AI is everywhere. It’s in copilots embedded in office tools. In standalone apps that promise instant insights. In smarter workflows, deeper automation and intelligent machines. It is an explosion of solutions and capabilities woven into almost every device and platform. According to the Stanford Artificial Intelligence Index Report 2025, 223 AI-enabled medical devices were approved in just one year, investment rose to $109.1 billion in the US, 78% of companies reported using AI compared with 55% in 2024, and the technology is becoming more efficient and affordable.[1] It has also become an incredibly competitive and crowded market.
Every vendor is AI-powered and every product is geared to deliver value. Which is great. But the result for companies is that most decision-makers are tired and confused, often dealing with failed projects still not delivering measurable return on investment (ROI).
“The AI space is really noisy right now,” says Edward Müller, Sales Solutions Architect at Mint Group. “We are seeing AI fatigue and scepticism because early implementations didn’t yield clear results. The market is flooded with overlapping tools and many companies simply don’t know where to start.”
Despite this fatigue, the case for AI remains compelling. The technology is transforming industries. In manufacturing, predictive maintenance is saving lives and costs; in finance, it provides organisations with enhanced fraud detection and protection alongside deeper customer personalisation; and in education, it’s re-imagining adaptive learning and access to skills development. Generative AI is changing how people write, code, design and interact with information and allowing for more creativity and strategic thinking. Ignoring the technology risks creating skills gaps and inefficiencies. It is also a lost competitive advantage.
“AI is a capability layer that will underpin almost every business process in the future; companies can’t afford to step back because of one failed project,” says Müller. “They risk missing opportunities to improve and optimise business processes and customer experiences.”
The challenge is finding a smart route through the clutter. What AI platform will best fit the business? What data does it need? How can it be integrated without creating more problems? These questions are challenging to answer but essential to ensuring AI actually works once it’s implemented.
“A lot of companies jump into AI before their data is ready,” Müller explains. “If it’s fragmented or inconsistent, even the most advanced model won’t deliver value. AI needs the right foundation.”
This is why Müller recommends a systematic approach to AI adoption, which starts with small, validating results and scaling only once the business impact is proven. The smart way, he says, is to define a clear use case, pilot it, measure it and then iterate.
There are four key pillars to evaluating any AI solution: strategic fit, integration fit, value fit and governance fit. Strategic fit ensures the solution is aligned with the company’s goals, while integration checks the solution’s compatibility with existing systems. Value fit prioritises measurable ROI; and governance makes sure critical factors like data privacy, compliance and explainability are aligned.
“Companies need to understand what happens to their data,” Müller explains. “Free AI tools may seem convenient, but when you share sensitive information with an AI outside of a walled garden, there are security and privacy risks. AI platforms must be private, secure and compliant.”
This structured, grounded approach is where Mint excels. The company helps clients de-risk AI adoption through a 10-10-10 methodology, which means 10 hours defining the use case, 10 days designing the architecture, and 10 weeks building a proof of concept. “It’s lean, measurable and avoids the big-bang approach that causes fatigue,” says Müller.
Beyond pilots, Mint also helps enterprises establish AI centres of excellence to embed capability internally. “We don’t just deliver projects and leave,” he says. “We co-build with clients so their teams are equipped to manage, scale and govern their AI over the long-term.”
In a market juicy with hype, Mint’s message is refreshingly practical – slow down, focus and make every model matter. “AI succeeds when it’s aligned,” concludes Müller. “That’s how you find the real signal in all the noise.”
[1]https://hai.stanford.edu/assets/files/hai_ai_index_report_2025.pdf
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