The AI Revolution Is Leaving Mid-Market Companies Behind—Here’s How They Catch Up
Staff Writer • 2025-08-18
Why “too big to pivot, too small to innovate” might be the most dangerous place to be in the age of AI Everyone’s talking about how AI is transforming business. But there’s a blind spot no one wants to admit: mid-market companies are being left behind. They’re too big to move fast like startups, too small to have in-house innovation labs, and often buried under legacy systems and siloed data. As the hype around AI keeps growing, many of these companies are being told to “just integrate ChatGPT” without a real strategy or infrastructure to support it. So I brought in Ali Din, CEO of Premier NX and a former executive at ADP and Indeed, to talk about what AI implementation actually looks like when you’re stuck in the “muddy middle.” His take was clear: if you're not using AI for financial planning, operations, or customer service by now—you’re already behind. Why AI Is Not Just a Trend—And Mid-Market Can't Afford to Wait “This is not like the dotcom bubble,” Ali told me. “That burst because of valuations. But the technology endured. AI is already transforming operations in real time.” He pointed out that mid-market companies have real barriers to AI adoption: limited budgets, small teams, and messy data. Unlike startups, they don’t have the luxury of starting from scratch. And unlike enterprises, they can’t throw money at the problem. But Ali’s warning was urgent: “This isn’t optional anymore. The inboxes, headlines, and case studies all say the same thing—AI is already here, and it's accelerating.” The Mid-Market Matrix: Where to Start Ali introduced a simple but powerful framework: focus on low-complexity, high-impact projects. Instead of trying to “AI-enable” the entire company, start small: Use AI to classify invoices Automate sentiment analysis in customer service Improve QA for training staff Streamline HR onboarding or employee handbook queries These are quick wins that build momentum and trust across teams. The Financial Planning & Analysis (FP&A) Opportunity Ali emphasized that one of the most underrated places to implement AI is in FP&A. Scenario planning, forecasting, budget simulations—these are time-consuming and error-prone. AI is designed to spot patterns and simulate outcomes faster than any analyst can. “Most CFOs are flying blind,” he said. “With proper historical data, AI can simulate a range of outcomes—so if a shock hits your revenue, you're not reacting, you're adapting.” This is mission-critical in today’s world of global trade shifts, talent shortages, and supply chain disruptions. “You wouldn’t rely on one supplier anymore,” he noted. “Why would you rely on one manual planning method?” Change Management: The Real Challenge Tech isn’t the hard part. People are. Many companies make the mistake of driving AI adoption top-down without bringing their teams along for the ride. That creates fear, sabotage, and resistance—especially from finance and ops staff who have been using Excel for decades. Ali's advice? "Crawl, walk, run." Start with small pilot programs and involve cross-functional “tiger teams” to foster ownership and reduce fear. “The goal isn’t to replace jobs,” he said. “It’s to enhance productivity, free people from tedious tasks, and empower better decisions. But you have to say that out loud—more than once.” How Mid-Market Firms Build the Muscle Ali sees AI-readiness as a muscle that needs to be trained. And he believes co-sourcing—a hybrid of internal champions and external AI expertise—is the best way to build that muscle without blowing up the budget. “In 2025, a successful AI-ready mid-market company isn’t the one with the biggest budget,” he said. “It’s the one that can identify use cases, empower teams to test them, and scale what works.” He gave examples of AI scanning compliance emails to alert relevant departments or helping forecast faster than human analysts. These small changes add up—and eventually become differentiators. Final Thought: Agility Meets Scale Mid-market companies often feel stuck between startup agility and enterprise scale. But with the right AI strategy, they can have both. “You get to be Goldilocks,” Ali told me. “Just the right size to move fast. But with enough structure to compete with the big players.” And that’s the real takeaway here: AI isn’t just for tech giants or scrappy startups. It’s for the businesses in the middle who are ready to lead—if they stop waiting.
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