Artificial intelligence (AI) isn’t short of hype. From personalisation to productivity, the list of promises grows daily. Yet across UK and European enterprises, a very different story is playing out behind the scenes: the majority of projects stall, underdeliver, or quietly disappear after the pilot stage.
Too often, the root of the issue lies not with the technology itself, but with the vendors selling it. When AI is pitched like a miracle cure rather than a strategic capability, organisations are left trying to retrofit the latest solution into environments that simply aren’t ready.
TrustedTech is no stranger to the vendor side. But the hard truth is this: many implementation failures begin with vendors overpromising, under-educating, and moving on before the customer is truly set up for success. To close this gap – and see genuine AI value – both buyers and sellers need to rethink how they approach adoption.
Over-selling hurts trust
AI creates excitement, but problems arise when sales pressure outweighs pragmatism. Vendors eager to hit revenue targets focus on selling licences, not laying the operational groundwork. That’s how expectations become misaligned before a project even starts.
IT teams are told AI can drive fast transformation, while leadership hear cost-saving stories that suggest tools will instantly replace manual processes. When results don’t arrive as advertised, the relationship between vendor and customer quickly erodes into mistrust, frustration, and finger-pointing.
Many customers also underestimate the operational and cultural readiness required to make AI stick. Without the right processes in place, even the best tools will struggle to deliver meaningful outcomes.
Data maturity is still the biggest barrier
Vendors love to talk about AI outcomes: productivity, insights, automation. But all of those hinge on one thing – data. If the data going into a model is inaccurate, ungoverned or poorly maintained, then the outputs will be equally flawed, biased or unusable.
Many businesses still operate in siloed, messy data environments with unclear ownership and governance frameworks. Introducing AI in this context is like upgrading the engine on a car with no wheels. You can spend millions on Microsoft Copilot, ChatGPT, or custom LLMs – the ROI will still crumble without structured, accessible, trusted information underneath.
Organisations serious about responsible, high-impact AI need to treat data as a strategic asset. That means investing in data architecture, appointing accountable owners (for many, a Chief Data Officer is now essential), and enforcing governance. Only then can AI solutions integrate, synthesise and deliver true value.
Enable your people before you enable the technology
Despite fears around AI replacing jobs, the reality in most enterprises is very different. AI augments expertise; it doesn’t eliminate the need for it. Take Copilot, for example – it can automate repetitive tasks, summarise meetings, or help build Excel models, but it won’t replace critical thinking or decision-making.
Many AI rollouts fail because people haven’t been told what changes the tools will bring or how their roles will evolve. Training is technical, heavily compliance-driven, and lacks practical application. For adoption to stick, enablement needs to focus on behaviour change, not just tool deployment.
Ask the difficult question: “Have we invested as much in preparing our people as we have in the technology?” If the honest answer is no, you’re unlikely to realise your expected benefits.
Transparency is the vendor superpower buyers should demand
Rather than pushing a one-size-fits-all solution, vendors should be more upfront about what AI can realistically achieve given a customer’s current state. That means setting expectations early around data readiness, governance, cost, and change management – even if it means slowing the sales cycle.
This is especially true with products like Microsoft Copilot, where pricing is transparent but value is dependent on context. At $30 USD per user per month, it’s easy to overspend if usage, training, and department fit aren’t evaluated upfront. That means doing due diligence before rollout: Which roles will benefit most? Where can time actually be saved? Have you benchmarked current workflows to track ROI over time?
This is where the best vendor partnerships distinguish themselves. The goal shouldn’t be immediate product uptake, but long-term operational success. Vendors must shift from being “tool installers” to trusted advisors who help buyers evaluate whether their foundations are truly ready.
Meanwhile, buyers should become more demanding. Before signing any AI contract, ask your provider:
- What internal investment do we need to make on data, people and governance?
- What specific risks – technical, regulatory, operational – are associated with this deployment?
- How will you support us beyond go-live?
If a vendor can’t answer those questions clearly, consider it a red flag.
Building AI maturity
AI success requires strategic patience. It’s not plug-and-play technology. It’s an interconnected set of capabilities that must be aligned across systems, teams and processes. Organisations that rush headlong into deployment – without first building readiness – will continue to be disappointed.
Even for well-designed tools like Copilot, it’s essential to phase adoption carefully. Start small, track metrics like time saved and frequency of use, and avoid over-licensing.
Conversely, businesses that take a layered approach (data-first, governance-led, people-enabled) will see durable returns. Whether it’s sales propensity models, finance forecasting or automated customer support, AI can deliver immediate productivity gains – but only when powered by a mature environment.
For vendors, the priority now must be rebuilding trust by being honest about the journey rather than selling the destination. For buyers, the work begins with asking better questions, demanding transparency, and investing in readiness first, not software licences.
Make your vendor partnership work for you
AI is too important to be left to chance – or to the sales targets of your technology partners. Every organisation feels pressure to deliver fast innovation. But real transformation happens only when your ecosystem is genuinely ready to absorb it.
That means treating AI tools like Copilot not just as add-ons, but as part of a broader strategic equation – one that includes data, processes, training, and culture.
By insisting on transparency, prioritising foundations and choosing vendor partners who speak as frankly about risks as they do benefits, businesses can turn AI from an expensive experiment into a competitive advantage.
