From data to decisions: How Ohio builders are shaping the next era of AI

Aplos Data was founded in Columbus with a clear belief: businesses should be able to see and run their operations as one. In a world of fragmented systems and partial visibility, the goal was to help organizations move toward greater clarity, alignment, and real time decision making.

In its first article with OhioX, the company introduced that challenge and its approach, building a unified operational layer on Palantir that helps organizations move from hindsight to action.

Now, as Aplos Data prepares to formally launch at the Ohio Tech Summit this May, the conversation is widening.

Because the problem is not new, and neither is the technology that is bringing it to the surface.

A Brief History, and a Rapid Shift

The story of AI stretches back further than most realize. In 1948, early thinking on "intelligent machinery" began to take shape. By 1956, the Dartmouth Conference formally introduced AI as a field of study. Decades later, milestones like Deep Blue defeating Garry Kasparov in 1997 signaled that machines could outperform humans in defined domains. What followed, from early statistical models to the release of Siri and the transformer architecture that underpins modern AI, has led us to today's moment.

What is different now is not just capability, but velocity.

"We have crossed from AI being a science experiment to adding real value to our clients," said Joe Leithauser, Co-Founder and Chief Delivery Officer at Aplos Data. "What took decades between breakthroughs now happens in months. The question is no longer whether AI can deliver value, but whether organizations are structured to use it properly."

AI is no longer confined to the background. It is moving into the core of how businesses operate, less like a sudden breakthrough and more like a rising tide. At first, only parts of the business feel it — the highest value use cases. Then it extends into planning, operations, and decision making. Eventually, it becomes how business is done.

For many organizations, that moment is now. And yet, despite the progress, the market is still early. Much of today's AI remains what experts describe as "weak AI" — highly capable within defined tasks, but limited in its ability to generalize, explain reasoning, or operate independently across complex environments. The future promises autonomous systems.

That gap between capability and reality is where many organizations struggle. AI has been explored and tested, but rarely operationalized.

Ian Klaus, Partner and Chief Operating Officer at Aplos Data, sees this clearly. "Most companies have started somewhere," Klaus said. "They have tested tools, explored use cases, and seen what's possible. But very few have been able to reliably mature that into something that runs day-to-day operations."

Klaus orchestrates the company's operations utilizing applications that Aplos Data purpose-built to scale its processes as a growth stage company. These aren't pilots being tested — these are production applications driving action and decisions within each of Aplos Data's core functions. "Aplos Ops," as it's called, enables the team to spend less time on administrative tasks and more time with their clients.

Together, their perspectives reinforce a central idea from Aplos Data's founding story: most organizations do not lack software, they lack coherence and simplicity. Data exists everywhere and often resembles a web — formed over time rather than designed with intent. Many companies purchase software to automate their data entry and improve productivity. Each software platform has its own data and structure, often requiring a business to change its processes to use it correctly. Aplos Data's mission is to bring this data together in real time, enabling operators to make better decisions.

From Experimentation to Operation

The next phase of AI becomes meaningful when every application and data point reflects how the business truly operates.

The industry itself is beginning to reflect this shift. After a period of intense excitement, AI is entering a more grounded phase — where expectations meet practical constraints. This is where real value is created, not through isolated tools, but through disciplined implementation.

"There is a tendency to start with the technology," Leithauser said. "But the real starting point is the problem. Where is time being lost? Where are teams reconciling information instead of acting on it? Once you understand that, AI becomes far more effective."

The goal is not to add tools, but to unify what exists into a single operational system where data, AI, and workflows operate together with clarity to make decisions. Klaus describes it simply.

"For a long time, getting your data into one place was the finish line. Now it's the starting line. The real value comes when AI utilizes trusted data to make decisions and act automatically, in the background of day-to-day operations."

That evolution is particularly relevant in Ohio, where industries such as manufacturing, healthcare, logistics, and energy operate in complex environments, often relying on systems that were never designed to work together, creating friction between insight and action.

AI can remove that friction, but only if implemented with precision and a deep understanding of how those businesses actually run.

That is where Aplos Data's Ohio roots matter.

Built in Ohio, Applied Globally

Both Leithauser and Klaus were born in the state and have built careers solving real operational challenges. Today, they are building AI applications in Ohio that are deployed globally, while investing in local talent and capability.

"There is a certain expectation that comes with building here," Leithauser said. "There is a certain amount of healthy skepticism with new tech. In Ohio, it has to work. It has to deliver value. Otherwise, it doesn't last."

Klaus agrees.

"Ohio has always been a place where things get built and proven — railroads, aviation, oil, steel. That 'build-and-prove' mindset carries into how we approach AI. It is about making systems that companies can rely on, use every day, and improve how operators work into the future."

As Aplos Data prepares to step out of stealth and formally launch this May, that combination of local grounding and global ambition remains central.

The company is expanding internationally, but its foundation is firmly in Columbus, rooted in the belief that operators deserve better systems.

More broadly, the AI conversation is entering a more disciplined phase where the focus is shifting from pilots to execution.

For Ohio's technology community, that presents a clear opportunity to operationalize data and AI — not just adopt a single tool. That means bringing structure to fragmented data and giving businesses the ability to make decisions and act based on a sharper, more accurate picture of how they actually operate.

Use data and AI to operationalize, one decision at a time.