feel empowered to speak up.
Exactly, and this culture of responsible AI,
it should extend beyond the company itself.
It should include suppliers, partners, even customers.
It's all ecosystem thing.
It is.
It really is.
And ultimately, I think it comes down to trust in the technology,
trust in the companies that are developing and using it,
and trust in the people who are interacting with it.
And the trust is so important as AI
becomes more and more a part of our lives.
Absolutely, and that kind of leads to another key takeaway
from both reports, which is this need for ongoing dialogue
and collaboration.
The field is moving so quickly.
The ethical and societal implications
are constantly evolving.
So it's not about finding a one-size-fits-all solution.
It's about having that culture of continuous learning
and adaptation.
Right, always be learning.
Exactly, and this conversation, it
needs to include everyone, academia, government,
industry, civil society.
We all need to be working together
to make sure that AI is used for good.
Right, that it benefits everyone.
Exactly.
Well said, and I think that's a great place
to wrap up our discussion today.
I mean, we've covered a lot of ground.
We have.
From the incredible potential of generative AI to the importance
of responsible development and governance.
Yeah, we've gone deep.
We have.
We've gone deep.
I think it's clear that we're still
in the early stages of this AI revolution.
Oh, yeah, for sure.
There's a lot that we're still figuring out.
But it's exciting to see that we are
starting to have these important conversations about how
to make sure that AI is used for good.
Absolutely, and I think the companies that
are really going to succeed in this new era are the ones
that embrace the power of AI, while also being realistic
about its limitations and prioritizing
ethical development and responsible use.
And those that can build that culture of trust
and transparency and collaboration,
both within their own company, and then the wider AI ecosystem.
Yeah, that's key.
Well, thank you for joining us for this deep dive
into generative AI.
I hope you found it insightful and thought-provoking.
And until next time.