Nora Iluri of Experian Health: Inside the Strategic Architecture of Balancing AI Innovation with Data Stewardship

Artificial Intelligence is no longer the future; it is the present. It’s reshaping landscapes, altering industries, and transforming the way we live and work. With its rapid advancement, AI is causing disruption — for better or worse — in every field imaginable. While it promises efficiency and growth, it also brings challenges and uncertainties that professionals and businesses must navigate. What can one do to pivot if AI is disrupting their industry? As part of this series, we had the pleasure of interviewing Nora Iluri.

Nora Iluri is the Chief Product Officer at Experian Health. She leads product strategy, innovation, and execution across the organization’s portfolio of healthcare technology solutions. A recognized digital health leader, Nora brings more than two decades of experience building and scaling products that improve healthcare operations, financial performance, and patient outcomes. Prior to joining Experian Health, Nora served as Vice President of Product Management at athenahealth, where she led athenaCollector, the company’s revenue cycle management and practice management business responsible for processing over 315 million claims a year. In this role, she directed strategy and product for an organization of more than 1,200 professionals and helped transform the business into one of athenahealth’s primary growth engines. Earlier in her career, Nora led product at Pieces Tech and served as a Product Manager at Google, leading key initiatives for AdWords reporting and performance analytics. She also founded and led two companies, Zoragen, a biotechnology startup focused on prenatal diagnostics and DeviceMatters, a healthcare technology company focused on medical device adverse event analytics. Nora began her career at McKinsey & Company, advising Fortune 100 healthcare, pharmaceutical, and medical device organizations on growth strategy, mergers and acquisitions, operations, and organizational transformation. Nora holds a Ph.D in Biological Engineering, M.Eng., and B.S. in Electrical Engineering, Computer Science, and Biological Engineering from the Massachusetts Institute of Technology (MIT), as well as an M.Phil. in Bioscience Enterprise from the University of Cambridge.

Thank you so much for joining us in this interview series! Before we dive into our discussion our readers would love to “get to know you” a bit better. Can you share with us the backstory about what brought you to your specific career path?

I had an interest in healthcare technology from an early age which led me to earning my Ph.D in Biological Engineering from MIT. Continuing my education, I went to the University of Cambridge where I completed a MPhil in bioscience enterprise.

While I was at Cambridge, I started my first company, focused on detecting fetal genetic anomalies through maternal blood. I eventually sold the company and joined McKinsey & Company, where I spent six years advising healthcare, pharmaceutical and medical device organizations. I really started my career with two tracks, building a company and strategic consulting. Consulting gave me the opportunity to solve complex problems, but it made me realize that I wanted to do more than recommend a strategy. I wanted to stay with the work and see whether what we built actually solved the problem. That’s what ultimately pulled me into product management.

I launched a second startup focused on analyzing medical device adverse-event data. The vision was to create something akin to a safety-rating agency for medical devices. The venture ultimately did not succeed, but the experience reinforced my passion for building products and taught me valuable lessons about innovation, resilience and leadership.

I took a brief departure from healthcare when I went to work at Google and then returned to the industry as head of product at the AI healthcare startup PCCI, which later spun out as Pieces Technologies. Afterwards, I spent nine years at athenahealth, including nearly six years leading product for its revenue cycle management division. I realized product leadership was really the natural intersection of entrepreneurial execution and strategic problem-solving.

What brought me to Experian Health was the opportunity to pair product leadership with Experian’s distinctive data assets at such a pivotal moment for healthcare. As AI reshapes the industry, trusted, high-quality data is becoming an increasingly important strategic advantage. I want to help translate that advantage into products that make healthcare work better for providers and the people they serve simplifying healthcare for all.

What do you think makes your company stand out? Can you share a story?

Experian Health has some of the best and most unique data sets in the industry, has deep trust and is committed to managing sensitive information responsibly. Healthcare depends on accurate information about identity, demographics and financial circumstances to keep everything from patient access to reimbursement moving smoothly.

Not only does Experian Health have the data but also decades of experience securing and governing that information. That foundation is especially important right now as organizations move quickly to adopt AI. Many are understandably focused on immediate efficiency gains, but questions about data quality, security, model performance, bias and governance cannot be afterthoughts. We are in a position to lead in innovation, use data responsibly, maintain appropriate controls and evaluate whether solutions perform as intended over time. That balance between innovation and stewardship is what attracted me to Experian Health and what I believe makes it a leader in the industry.

You are a successful business leader. Which three character traits do you think were most instrumental to your success? Can you please share a story or example for each?

The three traits that are instrumental are probably a focus on people, a focus on outcomes and a commitment to doing things the right way.

First, focus on people. I actually learned this one the hard way. Early in my career, I believed the most brilliant individual in the room would naturally produce the best result. Experience taught me otherwise. You can’t achieve what a collaborative group of people with different perspectives can achieve together. You need the productive push and pull within a team to strengthen ideas, expose blind spots and help people accomplish things that individual talent, and sometimes individual ego, cannot.

Second, focus on outcomes. This is a big reason I moved from strategic consulting into product management so I could carry ideas through execution and see whether they created meaningful results. That orientation still guides how I lead. I always begin with the problem and the outcome that matters, then align the product, people and process around achieving it.

Third, do the work the right way. The motivation needs to be right, the ethics need to be right, and you have to think about the implications of the decisions you’re making. Product decisions can affect sensitive data, financial performance and the patient experience, so integrity, responsible governance and the right motivations must be built into the work from the beginning.

Let’s now move to the main point of our discussion about AI. Can you explain how AI is disrupting your industry? Is this disruption hurting or helping your bottom line?

Well, we’ll know in 10 years. It’s complicated because AI is disrupting healthcare in several ways at once. At the most fundamental level, AI is automating a lot of repetitive, manual work that creates so much administrative burden, particularly in the revenue cycle. More broadly, I think it’s also going to change how we interact with healthcare technology. We are already beginning to see a shift in how clinicians interact with technology. Instead of spending so much time entering information into an electronic system, clinicians can focus on the patient while AI identifies and structures the relevant information from the conversation for clinical, billing and administrative use. So, there’s a huge opportunity to simplify the work and take the complexity out of healthcare.

At the same time, AI introduces new threats. It can make identity theft and fraud easier to execute at scale. People may become even more nervous about sharing their personal information. Industry consolidation around fewer technology platforms, which can be good when it gets rid of a long tail of subpar solutions, can also create systemic risk if everyone ends up depending on one or two systems.

Ultimately, whether the disruption helps or hurts the bottom line will depend on how organizations respond. Those that use AI to redesign work responsibly and deliver measurable value may perform exceptionally well. Others may be displaced. The opportunity is significant, but so is the need to move thoughtfully.

Which specific AI technology has had the most significant impact on your industry?

Without a doubt, generative AI, especially in healthcare has had the most impact. Machine learning has been around for decades and has improved many processes, but it hasn’t transformed how we work in the same way as GenAI. It is changing both how quickly we can develop software and how effectively we can use unstructured information.

That distinction is critical in healthcare, where so much valuable information lives in free-text clinical notes, conversations and other formats that traditional systems have struggled to interpret at scale. Generative AI can help turn that information into usable insights and actions in ways that were not previously possible. That makes GenAI especially powerful for our industry.

Can you share a pivotal moment when you recognized the profound impact AI would have on your sector?

A pivotal moment for me was seeing a self-healing, agentic AI bot move across multiple payer websites to find the information it needed and essentially adapt when things changed. The significance was not one particular outcome. It was the premise that software development had changed. Instead of building a tool to complete one narrowly prescribed task, we could give software an objective and allow it to determine how to achieve it across changing environments.

I thought of it as watching a toddler learn to walk. The moment the child begins moving independently, an entire house that once seemed static becomes a world to explore, and the adults suddenly recognize the need for outlet covers and corner guards. Agentic AI creates a similar realization. The potential was enormous, but so were the implications. You start thinking that some of those sci-fi stories are not quite as far away anymore.

Then I start thinking about what this means for healthcare. Work that is currently performed claim by claim could now be handled by agents. Electronic medical records could recede into the background, enabling physicians to focus more fully on patients rather than data entry. It also changed how I approached my own role. I had to learn quickly, not only how the technology works, but what it can and cannot do, where it is heading, and how to position my teams and product roadmaps so we can still hold the reins.

How are you preparing your workforce for the integration of AI, and what skills do you believe will be most valuable in an AI-enhanced future?

At Experian Health, we are emphasizing hands-on learning. We’re doing everything from holding workshops, centralizing resources and providing training materials to leveraging tools for responsible experimentation, and doing frequent demonstrations that help our employees see what’s possible. We are also looking at how teams are structured, reducing handoffs and creating smaller, more agile groups that can move from an idea to an outcome more quickly.

But the most valuable skill isn’t just learning the mechanics of a new AI tool. I see plenty of people willing to try the tools. The harder part is getting people to reimagine the work itself. Using a tool only to perform today’s process a little faster captures a fraction of its potential. People need to step outside the existing workflow and ask: If we designed this work today with AI available, how would we achieve the same, or a better, outcome differently? That combination of curiosity, and the ability to step outside the current process to rethink how you achieve the outcome is essential. Equally important is the ability to supervise AI effectively and recognize when human judgment is required.

What are the biggest challenges in upskilling your workforce for an AI-centric future?

One of the biggest challenges is moving people from incremental thinking to genuine reinvention. There is often enthusiasm for trying a new tool, but their natural tendency is to apply it to the same tasks and processes they already know. Upskilling therefore has to go beyond technical instruction. It must help employees question assumptions, redesign workflows and understand where AI changes the nature of the work rather than simply its speed.

Another challenge is developing sound oversight. Employees need to understand a system’s capabilities and limitations, evaluate the quality of its output and know when to intervene. That judgment cannot be learned through a single training session. It develops through hands-on use, shared examples and continuous learning as the technology evolves.

What ethical considerations does AI introduce into your industry, and how are you tackling these concerns?

First, AI should not operate without appropriate human supervision, especially in healthcare. The level of oversight must reflect the use case and the consequences of an error. It’s important that we balance the efficiency we can get from AI with the level of accuracy and quality that a particular use case requires. The right comparison is not AI versus an imaginary standard of 100% human accuracy. It is whether the new approach produces a better, safer and more reliable outcome with clear boundaries and accountability. Quality is still going to be king.

And then there’s bias and ethical concerns. AI amplifies patterns in the information it receives, and unfortunately no dataset is completely free of bias, errors or gaps. Feed the wrong information into a model and those biases and mistakes are magnified at scale. That means governance cannot be a one-time review at launch. Organizations must continually monitor data, outputs and model performance as new information enters the system and the technology evolves.

What are your “Five Things You Need To Do, If AI Is Disrupting Your Industry”?

1. Align and mobilize your workforce to adopt

Most people are super busy with their day-to-day work. You need to show them the art of the possible and create opportunities through offsites or other dedicated time where they can be hands on keyboard building with the new tools.

It is also super important that AI isn’t just used to accelerate or replace the repetitive tasks we do today. We need to think through how entire workflows and jobs are going to change.

For example, AI can accelerate gathering product requirements and writing Jira stories and acceptance criteria for engineers to develop new software. But the real opportunity is moving toward spec-driven development, where you can build prototypes from spec and potentially skip the entire story-writing process. That changes the product role. Product people need to spend less time in front of their computers, but the real differentiator becomes time spent with clients.

2. Think through how to enable tooling safely

Just getting people access to the AI tools and the permissions they need to use them effectively is often a challenge, especially when you have to make sure that the necessary data protections are in place. So, you have to move cautiously and quickly at the same time. Enabling AI tools to safely access PII and PHI is often one of the toughest hurdles that can delay adoption and usefulness.

3. Control and protect your data.

We all need to move quickly in this new AI age, but we also need to be extremely cautious about how we do it. Without the right controls and governance, some of the tools being used today can quickly expose proprietary data to public data sources. And if that happens, there is no putting the genie back in the bottle. In healthcare, where even relatively simple use cases can involve PII and PHI, safeguarding our clients and their patients’ data must come before everything else.

4. Think about bias and drift before they become a problem

Many organizations have figured out how to let their teams develop AI models safely and protect their data. Some have even built appropriate feedback loops so those models can keep learning and improving. Very few have taken the next step to think through how to maintain these models and monitor them to ensure bias doesn’t creep in or the performance does not drift into a dangerous area. Establishing governance before an AI tool enters production is nice, but having ways to monitor and control performance afterward is still pretty rare, and it’s extremely critical.

5. Watch for confirmation bias

The internet already started introducing confirmation bias into the way we think. Search results often help justify what we’re trying to research, not to critique it. AI can make that even more pronounced.

AI models are primarily designed to please the users so that those users keep coming back for more. This means that if you have an opinion, an AI model is unlikely to challenge it. And in fact, it’s going to be more likely to give you information to strengthen your conviction. We already see the impact of this as our society becomes more divided, but it is also seen in the AI models we build.

Confirmation bias is especially difficult to spot and slay because, generally speaking, we love being right. Challenging our own assumptions and “rightful position” goes against our natural instincts. The ability to challenge our own assumptions is a critical skill we’re going to need to deliberately build.

What are the most common misconceptions about AI within your industry, and how do you address them?

There are two that come to mind.

The first that I hear often is that AI hallucinations make it unfit to replace work done by humans. When humans do complex tasks, they can have meaningful error rates too, around 5–20%. AI can often beat that performance with a comfortable margin.

The issue often isn’t the error rate, but the types of errors AI makes. When we make mistakes, others can usually understand how they happened. When AI gets something wrong, the mistakes are often spectacular — as the errors are often ones humans are unlikely to make — creating an impression that “AI is dumb.”

Another misconception I think many of us often hear is that AI is going to replace all of us. It will definitely replace some repetitive work, but it won’t replace humans. It will create numerous new roles and dramatically increase the demand for many capabilities that were previously too expensive to do at scale.

Can you please give us your favorite “Life Lesson Quote”? Do you have a story about how that was relevant in your life?

My grandfather once gave me a piece of advice that has always stuck with me. He said, “It doesn’t matter what your job is. Whatever you do, make sure you do a great job and take pride in the work you deliver.” I’ve carried that with me throughout my career. Whatever the work is, take it seriously and be proud of what you put into the world.

Off-topic, but I’m curious. As someone steering the ship, what thoughts or concerns often keep you awake at night? How do those thoughts influence your daily decision-making process?

At night, I find myself constantly evaluating whether I’m driving AI transformation fast enough to ensure both my company and my people can succeed. Along with whether we’re balancing speed with the appropriate protections to keep our clients and patients most valuable data safe. Those thoughts shape my daily decisions, pushing us to move with urgency, but also with care and accountability.

You are a person of great influence. If you could start a movement that would bring the most amount of good to the most amount of people, what would that be? You never know what your idea can trigger. :-)

If I could start a movement, it would be about not postponing happiness.

We’re often conditioned to attach happiness to the next thing: when I get that job, when I make more money, when I find the right person, when I reach that goal, when life finally settles down…then I’ll be happy.

I’d love for people to reverse that equation and find meaning, gratitude, and happiness in where they are and what they’re doing today, while still striving for what comes next.

Ambition and contentment don’t have to be opposites. You can want more from life without making your happiness dependent on getting more.

So maybe the movement is simply: Don’t wait to arrive. Remember to live along the way.

How can our readers further follow you online?

The best way to follow my work is on our website, Experian Health, and they can connect with me on LinkedIn.

Thank you for the time you spent sharing these fantastic insights. We wish you only continued success in your great work!