In a world where generative AI promises endless possibilities,
how do you navigate its limitations in risk averse environments
like law, finance, or medicine?
Today's guest shares why it's not just about using AI, but about knowing where
and how to use it, especially when the margin of error is razor thin.
My name is Alexandre Nevski, and this is Innovation Tales.
Generative AI is changing the game in many industries, but in certain
areas where precision is critical, it's not as simple as jumping in.
Today's guest, Sam Gobrail, has over 15 years of experience in
making digital tools work for businesses and government agencies.
His achievements include improving customer journeys for Fortune 100
companies and using AI to cut federal processing times from hours to minutes.
In this episode, Sam shares how to implement generative AI safely and
strategically in complex environments.
Whether you're rolling out AI across your organization or just starting
to explore its uses, you'll find practical advice and fresh perspectives.
Without further ado, here's my conversation with Sam Gobriel.
Sam, welcome to the show.
Thank you.
Thanks for having me.
Excited to talk today.
Well, thanks for making the time.
I love these opportunities to talk with fellow digital leaders.
today you and I, we agreed to dive into a topic with which you have
significant experience, and that's the challenges of leveraging generative
AI in risk averse environments.
So to help our audience get their bearings, would you mind starting us off
with a couple of examples of risk averse environments in an enterprise context?
Absolutely.
I'll start with one that's close to my heart.
I'm an attorney by training, so I went to law school and I still do
a few pro bono cases on the side.
So the legal field comes to mind as one that is very much risk averse.
And when we're talking about risk averse, we're talking about
the fault tolerance, right?
The margin of error on whether you have 100 percent correct information
or 95 percent correct information.
that gap in places like the legal field is simply not acceptable, as opposed
to, for example, you and I researching your next vacation, and a generative
AI model says, "oh, this place is $500 and it ends up being $550".
That's probably okay.
and I think those are the differences we're talking about.
so The legal field certainly comes to mind.
Others that also come to mind include, tax professionals, and some parts of
the medical field, depending on how severe and urgent those items may be.
Right, let's dive straight into it.
What makes generative AI so tough in such environments?
Yeah.
So generative AI, has been such a great tool.
It's taken the world by storm.
I use it.
I think you use it.
We all kind of use it to some degree.
But we can't mistake it for what it truly is, right?
It is an algorithm predicting the next likely word and it comes
up with great responses, but it's not a peer of yours, right?
If I'm an attorney, you're doing research for a legal case.
It's not the equivalent of my peer thinking along with me or one of my
employees doing the research and writing.
and so it's prone to make mistakes, right?
some of them are called hallucinations where it sometimes just makes things up.
Those have improved for sure.
And it doesn't always have the full breadth of knowledge, right?
The context that we as humans may pick up, either from non text context, right?
A phone conversation with the client, the relationship I've had with the patient.
those kinds of things that may add important context to make an
informed, professional decision.
it lacks those things as well.
So that's the space we're talking about.
Right.
I think One way that I tend to think about this is like, from the
point of view of the organizations, the businesses that we serve.
Can you think of like how you would compare and contrast, working with clients
that may have already had experiences with other artificial intelligence approaches,
but more traditional machine learning.
And now they're considering generative AI.
What kind of aspects do you highlight for them at the beginning of their
journey so that, you know, to, to, to lay a solid foundation?
Yeah, I think that is, a great question.
some of the large organizations, many of the large organizations, right,
have used predictive modeling, right?
Traditional AI, right?
For things like financial planning, forecasting, resource planning, right?
These things that require a lot of data.
The trends are pretty reliable, right?
So what happened over the past 20 years is not a guarantee, but has a high
likelihood of happening next year.
And those are well proven, well, well used.
I think where it comes tricky.
Just sticking with finance for a second.
Finance has used predictive modeling for a long time, forecasting
models, all of those things.
Would they, a professional, finance professional, trust a generative AI
model today to write their SEC file?
Governmental agency financial filing that has to get published.
I don't think so, right?
Because their job is literally on the line when they create that document.
And so this is the nuance where an enterprise has opportunities to use
generative AI at enterprise scale for really great things, but either they
have to appreciate the time investing to review and really comb through the output.
And therefore, it's not a fully-backed solution.
It's a half baked solution and that's okay.
It still saves time and energy and that's great.
Or, they have to identify.
Here are the core items that are the reason we get paid
what we get paid, right?
So back to the attorney, right?
The legal opinion is the reason that a client hires them.
But there are many other parts of their business, that we could call admin or
bureaucracy, whatever you want to call it, that the margin of error is acceptable.
The emailing and accounts payable, right?
And the filing system, like there's a lot of other things going on that you
do have a higher risk tolerance for.
And so parsing out what is the core skill set?
I have very low risk tolerance here.
And what are the kind of peripheral items?
I have a higher response and I can actually gain a lot of efficiency
and productivity to focus on why I do this job or why we're in
this business in the first place.
Yeah, that's true.
Another way to look at this is that with generative AI, you can imagine
automating quite a few more tasks than with traditional machine learning
approaches where you would be classifying, you know, helping making decisions,
but you'd need to be very clear about, this is the task, where this
is the decision we need to be making.
This is the data that we are going to use to train the algorithm.
With, you know, the new breed of tools, we can think of a million
different ways where we can apply, but that's, I think, what you were saying,
I totally agree with it, is that, yeah, you, theoretically can, but in
practice, there are some challenges.
And I think that's what we want to explore more today.
And so I wonder, Sam, if you can share with us an example of a
project of deploying generative AI in such a risk averse environment
that we can sort of explore together.
Sure.
so one of the, one of my, my former clients, that, that, we're good friends.
One of the things we had been talking and working on together, is this idea again
around the legal field, for example, there are, two, two examples, one at the very
kind of enterprise level, and one that has a lot of repeatability, but could
apply to even smaller entities, right?
Smaller firms.
the smaller one are very basic filings, right?
So imagine if you have an outdated system.
In the legal system, fairly outdated most of the time, but
you have a lot of filings, right?
And those are essentially templates, right?
You have a one page or a two page document and you're replacing
maybe 10 words, 12 words, 15 words, maybe putting a blurb, a paragraph.
That's a perfect space for generative AI, right?
It is not the core part of a legal case.
It is, I need to do this kind of thing.
It looks nearly the same every single time, but that's 20 minutes of time,
that I could be using to something else.
so those kinds of, again, back to even within the legal work itself, What is
the core and the complex and what is the mundane, repeatable, you can turn
into a template, and you can use the facts of what's going on to fill in
that paragraph or two that's a summary: hey, we're filing this kind of thing.
At the enterprise level, we had talked about, and this goes back to a slightly
older concept that I think is coming back again, around like smart contracts.
So this is not smart contracts, but the idea of large corporate contracts.
So, whether those are contracts for digital products, license
agreements, commercial agreements.
Very lengthy, very long.
And managing every version of those is a complicated endeavor, right?
So imagine if you have a hundred vendors, a hundred different commercial agreements.
What is clause number 15 say for vendor C. That gets messy quickly.
So, similar concept here, right, around how do you use generative
AI to say, "Hey, this is the way I want to structure my contracts".
Assess how different things are, but also create the next one.
Cause I've already given you my standard clauses, right?
My standard language.
And what you're going to change is maybe client name, industry, date, the
kinds of things that, again, you or I would go into a template and modify.
But it also then pulls that data to tell you how fractured your fractured, your,
your commercial agreements are, right?
Do you really have one unified commercial agreement?
Or you actually have a hundred versions of that commercial agreement, which
means, that's a corporate risk that people in this field would assess, right?
So those are some examples of using it in a way that is value
added, taking into account the risk tolerance and the risk averseness of
the folks in these specific fields.
Right, so when you have some guardrails, if I may use the word,
in the form of a template, reduces the chances of a hallucination.
is time saving, I wonder, the primary benefit that, in your experience, people
are seeking today with generative AI?
I think it's either time saving, and/or doing more than you have the
capacity to do, which is a similar thing, but a little different.
In the example of the commercial agreements, right?
If I had a team of four, they could review all of our agreements
and give me that analysis.
In lieu of having a team of four, that work simply isn't getting done.
And so when my, let's say, executive asks, how fractured are our agreements?
The answer is, I think we're pretty fractured.
That's not a very good answer.
And so this is work that cannot be done unless you scale up at a cost you probably
are not ready or willing to take on.
Or getting time back for the team that you do have to do the things that
are more valuable, more important, reduce errors and mistakes, right?
Because if you're doing, humans don't like doing the same
mundane task over and over again.
And so it would benefit both accuracy, but also morale and culture and has all these
other benefits of letting your people do their best work, which is a great thing.
From what you're saying, there is, you know, the time savings on the artifacts
that, or the output of some business activity in this case, contracts.
So that is tricky, but some of the other benefits is that you can do tasks
like, okay, compare these documents.
This is what you've, you've just used as an example.
I was thinking maybe if there's a document that has gone through a number
of revisions, there's been number of changes, et cetera, like asking the
model to summarize the changes, that output of the model isn't directly
going to get used in the actual document that will get eventually
signed, but it still has benefits.
And it's time savings, but I like the way you said it, that it's also
maybe tasks that no one would have bothered doing because, well, we're
all, prioritizing as, as we go.
And yeah, the, the, the other cultural, morale aspect where, repetitive work,
becomes much more, much more doable.
Let's say because the repetitive aspect, you can rely on the, on the model.
So those are really, I like that set of benefits.
Let's now talk about the pitfalls or the risks of, adopting generative AI.
And you gave already the example, but if you try and generalize again,
thinking about a potential client with which you're exploring, you
know, the art of the possible.
What do you emphasize for them in terms of risks and pitfalls?
Yeah, I think some of the risks and pitfalls are, trying
to do too much too fast.
It is very much an exciting time and therefore we find people are
overhyped and they think it'll solve all their problems quickly.
And it has benefits, but it is slower than I think anyone wants it to be
while also still being very exciting.
Especially as we talk about enterprise scale, I want to
roll this out to my employees.
Okay.
We'd love to do that, but it's probably not in three weeks.
And that's some of the tension that folks feel.
the other one I've seen is not integrating it into whatever
way and how, and the tools that your teams use now, right?
My, my personal favorite example, is I don't like Outlook, I never liked Outlook.
But I've used Outlook for nearly 20 years now because that's what
every company and organization uses.
And we all laugh about how bad the search is, right?
I can never find an email.
It's a problem we've known about for a long time, yet we still all use
it because it's the way we all work.
There is search in outlook?
Yeah, exactly.
Exactly.
So that is the, how do you bring it to where your teams are?
Because when you tell them, Hey, you have to go sign up for a new
thing, new login, new website.
And by the way, it's not going to work as well as you hope, or would
like it to work the first few times.
All you're going to do is upset people, right?
You're not going to, you're not helping people at that point.
How do you bring it to where they work or, arguably even better, on the back
end, and just do some of the work on their behalf without even necessarily
needing the human intervention as much?
Yeah.
So those are some examples where, you know, helping them, helping
the people using it actually get comfortable using it.
I think it is a big thing to tackle.
So, again, it's great that you sort of went into this whole
discussion of enterprise scale.
Because this is something I get asked quite a lot.
People, of course since ChatGPT came out, have had personal experiences,
sometimes in like smaller startup or, yeah, smaller organizations.
Just using a tool or, or, or, or 10, it's very easy, very straightforward,
but you've already touched upon some elements that are specific to this
enterprise context, where if you start doing that at scale with all the tools you
already have, it, it, it just adds to the complexity of managing, the organization.
Can you think of any other aspects that you would want to highlight
that are unique to enterprises?
The other thing I would highlight is the maintenance of those systems, right?
At enterprise, they are almost never straight out of the box.
As they're improving, back to the guardrails for a second.
The really nice thing is being able to take a pretty basic model, right?
It understands general conversation.
It can put out pretty well written things, but then giving it your own data, right?
A perfect example of this for enterprise are your like
knowledge management systems.
So SharePoint, wherever you have your knowledge, being able to
take a thousand PDFs, right?
Or every standard operating procedure you've ever written, putting it in
there and then saying, how do I do X process or who do I contact for this?
And it'll actually, give you a good answer based on the standard operating
procedure one of your employees wrote.
So I think that has a lot of potential at the enterprise scale.
But there's a maintenance piece to it, right?
Which means there's a skill set, probably somewhere in IT, or you
could, have somebody come help you.
But to not just build it and get it stood up, but to fine tune it,
add features and functionality.
So there is that very literal cost of keeping this thing
running and feeding it, et cetera.
That I think the benefits clearly out outweigh the cost of that, but it is,
especially as we talk about small and mid sized enterprises, adding one or
two people or three people to an IT team, may not be something that's on
their mind when they're thinking about standing up these kinds of capabilities.
Yeah, definitely.
I think that, you know, this is something where enterprises have both
extra, okay, risks to manage and, you know, mitigations to put in place.
But it's also because they can afford to deploy these systems
that are not generic, but are working with the enterprise's data.
So, that is, maybe not the same when you're in a smaller or
larger organization, of course.
But you've, you know, mentioned skills and that's one of the areas
where I wanted to get your input.
It would seem at this enterprise scale that it kind of requires new skills.
So would you see those skills being internal, external, a mix of both?
I've seen both.
My favorite model, it's just how I like to work, it doesn't mean it's the right
way, is the upskilling model, right?
So to get off the ground, bring in the experts, get
some help, that kind of thing.
But having outside people do the long term maintenance has some benefits.
But what I really like is the idea of you have a team.
They're probably as excited as the rest of the world about this technology.
Give them the chance and the opportunity and the development while this thing
is being built and developed and rolled out, to get upskilled, to get
comfortable and to take ownership of it.
Because they're inside your organization.
They know you, they know your systems, they know your people.
So that transition from, a handover, and an upskilling that goes with it,
I think I've seen it work beautifully.
It's more intentional though.
You really have to not just focus on building it, but focus on the people
that will maintain it coming along for the journey the entire time.
For you, this is a decision that kind of works best in all circumstances?
What about, you know, the potential like future evolution of these technologies
and potentially, you know, trying to position yourself and your organization
in a good place for whatever comes next.
How does that, factor in for you?
I don't know if it applies in all situations.
I've seen it, where there's a desire and an intentionality, I think
it can work in most instances.
There's a few instances where let's say you have such a limited
internal capability, right?
But these are not large enterprises, right?
Or, it's a capacity, not a skill issue, right?
Hey, our folks could learn this, but it means they wouldn't do this
other thing we need them to do.
And we're not planning on hiring.
So it's a strategic decision.
We don't want to do this.
And those are obviously okay.
For our own purpose, I love solving the hard complex problems.
And I think I and the firm have built a reputation for that, right?
Where, if you are trying to tackle something as complex as how do I bring
artificial intelligence, generative AI into my enterprise, that we would be
on the list of folks you would call.
And so my hope is to go from complex problem to complex problem
without needing as much of the maintenance piece or sticking around
unless they want it, which happy
But that's why that's where the sort of combined model where they do have
internal people who are involved.
But you're there to support them, to show them the way, advise, and be
there, but not necessarily expect them to be also at the same time checking
all the research papers every day.
That's right.
And that is part of the the doing versus advising.
That's a great, that's a great point.
Yes.
and that relationship is ongoing, whether, you know, we're working
together actively at this moment or not.
Um, yeah, absolutely.
And so what related strategies from a leadership perspective, what related
strategies are relevant when thinking about, you know, deploying generative
AI, you've already mentioned obviously the, the skills side of things, but are
there any other risk mitigation approaches or things to do that you would advise?
So I personally take a very human centered approach.
And regardless of technology, I always start with the people
that will use it, right?
So, for many of our customers, they actually want to deploy it into their
offerings to their customers, right?
So they have a product, a business to business SaaS offering or
something like that, right?
And they're saying, "Hey, we have a suite of products. We want to
add generative AI to it." Love it.
Great.
Let's talk about that.
Do they want it?
Do they need it?
What part of their work are they interested in?
To me, that's the biggest risk mitigation because the technology
is solvable and in many instances now is solved and repeatable.
The question becomes, are you investing in the right area?
Back to the professionals and the way they work and the risk averseness, are you
touching a point that is very sensitive to your customers and therefore they
are much less likely to try or use the thing that you're now pitching them?
Or are you hitting a wonderful pain point like we both laugh about
the pain points we have, right?
They would go, "Oh, somebody thought of me and the pain I
feel, of course, I'm going to try.
And that difference of adoption and usability and interaction and feedback
can make or break any product.
So to me, that's a big risk mitigation is, taking that human centered approach
first, understanding where the people are, the way they work, their environment,
their context, and targeting the right problem with the right solution.
Great, the human centered approach is an excellent point on
which to start the wrapping up.
So usually at the end, I ask for a tool, book, or maybe a habit
that has made an impact on you in the last 12 months and why.
I'll give you a tool and a habit.
What I've been trying to do is, like this conversation, I have really
good conversations and idea sparks.
I have a notebook.
I usually jot it down quickly.
Before this habit and this tool, it would take me a long time to
go from an idea or a bullet to sharing that idea more broadly.
So my habit is, I take that bullet and I expand it into a few bullets.
Let's say.
Eight bullets, nine bullets, right?
Sentences, an outline.
But that takes 10 minutes, 15 minutes.
That doesn't take a lot of time.
And then because we're talking about generative AI, uh, the one I've been
using a lot recently is, Google's Gemini.
So I'll take the bullets into Gemini and I'll say, give me a first draft if I want
to share this idea out with the world.
it's usually only 70 percent good.
But like anyone who's tried to write going from a blank piece of paper
to something is the hardest part.
And so it shortens my cycle to getting an idea, to share with
my peers, to share with a client, to share with the world at large.
And I'm trying to get in the habit of shortening that, that loop,
Of going from idea to getting an idea out and starting over.
And I've done it a couple of times and it's worked well, but I still,
I need to work on that part.
That's an amazing tip.
Thank you.
Thank you very much.
And the final question is, of course, we're living through, you know, times
where everything's changing, but what is the one thing that you would imagine
to remain constant 10 years from now?
That's a great question.
I think the world will not look that much different in 10 years from now.
It's an unpopular opinion, for instance.
I think in many ways, the tools we use will look different.
If you think back 10 or 20 or 30, 50 years, right?
The cars looked different.
The appliances were different.
But you still needed community, right?
You still wanted to hang out with friends, sports still existed, right?
Most of the work that gets done still got done.
so in some ways I'm very excited for the future and in other ways I take
comfort in knowing that the things that we know and love and cherish
aren't going away anytime soon.
So I'm hopeful and optimistic for the future.
Wonderful, thank you very much, Sam, absolute pleasure!
Thank you.
I really appreciated the conversation.
Throughout today's conversation, Sam reminded us that the real challenge with
generative AI isn't the technology itself.
It's knowing how and where to apply it.
Whether it's streamlining repetitive legal filings, managing complex
contracts, or scaling knowledge systems across enterprises, Sam's
insights revealed that success lies in balancing innovation with practicality.
His human centered approach, focusing on risk tolerance, clear
workflows, and employee empowerment, offers a roadmap for leaders
navigating risk averse environments.
Sam's journey challenges us to think deeply.
Are we prioritizing tools that align with our core mission?
Or are we chasing hype at the expense of long term value?
If you'd like to connect with Sam and learn about his work,
you can find him on LinkedIn.
The link is in the description.
He's eager to grow his network and collaborate with leaders who
share his passion for building practical people first solutions.
As always, we have more exciting topics and guests lined up, so stay tuned
for more tales of innovation that inspire, challenge, and transform.
Until next time, peace.