May 1, 2025 • 53 mins
Learn about the impact of AI in healthcare in CXOTalk episode 877 on AI in clinical trials. Discover how artificial intelligence is revolutionizing clinical trials and the potential benefits for the healthcare industry.
AI promises faster, safer clinical trials and better patient experiences. Clario's EVP & Chief Information Technology & Product Officer, Jay Ferro, joins CXOTalk host Michael Krigsman to explain how his team unifies IT, product, data, security, and marketing under one roof, giving sponsors, sites, and patients a single, seamless platform. He outlines responsible AI governance with legal, medical, privacy, and security leaders. He shows how predictive tools speed recruitment, sharpen protocol design, cut dropout risk, and support image interpretation while physicians keep the final judgment.
Watch to learn
• How a unified tech-product structure cuts cost and removes friction
• Steps for enterprise-wide AI oversight in a regulated environment
• Real use cases for AI in recruitment, imaging, and trial optimization
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00:00 🧬 The Role of AI in Clinical Trials
01:18 🔗 Unified Roles and Seamless Experiences
10:45 📉 Customer Concerns and Future Challenges
11:54 🔬 Navigating Uncertainty in Clinical Research
13:13 ⚖️ Balancing Priorities and Saying 'No' Strategically
18:02 🤖 AI's Role in Healthcare and Clinical Trials
24:07 🤖 Ensuring Responsible AI Development
27:19 🧪 AI's Role in Clinical Trials
32:23 📜 Navigating Regulatory Challenges and Organizational Readiness
36:24 🤝 Proactive Collaboration and Regulatory Compliance in AI Development
38:57 🌐 Transparency and Cross-Industry Collaboration in AI
42:12 🔗 Industry Similarities and the Role of Technology
44:40 💡 Purpose-Driven Technology and AI in Healthcare
50:15 🚀 Scaling AI Projects and Overcoming Adoption Challenges
#AI #ClinicalTrials #DigitalHealth #CIO #cxotalk
Mark as Played
Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
(00:00):
Clinical trials are fundamental to developing new medications,
but they face significant challenges, including lengthy
timelines, high costs, and complex data management.
I'm Michael Krigsman, and today on CXO Talk Episode 877, we're
discussing the impact of AI on this critical part of our
(00:23):
healthcare system. Our guest is my old friend Jay
Farrow, Executive Vice Presidentand Chief Information Technology
and Product Officer at Clario, aleading provider of clinical
trial technology solutions. I oversee IT, product, data
(00:43):
security, marketing and corporate communications, all
the things that you normal normally would find in the
traditional CIO role, all the things that you normally would
find in the the CTO role, enterprise architecture,
etcetera. And then product and marketing.
We also get to build the solutions that empower clinical
(01:05):
trials all around the world. And I get to have a hand,
although I leave it to the experts in the marketing of that
of those products all around theworld for our customers and
ultimately the patients that we support.
Jay, it's in a very unusual roleagain, Chief Information
Technology and Product Officer. How does a role like that
(01:28):
actually come together? I've I've have not heard of
anybody with that exact title before.
To me, it comes down to trust and delivery and transparency.
I work for a terrific CEO. My team and I deliver.
We're transparent and we own issues.
We're business focused first, technology focused second.
(01:49):
In no way am I minimizing the important of staying ahead of
technology trends and leveragingemerging and transformational
technology. That is not the point.
But the point of an organization, especially with
what we do is serving our customers.
And it's not just about the technology, it's about
empowering clinical trials. So walking a day in the life of,
(02:10):
of what a patient goes through, of what a, a clinical trial
investigator goes through a trial site, what our sponsors
deal with when they're the ones,you know, funding a trial and
truly understanding the businessof what we do, I think has built
trust with my CEO that I can, I could have handled A broader
(02:31):
role. We have talked for years about
understanding the language of the business, but yet I still
see so many CIOSCTO struggling with how to do that with a lead,
with technology and then kind ofmaybe put a business wrapper on
it. And it really starts with an
understanding of the value chain, acquiring customers,
delivering your solution, whatever that may be, or your
(02:53):
product, servicing the customersat the end, making sure that
they are having an amazing experience.
You're staying ahead in the innovation cycle.
So you're, you're constantly delivering and all of that stuff
is powered by technology, but it's not technology that's the
end game, right? It, it's removing friction, it's
delivering value, it's delivering services on time.
(03:15):
It's making sure things are up. Of course, it's delivering high
quality data when you say you'regoing to do it, it's the new
endpoints that are being broughtinto a clinical trial.
And when you can do that consistently and show that you
have a good understanding of whatever domain you're in and
you're having open and transparent conversations with
(03:36):
your CEO about your aspirations.Part of this came from me just
raising my hand and saying I'd like to, when you do have other
opportunities, I'd like to be considered.
And and so, you know, part of that has come through delivery,
part of it has just come throughtransparency and good
relationships. So what is the benefit or the
(03:56):
value of combining these multiple functions under this
particular umbrella? Well, my CEO only has one throat
to choke. He knows where to go if
something's not on time or it's delivered with with excessive
bugs or defects. But all kidding aside, we own
(04:19):
the solutions, the technology portions of the solutions all
the way from inception, from a product management point of
view, all the way to marketing. Now we have other groups that
are responsible for delivering our services, our solutions, our
science, all of those things. And they're amazing at what they
do. That doesn't follow under my
(04:40):
remit. But the other advantage it
gives, Michael, is we're in a lot of different areas, right?
We do what we deal a lot with cardiac safety, with
respiratory, with medical imaging, what's called ECOA or a
electronic clinical outcome assessment.
All of those things are different, but there are common
threads. So being able to unify those
(05:01):
technology solutions or those products and those solutions
into one tech stack and provide a more seamless experience for
our customers. And our customers are of course
the sponsors who are underwrite the, the clinical trial that
could be large pharma, perhaps acro or a biotech, the clinical
trial sites all around the worldwho are actively running the,
(05:25):
the trials on behalf of the sponsors.
And it of course, more most importantly are the patients,
the, the people enrolled in a clinical trial wherever they may
be around the world, right. And, and we want to make sure
they have the most frictionless,seamless experience they have.
They're all overworked. They may be going through tough
times. If they're going through a
clinical trial, we want to keep them front and center.
(05:47):
And because this role is now unified and we have one throat
to choke and we have a unified product management organization,
we were able to see and leveragetechnology and products all
across our ecosystem versus having siloed products or siloed
technology. And we now talk about Clario in
terms of an overall ecosystem inplatform versus a bunch of point
(06:12):
solutions. And our customers want that they
they want a Clario to deal with,not a Clario comma XYZ solution
ad infinitum. So the ultimate point then,
correct me if I'm wrong, is by unifying these teams across what
(06:34):
are typically silos, you're enabling a hopefully seamless
experience for your customers. I think that's one of the
largest benefits, Michael. I think one of the other
benefits is internally, if you want to put kind of a, a tech
spin on it is I'm, I'm able to, and I say I'm as a proxy for my
(06:57):
team is able to see across our entire tech landscape, look for
commonalities, look for differences.
You don't want to build 6 of thesame thing because you have
product silos all over the organization that are kind of
doing their own thing. O unifying that vision and being
able to see where you can build once and deploy many is huge.
(07:20):
There are the economic benefits of that.
I build it once and I get to useit all over the organization.
That's wonderful. Keeps my CapEx lower than it,
you know, otherwise would be. Keeps my spend, supportability
gets better, all of those thingsthat we care about.
But at the end of the day, the downstream impact is going to be
felt by the customers, the sitesand the sponsors.
They're not getting a different experience every time they
(07:42):
interact with a different product line or anything.
They're used to seeing things a certain way.
They're used to dealing with Clairio a certain way.
And that is really what we're striving for.
If you've ever walked into a clinical trial site, and I'll
describe it for you, right? And these investigators are
working their tails off. They're terrific, hard working,
brilliant folks. You see stacks of devices,
(08:07):
laptops, you know, respiratory devices, cardiac devices and
stickies on each one of them to,to kind of talk about, you know,
which company it's from, which study it's in all very, very
important to keep everything organized.
But I can't control what my competitors do.
I can't control what other partsof the clinical trial ecosystem
(08:29):
do. I can't control what Clario
does. And what I really want more than
anything is when an investigatoror a patient deals with Clario,
whether it's a BYOD app during the life of a trial, a connected
device during the trial, a portal that a sponsor is going
into is they have a frictionless, seamless
(08:50):
experience. And if that sounds familiar, it
should because really every industry wants that, right?
And, and every industry I think thinks it's unique in that while
we can't possibly think about itin consumer terms, and I would
argue that you absolutely have to think about it in consumer
terms. If every time I went to Bank of
America and I had 17 different apps, well, this isn't the
(09:13):
transfer app, you got to use that.
This isn't the loan app, that's a different app.
This isn't the check your balance app.
You got to fire up something different.
You'd be like, man out, I'm out of here.
And and and. So we really want that unified
experience while retaining speedand quality integrity of the
data. Very, very interesting as you're
(09:35):
describing the comparison of this clinical medical system.
But from a user experience standpoint, we're all just
people and and we want that. We want that non siloed seamless
experience as you were describing with a bank.
You nailed it and and that's it.That's it.
(09:58):
And, and I think the the secret is you always have, we are in a
highly regulated industry. So in no way am I saying that
you freewheel it just to make itease of use and that's your only
bar. There are regulatory hurdles,
there are scientific, there's scientific validity that you
have to meet. And so we are always focused on
(10:19):
the science. My chief medical officer, Doctor
Todd Rudo and I have a great relationship and the the
hundreds of scientists that we have in our organization are
amazing partners. But within the context of
keeping everything valid and youknow, meeting our regulatory
burden as much as I can, I want to make that a consumer like
(10:41):
experience and and as friction free as I can.
And I just want to tell people that you should ask questions.
If you're on Twitter, use the hashtag CXO talk.
If you're watching on LinkedIn, just pop your questions into the
chat. And truly it's a unique
(11:03):
opportunity to ask Jay Pharaoh pretty much whatever you want.
So, so I hope you folks take advantage of it.
So we have, let's see. Our first question comes in from
Chris Peterson and he says this is a touchy subject in Clario's
niche. Are you seeing or expecting
customers hedging because of research funding cuts?
(11:26):
We're certainly keeping an eye on that and you can't help but
read everything that's going on in the news and wondering what
the knock on effect is. And thank you, Chris, my good
friend Chris, who has been a regular viewer of yours.
I know that. And and I am going to say that
the last time I was on here, which was years ago, he asked a
question. So I am honored that he showed
(11:48):
up again so I didn't scare him off, which is great.
But yeah, we obviously have to keep an eye on that.
It's an uncertain environment. I don't think you can pop open a
news app and not see something about volatility, funding cuts.
I, I personally, Jay Pharaoh's opinion, want to keep him, you
know, the United States on the forefront of, of, of medical
(12:11):
research. But there's so much good
research going on all around theworld.
You just don't want to see any let up in, in clinical research
going because people are depending on it.
There are life saving, potentially life altering cures
or treatments in, in the pipeline that we want to prove
(12:33):
that are safe if we can and thenget them out into the to the
people. So we're keeping an eye on it.
Clearly, Chris, we, we, we don'twant to see that type of impact.
The clinical trial space, there's a long tail in it.
Generally these things are not done overnight.
(12:57):
And, and hopefully I think we can withstand kind of the, the
chaos that's happening right nowwhen we get a little bit more
clarity and predictability in the coming weeks and months so
that we can get back to business.
But right now we're we're keeping on, keeping on.
Now is the time to subscribe to the CXO Talk newsletter, go to
(13:17):
cxotalk.com, join our community,participate every week.
And we have another question, this time from Arsalan Khan, who
is also a very long time. He's another regular.
He's another regular, and we're grateful for Chris and for
Arsalan and the other folks who listen and who just ask such
excellent questions. And Arsalan says this.
(13:40):
How important is it to say yes but also say no to all of your
teams? You're juggling multiple teams.
And how do you decide when to say yes and when to say no, and
when to prioritize one versus the other?
It is something that every CXO or head of IT or anybody that is
(14:03):
responsible for software, anybody in our space has to deal
with. There is always more supply or
more demand than supply. Always.
I don't care what company. I have been a CXO now since 2008
and I cannot remember one time where I ever said, yeah, I'm
(14:25):
good, I got plenty and so ruthless.
Prioritization is absolutely keyand you don't want to do that on
an island for me. What does that mean?
It means partnership with my GMswho run our business units
day-to-day. It means partnership with my
CEO, naturally means partnershipwith my CFO.
(14:48):
And he and I are absolutely joined at the hip with what we
want to see. We want to see near term value.
Certainly there are strategic investments.
We're completely aligned on payback and in the making the
decisions that have the most benefit for the company, but
also our customers. Now there are certain things
that you just have to kind of pay the dues every year.
(15:10):
Cyber, you never want to take your foot off the gas with
cyber. I am in an industry of trust.
All of us, I think to some degree are in an industry of
trust. When that trust is broken, it
takes a while to rebuild from that.
So cyber is always kind of pinned to the top of our
priority list regulatory requirements, I think those
always get, you know, pinned to the top.
(15:32):
And then there are customer requests which come in and, and
have to be juggled with those things.
But our salon bottom line is it comes down to ruthless
prioritization. And I try not to say no, it's
more of a not yet or no comma. Here's what I can do.
Can we agree on this in 2025 andthen 26, we'll do a phase two.
(15:53):
I really do my best to try to deliver some value, even if it's
a no. I prefer to be like a no comma
no. But we have this.
This gets you 40% of the way there.
Can we start there and then put a plan together to to pick up
the rest of it in 20 in in 26? So it's not just a knee jerk
(16:16):
reaction philosophical. Well, this is something we don't
do. And so therefore the answer is
no. It's there's a, there's a reason
there's there's a. Yeah.
You know, I will say this, if it's a customer request that
comes in and it's just somethingthat's not in our wheelhouse or
you know, something that's not acore competency or yeah, yeah,
there are going to be times where you say, look, it's just
(16:37):
you, you have to be mature and self select out of it.
Or if it's a capability that just is, doesn't make sense for
us to build, we are always goingto try to find a way to partner
or build or, or serve our customers.
If it's internal, nobody wants the House of no.
I mean, Michael, you've been at this for a minute.
(16:58):
You and I probably sat here 15 years ago and talked about the
House of no and the CIO being the CI no and all of this other
kind of thing. Nobody wants that.
And I try never to say that, butit's, it's always, you know,
when I do have to decline or just say, you know, we just
cannot get to that this year or this cycle.
(17:21):
It's, it's usually followed by acomma.
But here's what we can do. And let's figure out a way
together, GM or CXO, how we can tell the story.
So maybe we can get some additional funding to do this.
So I, I, you know, there's, there's some to me, it's about
relationships and building and being transparent about how
(17:42):
decisions are being made in the company and, and why we're
choosing to do what we're doing.OK, let's go to some additional
questions. Joseph Puglisi on Twitter.
Ah, Joe up in Philly. Philly Joe.
OK, so you so friends. I know, Joe.
Great. And Joe says, will AI or other
(18:04):
innovations lower the cost of healthcare?
This is so needed in today's economic environment.
I think it has the potential to to perhaps do that.
Certainly we don't look at it that way.
We, we look at it from a quality, efficiency and privacy
(18:25):
point of view. So I, I think you're foolish not
to look at AI and say there's not going to be some sort of
art, you know, cost arbitrage. We hear, we read about it every
single day. I think Bill Gates said
something the other day, Michael, that he thought it
would replace physicians or replace many doctors or
healthcare roles. You know, I don't know how
(18:47):
comfortable we are with that just yet.
I, I think we're at the beginning of our AIAI journey.
To me, we still want humans in the loop.
Given, given the regulatory hurdles and the importance of
trust in the drug development process, humans are still very,
very much in the loop. And where AI plays a huge role
(19:09):
is tackling things that maybe machines are better at.
Looking at large volumes of data, finding anomalies,
assisting with document, you know, interpretation or image
interpretation, excuse me, beingable to make predictions, those
types of things. But we're still at a point where
I want a physician or I want an expert overseeing that work.
(19:34):
But yeah, I mean, the short answer to Joe's question is, I
mean, if there there's got to bea cosplay down the road, I mean,
AI isn't free and, and, but to me, it's less about removing
people. That's not the point.
To me. It's about getting our
scientists and our amazing people at Clairio to focus on a
(19:56):
higher level of challenge and augment them with the
technology. On LinkedIn, Greg Walter says
when implementing AI in trials, is each application of AI unique
to that specific trial or part of a standard AI process?
(20:20):
And he also asks is the AI home grown or partner?
The answer is yes. The AIS are models that are it.
Well, it depends. They are limited in
functionality to a certain a certain function and that model
can then be used across trials. They are trained more often than
(20:42):
not, they're closed models. So they are not continually
learning. So they are closed as of a
certain point. So whether that's a data privacy
model that anonymizes medical images, it's a QC or quality
control assist, a model that looks at early identification of
quality concerns to help us ensure timelines.
(21:04):
Maybe a Reed assist. We're having a, a, a radiologist
look at images and MRIACT scan those types of things and you're
identifying it's, it's identifying different things for
you and it can reduce inter and intra observer variability.
Those models are built and are fit for purpose for that
(21:24):
function, but they're utilized across across studies.
So and there's many more. We at this point we have 50
models, 50 plus models that are proprietary home grown across
ecoa, cardiac imaging, respiratory and precision
motion. We do have a proprietary Gen.
(21:48):
AI platform that we have built internally on enterprise class.
You can think open AI, you know,Gemini etcetera that are
private. We named her Claire.
We're not super creative, Michael.
So Clario, declare. I know that's a.
You can see the leap we took there.
(22:11):
And well, hey, you're in a conservative business, right?
The health. Care and if you saw the AI image
and I'll share it with you sometime.
If you saw the AI image that AI created, we said what would
Claire look like It it is absolutely terrifying.
It it it is terrifying. But what I love about it is a
(22:31):
year ago within the organization, if you had asked,
I don't know 1000 people, what is Claire?
They, they wouldn't have known maybe a year and a half ago
today you're now hearing people say, well, we're going to put
that in Claire or we're clearingit.
And, and it's become part of ourlexicon because we've built
(22:52):
capabilities to do a protocol summary, user story writer, RFP
and contract analysis, translations, you know, document
chat where you have a large volume of documents and you're
trying to ascertain what exactlyis and so many more.
And the line of people behind our AI team with just use cases
(23:15):
that for, for Claire and what we're doing is huge.
But going back to the question, we try to leverage third party
wherever we can as long as it's safe, it's private, but a lot of
our other proprietary models arebuilt on our home grown Aquarius
engine. When it comes to Gen.
AI and summaries, how do you ensure that there are no
(23:41):
hallucinations? For example, summarizing a
patient record, the AI could potentially think to itself,
think whatever that, whatever that actually means to the AI.
But the AI says given all of these symptoms, the patient must
also have XY or Z condition and then inserts it.
(24:04):
How do you how do you deal with that?
Constant oversight, human oversight.
First of all, we don't deal withanything that can be attributed
back to a, you know, a Michael Krigsman or a Jay Pharaoh or
anything like that. We deal with pseudonymized in
anonymized data. So first and foremost, you know,
we're not dealing with Joe Smithor, or, you know, Jason James or
(24:25):
anything like that. We're dealing with pseudonymized
and derived data, which is, which is super important #2
constant refinement of the model.
We have an internal AI team under my chief AI officer who
reports to me that I hired and brought on to the organization
last year, Marco Tapolovich. We are ruthlessly challenging
(24:50):
our models to look for bias and hallucinations.
One of the ways that I think I'mreally proud of is right out of
the gate when when I joined the organization and I realized that
AI was a big part of our story and we got a little bit of a
running start. When I I think when I joined, we
had maybe one model in production that was
scientifically validating. Today we're at 50 and and
(25:13):
growing, which is super exciting.
But one of the ways you do that is transparency within the
organization. You want organizational literacy
on what AI is, what it isn't, what we're doing, why we're
doing it, more importantly, whatwe're not doing.
And so we created a group right out of the gate, including my
General counsel, Lauren Nishtal,my Chief Medical officer, Todd
(25:35):
Rudo, my CISO Mortazanisar, my course, my Chief AI officer,
Marco that I talked about, my head of quality, Todd, and, and,
and all unified with our passionfor the technology, but all
coming at it from a different point of view and making sure
(25:56):
that everything we're doing is built on trust, transparency,
responsibility, bias control, etcetera.
And I urge anybody out there that is just at the beginning of
their AI program, do not approach it as a CIO only or a
CTO only. Don't do it, OK?
Don't throw all your stuff in anLLM and be like, we're on AAI,
(26:18):
man, we're great. You know, when cloud came out,
you just started throwing stuff in the cloud and you wondered
why, you know, proprietary information was suddenly gone.
You don't want to do that. Do it in a controlled way.
And people immediately hear that, Michael, and what do they
say? Oh my God, I involve attorneys
and whoo, that's just going to slow down in absolutely not.
(26:39):
If anything, it has speed it up because there's no daylight
between US and because our customers know that we're
approaching it for a from a responsible point of view.
And the last thing I'm going to do is put a model into
production that my general counsel, my head of privacy, my
head of quality, my Chief medical officer, my CSO, my CEO
(27:02):
for that matter of course that that we all haven't seen and are
are signed off on that we did itthe right way and that we did it
responsibly. So, you know, if you're tackling
this as an IT problem, I just, Iencourage listeners to, you
know, broaden open the aperture a little bit.
We have another interesting question again from Arsalan Khan
(27:23):
on Twitter. How do you use AI on clinical
trials? And of course, what's the
impact? But he's also wondering, would
removing AI guardrails and regulations make clinical trial
trials better or faster? You remove regulation, would
(27:44):
they make them faster? Maybe.
Would they make them better? No.
I, I, I would argue that the FDAand I have had numerous
conversations as recently as this week with our colleagues
over at the FDA. They are very bullish on the
technology. They're doing it the right way.
I think they're moving at a speed.
I think people hear FDA and whatdo they think the red tape
(28:05):
right, you know, grind to a halt, see you in three years.
And, and that is just not true. I, I might have said that coming
into the organization five yearsago, it would, it would have
been an ignorant judgement. And I have learned that our, our
colleagues in the FDA absolutelywant to do what is best for
(28:28):
people. They want to protect people, but
they want these, these drugs andthese therapies to be trusted,
safe, effective, and they want them to improve lives.
That's what they want. And, and in so much as AI can
impact that, they, they want to lean into AI.
(28:50):
Now, the first part of this question, which I think is
really, really good is where canAI, where does AI fit into the
clinical trial? Now, I'll, I'll give some
examples where I, where I'm seeing really good effectiveness
in the trial process. We don't play in all of these
roles and I've talked a little bit about how we use it, but
(29:11):
patient recruitment and retention, finding and enrolling
the right patients in a trial isnot easy.
There's not a line of people hands, you know, standing right
now, you know it. It can be time consuming,
expensive, difficult patient retention.
You're taking a a treatment or apill or an injection or whatever
(29:34):
over time that may or may not have side effects that you may
not have just real world things that impact you.
Your ability to participate in that trial and retaining
patients is key and it's an important challenge.
And so minimizing dropouts is absolutely key.
(29:54):
What can AI do about that? Well, first of all, it can.
It can manage or analyze large data sets, potentially
electronic health records, genetics, social determinants.
It can identify potential candidates who are likely to
meet trial criteria faster, moreeffectively using predictive
(30:16):
capabilities. It could predict drop out risks
so that those can be mitigated ahead of time where you can say,
hey, look, there's a a chance that Michael may or may not make
the cut because of we're seeing those behaviors.
And that way I can intervene potentially and keep Michael in
the trial. That's a huge application in
ripe for AI disruption. I think trial design and
(30:39):
optimization, where we do play arole.
Trials are complex, intentionally so, because
they're important and there's a lot of science behind them, and
you want scientific rigor, you want meaningful results.
But a protocol can be very, verycomplex.
You're balancing patient safety,statistics, cost effectiveness,
(31:05):
so many other things. And AI can simulate different
trial designs and predict outcomes with various
configurations. Now you wouldn't want to rely on
that alone, but it can at least directionally move you in a in a
path to optimize a protocol and looking for the most effective
(31:26):
study designs, not just cost effective overall effectiveness,
which ultimately would have the benefit of speeding up the
development process, which is isexciting.
And those are just a couple of top of mind examples where I, I
see AI making a, you know, a huge, a huge difference.
We have another related questionthat's come in from LinkedIn
(31:49):
from Lori Noregov. Before we get to Lori's
question, I just want to remind everybody that we have shows
like this every week. Subscribe to the CXO Talk
newsletter. Just go to cxotalk.com so you
can sign up. We want you as part of our
(32:10):
community so that you can participate and it's fun and
it's great. So sign up for our newsletter.
And here is Lori's question. He says, Does regulatory
compliance create limitations for some of the more advanced AI
(32:31):
use cases? If yes, what would those be?
And how do they work with regulators for making innovation
more accessible? The regulatory environment being
what it is, and keep in mind this is not the FDA only.
You have the EMA and you have other regulatory bodies all
around the world all with their own criteria.
(32:53):
The FDA is very thoughtful and very measured in what they do,
as are many other regulatory bodies filled with smart people
who want to do the best for the most.
This is to me about partnership with the regulatory bodies and I
don't mean like Co development or anything like that, but
(33:17):
understanding what the regulations are, understanding
the hurdles and the barriers that we can that, that we can
overcome. Showing them.
I'll, I'll be honest in the question is such a good one.
And I think, and I don't want tocome off as an FDA Homer or
anything like that, but as I've gotten to know them, I, I will
say they want to do the right thing.
(33:39):
And I don't see them at all as aquote obstacle or anything.
I think a bigger obstacle is organizational readiness.
If you said Jay is at the FDA orregulatory body and to be sure
we are in a really highly regulated and for good reason.
We're in a highly regulated industry and we have to be
right. Patient safety is absolutely
(34:01):
paramount and trust is absolutely paramount and the
science behind everything we do is absolutely important.
The reason I say that it's organizational readiness is
because you're dealing with verycomplex.
You're dealing with an industry that does not pivot overnight.
(34:21):
We've seen it with healthcare over time.
We saw it with fintech in the 90s and the 2000s, etcetera.
If you had gone back in time andasked somebody in the 90s, hey,
how safer is it to to use a cellphone for millions of dollars of
financial transactions, they would have looked at you like
you sprouted and tell that'll never happen.
You got to have people, you got to have whatever.
(34:43):
And here we are today moving money all around the world at a
moment's notice. It's going to change, but you're
still seeing organizational resistance.
I, I think in some ways for goodreason, because there's a, a bar
of rigor that you want to hit. Part of it is just culture
change and making sure that people understand the power of
AI in this truly unique fundamental force that we have.
(35:07):
So a lot of it comes from education as well in overcoming
some of the organizational inertia in older thinking.
I, I mean, look, I'm, I'm not a want to be too hard on people,
Michael, but I mean, if you had walked into A and, and, and
we're hanging out with a 25 yearveteran in the fintech space in
1994 and said, I'm going to paint a picture of what the
(35:29):
future is going to look like, they would have been like, no,
let me tell you the 52 reasons why that's not going to work.
Lori clarifies his question to say How do you work with
regulators to make innovation more accessible?
I was at a conference not too long ago, Lori, you, you'll
appreciate this. And it was an FDA panel and, and
(35:56):
one of the FDA guys that, and I won't say his name because I, I,
I just, it's not important. But he came off and I, I greeted
him because I just was blown away by the, the discussion.
And it was all about AI and whatthey thought of it.
And it just was in, it went in atotally different direction.
And I said, and I almost asked Lori's question verbatim.
(36:17):
And I was like, how do we partner with you?
You can e-mail me. And I was like, oh, oh, well,
that's, I mean, that's interesting.
You know, we, we want to hear, we, we want to learn.
But to put a finer point on, I would say proactive
collaboration, engage early. How do you do that?
(36:39):
Well, talk to them, OK, this isn't an ivory tower that I, I
will say every FDA person or every regulatory person that
I've ever talked to has been willing to have a conversation.
So establishing clear communication and being
proactive with the regulatory authority, whether it's the FDA
and, you know, EMA, etcetera #2 understand regulatory
(37:04):
frameworks, show that you're compliant with existing
guidelines, making sure that you're familiar and you
understand what they're trying to accomplish and that you're
not coming at it from a purely tech point of view.
And be like, yeah, but if we could just release the
guardrails and let this, you know, thing go nuts, imagine all
the good probably, yeah, maybe some truth to that.
(37:26):
But I think the reality is that the regulatory frameworks exist
for a reason. They change over time.
Guidance has changed over time. If you had said 20 years ago,
we're going to have an AI reasonable use policy, I think
most companies would've been like, why the hell do I need an
AI reasonable use policy, right?So making sure you're familiar
(37:48):
with existing guidelines and, and that you're adapting your AI
to fit that compliance, I think that starts you off in a, in a
very good conversation with, with regulators, making sure
that you emphasize safety and risk management with regulators.
The FDA often requires risk management strategies.
(38:09):
They don't want to hear about the art of the possible without
the other side of the coin. What are you doing to manage
risk to ensure that the AI technologies that you're
proposing are safe and that any potential risk to perhaps the
patient, the study, etcetera areare are mitigated?
(38:29):
And the last thing I'll say, thelast one is develop a regulatory
friendly development practice, which I kind of harped on at the
beginning, following best practices, having SO PS in place
that can pass regulatory scrutiny, transparent and
training models, bias mitigation, all of those types
(38:55):
of things. Build trust with regulators.
And on this topic of transparency, Arsalan Khan from
Twitter comes back and he says if we want transparency across
industry, should companies sharetheir next AI with other so
collectively whole industries can get better?
And what about the role of gatekeepers in all of this?
(39:20):
I do think there's an opportunity across industries to
promote transparency and Dr. positive outcomes across the
industry, whether it's healthcare, life sciences,
fintech, etcetera. I think I, I think there are
some benefits of doing that. Certainly speed collaboration
(39:45):
for innovation and you're going to get more faster generally
public confidence. I think when they see
collaboration and trust and transparency across industry,
there's certainly an opportunityfor better regulation.
And you're seeing that already with Europe, right?
(40:06):
Their AI, their AI regulations are not just for life sciences
or healthcare. It's AI in general matter of
time before the United States does it either at the state
level or the OR the federal government level.
Certainly you have some draft guidance already out there, but.
(40:26):
I think it speeds that up and I think you get a higher quality
of regulation when there's more collaboration and sharing.
So there are a lot of reasons toto do it.
I'll just invention for folks that are interested in Europe,
European adoption of AI and regulatory efforts.
(40:46):
Just a few weeks ago, we had twomembers of the House of Lords
discussing these exact issues here on CXO Talk.
These are folks who are creatingpolicy in the UK.
So if you're interested in this topic in Europe and the UK
specifically, just go back and look at CXO talk we had.
(41:06):
It was a really great discussion.
Well, I'm sure, I mean, look, they are always at the forefront
of regulation. And I don't mean that in a
snarky way, but I mean I I thinkthey've done a nice job in
balancing innovation with risk mitigation, transparency and
accountability. It's not perfect, nothing ever
is, but the E the EUAI Act, I mean, they came out of the, I
(41:35):
mean, what is that in 21? I think they launched that and
and which is crazy. It it, it, it was very, very
well thought out. And what I like about it is it
triages risk, unacceptable, high, limited, minimal,
etcetera, right. So this, this risk approach
(41:58):
where, hey, if it's the system that poses little to no risk,
it's an AI in a video game, it'san AI in a spam filter.
Have at it. You know, you're going to have
to check a few boxes, you're going to have to do a few
things. But yeah, we, we don't want to
slow stuff like that down, right?
And and it it seems to me they at least tried to right size the
(42:22):
regulation which is which is good.
So we have another question fromGreg Walters, Who is aware that
you have a background in cement.You can tell us about that and.
Outing me like that, Greg. All right.
And Greg Walters wants to you tocompare AI to quote cementitious
(42:46):
materials, given the fact that concrete has been around for
thousands of years. Every industry is more alike
than not. I, I know we like to not think
that, right. I, I, I think every industry
thinks it's a, a perfect little snowflake, Michael, that you
know, that that is, it's a Unicorn, every little thing.
(43:09):
And to be sure, every industry has nuance and has its own
lingo. It's got its own ecosystem and
it's got its own special spin ona regulation or whatever.
But I can tell you the discipline of product
management, technology management, financial
management, software development, security, etcetera
(43:34):
is 80% the same across industry.So you if you look at
manufacturing my time as the CIOof the Quikrete companies and
even before that, you know at AIG or the American Cancer
Society or EarthLink, you were doing many of the same things.
Now the size, the scale, the severity may be different, but
(43:56):
when are you not protecting customer data, when are you not
trying to deliver high quality results on time?
When are you not trying to leverage emerging technologies
to deliver a higher quality product or have a, an amazing
customer experience or improve operations or drive revenue.
(44:18):
And, and I would challenge CI OSand CT OS and aspiring CX OS to
think about things that way and not box yourself into one
particular industry. But how is cement like AII know
there's a punchline, Greg, somewhere out there and I don't
know what it is. Maybe, maybe somebody will
suggest it, but. Joe P on Twitter comes back and
(44:42):
he says, how do you keep your team focused on the outcomes and
avoid becoming enamored with thetechnology?
In other words, avoiding shiny new object syndrome.
Education transparency, I'm building financial literacy
education, educating the team onwhy we exist as a company and
what what does that really mean?That those are all a lot of
(45:03):
really flowery words. Site visits, discussing with
patients, showing them what we do, visits from, you know,
different departments, building those relationships so that
people, no matter where they arein the organization, in my team,
I don't care if your health testlevel 1, I don't care if you're
a coder and you're a junior person that just joined the
(45:24):
team. I want them feeling the
importance of what we do. I want them recognizing that
there's a patient at the other end that is using an app on
either on a provision device or a phone or is using a connected
device, whether that's a a bloodpressure device or a respiratory
device in clinic that we built or that we built an interface
for or that we have AI integrated with.
(45:46):
I want them understanding that what they're doing has real
human impact around the world. And when you do that, yeah,
you're still going to get excited about the technology.
It's what we do, but it's technology with purpose.
It's not technology for technology.
So we try to do a lot of education, Michael and Joe P,
(46:07):
but and keep people focused. So what does that mean in
practical terms? Site visits, site education,
voice of the customer type activities, making sure we're
hearing from patients, making sure we're showing teams the
impact of the technology that they're building and that
they're delivering, whether thatmeans, you know, hearing
directly from customers, etcetera, so that they
(46:28):
understand the impact. It wasn't just a bunch of code
on time, which is nice. I want that, but there's purpose
behind it. On this topic, Arsalan comes
back again, and he's wondering about the future of AI.
Let's keep it focused on clinical trials.
And, he says, as AI becomes increasingly more of a
(46:52):
commodity. If DeepSeek has showed us
anything, it's that I, I think the idea of a, if a trillion
dollar, one-size-fits-all one magical product to rule them all
is not going to be long for the world.
I, I, I think there will be somecommoditization of, of, of
technology over time. And I think what you'll see is
(47:15):
far less one giant model and farmore fit for purpose.
What, what the future I see, I see some amazing things
happening in the future, particularly in healthcare, in
clinical trials, keeping it speed, improving in quality
patient experience. What, what does that mean?
(47:38):
It means if a patient is having a bad day or there's, you know,
there's, there's just you're, you're, there's just some
empathy that even models today can show more time with an
investigator or a physician. If it's healthcare, physicians
will tell you constantly and nurses will tell you constantly.
I'm sure you everybody knows this.
They spend more time entering things into an EMR than they do
(48:00):
with a patient. Emr's are designed for one
thing. What do they do?
Or two things to bill and to keep street legal.
And I'm not trying to be depressing or snarky or anything
like that. And I don't mean anything bad to
our friends at the big EMR companies, but there's a huge
opportunity for AI whether to listen to the physician as he's
talking, to transcribe notes, where they can turn around and
(48:24):
spend even another minute or twowith a patient and improve that
patient experience. So I am really excited about
what the future holds, particularly for patients and
better outcomes. The through line of our
conversation seems to be customer experience and broaden
it, patient experience, whateverdomain that might be.
(48:47):
I think that's true. I, I look, if not them, then
who? I I mean, if you're not doing it
for for them, then why are you doing it?
And and look, you and I, if I take off the motherhood and
apple pie hat and say, OK, Jay, yeah, patient experience is
super important. But really, companies exist for
shareholder value and to make money.
(49:09):
Fine, if you're not wrong. I would argue that one of the
best ways to do that is an amazing patient experience and
an amazing customer experience. If I'm making my sites happy or
at least less mad at me or am I providing A frictionless
experience and they always know what the other end is, a company
or a team that is working hard to do the best.
(49:32):
We're not always going to be right.
We're not always going to get itright, but we're always doing
what's important and what's the best for them and for their
patients. If they know that, I would argue
that they're going to want to continue to do business with
Clario and they're going to continue want to continue to
partner with us. So I think the two are
inextricably linked. I, I, I think you can have an
amazing patient experience and amazing site experience and
(49:54):
sponsor experience maintaining scientific rigor, high quality,
privacy, speed, and all the other things that we want to do.
And you can still make money as an organization and build
shareholder value and do all of those things.
And I would argue that if I can do the first one really, really
well and I'm making good choices, the second one will
come. What advice do you have to
(50:17):
business and technology leaders in the enterprise when it comes
to developing AI projects and scaling those projects as you
have done? Don't do it in a silo.
Whereas my old boss used to tellme a highly polished cylinder of
excellence. Don't look.
Look partner, you know. Talk to your partner ecosystem,
(50:38):
other CI OS, other CX OS, talk to your suppliers, understand,
educate yourself, but talk to your, your peers in the
organization and work with them,not against them.
Whatever you do, don't make it a, a, a tech only, tech only
endeavor. In no way does that minimize the
importance or the, the excitement about the technology.
(50:59):
But I promise you, if you can doall of those things, it will
speed up what you're trying to accomplish.
And is always Michael, and I think I said this on our first
interview. Think big, start small, scale
fast. Think big, start small, scale
fast. Great advice.
Hey, we have one last question that's come in very quickly from
(51:20):
again from Luri Adnorigov, who says, do you feel like we have
the right people training mindset, the site patients,
nurses, etcetera to enable and provide all of the data needed
for the AI applications or is the technology far in front of
(51:43):
actual adoption? There's a huge educational
hurdle that we have to hit. There are people at the sites
and the, you know, with the patients, I I think they are
just handed a lot, right? They are handed a lot of
different devices and it and, and I promise that it's not
because they are not smart people or hard working people.
It's just a lot. And if I mean, the first part of
(52:08):
the question is do we have the right people?
Yeah, I mean, the ecosystem of of investigators and sites are
amazing and and they work their tails off and they're more than
capable of picking this up. I think it's on us to train.
It's on us to show them the power.
It's on us to make it simple. I think it's going to require
patience, Michael. I do not think we're going to be
able to hand them a magical gokulator and say, you know, and
(52:31):
have them be enamoured with the tech in, in one day.
It's going to take some time. And so we have to be, I think a
little patient as we kind of change the paradigm.
But I'm I'm, I'm optimistic that, you know, we have the
right people. We just got to meet them where
they are. And on that note, Jay, a huge
thank you. I'm so grateful for your taking
(52:53):
the time to be here with us today.
Really, really appreciate it. My pleasure, Michael.
Thank you for having me. And thank you to everybody who
watched you guys who asked such amazing questions.
You are so smart. Now is the time to subscribe to
the CXO Talk newsletter, go to cxotalk.com, join our community,
(53:15):
participate every week, and we'll see you again next time.
Take care everybody, have a goodday.
Clinical trials are fundamental to developing new medications,
but they face significant challenges, including lengthy
timelines, high costs, and complex data management.
I'm Michael Krigsman, and today on CXO Talk Episode 877, we're
discussing the impact of AI on this critical part of our
(00:23):
healthcare system. Our guest is my old friend Jay
Farrow, Executive Vice Presidentand Chief Information Technology
and Product Officer at Clario, aleading provider of clinical
trial technology solutions. I oversee IT, product, data
(00:43):
security, marketing and corporate communications, all
the things that you normal normally would find in the
traditional CIO role, all the things that you normally would
find in the the CTO role, enterprise architecture,
etcetera. And then product and marketing.
We also get to build the solutions that empower clinical
(01:05):
trials all around the world. And I get to have a hand,
although I leave it to the experts in the marketing of that
of those products all around theworld for our customers and
ultimately the patients that we support.
Jay, it's in a very unusual roleagain, Chief Information
Technology and Product Officer. How does a role like that
(01:28):
actually come together? I've I've have not heard of
anybody with that exact title before.
To me, it comes down to trust and delivery and transparency.
I work for a terrific CEO. My team and I deliver.
We're transparent and we own issues.
We're business focused first, technology focused second.
(01:49):
In no way am I minimizing the important of staying ahead of
technology trends and leveragingemerging and transformational
technology. That is not the point.
But the point of an organization, especially with
what we do is serving our customers.
And it's not just about the technology, it's about
empowering clinical trials. So walking a day in the life of,
(02:10):
of what a patient goes through, of what a, a clinical trial
investigator goes through a trial site, what our sponsors
deal with when they're the ones,you know, funding a trial and
truly understanding the businessof what we do, I think has built
trust with my CEO that I can, I could have handled A broader
(02:31):
role. We have talked for years about
understanding the language of the business, but yet I still
see so many CIOSCTO struggling with how to do that with a lead,
with technology and then kind ofmaybe put a business wrapper on
it. And it really starts with an
understanding of the value chain, acquiring customers,
delivering your solution, whatever that may be, or your
(02:53):
product, servicing the customersat the end, making sure that
they are having an amazing experience.
You're staying ahead in the innovation cycle.
So you're, you're constantly delivering and all of that stuff
is powered by technology, but it's not technology that's the
end game, right? It, it's removing friction, it's
delivering value, it's delivering services on time.
(03:15):
It's making sure things are up. Of course, it's delivering high
quality data when you say you'regoing to do it, it's the new
endpoints that are being broughtinto a clinical trial.
And when you can do that consistently and show that you
have a good understanding of whatever domain you're in and
you're having open and transparent conversations with
(03:36):
your CEO about your aspirations.Part of this came from me just
raising my hand and saying I'd like to, when you do have other
opportunities, I'd like to be considered.
And and so, you know, part of that has come through delivery,
part of it has just come throughtransparency and good
relationships. So what is the benefit or the
(03:56):
value of combining these multiple functions under this
particular umbrella? Well, my CEO only has one throat
to choke. He knows where to go if
something's not on time or it's delivered with with excessive
bugs or defects. But all kidding aside, we own
(04:19):
the solutions, the technology portions of the solutions all
the way from inception, from a product management point of
view, all the way to marketing. Now we have other groups that
are responsible for delivering our services, our solutions, our
science, all of those things. And they're amazing at what they
do. That doesn't follow under my
(04:40):
remit. But the other advantage it
gives, Michael, is we're in a lot of different areas, right?
We do what we deal a lot with cardiac safety, with
respiratory, with medical imaging, what's called ECOA or a
electronic clinical outcome assessment.
All of those things are different, but there are common
threads. So being able to unify those
(05:01):
technology solutions or those products and those solutions
into one tech stack and provide a more seamless experience for
our customers. And our customers are of course
the sponsors who are underwrite the, the clinical trial that
could be large pharma, perhaps acro or a biotech, the clinical
trial sites all around the worldwho are actively running the,
(05:25):
the trials on behalf of the sponsors.
And it of course, more most importantly are the patients,
the, the people enrolled in a clinical trial wherever they may
be around the world, right. And, and we want to make sure
they have the most frictionless,seamless experience they have.
They're all overworked. They may be going through tough
times. If they're going through a
clinical trial, we want to keep them front and center.
(05:47):
And because this role is now unified and we have one throat
to choke and we have a unified product management organization,
we were able to see and leveragetechnology and products all
across our ecosystem versus having siloed products or siloed
technology. And we now talk about Clario in
terms of an overall ecosystem inplatform versus a bunch of point
(06:12):
solutions. And our customers want that they
they want a Clario to deal with,not a Clario comma XYZ solution
ad infinitum. So the ultimate point then,
correct me if I'm wrong, is by unifying these teams across what
(06:34):
are typically silos, you're enabling a hopefully seamless
experience for your customers. I think that's one of the
largest benefits, Michael. I think one of the other
benefits is internally, if you want to put kind of a, a tech
spin on it is I'm, I'm able to, and I say I'm as a proxy for my
(06:57):
team is able to see across our entire tech landscape, look for
commonalities, look for differences.
You don't want to build 6 of thesame thing because you have
product silos all over the organization that are kind of
doing their own thing. O unifying that vision and being
able to see where you can build once and deploy many is huge.
(07:20):
There are the economic benefits of that.
I build it once and I get to useit all over the organization.
That's wonderful. Keeps my CapEx lower than it,
you know, otherwise would be. Keeps my spend, supportability
gets better, all of those thingsthat we care about.
But at the end of the day, the downstream impact is going to be
felt by the customers, the sitesand the sponsors.
They're not getting a different experience every time they
(07:42):
interact with a different product line or anything.
They're used to seeing things a certain way.
They're used to dealing with Clairio a certain way.
And that is really what we're striving for.
If you've ever walked into a clinical trial site, and I'll
describe it for you, right? And these investigators are
working their tails off. They're terrific, hard working,
brilliant folks. You see stacks of devices,
(08:07):
laptops, you know, respiratory devices, cardiac devices and
stickies on each one of them to,to kind of talk about, you know,
which company it's from, which study it's in all very, very
important to keep everything organized.
But I can't control what my competitors do.
I can't control what other partsof the clinical trial ecosystem
(08:29):
do. I can't control what Clario
does. And what I really want more than
anything is when an investigatoror a patient deals with Clario,
whether it's a BYOD app during the life of a trial, a connected
device during the trial, a portal that a sponsor is going
into is they have a frictionless, seamless
(08:50):
experience. And if that sounds familiar, it
should because really every industry wants that, right?
And, and every industry I think thinks it's unique in that while
we can't possibly think about itin consumer terms, and I would
argue that you absolutely have to think about it in consumer
terms. If every time I went to Bank of
America and I had 17 different apps, well, this isn't the
(09:13):
transfer app, you got to use that.
This isn't the loan app, that's a different app.
This isn't the check your balance app.
You got to fire up something different.
You'd be like, man out, I'm out of here.
And and and. So we really want that unified
experience while retaining speedand quality integrity of the
data. Very, very interesting as you're
(09:35):
describing the comparison of this clinical medical system.
But from a user experience standpoint, we're all just
people and and we want that. We want that non siloed seamless
experience as you were describing with a bank.
You nailed it and and that's it.That's it.
(09:58):
And, and I think the the secret is you always have, we are in a
highly regulated industry. So in no way am I saying that
you freewheel it just to make itease of use and that's your only
bar. There are regulatory hurdles,
there are scientific, there's scientific validity that you
have to meet. And so we are always focused on
(10:19):
the science. My chief medical officer, Doctor
Todd Rudo and I have a great relationship and the the
hundreds of scientists that we have in our organization are
amazing partners. But within the context of
keeping everything valid and youknow, meeting our regulatory
burden as much as I can, I want to make that a consumer like
(10:41):
experience and and as friction free as I can.
And I just want to tell people that you should ask questions.
If you're on Twitter, use the hashtag CXO talk.
If you're watching on LinkedIn, just pop your questions into the
chat. And truly it's a unique
(11:03):
opportunity to ask Jay Pharaoh pretty much whatever you want.
So, so I hope you folks take advantage of it.
So we have, let's see. Our first question comes in from
Chris Peterson and he says this is a touchy subject in Clario's
niche. Are you seeing or expecting
customers hedging because of research funding cuts?
(11:26):
We're certainly keeping an eye on that and you can't help but
read everything that's going on in the news and wondering what
the knock on effect is. And thank you, Chris, my good
friend Chris, who has been a regular viewer of yours.
I know that. And and I am going to say that
the last time I was on here, which was years ago, he asked a
question. So I am honored that he showed
(11:48):
up again so I didn't scare him off, which is great.
But yeah, we obviously have to keep an eye on that.
It's an uncertain environment. I don't think you can pop open a
news app and not see something about volatility, funding cuts.
I, I personally, Jay Pharaoh's opinion, want to keep him, you
know, the United States on the forefront of, of, of medical
(12:11):
research. But there's so much good
research going on all around theworld.
You just don't want to see any let up in, in clinical research
going because people are depending on it.
There are life saving, potentially life altering cures
or treatments in, in the pipeline that we want to prove
(12:33):
that are safe if we can and thenget them out into the to the
people. So we're keeping an eye on it.
Clearly, Chris, we, we, we don'twant to see that type of impact.
The clinical trial space, there's a long tail in it.
Generally these things are not done overnight.
(12:57):
And, and hopefully I think we can withstand kind of the, the
chaos that's happening right nowwhen we get a little bit more
clarity and predictability in the coming weeks and months so
that we can get back to business.
But right now we're we're keeping on, keeping on.
Now is the time to subscribe to the CXO Talk newsletter, go to
(13:17):
cxotalk.com, join our community,participate every week.
And we have another question, this time from Arsalan Khan, who
is also a very long time. He's another regular.
He's another regular, and we're grateful for Chris and for
Arsalan and the other folks who listen and who just ask such
excellent questions. And Arsalan says this.
(13:40):
How important is it to say yes but also say no to all of your
teams? You're juggling multiple teams.
And how do you decide when to say yes and when to say no, and
when to prioritize one versus the other?
It is something that every CXO or head of IT or anybody that is
(14:03):
responsible for software, anybody in our space has to deal
with. There is always more supply or
more demand than supply. Always.
I don't care what company. I have been a CXO now since 2008
and I cannot remember one time where I ever said, yeah, I'm
(14:25):
good, I got plenty and so ruthless.
Prioritization is absolutely keyand you don't want to do that on
an island for me. What does that mean?
It means partnership with my GMswho run our business units
day-to-day. It means partnership with my
CEO, naturally means partnershipwith my CFO.
(14:48):
And he and I are absolutely joined at the hip with what we
want to see. We want to see near term value.
Certainly there are strategic investments.
We're completely aligned on payback and in the making the
decisions that have the most benefit for the company, but
also our customers. Now there are certain things
that you just have to kind of pay the dues every year.
(15:10):
Cyber, you never want to take your foot off the gas with
cyber. I am in an industry of trust.
All of us, I think to some degree are in an industry of
trust. When that trust is broken, it
takes a while to rebuild from that.
So cyber is always kind of pinned to the top of our
priority list regulatory requirements, I think those
always get, you know, pinned to the top.
(15:32):
And then there are customer requests which come in and, and
have to be juggled with those things.
But our salon bottom line is it comes down to ruthless
prioritization. And I try not to say no, it's
more of a not yet or no comma. Here's what I can do.
Can we agree on this in 2025 andthen 26, we'll do a phase two.
(15:53):
I really do my best to try to deliver some value, even if it's
a no. I prefer to be like a no comma
no. But we have this.
This gets you 40% of the way there.
Can we start there and then put a plan together to to pick up
the rest of it in 20 in in 26? So it's not just a knee jerk
(16:16):
reaction philosophical. Well, this is something we don't
do. And so therefore the answer is
no. It's there's a, there's a reason
there's there's a. Yeah.
You know, I will say this, if it's a customer request that
comes in and it's just somethingthat's not in our wheelhouse or
you know, something that's not acore competency or yeah, yeah,
there are going to be times where you say, look, it's just
(16:37):
you, you have to be mature and self select out of it.
Or if it's a capability that just is, doesn't make sense for
us to build, we are always goingto try to find a way to partner
or build or, or serve our customers.
If it's internal, nobody wants the House of no.
I mean, Michael, you've been at this for a minute.
(16:58):
You and I probably sat here 15 years ago and talked about the
House of no and the CIO being the CI no and all of this other
kind of thing. Nobody wants that.
And I try never to say that, butit's, it's always, you know,
when I do have to decline or just say, you know, we just
cannot get to that this year or this cycle.
(17:21):
It's, it's usually followed by acomma.
But here's what we can do. And let's figure out a way
together, GM or CXO, how we can tell the story.
So maybe we can get some additional funding to do this.
So I, I, you know, there's, there's some to me, it's about
relationships and building and being transparent about how
(17:42):
decisions are being made in the company and, and why we're
choosing to do what we're doing.OK, let's go to some additional
questions. Joseph Puglisi on Twitter.
Ah, Joe up in Philly. Philly Joe.
OK, so you so friends. I know, Joe.
Great. And Joe says, will AI or other
(18:04):
innovations lower the cost of healthcare?
This is so needed in today's economic environment.
I think it has the potential to to perhaps do that.
Certainly we don't look at it that way.
We, we look at it from a quality, efficiency and privacy
(18:25):
point of view. So I, I think you're foolish not
to look at AI and say there's not going to be some sort of
art, you know, cost arbitrage. We hear, we read about it every
single day. I think Bill Gates said
something the other day, Michael, that he thought it
would replace physicians or replace many doctors or
healthcare roles. You know, I don't know how
(18:47):
comfortable we are with that just yet.
I, I think we're at the beginning of our AIAI journey.
To me, we still want humans in the loop.
Given, given the regulatory hurdles and the importance of
trust in the drug development process, humans are still very,
very much in the loop. And where AI plays a huge role
(19:09):
is tackling things that maybe machines are better at.
Looking at large volumes of data, finding anomalies,
assisting with document, you know, interpretation or image
interpretation, excuse me, beingable to make predictions, those
types of things. But we're still at a point where
I want a physician or I want an expert overseeing that work.
(19:34):
But yeah, I mean, the short answer to Joe's question is, I
mean, if there there's got to bea cosplay down the road, I mean,
AI isn't free and, and, but to me, it's less about removing
people. That's not the point.
To me. It's about getting our
scientists and our amazing people at Clairio to focus on a
(19:56):
higher level of challenge and augment them with the
technology. On LinkedIn, Greg Walter says
when implementing AI in trials, is each application of AI unique
to that specific trial or part of a standard AI process?
(20:20):
And he also asks is the AI home grown or partner?
The answer is yes. The AIS are models that are it.
Well, it depends. They are limited in
functionality to a certain a certain function and that model
can then be used across trials. They are trained more often than
(20:42):
not, they're closed models. So they are not continually
learning. So they are closed as of a
certain point. So whether that's a data privacy
model that anonymizes medical images, it's a QC or quality
control assist, a model that looks at early identification of
quality concerns to help us ensure timelines.
(21:04):
Maybe a Reed assist. We're having a, a, a radiologist
look at images and MRIACT scan those types of things and you're
identifying it's, it's identifying different things for
you and it can reduce inter and intra observer variability.
Those models are built and are fit for purpose for that
(21:24):
function, but they're utilized across across studies.
So and there's many more. We at this point we have 50
models, 50 plus models that are proprietary home grown across
ecoa, cardiac imaging, respiratory and precision
motion. We do have a proprietary Gen.
(21:48):
AI platform that we have built internally on enterprise class.
You can think open AI, you know,Gemini etcetera that are
private. We named her Claire.
We're not super creative, Michael.
So Clario, declare. I know that's a.
You can see the leap we took there.
(22:11):
And well, hey, you're in a conservative business, right?
The health. Care and if you saw the AI image
and I'll share it with you sometime.
If you saw the AI image that AI created, we said what would
Claire look like It it is absolutely terrifying.
It it it is terrifying. But what I love about it is a
(22:31):
year ago within the organization, if you had asked,
I don't know 1000 people, what is Claire?
They, they wouldn't have known maybe a year and a half ago
today you're now hearing people say, well, we're going to put
that in Claire or we're clearingit.
And, and it's become part of ourlexicon because we've built
(22:52):
capabilities to do a protocol summary, user story writer, RFP
and contract analysis, translations, you know, document
chat where you have a large volume of documents and you're
trying to ascertain what exactlyis and so many more.
And the line of people behind our AI team with just use cases
(23:15):
that for, for Claire and what we're doing is huge.
But going back to the question, we try to leverage third party
wherever we can as long as it's safe, it's private, but a lot of
our other proprietary models arebuilt on our home grown Aquarius
engine. When it comes to Gen.
AI and summaries, how do you ensure that there are no
(23:41):
hallucinations? For example, summarizing a
patient record, the AI could potentially think to itself,
think whatever that, whatever that actually means to the AI.
But the AI says given all of these symptoms, the patient must
also have XY or Z condition and then inserts it.
(24:04):
How do you how do you deal with that?
Constant oversight, human oversight.
First of all, we don't deal withanything that can be attributed
back to a, you know, a Michael Krigsman or a Jay Pharaoh or
anything like that. We deal with pseudonymized in
anonymized data. So first and foremost, you know,
we're not dealing with Joe Smithor, or, you know, Jason James or
(24:25):
anything like that. We're dealing with pseudonymized
and derived data, which is, which is super important #2
constant refinement of the model.
We have an internal AI team under my chief AI officer who
reports to me that I hired and brought on to the organization
last year, Marco Tapolovich. We are ruthlessly challenging
(24:50):
our models to look for bias and hallucinations.
One of the ways that I think I'mreally proud of is right out of
the gate when when I joined the organization and I realized that
AI was a big part of our story and we got a little bit of a
running start. When I I think when I joined, we
had maybe one model in production that was
scientifically validating. Today we're at 50 and and
(25:13):
growing, which is super exciting.
But one of the ways you do that is transparency within the
organization. You want organizational literacy
on what AI is, what it isn't, what we're doing, why we're
doing it, more importantly, whatwe're not doing.
And so we created a group right out of the gate, including my
General counsel, Lauren Nishtal,my Chief Medical officer, Todd
(25:35):
Rudo, my CISO Mortazanisar, my course, my Chief AI officer,
Marco that I talked about, my head of quality, Todd, and, and,
and all unified with our passionfor the technology, but all
coming at it from a different point of view and making sure
(25:56):
that everything we're doing is built on trust, transparency,
responsibility, bias control, etcetera.
And I urge anybody out there that is just at the beginning of
their AI program, do not approach it as a CIO only or a
CTO only. Don't do it, OK?
Don't throw all your stuff in anLLM and be like, we're on AAI,
(26:18):
man, we're great. You know, when cloud came out,
you just started throwing stuff in the cloud and you wondered
why, you know, proprietary information was suddenly gone.
You don't want to do that. Do it in a controlled way.
And people immediately hear that, Michael, and what do they
say? Oh my God, I involve attorneys
and whoo, that's just going to slow down in absolutely not.
(26:39):
If anything, it has speed it up because there's no daylight
between US and because our customers know that we're
approaching it for a from a responsible point of view.
And the last thing I'm going to do is put a model into
production that my general counsel, my head of privacy, my
head of quality, my Chief medical officer, my CSO, my CEO
(27:02):
for that matter of course that that we all haven't seen and are
are signed off on that we did itthe right way and that we did it
responsibly. So, you know, if you're tackling
this as an IT problem, I just, Iencourage listeners to, you
know, broaden open the aperture a little bit.
We have another interesting question again from Arsalan Khan
(27:23):
on Twitter. How do you use AI on clinical
trials? And of course, what's the
impact? But he's also wondering, would
removing AI guardrails and regulations make clinical trial
trials better or faster? You remove regulation, would
(27:44):
they make them faster? Maybe.
Would they make them better? No.
I, I, I would argue that the FDAand I have had numerous
conversations as recently as this week with our colleagues
over at the FDA. They are very bullish on the
technology. They're doing it the right way.
I think they're moving at a speed.
I think people hear FDA and whatdo they think the red tape
(28:05):
right, you know, grind to a halt, see you in three years.
And, and that is just not true. I, I might have said that coming
into the organization five yearsago, it would, it would have
been an ignorant judgement. And I have learned that our, our
colleagues in the FDA absolutelywant to do what is best for
(28:28):
people. They want to protect people, but
they want these, these drugs andthese therapies to be trusted,
safe, effective, and they want them to improve lives.
That's what they want. And, and in so much as AI can
impact that, they, they want to lean into AI.
(28:50):
Now, the first part of this question, which I think is
really, really good is where canAI, where does AI fit into the
clinical trial? Now, I'll, I'll give some
examples where I, where I'm seeing really good effectiveness
in the trial process. We don't play in all of these
roles and I've talked a little bit about how we use it, but
(29:11):
patient recruitment and retention, finding and enrolling
the right patients in a trial isnot easy.
There's not a line of people hands, you know, standing right
now, you know it. It can be time consuming,
expensive, difficult patient retention.
You're taking a a treatment or apill or an injection or whatever
(29:34):
over time that may or may not have side effects that you may
not have just real world things that impact you.
Your ability to participate in that trial and retaining
patients is key and it's an important challenge.
And so minimizing dropouts is absolutely key.
(29:54):
What can AI do about that? Well, first of all, it can.
It can manage or analyze large data sets, potentially
electronic health records, genetics, social determinants.
It can identify potential candidates who are likely to
meet trial criteria faster, moreeffectively using predictive
(30:16):
capabilities. It could predict drop out risks
so that those can be mitigated ahead of time where you can say,
hey, look, there's a a chance that Michael may or may not make
the cut because of we're seeing those behaviors.
And that way I can intervene potentially and keep Michael in
the trial. That's a huge application in
ripe for AI disruption. I think trial design and
(30:39):
optimization, where we do play arole.
Trials are complex, intentionally so, because
they're important and there's a lot of science behind them, and
you want scientific rigor, you want meaningful results.
But a protocol can be very, verycomplex.
You're balancing patient safety,statistics, cost effectiveness,
(31:05):
so many other things. And AI can simulate different
trial designs and predict outcomes with various
configurations. Now you wouldn't want to rely on
that alone, but it can at least directionally move you in a in a
path to optimize a protocol and looking for the most effective
(31:26):
study designs, not just cost effective overall effectiveness,
which ultimately would have the benefit of speeding up the
development process, which is isexciting.
And those are just a couple of top of mind examples where I, I
see AI making a, you know, a huge, a huge difference.
We have another related questionthat's come in from LinkedIn
(31:49):
from Lori Noregov. Before we get to Lori's
question, I just want to remind everybody that we have shows
like this every week. Subscribe to the CXO Talk
newsletter. Just go to cxotalk.com so you
can sign up. We want you as part of our
(32:10):
community so that you can participate and it's fun and
it's great. So sign up for our newsletter.
And here is Lori's question. He says, Does regulatory
compliance create limitations for some of the more advanced AI
(32:31):
use cases? If yes, what would those be?
And how do they work with regulators for making innovation
more accessible? The regulatory environment being
what it is, and keep in mind this is not the FDA only.
You have the EMA and you have other regulatory bodies all
around the world all with their own criteria.
(32:53):
The FDA is very thoughtful and very measured in what they do,
as are many other regulatory bodies filled with smart people
who want to do the best for the most.
This is to me about partnership with the regulatory bodies and I
don't mean like Co development or anything like that, but
(33:17):
understanding what the regulations are, understanding
the hurdles and the barriers that we can that, that we can
overcome. Showing them.
I'll, I'll be honest in the question is such a good one.
And I think, and I don't want tocome off as an FDA Homer or
anything like that, but as I've gotten to know them, I, I will
say they want to do the right thing.
(33:39):
And I don't see them at all as aquote obstacle or anything.
I think a bigger obstacle is organizational readiness.
If you said Jay is at the FDA orregulatory body and to be sure
we are in a really highly regulated and for good reason.
We're in a highly regulated industry and we have to be
right. Patient safety is absolutely
(34:01):
paramount and trust is absolutely paramount and the
science behind everything we do is absolutely important.
The reason I say that it's organizational readiness is
because you're dealing with verycomplex.
You're dealing with an industry that does not pivot overnight.
(34:21):
We've seen it with healthcare over time.
We saw it with fintech in the 90s and the 2000s, etcetera.
If you had gone back in time andasked somebody in the 90s, hey,
how safer is it to to use a cellphone for millions of dollars of
financial transactions, they would have looked at you like
you sprouted and tell that'll never happen.
You got to have people, you got to have whatever.
(34:43):
And here we are today moving money all around the world at a
moment's notice. It's going to change, but you're
still seeing organizational resistance.
I, I think in some ways for goodreason, because there's a, a bar
of rigor that you want to hit. Part of it is just culture
change and making sure that people understand the power of
AI in this truly unique fundamental force that we have.
(35:07):
So a lot of it comes from education as well in overcoming
some of the organizational inertia in older thinking.
I, I mean, look, I'm, I'm not a want to be too hard on people,
Michael, but I mean, if you had walked into A and, and, and
we're hanging out with a 25 yearveteran in the fintech space in
1994 and said, I'm going to paint a picture of what the
(35:29):
future is going to look like, they would have been like, no,
let me tell you the 52 reasons why that's not going to work.
Lori clarifies his question to say How do you work with
regulators to make innovation more accessible?
I was at a conference not too long ago, Lori, you, you'll
appreciate this. And it was an FDA panel and, and
(35:56):
one of the FDA guys that, and I won't say his name because I, I,
I just, it's not important. But he came off and I, I greeted
him because I just was blown away by the, the discussion.
And it was all about AI and whatthey thought of it.
And it just was in, it went in atotally different direction.
And I said, and I almost asked Lori's question verbatim.
(36:17):
And I was like, how do we partner with you?
You can e-mail me. And I was like, oh, oh, well,
that's, I mean, that's interesting.
You know, we, we want to hear, we, we want to learn.
But to put a finer point on, I would say proactive
collaboration, engage early. How do you do that?
(36:39):
Well, talk to them, OK, this isn't an ivory tower that I, I
will say every FDA person or every regulatory person that
I've ever talked to has been willing to have a conversation.
So establishing clear communication and being
proactive with the regulatory authority, whether it's the FDA
and, you know, EMA, etcetera #2 understand regulatory
(37:04):
frameworks, show that you're compliant with existing
guidelines, making sure that you're familiar and you
understand what they're trying to accomplish and that you're
not coming at it from a purely tech point of view.
And be like, yeah, but if we could just release the
guardrails and let this, you know, thing go nuts, imagine all
the good probably, yeah, maybe some truth to that.
(37:26):
But I think the reality is that the regulatory frameworks exist
for a reason. They change over time.
Guidance has changed over time. If you had said 20 years ago,
we're going to have an AI reasonable use policy, I think
most companies would've been like, why the hell do I need an
AI reasonable use policy, right?So making sure you're familiar
(37:48):
with existing guidelines and, and that you're adapting your AI
to fit that compliance, I think that starts you off in a, in a
very good conversation with, with regulators, making sure
that you emphasize safety and risk management with regulators.
The FDA often requires risk management strategies.
(38:09):
They don't want to hear about the art of the possible without
the other side of the coin. What are you doing to manage
risk to ensure that the AI technologies that you're
proposing are safe and that any potential risk to perhaps the
patient, the study, etcetera areare are mitigated?
(38:29):
And the last thing I'll say, thelast one is develop a regulatory
friendly development practice, which I kind of harped on at the
beginning, following best practices, having SO PS in place
that can pass regulatory scrutiny, transparent and
training models, bias mitigation, all of those types
(38:55):
of things. Build trust with regulators.
And on this topic of transparency, Arsalan Khan from
Twitter comes back and he says if we want transparency across
industry, should companies sharetheir next AI with other so
collectively whole industries can get better?
And what about the role of gatekeepers in all of this?
(39:20):
I do think there's an opportunity across industries to
promote transparency and Dr. positive outcomes across the
industry, whether it's healthcare, life sciences,
fintech, etcetera. I think I, I think there are
some benefits of doing that. Certainly speed collaboration
(39:45):
for innovation and you're going to get more faster generally
public confidence. I think when they see
collaboration and trust and transparency across industry,
there's certainly an opportunityfor better regulation.
And you're seeing that already with Europe, right?
(40:06):
Their AI, their AI regulations are not just for life sciences
or healthcare. It's AI in general matter of
time before the United States does it either at the state
level or the OR the federal government level.
Certainly you have some draft guidance already out there, but.
(40:26):
I think it speeds that up and I think you get a higher quality
of regulation when there's more collaboration and sharing.
So there are a lot of reasons toto do it.
I'll just invention for folks that are interested in Europe,
European adoption of AI and regulatory efforts.
(40:46):
Just a few weeks ago, we had twomembers of the House of Lords
discussing these exact issues here on CXO Talk.
These are folks who are creatingpolicy in the UK.
So if you're interested in this topic in Europe and the UK
specifically, just go back and look at CXO talk we had.
(41:06):
It was a really great discussion.
Well, I'm sure, I mean, look, they are always at the forefront
of regulation. And I don't mean that in a
snarky way, but I mean I I thinkthey've done a nice job in
balancing innovation with risk mitigation, transparency and
accountability. It's not perfect, nothing ever
is, but the E the EUAI Act, I mean, they came out of the, I
(41:35):
mean, what is that in 21? I think they launched that and
and which is crazy. It it, it, it was very, very
well thought out. And what I like about it is it
triages risk, unacceptable, high, limited, minimal,
etcetera, right. So this, this risk approach
(41:58):
where, hey, if it's the system that poses little to no risk,
it's an AI in a video game, it'san AI in a spam filter.
Have at it. You know, you're going to have
to check a few boxes, you're going to have to do a few
things. But yeah, we, we don't want to
slow stuff like that down, right?
And and it it seems to me they at least tried to right size the
(42:22):
regulation which is which is good.
So we have another question fromGreg Walters, Who is aware that
you have a background in cement.You can tell us about that and.
Outing me like that, Greg. All right.
And Greg Walters wants to you tocompare AI to quote cementitious
(42:46):
materials, given the fact that concrete has been around for
thousands of years. Every industry is more alike
than not. I, I know we like to not think
that, right. I, I, I think every industry
thinks it's a, a perfect little snowflake, Michael, that you
know, that that is, it's a Unicorn, every little thing.
(43:09):
And to be sure, every industry has nuance and has its own
lingo. It's got its own ecosystem and
it's got its own special spin ona regulation or whatever.
But I can tell you the discipline of product
management, technology management, financial
management, software development, security, etcetera
(43:34):
is 80% the same across industry.So you if you look at
manufacturing my time as the CIOof the Quikrete companies and
even before that, you know at AIG or the American Cancer
Society or EarthLink, you were doing many of the same things.
Now the size, the scale, the severity may be different, but
(43:56):
when are you not protecting customer data, when are you not
trying to deliver high quality results on time?
When are you not trying to leverage emerging technologies
to deliver a higher quality product or have a, an amazing
customer experience or improve operations or drive revenue.
(44:18):
And, and I would challenge CI OSand CT OS and aspiring CX OS to
think about things that way and not box yourself into one
particular industry. But how is cement like AII know
there's a punchline, Greg, somewhere out there and I don't
know what it is. Maybe, maybe somebody will
suggest it, but. Joe P on Twitter comes back and
(44:42):
he says, how do you keep your team focused on the outcomes and
avoid becoming enamored with thetechnology?
In other words, avoiding shiny new object syndrome.
Education transparency, I'm building financial literacy
education, educating the team onwhy we exist as a company and
what what does that really mean?That those are all a lot of
(45:03):
really flowery words. Site visits, discussing with
patients, showing them what we do, visits from, you know,
different departments, building those relationships so that
people, no matter where they arein the organization, in my team,
I don't care if your health testlevel 1, I don't care if you're
a coder and you're a junior person that just joined the
(45:24):
team. I want them feeling the
importance of what we do. I want them recognizing that
there's a patient at the other end that is using an app on
either on a provision device or a phone or is using a connected
device, whether that's a a bloodpressure device or a respiratory
device in clinic that we built or that we built an interface
for or that we have AI integrated with.
(45:46):
I want them understanding that what they're doing has real
human impact around the world. And when you do that, yeah,
you're still going to get excited about the technology.
It's what we do, but it's technology with purpose.
It's not technology for technology.
So we try to do a lot of education, Michael and Joe P,
(46:07):
but and keep people focused. So what does that mean in
practical terms? Site visits, site education,
voice of the customer type activities, making sure we're
hearing from patients, making sure we're showing teams the
impact of the technology that they're building and that
they're delivering, whether thatmeans, you know, hearing
directly from customers, etcetera, so that they
(46:28):
understand the impact. It wasn't just a bunch of code
on time, which is nice. I want that, but there's purpose
behind it. On this topic, Arsalan comes
back again, and he's wondering about the future of AI.
Let's keep it focused on clinical trials.
And, he says, as AI becomes increasingly more of a
(46:52):
commodity. If DeepSeek has showed us
anything, it's that I, I think the idea of a, if a trillion
dollar, one-size-fits-all one magical product to rule them all
is not going to be long for the world.
I, I, I think there will be somecommoditization of, of, of
technology over time. And I think what you'll see is
(47:15):
far less one giant model and farmore fit for purpose.
What, what the future I see, I see some amazing things
happening in the future, particularly in healthcare, in
clinical trials, keeping it speed, improving in quality
patient experience. What, what does that mean?
(47:38):
It means if a patient is having a bad day or there's, you know,
there's, there's just you're, you're, there's just some
empathy that even models today can show more time with an
investigator or a physician. If it's healthcare, physicians
will tell you constantly and nurses will tell you constantly.
I'm sure you everybody knows this.
They spend more time entering things into an EMR than they do
(48:00):
with a patient. Emr's are designed for one
thing. What do they do?
Or two things to bill and to keep street legal.
And I'm not trying to be depressing or snarky or anything
like that. And I don't mean anything bad to
our friends at the big EMR companies, but there's a huge
opportunity for AI whether to listen to the physician as he's
talking, to transcribe notes, where they can turn around and
(48:24):
spend even another minute or twowith a patient and improve that
patient experience. So I am really excited about
what the future holds, particularly for patients and
better outcomes. The through line of our
conversation seems to be customer experience and broaden
it, patient experience, whateverdomain that might be.
(48:47):
I think that's true. I, I look, if not them, then
who? I I mean, if you're not doing it
for for them, then why are you doing it?
And and look, you and I, if I take off the motherhood and
apple pie hat and say, OK, Jay, yeah, patient experience is
super important. But really, companies exist for
shareholder value and to make money.
(49:09):
Fine, if you're not wrong. I would argue that one of the
best ways to do that is an amazing patient experience and
an amazing customer experience. If I'm making my sites happy or
at least less mad at me or am I providing A frictionless
experience and they always know what the other end is, a company
or a team that is working hard to do the best.
(49:32):
We're not always going to be right.
We're not always going to get itright, but we're always doing
what's important and what's the best for them and for their
patients. If they know that, I would argue
that they're going to want to continue to do business with
Clario and they're going to continue want to continue to
partner with us. So I think the two are
inextricably linked. I, I, I think you can have an
amazing patient experience and amazing site experience and
(49:54):
sponsor experience maintaining scientific rigor, high quality,
privacy, speed, and all the other things that we want to do.
And you can still make money as an organization and build
shareholder value and do all of those things.
And I would argue that if I can do the first one really, really
well and I'm making good choices, the second one will
come. What advice do you have to
(50:17):
business and technology leaders in the enterprise when it comes
to developing AI projects and scaling those projects as you
have done? Don't do it in a silo.
Whereas my old boss used to tellme a highly polished cylinder of
excellence. Don't look.
Look partner, you know. Talk to your partner ecosystem,
(50:38):
other CI OS, other CX OS, talk to your suppliers, understand,
educate yourself, but talk to your, your peers in the
organization and work with them,not against them.
Whatever you do, don't make it a, a, a tech only, tech only
endeavor. In no way does that minimize the
importance or the, the excitement about the technology.
(50:59):
But I promise you, if you can doall of those things, it will
speed up what you're trying to accomplish.
And is always Michael, and I think I said this on our first
interview. Think big, start small, scale
fast. Think big, start small, scale
fast. Great advice.
Hey, we have one last question that's come in very quickly from
(51:20):
again from Luri Adnorigov, who says, do you feel like we have
the right people training mindset, the site patients,
nurses, etcetera to enable and provide all of the data needed
for the AI applications or is the technology far in front of
(51:43):
actual adoption? There's a huge educational
hurdle that we have to hit. There are people at the sites
and the, you know, with the patients, I I think they are
just handed a lot, right? They are handed a lot of
different devices and it and, and I promise that it's not
because they are not smart people or hard working people.
It's just a lot. And if I mean, the first part of
(52:08):
the question is do we have the right people?
Yeah, I mean, the ecosystem of of investigators and sites are
amazing and and they work their tails off and they're more than
capable of picking this up. I think it's on us to train.
It's on us to show them the power.
It's on us to make it simple. I think it's going to require
patience, Michael. I do not think we're going to be
able to hand them a magical gokulator and say, you know, and
(52:31):
have them be enamoured with the tech in, in one day.
It's going to take some time. And so we have to be, I think a
little patient as we kind of change the paradigm.
But I'm I'm, I'm optimistic that, you know, we have the
right people. We just got to meet them where
they are. And on that note, Jay, a huge
thank you. I'm so grateful for your taking
(52:53):
the time to be here with us today.
Really, really appreciate it. My pleasure, Michael.
Thank you for having me. And thank you to everybody who
watched you guys who asked such amazing questions.
You are so smart. Now is the time to subscribe to
the CXO Talk newsletter, go to cxotalk.com, join our community,
(53:15):
participate every week, and we'll see you again next time.
Take care everybody, have a goodday.
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