SaaS sprawl was bad. AI sprawl? That’s an existential crisis waiting to happen.
Right now, every SaaS vendor is scrambling to bolt AI onto their platform. Some of these integrations are useful. Others? They’re rushed, half-baked, and about to give your tech stack a full-blown identity crisis. For example, what happens when your CRM’s AI starts contradicting your finance AI?
Such internal AI conflicts could have real business consequences. Consider the impact when a sales team, using its CRM’s inbuilt AI, starts to prioritise leads based on the customer’s past purchase history. However, over in the marketing department, they’re using a different marketing platform — with its own baked-in AI — deprioritising the same lead due to low email open rates. The sales and marketing teams, which should work in sync with each other, begin clashing over lead quality. Conflicting communications are sent to prospects — or worse — they’re ignored completely. The end result? Untold lost opportunities.
It’s not just a mess of overlapping features anymore; it’s a battleground of competing AI voices, each pushing, pulling, and occasionally steamrolling your business in different directions. It’s a classic case of “too many cooks in the kitchen,” except now the cooks are autonomous AI models.
A decade ago, SaaS exploded across enterprises, with teams grabbing whatever tools they needed without waiting for IT approval. Innovation soared. So did inefficiency as redundant tools and broken integrations forced businesses to slam the brakes on SaaS adoption and regain control.
AI adds urgency to an old problem
Now, AI is supercharging the same problem. But unlike the SaaS boom, where bad software just sat there, unused, bad AI doesn’t sit still — it acts. It prioritises leads, approves expenses, and automates decisions. Sometimes it’s helpful. Sometimes it’s way off the mark. Unless businesses put guard rails in place, they’ll end up with an army of AI-powered applications pulling them in conflicting directions.
Imagine a risk management AI approves a contract that the compliance AI flags as non-compliant. Or a procurement AI approving a vendor that your finance AI immediately flags as a risk. These aren’t hypothetical scenarios — this is the reality businesses could be walking into.
But let’s be clear: AI isn’t the problem. Unchecked AI sprawl is.
Why automation must come first
Businesses can’t just keep stacking AI onto their SaaS tools and hoping for the best. AI needs structure. Governance. A strategy that ensures it is making things better, not more chaotic.
How? The first step is starting with automation before layering in AI. Businesses are rushing to inject AI into workflows that are already inefficient, expecting it to magically fix the mess. But AI isn’t a shortcut — it amplifies what’s already there. If a process is broken, AI will break it faster. If that process is inefficient, it will supercharge those inefficiencies. Businesses need to lock in their automation strategies first, then bring AI in as a force multiplier.
SaaS sprawl taught businesses a hard lesson: if you can’t see it, you can’t manage it. The same applies to AI. Businesses need to track and capture AI processes — where it’s operating, how it’s making decisions, and what data it’s using. Otherwise, they’re just handing over control to a black box and hoping for the best.
The critical thing to keep in mind is that control is key. The instinctive reaction to SaaS bloat was to cut tools, streamline vendors, and consolidate. But AI isn’t just another app. The solution isn’t fewer tools; it’s ensuring that AI-driven automation is structured, predictable, and aligned with business goals. Rather than layering in third-party AI tools at random, businesses should prioritise systems that allow them to build AI-driven solutions on top of their existing automation frameworks.
Right now, businesses still have time to take control — to put governance frameworks in place, align AI with automation strategies, and ensure their AI investments aren’t just making decisions, but making the right ones.
Enterprise AI is still nascent. There is still time to put the right control, governance, and automation measures in place. Skipping this fundamental first step can leave organisations at the mercy of AI systems they don’t understand, making decisions they didn’t authorise, in workflows they no longer control.
