A career built on better questions
Richard Kadeg lives on a ranch in Texas, surrounded by horses, dogs and a 250-pound potbelly pig. It is an unlikely home base for someone whose career has taken him through enterprise-technology projects in some 40 countries, across four continents and eight industries. Yet the setting suits a professional who has spent decades thinking about how organizations make decisions: away from the noise, close to the practical question that has guided his work from the start. “I love analytics,” he says. “Businesses need to operate, and to do so with insight and intelligence. How quickly can I get an answer to my question so that I can take informed action?”
That question has carried Kadeg through financial services, enterprise applications, data warehousing, cloud transformation and, increasingly, artificial intelligence. It also explains his interest in change management, the subject of his recently published book, Disruptive Change Management: The 10 Pillars. For Kadeg, technology is never simply a system to install. Its value lies in changing the way people work, the quality of the decisions they make, and the confidence with which an organization can act.
His route into that work was anything but conventional. His family moved frequently, and he attended schools across Alaska, Michigan, Maine, Connecticut, Northern California and Arizona. Traditional education frustrated him; he was the student at the back of the classroom reading a book a day. That self-directed appetite for information became the foundation for everything that followed. A brief attempt to study computer science after high school only reinforced his instinct to question the teaching rather than passively absorb it.
His first real technology experience came at Valley National Bank, where, at 18, he reconciled trades alongside Wall Street veterans in institutional and trust custody banking. The bank still depended heavily on mainframes and paper. When he was handed green-bar accounting reports and asked to reconcile them manually, Kadeg saw a different path: put the data in a database and query it.
From there, Kadeg moved into increasingly ambitious transformation work: restructuring a business with analytics at Transamerica Life Insurance, working in the mortgage industry and startups, leading cloud projects, and helping build the enterprise data warehouse for AT&T Wireless’s cell-tower network across North America. He later founded Gold Standard Management Consulting, often working between customer, vendor, and systems integrator to keep projects inside a disciplined framework. Across those roles, the pattern was consistent: start with the business problem, then make the data useful enough to change the work.
From reports to conversations
Kadeg’s view of analytics has sharpened with the technology. Traditional enterprise reporting asks users to define a report, wait for the data to be assembled, download it, and often manipulate it in Excel before they can begin answering the questions that matter. At Houlihan Lokey, he saw financial-close reports take hours to run. The process became hard to justify as analytics grew more sophisticated, prompting discussions with Oracle product and engineering teams and a move to Oracle Fusion Data Intelligence (FDI), a family of AI-powered, prebuilt, cloud-native analytic applications for Oracle Fusion Cloud Applications. A quarter-end close report that had taken about three hours could be produced in roughly 45 seconds.
For Kadeg, however, speed is only the beginning. “Oracle has added AI on top of FDI,” he explains. “Now I can have a conversation with that data: what do I need to know and what am I going to do with it when I have an answer.” That shift became tangible when a team of Oracle engineers spent a week at his ranch testing the new AI Assistant in FDI. They connected Fusion Financials with Concur, a non-Oracle expense system, and within about 15 minutes were asking natural-language questions about an employee’s airfare, meals and entertainment—and receiving answers in real time. The point was not a faster report or a longer data-warehouse project, it was how quickly a company could get an answer from the systems it already uses.
Conversational analytics does not eliminate the need for judgment. It makes the quality of the question more important. “How many suppliers do I have?” may be interesting, Kadeg says, but the actionable questions are more specific: which suppliers are active, which have been used recently, which have transacted above a meaningful value, and which have been inactive for 24 months and could be deactivated. The difference is between information and intelligence that leads to action. In expense management, the same principle could let AI apply policy across thousands of reports and surface only the exceptions, leaving people to focus on the handful of cases that need human judgment.
That is a skill organizations must learn, not assume. Early on, Kadeg’s team built “training wheels”: a natural-language carousel of well-formed questions that users could select. As people became comfortable, most of those guardrails were retired. Users began framing sharper questions themselves. In Kadeg’s view, this is where AI earns its place—not by removing people from the process, but by giving them a more direct way to understand what matters and decide what to do next.
Making change stick
None of this works without data that people trust. Kadeg has watched executive meetings collapse into arguments about whose numbers are correct, and worked with regional finance leaders maintaining three separate sets of books rather than accepting a shared corporate standard. The remedy is not simply a better toolset. “If you fix that problem organizationally,” he says, “you fix a lot of other things along the way. It’s not a toolset issue, it’s change management. Get people aligned on what the data means, and everything downstream, from security to reporting to AI, gets dramatically easier.” That discipline is central to his book and to the way he approaches transformation.
It is also why he argues for beginning small. Rather than launching an enormous program and expecting instant adoption, identify a handful of use cases with a modest scope and a potentially large return. His team examined where people spent the most time supporting existing processes, applied AI to selected activities, and cut the team’s support effort by roughly half. Once people saw a concrete result, they wanted to do more. More ambitious ideas can follow: for example, an executive delayed by bad weather messaging an AI application that knows their identity, preferences and booking, finds alternatives and potentially rebooks the trip. The value of that scenario is that it lets people see how AI might improve their own working lives.
Kadeg is equally clear-eyed about risk and cost. He sees an advantage in keeping AI close to the enterprise stack, where data, security and AI capabilities remain connected. Organizations need to understand where sensitive information goes, how it is protected and how costs are managed. The discipline is simple: tie every investment to a measurable business outcome, and make sure people actually use it.
After a career of large-scale technology and business transformations, Kadeg remains convinced that technology itself is rarely the hardest part. The deeper task is organizational alignment: persuading, influencing and sometimes pushing people toward new processes, responsibilities, and ways of working. His measure of success has not changed since that first bank job. Start with the business outcome. Establish data people can trust. Use technology to make the complex simpler. And bring the organization with you.
Learn More
Learn more about Richard from his book, Disruptive Change Management: The 10 Pillars, and in the Oracle Analytics Community.