Alegeus Chief Growth Officer Melanie Hallenbeck recently sat down with Chief Technology Officer Pradeep Ittycheria for a conversation about AI, discussing key considerations for business leaders as they navigate their approach to this rapidly evolving technology.

Melanie: There’s a common misconception that AI is just a “tech thing” — something only developers or product teams need to worry about. But I see it as something that should be woven into the fabric of the entire organization. And that everyone, regardless of role, should be thinking about how it can help us work smarter. In your view, what role should AI play in shaping an organization’s strategy at the enterprise level?

Pradeep: AI is definitely not just for tech teams — it’s for everyone across the enterprise. I’ve seen this firsthand in companies I’ve worked with, in conversations with friends, and through stories from leaders across industries.

Legal teams are using AI to review contracts more efficiently, identifying potential risks before they become real issues. HR teams are streamlining hiring processes, answering employee questions, and creating more personalized onboarding experiences. Finance teams are leveraging AI for fraud detection and expense auditing, becoming remarkably effective at catching inappropriate spend. And in marketing, which is my background, AI is transforming how we understand customers, personalize campaigns, and produce content faster than ever before.

To me, the true power of AI is in its ability to clear the clutter. It removes repetitive, time-consuming tasks so that every team can focus on the work that truly moves the needle. It’s not about replacing people — it’s about freeing them up to do their best, most impactful work. That’s where real transformation happens.

So yes, AI isn’t just a technology initiative. It’s a business imperative — one that touches every part of the enterprise.

You just raised a really important point. So many people view AI as a threat, that it will take their job away, as opposed to understanding that you need humans to power AI. You need humans to supply it with the data. You need someone there to “train the brain.”

Absolutely. In the end, AI is all about humans using technology to drive value creation in ways that enable them to focus on high-value activities. If you look at every single epoch of evolution, like with the industrial revolution, it was about taking things that were repetitive, that could be done faster, and then focusing on other things that then improved all our lives. And so it’s that cycle again. It’s now AI, and it’s software and math and data, but the pattern is not new.

From your perspective, what kind of leadership mindset is needed to responsibly and effectively implement AI at scale?

Leading with AI at scale takes a different kind of a mindset. First, you have to be curious — curious enough to challenge how things are getting done today.

You also have to be very focused on making sure AI is actually driving value, not just chasing the hype. And there’s a lot of hype with AI, but as leaders, you want to be curious about how we’re doing things today, and how can they be done better.

You also have to be accountable for what you’re going to build with AI. It has to be responsible and explainable — it can’t be a black box where you don’t know why AI did something a certain way. And in the end, it always has to be human centered. It can’t just be about the technology.

I believe the best AI leaders don’t just think about how to make things faster. They think about how you can make things fundamentally better. And better not just for their teams, but also for their partners, and their partners’ customers.

That’s the mindset we really have to bring as leaders. We should be bold about the opportunity. Get excited about it. Be focused on the outcomes and be very, very thoughtful about the impact.

Where are you seeing the biggest ROI impact from AI?

At the most fundamental level, it’s about automation and better decision-making at scale. When you talk about taking manual, expensive tasks — something like extracting data out of documents, validating that data, and making that entire process fast and automated — what that really means is lower costs, faster service, preferably fewer errors, all leading to real savings for not just us but for our partners. Automation and decision-making are big areas where we see ROI.

At the same time, AI is really helping us unlock insights that are buried in massive amounts of data. Being able to take that data and extract insights, spotting exceptions, being able to answer questions, flagging opportunities — all those are areas where you can really start seeing the ROI come in.

The real win is not just about doing things cheaper. It’s about freeing up energy to grow. By taking those costs and friction out, we are helping our own teams, our partners to move faster and serve better and scale smarter.

What would you say are some common pitfalls or misconceptions business leaders should avoid when adopting AI?

We’re still in the early innings of AI. As you’d expect, there’s a lot of hype — and in many cases, it’s warranted. The value being created is real, and the potential is transformational.

But one of the biggest misconceptions is viewing AI as a magic bullet. It’s not. Nor is it brand-new. Technologies like data science, machine learning, and predictive analytics — all built on AI principles — have been around for decades. Generative AI feels more recent, but even that has been in development for over ten years. The truth is, AI isn’t inherently good or bad. It all comes down to the quality of the data it’s built on.

Another common pitfall is trying to boil the ocean — launching massive, overly complex AI initiatives that struggle to show quick wins. Like any business initiative, if AI doesn’t deliver tangible value early on, it’s tough to justify continued investment. The business will lose interest. The smart approach is to start small, focus on meaningful, measurable outcomes, and scale from there.

And let me say this again as the tech person in the room: AI is not just a technology project. It’s a business strategy. If it’s not directly linked to outcomes like cost reduction, revenue growth, or better customer experience, then it’s just noise. Success comes from laying the right foundation, staying focused, and aligning every effort with clear business goals. That’s how you win with AI.

We’re hearing a lot about “agentic AI” these days. Can you help the layperson understand what that means?

“Agentic AI” has become quite the buzzword — and for good reason! It really is transformative.

The simplest way to understand it is by comparing it to generative AI. Generative AI produces outputs, like what you see with ChatGPT or similar tools. It might generate code, write documents, create images, or summarize information based on patterns it has learned from data.

Agentic AI, however, takes it a step further. It doesn’t just generate something and stop; it takes action. It can execute tasks end to end, making certain decisions within boundaries you define. In other words, it can operate on your behalf.

For example, imagine a customer submits a paragraph with feedback or a new idea. A generative AI tool might summarize that input for you — that’s a helpful first step. But then you still have to take action, like drafting a thank-you email or picking up the phone.

With agentic AI, that entire flow becomes connected. It can not only extract and summarize the feedback but also send an appropriate response automatically. And it does this far more quickly and efficiently than a human would, enabling teams to scale their responsiveness and productivity.

Super helpful, thank you. Maybe we should change “agentic AI” to “action AI.”

That’s a really good way to put it. It is action AI.

How does an organization take the right approach to being AI-first?

The first thing I’d say is start off with your data. Without clean, connected, accessible data, AI really won’t deliver real results. Get your data house in order, because that’s the foundation of everything we do with AI.

Then, get really crystal-clear about where you want to move the needle. Don’t start off with technology; start off with outcomes. Where is the friction? Where is the cost? Where is the opportunity to serve customers better or faster?

At this point, AI isn’t just an experiment anymore. It truly is a business advantage. But only if you ground it in real priorities and focus on real action from day one. So I would start off with the data, focus on where the biggest frictions are, where the biggest point pain points are, and then make sure everything you do and how you use AI is measurable.

Small wins first. Don’t go for the large, enterprise projects. Go with smaller efforts first and then, scale up continuously measuring value business value.

Of course, there are people who will be detractors. There are people who will be afraid. There are people who will be super excited, even overly excited, and want to use ChatGPT for every aspect of their business and upload every document for analysis.

It’s important to get organized around how you think about AI and stay ethically responsible with AI. It’s taking the people that are excited and getting them to the right level of excitement, and taking the people that are detractors and helping to move them through the spectrum to be excited enough to use it and get everyone to embrace it.

There’s a fine balance between speeding up innovation but also keeping ethical and data privacy considerations in mind. Speed and responsibility are not an “either/or.”

Absolutely. When it comes to data privacy, security and ethical use, there are no shortcuts.

To me, strategic governance isn’t a barrier to innovation. It’s the foundation that enables innovation to happen with confidence. Every enterprise should be moving fast where the risks are low, and taking a more measured approach where the stakes are high. For example, if you’re in the business of moving money, you’re probably not going to start by deploying AI agents to make decisions about transaction risk. A more responsible path would be to first evaluate how well your existing descriptive and predictive models are performing, then consider introducing AI-based approaches. That said, there are already some incredibly effective uses of AI in payments and fraud detection today.

In the AI world, trust is everything. It determines who has the right to scale.

In terms of tools you use, balance risk by making sure everything has governance to it. Take it through a process. Take it through a rubric that looks for risk or reward. In some cases, certain tools that you would use in your personal life or outside of work are just not suited for the enterprise. They may be very effective, but they just don’t have the right levels of controls, security and governance.

Governance doesn’t have to slow things down. It simply creates clear rules of engagement and a shared standard that everyone can align to. That’s what makes responsible, scalable innovation possible.

If you had to bet on one AI trend that would shape the business in the next five years, what would it be and why?

One area where I think AI will drive significant impact is in autonomous workflows — enabling entire business processes to run end to end. In our world, it could be claims processing, account updates, or compliance checks.

This shift has the potential to transform everything: faster operations, reduced costs, improved customer experiences, and a fundamental rethinking of how teams are structured. Today, we typically organize teams by function. But with autonomous workflows, we may start organizing teams around collections of workflows — with humans acting as master orchestrators, overseeing a network of AI agents working in tandem.

Organizations that embrace this model will be better positioned to scale quickly, respond faster, and capture greater value. Over the next two to three years, I expect we’ll see a major shift — and those who lean into this approach early will have a significant advantage.