Walk into almost any small or mid-sized business in Europe right now, and you will hear the same thing: “We are experimenting with AI.” Look closer, and it usually means that there is a chatbot open in one browser tab, a spreadsheet of ideas in another, and a list of half-finished pilots that no one quite knows how to switch on.

The reality is: AI in most companies is still a side project. It is tried in one corner of the business, never connected to the rest, and then quietly left to gather dust. Research from S&P Global shows that in 2025, 42% of companies abandon most of their AI initiatives before anything reaches production, not due to lack of intent but because those pilots never become part of daily operations.

For SMBs, the reason is sharper: they usually have no dedicated team, no budget for a long custom build, and often just a few tech people – if any. Hiring consultants often means paying for generic advice that doesn’t match their voice or customers. Meanwhile, their systems weren’t built for AI integration, and most tools scramble when they hit that reality.

This happens because many solutions expect companies to tailor their systems and databases or assign special AI teams to run isolated tools. But SMBs usually lack the time and resources for such projects, which leaves a lot of disconnected tests that don’t scale.

The problem is not ambition or budget, but the missing layer that makes AI part of everyday workflow. For SMBs, that layer needs to be something that can go live in days, not months. It should be something that feels like an extension of the founder’s own customer knowledge and style, not a generic bolt-on.

The fastest-moving teams have already found it and get things done without a six-month budget and endless tests.

AI is easy to try but hard to operationalise

89% of SMBs say they use AI in at least one business function. In reality, most are still at the trial stage. Off-the-shelf tools often crumble when confronted with legacy infrastructure, scattered data, or unpredictable workflow. That’s why as many as 80% of AI projects fail in the end. This percentage is twice the failure rate of non-AI tech initiatives. So, the barrier is clearly not access to AI but embedding it in systems without costly rework and maintenance. Platform’s Copilots, who act like ‘GPT for Customers’, solve this by delivering enterprise-grade AI that speaks in the SMB’s own voice, understands its customers, and is ready to work from day – no AI experts or tech team required.

Instead of spending months on API connections and custom work, SMBs can upload their knowledge base, set tone of voice, configure customer lifecycle stages, and paste one snippet of code. The result is going to be a live AI that blends into the business, competes with industry leaders on engagement, and preserves the unique personal touch that makes customers loyal.

Why support chatbots alone are not enough

Relying only on chat interfaces is part of the problem. They’re easy to demo but hard to make essential. A tool that can only answer questions without triggering processes or updating systems risks becoming a novelty.

Support chatbots often depend on users to ask questions and then act on the answers separately, which interrupts work. Co-pilots can trigger actions automatically, for instance, guiding users through onboarding or submitting a service request via a simple chat, like asking an assistant for help, without navigating a 15-click interface, and helping users find, compare, and buy the right product seamlessly. This reduces friction and makes AI a natural part of daily tasks, embedded directly into the products and services users already rely on, without the need to pause, switch tabs, or consult ChatGPT separately.

Technically, co-pilots act as a new interface layer between the product and its users, bringing fresh value to the experience. Instead of being hidden automation that only optimises internal workflow, they become a visible, useful extension of the product itself. For end users. This means more intuitive, context-aware interactions that feel personal rather than generic. For the teams building the product, it’s a way to observe real user behaviour, spot where people get stuck or drop off, and quickly generate new ideas for what their customers actually need. In practice, this bridges the common disconnect we see between users and the products they use, and solving that gap is where co-pilots make the biggest impact.

What scalable AI adoption looks like

From what I’ve seen so far at Outter, partnering with companies in various sectors to help them integrate AI seamlessly into their operations, websites, and apps, the businesses that succeed treat it less as a project and more as infrastructure. Their systems connect with the tools employees already use, act without constant oversight, and handle unglamorous but essential tasks, such as moving data, processing requests, or updating records.

In one European food tech business, replacing clunky user flows with co-pilot-powered interactions cut onboarding time in half without hiring extra staff. The breakthrough was not just automation – it was our orchestration engine, which quietly coordinates multiple specialised AI models behind one simple, user-friendly interface. It understands user intent, adapts to the company’s tone of voice, and delivers consistent results without ongoing manual tuning.

This is the technical complexity SMBs need but could never afford to build themselves: enterprise-grade AI capabilities that plug into existing systems in a couple of days, keep data private by design, and require only basic setup from the business owner.

The quiet shift is already underway

The difference between a stalled pilot and a working system is execution. The most durable solutions operate inside existing environments, coordinate multiple models, and act without fanfare. And when done right, they don’t replace the personal touch SMBs are known for. Instead, they scale it. They let the business be everywhere its customers need it, instantly and personally, without becoming soulless automation.

Gardner warns that over 40% of agentic AI projects may fail by 2027 because they are too complex, too visible, and too disconnected from real work.

So, the key to successful AI adoption isn’t in endless pilots; it’s in systems that blend in, act when needed, and keep running long after the AI integration trend fades. For SMBs, that means AI that feels like a natural extension of the relationship they’ve built with their customers and keeps that relationship strong at scale.