From AI Hype to Real Business Results

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.

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Transcript

SPEAKER_01 0:19

Hello and welcome to Full Tech Ahead. I'm your host, Amanda Razzani. And with me today, I'm excited to be here with Mark Talbot. He is AVP Customer Success AI Incubation at Appian. How are you doing today?

SPEAKER_00 0:34

I'm well, Amanda. Thank you for having me.

SPEAKER_01 0:37

Happy to have you on this show. So can you share first a little bit about Appian and the services that you provide?

SPEAKER_00 0:44

Right. So at Appian, I focus on helping companies move AI from isolated experiments into governed production workflows. So at Appian's core strength is process orchestration. So my lens is always where does AI fit into real work in a way that's secure, measurable, and scalable?

SPEAKER_01 1:06

Fantastic. Well, we're going to jump right in today's topic: how organizations move beyond hype to deploy automation and AI in secure, scalable ways, particularly in complex regulated environments. And I want to start with your collaboration recently with Harvard Business Review on some research around enterprise AI adoption. Can you share more about that and some of the key takeaways?

SPEAKER_00 1:36

Yeah, the research was interesting. So one thing we found was 59% of organizations already have AI in production, but only 16% realize a high degree of measurable value from those investments. So many companies are already using this to drive productivity at 64%, operational efficiency at 58%, but they're not seeing that impact on top-line business growth. And one thing is interesting only 18% said AI is primarily integrated into workflows, which we believe that explains why many companies are struggling to receive impact from AI. But if you look at the data carefully, organizations embedding AI directly into workflows are seeing stronger outcomes. So 70% of those organizations reported moderate or substantial value from those efforts.

SPEAKER_01 2:32

Yes, I think this is a struggle for many. So let's let's talk about that. What are some of the biggest barriers or roadblocks that you're seeing? And what advice do you have to overcome those?

SPEAKER_00 2:48

Right. So typically they try to apply AI to broken or inefficient processes. They're focusing too much on the technology and not enough on the implementation strategy. So what they'll do is they'll underestimate governance, security. I mean, some of the data quality challenges that are out there. I mean, they'll treat AI as this standalone tool instead of part of an integrated workflow. So, I mean, that's what we see.

SPEAKER_01 3:17

Mm-hmm. Well, I mean, we've seen an explosion in the AI space because of course it has rapidly advanced over the last few years. And so now that we are hitting that um phase where organizations are looking for ROI and how to improve, where are they falling short and why?

SPEAKER_00 3:39

Right. So earlier on, 2024, 2025, we were focused on experimentation. Now organizations need measurable business outcomes. So executives are asking where AI is improving revenue, decreasing risk, and decreasing costs. So I think what companies are doing is that they're becoming more strategic and selective with their AI investments. So what it depends on now is that operational execution and just not the innovation that we got away with earlier on.

SPEAKER_01 4:11

Do you think this is a big communication issue or a training issue or all the above?

SPEAKER_00 4:18

I think it's all of the above. I mean, I think part of it is a um communication issue. So I think it's a lack of understanding of what those common barriers are. So you'll have siloed data, you'll have lack of a data fabric that some organizations provide. You'll have fragmented systems where you need to bring that data to the AI. And there's also governance concerns, right? You want to make sure you're able to act on that AI safely. So really, when you start to execute on AI, I mean, what it requires is organizational discipline. You need to have long-term planning on what you need to do to get AI approved within your organization. So really, the most successful companies, what they're doing is they're taking a really intentional, organized and phased approach.

SPEAKER_01 5:14

Okay. Well, what separates companies that are seeing meaningful business value from AI, from those that are stuck in pilot mode?

SPEAKER_00 5:24

Right. So successful organizations start with clear business problems and measurable goals. AI works best when embedded into workflows 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 they'll look at how AI can automate that work. I think that involves cross-functional alignment with IT and the business and governance with your AI center of excellence. I mean, I think that's really critical to scaling successfully.

SPEAKER_01 6:03

I keep hearing that term a lot. That's becoming really a popular term is the AI centers of excellence. Uh, what kind of impact do you think is being made there? Is that having a big impact, having these AI centers of excellence?

SPEAKER_00 6:18

I mean, I think it is. I think there's lessons learned when deploying AI. I think there's certain questions you need to ask from a data governance perspective to make sure that your uh data is safe. I mean, the last thing that you need in your organization is for you to make the headlines for the wrong reasons. These AI centers of excellence also ensure that you have measurable business goals going into your AI application and you have measurable business outcomes to ensure it's successful. So these organizations are going to ask, okay, well, how is it performing before you introduce AI? How is it performing after AI was introduced? What tools are you using to measure success? How do you know the data is safe? What type of logging are you doing? What type of auditing are you doing? What type of measurement are you doing? So I think all those things your AI centers of excellence take into consideration.

SPEAKER_01 7:20

Well, there at Appian, um, you probably have a lot of experience with this. Um, how does governance, security, and workflow integration shape those successful AI deployments?

SPEAKER_00 7:33

I mean, they're they're non-negotiable. So one of the nice things about the Appian platform, the auditability is built in. I would say the governance is built in if you're on cloud. We have third-party auditors that come in ensure that we're, you know, SOC2 compliance, ISO compliant, FedRamp compliance. And this is important to make sure that your data is safe. And that's a part of the conversations when we're talking with these AI center of excellence. They want to make sure that every interaction with the AI is audited so that they can see how their users are interacting with the AI. They need that to make sure they can improve it. They need to make sure that there's explainability and traceability to how the AI came to that decision. So that's why it's it's useful to have that framework to ensure that you're doing that with every AI workflow that you're building.

SPEAKER_01 8:30

Absolutely. Well, looking forward, as we mentioned, AI is advancing so quickly. What do you envision for the future of AI? What's the next thing that business leaders need to be thinking about?

SPEAKER_00 8:43

Yeah, I think there's going to be less focus on hype and model comparisons. So less focus on am I using Claude or am I using some of the GPT models and more focus on those business outcomes, more focused on trust, more focus on governance, more focused on operational efficiency. And I believe AI is going to be more embedded into existing workflows and systems, and there's going to be less focus on chatbot. I mean, one of the things I've noticed 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 why AI needs to be embedded into existing workflows. And so I think what organizations are going to do is they're going to prioritize scalable and secure platforms over these disconnected chat tools.

SPEAKER_01 9:42

Well, if there was one key takeaway you could leave our audience with today, what would that be?

SPEAKER_00 9:48

I think it's going to be start with clear business challenge and measurable goals. Focus on workflows where AI can drive operational improvements. So I would say build governance and change management early. And I would prioritize sustainable implementation and the long term rather than chasing every AI trend.

SPEAKER_01 10:09

Okay, great advice. Well, thank you so much for coming 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 or comments, leave those below, and I'll try to respond as soon as possible. And until the next podcast, have a wonderful week.