More Data, More Software Problems (Zack Hill Commentary)

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The companies best positioned to win in this AI-driven economy aren’t necessarily the ones rushing to implement chatbots or automation. They’re the ones sitting on large, underutilized data sets and deploying the right tools to extract value.

The artificial intelligence revolution is here, but many businesses are missing out — not because they lack data, but because their legacy software isn’t built to take advantage of it.

For industries like banking, manufacturing, logistics and home services, AI and machine learning aren’t just buzzwords; they’re tools that predict trends, optimize operations and personalize customer experiences. But AI is only as good as the software infrastructure supporting it.

Every business collects data — on processes, customer orders, sales cycles, supply chains and decision-making patterns. But for many, that data is locked away in outdated systems, siloed across departments or buried in formats that don’t integrate with AI models.

The first step isn’t jumping straight to AI implementation. The first step is asking: Is my software AI-ready?

Businesses that have accumulated structured or semi-structured data already have an advantage. AI thrives on large datasets, and companies with long-standing histories of transactions, customer behaviors and operational patterns have the raw material for ML models to uncover efficiencies and insights.

Deciding when to upgrade or replace legacy software is critical. If your systems are slowing down operations — where simple queries take too long or reports require excessive manual work — you’re already falling behind.

Additionally, AI and ML tools rely on seamless integrations with cloud-based data lakes and modern frameworks, meaning your software must be API-ready to support these advancements.

Scalability is key. AI and ML require significant computing power, and older, on-premise systems often struggle to keep up. If your business faces these challenges, modernization is necessary.

So where do you start? Instead of a full rip-and-replace approach, businesses can incrementally modernize their systems:

► Centralize and manage data.

► Introduce AI-friendly APIs.

► Leverage cloud computing.

► Find and use external data sources.

► Focus on customer experience.

We’re already seeing AI transform industries that have relied on traditional systems for decades. Banks are using AI-driven customer segmentation to identify high-value clients and predict credit risk. Service companies optimize workforce deployment through AI-powered scheduling, while retailers and manufacturers use AI to analyze sales and inventory patterns, preventing shortages and improving efficiency.

AI and machine learning aren’t just for tech giants. They’re tools for any business that has built up years of data. Even in Arkansas, we have clients that are putting their data to work in agriculture industries and more. But the key to unlocking their potential isn’t just about feeding numbers into an algorithm. It starts with software modernization.

The companies that recognize this now will be the ones leading their industries in the years to come. The question is no longer whether your business should implement AI, but whether your software is capable of supporting it.

Zack Hill is Partner/CEO at Few, a Little Rock-based digital product agency.