Dating Around with AI – The Midmarket’s Commitment Problem

Last week, in The Midmarket’s Magic-Wand AI Moment, Mike Midmarket discovered that enthusiasm alone can’t compensate for messy, missing, or mislabeled data. Now, armed with partial insights and pressure to “do something with AI,” he explores what the rest of the industry is selling him.

Cloud Giants and Cold Feet

Many mid-market firms have flirted with the big cloud “hyperscalers” or enterprise-software vendors. They’ve maybe moved some data to Azure, or they use an SAP or Oracle system for ERP. But ask them about going all-in on one vendor’s AI platform and you might see panic in their eyes. These companies are understandably wary of chaining their AI future to a single mega-vendor’s ecosystem. They’ve heard horror stories of cloud bills that read like mortgage statements and “Byzantine” pricing models with 500 line items.

Vendor lock-in is a four-letter word in the mid-market – it feels scarier to them because they know they won’t get the mega-discounts Fortune 50 clients enjoy. Mike Midmarket’s CWO had nightmares of being stuck with a one-size-fits-all “AI module” from his ERP vendor, paying through the nose for mediocre results and unable to pivot without starting from scratch. So while they do engage the cloud, mid-market leaders often keep one hand on the exit door – they prefer mix-and-match flexibility, even if it means stitching together smaller solutions.

There’s also a bit of pride and pragmatism: these folks have been burned by big-tech hype before. (Remember the ERP wave of the 1990s that promised seamless integration and delivered years of pain? Mike does. His company’s first ERP install in 1999 nearly broke the business.) So they treat hyperscalers’ AI promises like a used-car pitch – maybe useful, but check the engine and be ready to walk away.

Shiny Point Solutions: The Siren Song of Quick ROI

Given the data mess and the fear of committing to one platform, what do mid-market innovators do? They date around. The market is teeming with AI startups and point solutions promising to solve this or that problem yesterday. Have a warehouse full of parts you can’t track? Here’s a modern AI-powered inventory optimizer that plugs in and shows value in a month. Too many customer-service calls? Deploy a ChatGPT-like assistant to cut them by 30 percent.

These AI-first point solutions are seductive: specialized, fast to deploy, and they make any new CTO look like a genius early on. For leaders under pressure to show results to the board (or to Grandpa who still chairs the company), quick wins are golden. It’s no wonder traditionally “technophobic” sectors are suddenly adopting these vertical AI apps so fast – the good ones promise ROI from day one. In effect, mid-market firms are saying, “We’ll take the magic beans, as long as they sprout by the next quarter.”

And often, the beans do sprout. Mike’s company rolled out a computer-vision system from a startup to inspect cement bags for tears. It was installed in weeks and immediately started reducing returns. Victory! Emboldened, they added an AI scheduling tool for deliveries, and a generative-AI assistant to auto-draft vendor emails. Each tool tackled a pain point and delivered real improvements quickly. It’s all upside with no downsides, right? Cue the ominous music…

Silos, Franken-Stacks, and the Island of Misfit Tools

Here’s the rub: those dazzling point solutions, adopted one by one, can inadvertently turn your organization into an archipelago of AI islands. The inventory optimizer doesn’t natively share data with the sales system. The AI inspector’s insights on defect rates never reach the procurement dashboard. The chatbot knows what customers complained about this morning, but that info stays in its own little brain.

In the rush to “do something with AI,” many mid-market companies accidentally widen the gap between aspiration and execution. Observers call it AI sprawl – the more businesses invest in separate AI point solutions, the more disconnected their operations become, leading to scattered tools, siloed data, and shallow gains.

Mike felt this firsthand. His company’s AI experiments started strong in their individual silos, but soon he had what IT folks call a “Franken-stack” – a monster made of disparate systems loosely stitched together. Bright AI wins in one department were being dragged down by darkness in another. The fancy delivery-scheduling AI couldn’t pull inventory delays from the warehouse system (no integration), so it sometimes scheduled trucks for loads that weren’t ready. The result: confusion, finger-pointing, and that dreaded phrase, “the left hand doesn’t know what the right hand is doing.”

Surveys confirm it: “business silos between departments” and “lack of system integration” rank among the top obstacles holding back AI success. Data stuck in one corner of the business is almost as bad as no data at all. It’s the difference between pockets of intelligence and a truly smart enterprise.

So mid-market leaders face a growing tension. They know quick-hit AI tools can drive change (and make them look like heroes), but they also see the risk of ending up with a disjointed mess that doesn’t scale. It’s the classic IT cautionary tale: today’s neat solution could be tomorrow’s tangled problem.

The next edition glows pumpkin-orange as Mike tries to stitch life into his AI monsters before they trick the budget.

Enjoy your Sunday!