The future of data automation marries human expertise and AI technology

Determining how to deal with your ops data can make for a pretty messy equation. The tradeoffs feel inevitable: spending time on careful audits takes time away from strategic work, and the more bespoke your data processes, the less adaptable they are.

Alex Yaseen, founder and CEO of Parabola, recently spoke with Sarah Barnes-Humphrey on the Let’s Talk Supply Chain podcast to dig into how data automation and AI are transforming the landscape for supply chain operators.

The most striking revelation from the conversation? A staggering 67.4% of supply chain managers still rely on Excel spreadsheets to manage their operations. This level of manual work sounds unsustainable, but the stat tracks closely with Yaseen’s observations from his early-career consulting experience.

“Even at Fortune 50 companies, in teams that were right in the spotlight, people were still doing incredibly non-scalable manual processes,” says Yaseen. That even high-achieving orgs are caught in anachronistic workflows begs the question: how did we get here, and how do we get out?

Progress starts with people

Although the answer to those questions might end with technology, it starts with the foundation of any organization: people.

Call them “operators”: those “in-the-weeds subject matter experts who know where the skeletons are buried.” These people keep critical functions afloat, but sometimes they’re the only ones who know just how things work.

Yaseen recalls working with a prominent healthcare company on a short-term project. A team processing claims relied on a single spreadsheet, nicknamed “Craigslist,” for routing inquiries. The sheet had a built-in algorithm that “magically” gave the correct outputs, but the only person who knew how it worked was the titular Craig, who had built it five years earlier, and had since left the company.

This highlights a significant barrier to process improvement: knowledge retention. As Yaseen explains, “People end up being a huge part of this; somebody joins, they spend two years building all this knowledge, and if they don’t have a way to get that back out into the world of the company, when they leave they take all that siloed knowledge with them.”

Current tools like Excel are central to data automation, but tracking how workflows actually function is still a struggle. The Craigs of the world hold hard-won information in their heads — helping them document their SOPs should be the easy part.

Tech that works for operators

At the root of this is a mismatch in expertise. Time and again, the best salve for ops complexity is a tech remedy, but ops leaders rarely have the bandwidth to be tech mavens too.

Yaseen highlights the occasionally fraught relationship between operations teams and IT departments: “Supply chain teams are always asking for more stuff, they’re trying new things…the tech team wants to say yes to everything, but they can’t keep up with the pace of how fast things are changing.“

The problem isn't that companies don’t want to modernize; rather, they’re often caught in a cycle of failed implementations and incomplete solutions. “They set up an ERP and they were told that an ERP is going to solve everything and it actually only solved maybe 10% of the most common use cases across the ops team.”

Even the more bespoke tools are ill-equipped to deal with the complexity of crucial ops tasks. A purpose-built PDF parser works well when it’s looking at a document that has the same format every time, but that tool breaks when the invoice is “a screenshot of a photo that a truck driver took, of a crumpled piece of paper on their lap.” When inputs are so unpredictable, operators are forced to default to manual work.

The ideal solution for these data automation challenges might be a more nimble, no-code tool that closely mirrors the processes that operators are already familiar with — something like a spreadsheet, but with the functionality for operators to show their work, and deal with the constantly shifting supply chain landscape.

Onboarding user-friendly AI

“As intuitive as Excel, but a little supercharged.” That’s how Yaseen characterizes Parabola. Although AI tooling is almost obligatory in new software, Yaseen highlights some of the ways that Parabola’s AI functionality aims to supplement human expertise, rather than supplant it.

Built-in auto-documentation is just one exciting feature Yaseen highlights. When you have data passing through a workflow on the Parabola canvas, the software automatically generates brief summaries, explaining what each step of the process is doing — whether extracting data from a messy PDF, or calculating discrepancies between rate sheets and actual invoices. So, if Craig is out sick, anyone on his team can understand his process, and keep his data automation running.

Additionally, “we’ve prioritized AI features that help deal with unstructured data,” Yaseen explains, noting how Parabola’s AI uses vision technology and LLMs to intelligently extract data from documents, even when formats change or information appears in different locations. This is particularly valuable for tasks like freight audits and invoice processing, where operators traditionally spend countless hours manually entering data.

Parabola packages AI-assisted tools (that are actually useful) in a convenient no-code platform that looks like a flowchart. And unlike an ERP, it can quickly and easily integrate with your whole tech stack, pulling data from sources in real-time, and sending it to the places where it matters most.

What’s next

As we move into 2025, Yaseen emphasizes that the greatest value is not in the technology itself, but in how it enables human expertise: “The hard thing is actually knowing the problem.” This is something AI still can’t do on its own.

While AI and data automation are powerful levers, they're most effective when they enhance, rather than replace, people. The goal isn’t to eliminate spreadsheets entirely, but to automate data tasks so operators can focus on strategic decision-making and building personal connections.

Yaseen envisions a future where working in operations feels like “having a really nice relationship that’s a little bit more human, a little bit more like casual conversation.” The calculations around data and supply chain operations are only getting more complicated, but the human element will always add value.