The Race for Reliable AI

The pace of AI adoption is as breath taking as the consistent, ground breaking advancements in the technology itself. Already, around 78% of organisations globally use AI in at least one part of their operations. The pace is exponential but so are the risks.

In the UK, nearly half of SMEs report using generative AI tools, with 45% actively integrating AI into workflows. These numbers are incredible but don’t confuse these types of AI tools with the never-ending delivery of new, off-the-shelf solutions that appear each business day. And I don’t say never-ending lightly, I mean it.

We had the dotcom boom, then the app boom and we’ve just ended the decade of the CRM, where individuals from specific industries have worked hard to development bespoke tools for their needs. That’s why we now have CRMs for everyone from bakers to estate agents, and we’re seeing the same happen with AI.

This is one part in the Race for Reliable AI and it’s not about who launches the flashiest features. This race is about building tools that survive three to four years, remain compliant and cost-effective and maintain reliability as platforms evolve or vanish. What good is adopting a tool if it folds next year, gets bought and re‑engineered or spikes its pricing unexpectedly?

Yes, businesses are excited by the opportunities AI brings but they’re also wary. The worries are the same worries every business decisionmaker has always wrestled with – vendor sustainability and compliance, hidden cost escalations, legal and contractual uncertainties and potential losses in productivity.

Many SMEs invest time and budget onboarding staff only for the tool to fizzle. That’s why durability matters more than novelty.

Some businesses are attempting in-house builds which will work if that in‑house expertise includes compliance, adoption strategies and legal governance and not just tool-building. Rarely is that the case early on.

At AI Expert, we guide SMEs through the right tools that offer practical value and long-term sustainability. We help leaders ask: Will this tool still be viable in three years? What compliance roadmap does it have? Is its architecture responsible, from carbon footprint to data handling?

The answers to those questions aren’t usually straightforward and right now, with AI moving faster than any other technology cycle we’ve seen, they’re becoming harder to answer by the week.

Every day, new AI tools and agents are released into the market - some innovative, some niche, many untested. In 2024 alone, there were more than 12,000 new AI tools launched globally. Today, new tools are appearing daily. That creates excitement but also chaos. It’s the Wild West of tech all over again: a flood of products, each promising breakthroughs, with no guarantee of who will survive when the market inevitably consolidates.

We’ve seen this before. During the dotcom boom, companies raised millions on promises alone only for most to collapse. The same happened with apps. And with CRMs. Each wave left behind only the strongest, most sustainable players. AI will be no different.

The Real Race

The race isn’t for the flashiest features or the boldest promises. It’s for reliability. For tools that won’t collapse when investment dries up. For vendors with roadmaps that include compliance, security and cost stability. For solutions that integrate into real-world operations without derailing teams or budgets.

This is where most SMEs are getting stuck. Not in curiosity - they see AI’s potential - but in choosing where to commit. With adoption rates surging, the risk of costly missteps is growing just as fast.

Think of vendor churn - if your chosen AI provider folds or pivots, your investment in onboarding and integration vanishes overnight. Or token pricing volatility - many AI tools price usage by “tokens”, leaving SMEs exposed to unpredictable costs as models evolve.

Then there’s compliance blind spots - data privacy, copyright and emerging regulations (like the EU AI Act) create legal risks few SMEs are resourced to monitor. There’s also adoption challenges - tools fail not because they lack capability but because teams never fully adopt them, often because of poor planning or lack of training.

Building AI the Right Way

Some SMEs respond by trying to build AI solutions in-house. That can work - but only if those teams combine technical skills with compliance knowledge, change management and training expertise. Few early-stage teams can cover all those bases.

Others chase “plug-and-play” tools, hoping for instant results. But shortcuts here often lead to bigger headaches down the line - from fragmented systems to compliance gaps and spiralling costs.

The smarter approach? Measured action. Clear priorities. Due diligence on vendors. And a roadmap that balances ambition with sustainability.

Where We Fit In

At AI Expert, this is exactly what we do. We help SMEs identify tools that will stand the test of time and build adoption strategies that safeguard both budget and compliance. Our approach is deliberately phased.

It starts with a free AI Readiness Assessment, giving businesses an honest picture of where AI can create value immediately and where it isn’t yet viable. From there, we run a focused AI Workshop to map opportunities and align leadership on priorities. Insights from that session feed into a practical AI Roadmap that sets out clear costs, timelines and return on investment - the kind of plan decision-makers can act on with confidence.

When businesses are ready to move forward, we support implementation, ensuring the right tools are integrated into existing workflows and adopted across teams. And because AI doesn’t stand still, we provide ongoing AI Optimisation and AI Compliance oversight so the technology continues to deliver value long after launch.

The Road Ahead

The next few years will be pivotal. AI adoption will accelerate, thousands more tools will flood the market and inevitable consolidation will see many of today’s platforms disappear.

For SMEs, the real winners will be those who approach AI with clarity and caution, adopting it where it drives value, avoiding it where it doesn’t and building systems designed to endure beyond the hype cycle.

The race for AI isn’t about who moves first. It’s about who builds to last.

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