Flexibility is a skill Ibrahim Gokcen counts on to organize the busy sports schedules for his daughters, ages 5 and 7. The skill has also carried him through a 20-year career focused on AI, navigating massive technology shifts at Fortune-250 companies.
Ibrahim currently leads data, analytics, AI strategy, products, and governance across Aon, a leading global professional services firm providing integrated Risk Capital and Human Capital capabilities and expertise.
While AI is now a buzzword for many and a fairly new concept for some, the technology has been on Ibrahim’s mind for decades. Growing up in Turkey, he played basketball and worked diligently in school to earn a competitive spot studying computer engineering at Middle East Technical University. Then he made his way to the United States, where his PhD at Tulane University in New Orleans focused on AI. In his career, he has led large-scale digital and data transformations at Schneider Electric, Maersk, and GE before joining Aon.
To be one of the winners in this AI era—where rapid change and uncertainty are the norm—Ibrahim says a company must have a flexible yet focused AI strategy.
“Staying focused is the harder thing to do. Complexity is easy. Simplicity is harder. For large organizations, it’s critical to not waste resources.”
Ibrahim recommends exploring multiple options at the same time, staying ready to adapt as circumstances shift. He cautions companies to move at a speed and scale that makes sense for them and their clients.
An ‘Outside In’ Approach to AI Innovation: Customer Problem → Solution → Profitability
As the pressure to prove AI innovation grows, many executives want to see results...now. Ibrahim, who has led innovation across solution lines, business models, and partnerships, sees a clear starting point.
“You have to understand the biggest problems your clients and users face, and then work backwards,” he said. It’s an outside-in strategy that has worked well for Ibrahim throughout his career.
Once there’s a defined problem, it’s time to think about potential solutions. Specifically, it’s important to determine what data and technology are needed to solve the problem at the right cost, performance and scalability. “We never want to build technology for technology’s sake. It must address the client's problem,” Ibrahim said. “It’s true for picking tech partners. It’s true for developing the products. In everything, you have to start from the problem.”
The next consideration is profitability. An enterprise must have the right skills, processes, and investment appetite in place to implement its innovation, deploy and scale it, and track ROI metrics.
“Growth is great, client demand is great, but ultimately, innovation—what you build—has to be profitable."
There is a lot to learn from some of the most innovative companies, as well as successful startups regarding how they look at the client problem, how they organize themselves and how they fund and prioritize. And then how they deploy at scale," Ibrahim explained.
Sitting on a Mountain of Data
One critical asset in the arena of AI innovation, according to Ibrahim, is the substantial stores of data that already exist inside a business.
[PULL QUOTE] “Proprietary datasets really present some of the most unique opportunities to drive product innovation,” he said. “If you can connect data sets using AI and analytics to create new products and services that enhance colleague and client experiences, that creates differentiation.”
To achieve this differentiation at Aon, Ibrahim and his team probe for answers from a series of highly relevant questions:
- How do we extract value from the data we collect across our business processes and workflows?
- How can we consume that data to make better decisions for our firm through enterprise analytics?
- How do we do better client planning, better market planning, and drive better insights to our sales and operations teams?
- How do we feed that data into our analytical models so we can help our clients make better decisions about risk management, risk financing, and their workforce?
The answers to these questions have helped Aon design a suite of AI-powered risk analyzers for the firm’s clients in property, casualty, insurance, cyber and health among others in development
“Risk analyzers are changing the game for our clients and colleagues,” he said. “Now, instead of just focusing on reducing the premiums, they can focus on de-risking our clients’ businesses. We are looking into total cost of risk broadly.”
AI’s Need for Data Governance
Ibrahim has a thoughtful approach to leading Aon's AI and data strategy.
“The moment we use the word ‘AI,’ we have to have governance around it."
“Data has to be cataloged, labeled and tagged correctly so that we can establish access controls and meet client requirements and at all times stay compliant with regulations across the world," Ibrahim explained.
The goal is to establish a trusted data layer for the enterprise to feed analytics. “This data governance discipline is critical for creating outcomes with AI for a firm,” Ibrahim said.
Some of the key data sets for a company like Aon are property and casualty exposure and human capital data. All of this data must be safely integrated and connected to support decision-making while maintaining trust.
Without proper data governance, data quality suffers, risk increases, and “the consumption patterns start to become inefficient and ineffective,” said Ibrahim.
He explained: “With generative AI, ultimately, you’re pre-training models or contextualizing models. And if you don’t have the right data and inputs, then the output may contain misinformation. Then you have ethical considerations, bias considerations - and if the data is not well governed, then these are going to be amplified.”
Reflecting on a Career Built around AI
Ibrahim draws satisfaction from a few essential areas of life.
Professionally, he is honored to have worked with brilliant colleagues and to have helped large enterprises transform with AI, data, and software.
Personally, he loves traveling with his family and being in nature, preferably doing something adventurous in an interesting location.
Intellectually and academically, he feels lucky to have had an early and ongoing interest in AI.
“It’s great to see something I’ve studied and worked on for 25 years is now so mainstream and so important,” he said. “AI touches every part of our lives. It has huge implications on the way we live, the way we work, the way we get entertained, get medical care—so many important aspects of our life are transformed.”
Despite having watched the technology grow over the decades, Ibrahim [ED1] has very few preconceived notions about where the technology will take us next.
“The fact is, nobody knows how exactly AI will evolve,” Ibrahim said. “Every new model the tech companies launch changes the vendor landscape, the investment landscape. There are new winners, new losers, almost on a day-to-day basis.”
At this point in the evolution of AI, Ibrahim thinks researchers and those leading innovation have a big responsibility.
“To be honest, I sometimes wish I was a PhD student now because, as a researcher, there are endless opportunities,” he said. “There are so many interesting problems to solve. We need to make this technology safe and scalable, impactful, and responsible.”
Looking forward, Ibrahim sees many reasons to be excited and unafraid of AI. “I don’t want to sound like an AI maximalist,” he said. “But I truly believe there’s so much more to come.”
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