June 11, 2026 • 10 mins
In this episode of "Full Tech Ahead," host Amanda Razani interviews Mark Talbot, AVP Customer Success AI Incubation at Appian. They discuss transitioning enterprise AI from isolated experiments into governed production workflows, focusing on recent research conducted in collaboration with Harvard Business Review.
Talbot reveals a stark contrast in enterprise adoption: while 59% of organizations have AI in production, only 16% realize a high degree of measurable value. He attributes this gap to a failure to embed AI directly into core business workflows, as well as the mistake of applying AI to inefficient, broken legacy processes.
To scale successfully, Talbot advocates for the creation of AI Centers of Excellence (CoEs) to manage data fabric, fragmentation, and strict compliance (such as SOC 2 and FedRAMP).
Moving forward, he predicts a shift away from disconnected chatbot tools toward unified, automated platforms that offer full auditability, traceability and concrete business results.
Key Quotes
- "My lens is always where does AI fit into real work in a way that's secure, measurable, and scalable?"
- "Only sixteen percent realize a high degree of measurable value from those investments... because only eighteen percent said AI is primarily integrated into workflows."
- "If you have AI chat and you have ten thousand employees, you have ten thousand different ways of doing things. That's one of the reasons why AI needs to be embedded into existing workflows."
- "Prioritize sustainable implementation and the long term rather than chasing every AI trend."
Takeaways
- Embed AI in Workflows for True ROI: Running isolated AI experiments or simple chat windows doesn't drive top-line business growth. Organizations that embed AI directly into automated, existing workflows report significantly higher value (70% reporting moderate to substantial success) because it systematically removes human toil.
- Empower AI Centers of Excellence (CoEs): Scaling AI requires organizational discipline. Establishing an AI CoE ensures that the company maps performance metrics before and after AI deployment, maintains strict data logging, and keeps the enterprise out of the headlines for data security failures.
- Demand Traceability and Auditability: In complex, regulated environments, governance is non-negotiable. Successful deployments rely on platforms (like Appian) that provide built-in compliance frameworks (SOC 2, ISO, FedRAMP) and offer clear explainability for every decision the AI makes.
- Move Beyond Chatbots and Model Hype: The era of comparing LLMs or relying on generic chat screens is fading. The future belongs to structured platforms where the technology is invisible, secure, and seamlessly integrated into day-to-day operations to deliver scalable efficiency.
Find Amanda Razani on LinkedIn. https://www.linkedin.com/in/amanda-razani-990a7233/
Follow the FTA LinkedIn Page: https://www.linkedin.com/company/full-tech-ahead/
Visit the FTA website: https://fulltechahead.com/
Check out the Substack Channel: https://fulltechahead.substack.com/
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Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
SPEAKER_01 (00:19):
Hello and welcome to Full Tech Ahead.
I'm your host, Amanda Razzani.
And with me today, I'm excitedto be here with Mark Talbot.
He is AVP Customer Success AIIncubation at Appian.
How are you doing today?
SPEAKER_00 (00:34):
I'm well, Amanda.
Thank you for having me.
SPEAKER_01 (00:37):
Happy to have you on this show.
So can you share first a littlebit about Appian and the
services that you provide?
SPEAKER_00 (00:44):
Right.
So at Appian, I focus on helpingcompanies move AI from isolated
experiments into governedproduction workflows.
So at Appian's core strength isprocess orchestration.
So my lens is always where doesAI fit into real work in a way
that's secure, measurable, andscalable?
SPEAKER_01 (01:06):
Fantastic.
Well, we're going to jump rightin today's topic: how
organizations move beyond hypeto deploy automation and AI in
secure, scalable ways,particularly in complex
regulated environments.
And I want to start with yourcollaboration recently with
(01:26):
Harvard Business Review on someresearch around enterprise AI
adoption.
Can you share more about thatand some of the key takeaways?
SPEAKER_00 (01:36):
Yeah, the research was interesting.
So one thing we found was 59% oforganizations already have AI in
production, but only 16% realizea high degree of measurable
value from those investments.
So many companies are alreadyusing this to drive productivity
at 64%, operational efficiencyat 58%, but they're not seeing
(01:59):
that impact on top-line businessgrowth.
And one thing is interestingonly 18% said AI is primarily
integrated into workflows, whichwe believe that explains why
many companies are struggling toreceive impact from AI.
But if you look at the datacarefully, organizations
(02:20):
embedding AI directly intoworkflows are seeing stronger
outcomes.
So 70% of those organizationsreported moderate or substantial
value from those efforts.
SPEAKER_01 (02:32):
Yes, I think this is a struggle for many.
So let's let's talk about that.
What are some of the biggestbarriers or roadblocks that
you're seeing?
And what advice do you have toovercome those?
SPEAKER_00 (02:48):
Right.
So typically they try to applyAI to broken or inefficient
processes.
They're focusing too much on thetechnology and not enough on the
implementation strategy.
So what they'll do is they'llunderestimate governance,
security.
I mean, some of the data qualitychallenges that are out there.
I mean, they'll treat AI as thisstandalone tool instead of part
(03:12):
of an integrated workflow.
So, I mean, that's what we see.
SPEAKER_01 (03:17):
Mm-hmm.
Well, I mean, we've seen anexplosion in the AI space
because of course it has rapidlyadvanced over the last few
years.
And so now that we are hittingthat um phase where
organizations are looking forROI and how to improve, where
are they falling short and why?
SPEAKER_00 (03:39):
Right.
So earlier on, 2024, 2025, wewere focused on experimentation.
Now organizations needmeasurable business outcomes.
So executives are asking whereAI is improving revenue,
decreasing risk, and decreasingcosts.
So I think what companies aredoing is that they're becoming
(03:59):
more strategic and selectivewith their AI investments.
So what it depends on now isthat operational execution and
just not the innovation that wegot away with earlier on.
SPEAKER_01 (04:11):
Do you think this is a big communication issue or a
training issue or all the above?
SPEAKER_00 (04:18):
I think it's all of the above.
I mean, I think part of it is aum communication issue.
So I think it's a lack ofunderstanding of what those
common barriers are.
So you'll have siloed data,you'll have lack of a data
fabric that some organizationsprovide.
You'll have fragmented systemswhere you need to bring that
(04:40):
data to the AI.
And there's also governanceconcerns, right?
You want to make sure you'reable to act on that AI safely.
So really, when you start toexecute on AI, I mean, what it
requires is organizationaldiscipline.
You need to have long-termplanning on what you need to do
(05:01):
to get AI approved within yourorganization.
So really, the most successfulcompanies, what they're doing is
they're taking a reallyintentional, organized and
phased approach.
SPEAKER_01 (05:14):
Okay.
Well, what separates companiesthat are seeing meaningful
business value from AI, fromthose that are stuck in pilot
mode?
SPEAKER_00 (05:24):
Right.
So successful organizationsstart with clear business
problems and measurable goals.
AI works best when embedded intoworkflows and existing
operations.
So they'll take a workflow,they'll see areas that have a
large amount of, I would say,unnecessary human toil, and
(05:45):
they'll look at how AI canautomate that work.
I think that involvescross-functional alignment with
IT and the business andgovernance with your AI center
of excellence.
I mean, I think that's reallycritical to scaling
successfully.
SPEAKER_01 (06:03):
I keep hearing that term a lot.
That's becoming really a popularterm is the AI centers of
excellence.
Uh, what kind of impact do youthink is being made there?
Is that having a big impact,having these AI centers of
excellence?
SPEAKER_00 (06:18):
I mean, I think it is.
I think there's lessons learnedwhen deploying AI.
I think there's certainquestions you need to ask from a
data governance perspective tomake sure that your uh data is
safe.
I mean, the last thing that youneed in your organization is for
you to make the headlines forthe wrong reasons.
(06:38):
These AI centers of excellencealso ensure that you have
measurable business goals goinginto your AI application and you
have measurable businessoutcomes to ensure it's
successful.
So these organizations are goingto ask, okay, well, how is it
performing before you introduceAI?
How is it performing after AIwas introduced?
(07:01):
What tools are you using tomeasure success?
How do you know the data issafe?
What type of logging are youdoing?
What type of auditing are youdoing?
What type of measurement are youdoing?
So I think all those things yourAI centers of excellence take
into consideration.
SPEAKER_01 (07:20):
Well, there at Appian, um, you probably have a
lot of experience with this.
Um, how does governance,security, and workflow
integration shape thosesuccessful AI deployments?
SPEAKER_00 (07:33):
I mean, they're they're non-negotiable.
So one of the nice things aboutthe Appian platform, the
auditability is built in.
I would say the governance isbuilt in if you're on cloud.
We have third-party auditorsthat come in ensure that we're,
you know, SOC2 compliance, ISOcompliant, FedRamp compliance.
And this is important to makesure that your data is safe.
(07:57):
And that's a part of theconversations when we're talking
with these AI center ofexcellence.
They want to make sure thatevery interaction with the AI is
audited so that they can see howtheir users are interacting with
the AI.
They need that to make sure theycan improve it.
They need to make sure thatthere's explainability and
(08:18):
traceability to how the AI cameto that decision.
So that's why it's it's usefulto have that framework to ensure
that you're doing that withevery AI workflow that you're
building.
SPEAKER_01 (08:30):
Absolutely.
Well, looking forward, as wementioned, AI is advancing so
quickly.
What do you envision for thefuture of AI?
What's the next thing thatbusiness leaders need to be
thinking about?
SPEAKER_00 (08:43):
Yeah, I think there's going to be less focus
on hype and model comparisons.
So less focus on am I usingClaude or am I using some of the
GPT models and more focus onthose business outcomes, more
focused on trust, more focus ongovernance, more focused on
(09:06):
operational efficiency.
And I believe AI is going to bemore embedded into existing
workflows and systems, andthere's going to be less focus
on chatbot.
I mean, one of the things I'venoticed is if you have AI chat
and you have 10,000 employees,you have 10,000 different ways
of doing things.
So that's one of the reasons whyAI needs to be embedded into
(09:30):
existing workflows.
And so I think whatorganizations are going to do is
they're going to prioritizescalable and secure platforms
over these disconnected chattools.
SPEAKER_01 (09:42):
Well, if there was one key takeaway you could leave
our audience with today, whatwould that be?
SPEAKER_00 (09:48):
I think it's going to be start with clear business
challenge and measurable goals.
Focus on workflows where AI candrive operational improvements.
So I would say build governanceand change management early.
And I would prioritizesustainable implementation and
the long term rather thanchasing every AI trend.
SPEAKER_01 (10:09):
Okay, great advice.
Well, thank you so much forcoming on the show and sharing
your insights with us today.
SPEAKER_00 (10:16):
Thank you for having me, and we'll talk soon.
SPEAKER_01 (10:18):
All right.
And thank you to our audience.
If you have any questions orcomments, leave those below, and
I'll try to respond as soon aspossible.
And until the next podcast, havea wonderful week.
Hello and welcome to Full Tech Ahead.
I'm your host, Amanda Razzani.
And with me today, I'm excitedto be here with Mark Talbot.
He is AVP Customer Success AIIncubation at Appian.
How are you doing today?
SPEAKER_00 (00:34):
I'm well, Amanda.
Thank you for having me.
SPEAKER_01 (00:37):
Happy to have you on this show.
So can you share first a littlebit about Appian and the
services that you provide?
SPEAKER_00 (00:44):
Right.
So at Appian, I focus on helpingcompanies move AI from isolated
experiments into governedproduction workflows.
So at Appian's core strength isprocess orchestration.
So my lens is always where doesAI fit into real work in a way
that's secure, measurable, andscalable?
SPEAKER_01 (01:06):
Fantastic.
Well, we're going to jump rightin today's topic: how
organizations move beyond hypeto deploy automation and AI in
secure, scalable ways,particularly in complex
regulated environments.
And I want to start with yourcollaboration recently with
(01:26):
Harvard Business Review on someresearch around enterprise AI
adoption.
Can you share more about thatand some of the key takeaways?
SPEAKER_00 (01:36):
Yeah, the research was interesting.
So one thing we found was 59% oforganizations already have AI in
production, but only 16% realizea high degree of measurable
value from those investments.
So many companies are alreadyusing this to drive productivity
at 64%, operational efficiencyat 58%, but they're not seeing
(01:59):
that impact on top-line businessgrowth.
And one thing is interestingonly 18% said AI is primarily
integrated into workflows, whichwe believe that explains why
many companies are struggling toreceive impact from AI.
But if you look at the datacarefully, organizations
(02:20):
embedding AI directly intoworkflows are seeing stronger
outcomes.
So 70% of those organizationsreported moderate or substantial
value from those efforts.
SPEAKER_01 (02:32):
Yes, I think this is a struggle for many.
So let's let's talk about that.
What are some of the biggestbarriers or roadblocks that
you're seeing?
And what advice do you have toovercome those?
SPEAKER_00 (02:48):
Right.
So typically they try to applyAI to broken or inefficient
processes.
They're focusing too much on thetechnology and not enough on the
implementation strategy.
So what they'll do is they'llunderestimate governance,
security.
I mean, some of the data qualitychallenges that are out there.
I mean, they'll treat AI as thisstandalone tool instead of part
(03:12):
of an integrated workflow.
So, I mean, that's what we see.
SPEAKER_01 (03:17):
Mm-hmm.
Well, I mean, we've seen anexplosion in the AI space
because of course it has rapidlyadvanced over the last few
years.
And so now that we are hittingthat um phase where
organizations are looking forROI and how to improve, where
are they falling short and why?
SPEAKER_00 (03:39):
Right.
So earlier on, 2024, 2025, wewere focused on experimentation.
Now organizations needmeasurable business outcomes.
So executives are asking whereAI is improving revenue,
decreasing risk, and decreasingcosts.
So I think what companies aredoing is that they're becoming
(03:59):
more strategic and selectivewith their AI investments.
So what it depends on now isthat operational execution and
just not the innovation that wegot away with earlier on.
SPEAKER_01 (04:11):
Do you think this is a big communication issue or a
training issue or all the above?
SPEAKER_00 (04:18):
I think it's all of the above.
I mean, I think part of it is aum communication issue.
So I think it's a lack ofunderstanding of what those
common barriers are.
So you'll have siloed data,you'll have lack of a data
fabric that some organizationsprovide.
You'll have fragmented systemswhere you need to bring that
(04:40):
data to the AI.
And there's also governanceconcerns, right?
You want to make sure you'reable to act on that AI safely.
So really, when you start toexecute on AI, I mean, what it
requires is organizationaldiscipline.
You need to have long-termplanning on what you need to do
(05:01):
to get AI approved within yourorganization.
So really, the most successfulcompanies, what they're doing is
they're taking a reallyintentional, organized and
phased approach.
SPEAKER_01 (05:14):
Okay.
Well, what separates companiesthat are seeing meaningful
business value from AI, fromthose that are stuck in pilot
mode?
SPEAKER_00 (05:24):
Right.
So successful organizationsstart with clear business
problems and measurable goals.
AI works best when embedded intoworkflows and existing
operations.
So they'll take a workflow,they'll see areas that have a
large amount of, I would say,unnecessary human toil, and
(05:45):
they'll look at how AI canautomate that work.
I think that involvescross-functional alignment with
IT and the business andgovernance with your AI center
of excellence.
I mean, I think that's reallycritical to scaling
successfully.
SPEAKER_01 (06:03):
I keep hearing that term a lot.
That's becoming really a popularterm is the AI centers of
excellence.
Uh, what kind of impact do youthink is being made there?
Is that having a big impact,having these AI centers of
excellence?
SPEAKER_00 (06:18):
I mean, I think it is.
I think there's lessons learnedwhen deploying AI.
I think there's certainquestions you need to ask from a
data governance perspective tomake sure that your uh data is
safe.
I mean, the last thing that youneed in your organization is for
you to make the headlines forthe wrong reasons.
(06:38):
These AI centers of excellencealso ensure that you have
measurable business goals goinginto your AI application and you
have measurable businessoutcomes to ensure it's
successful.
So these organizations are goingto ask, okay, well, how is it
performing before you introduceAI?
How is it performing after AIwas introduced?
(07:01):
What tools are you using tomeasure success?
How do you know the data issafe?
What type of logging are youdoing?
What type of auditing are youdoing?
What type of measurement are youdoing?
So I think all those things yourAI centers of excellence take
into consideration.
SPEAKER_01 (07:20):
Well, there at Appian, um, you probably have a
lot of experience with this.
Um, how does governance,security, and workflow
integration shape thosesuccessful AI deployments?
SPEAKER_00 (07:33):
I mean, they're they're non-negotiable.
So one of the nice things aboutthe Appian platform, the
auditability is built in.
I would say the governance isbuilt in if you're on cloud.
We have third-party auditorsthat come in ensure that we're,
you know, SOC2 compliance, ISOcompliant, FedRamp compliance.
And this is important to makesure that your data is safe.
(07:57):
And that's a part of theconversations when we're talking
with these AI center ofexcellence.
They want to make sure thatevery interaction with the AI is
audited so that they can see howtheir users are interacting with
the AI.
They need that to make sure theycan improve it.
They need to make sure thatthere's explainability and
(08:18):
traceability to how the AI cameto that decision.
So that's why it's it's usefulto have that framework to ensure
that you're doing that withevery AI workflow that you're
building.
SPEAKER_01 (08:30):
Absolutely.
Well, looking forward, as wementioned, AI is advancing so
quickly.
What do you envision for thefuture of AI?
What's the next thing thatbusiness leaders need to be
thinking about?
SPEAKER_00 (08:43):
Yeah, I think there's going to be less focus
on hype and model comparisons.
So less focus on am I usingClaude or am I using some of the
GPT models and more focus onthose business outcomes, more
focused on trust, more focus ongovernance, more focused on
(09:06):
operational efficiency.
And I believe AI is going to bemore embedded into existing
workflows and systems, andthere's going to be less focus
on chatbot.
I mean, one of the things I'venoticed is if you have AI chat
and you have 10,000 employees,you have 10,000 different ways
of doing things.
So that's one of the reasons whyAI needs to be embedded into
(09:30):
existing workflows.
And so I think whatorganizations are going to do is
they're going to prioritizescalable and secure platforms
over these disconnected chattools.
SPEAKER_01 (09:42):
Well, if there was one key takeaway you could leave
our audience with today, whatwould that be?
SPEAKER_00 (09:48):
I think it's going to be start with clear business
challenge and measurable goals.
Focus on workflows where AI candrive operational improvements.
So I would say build governanceand change management early.
And I would prioritizesustainable implementation and
the long term rather thanchasing every AI trend.
SPEAKER_01 (10:09):
Okay, great advice.
Well, thank you so much forcoming on the show and sharing
your insights with us today.
SPEAKER_00 (10:16):
Thank you for having me, and we'll talk soon.
SPEAKER_01 (10:18):
All right.
And thank you to our audience.
If you have any questions orcomments, leave those below, and
I'll try to respond as soon aspossible.
And until the next podcast, havea wonderful week.
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