Artificial intelligence (AI) has been positioned as the saviour of business, bringing efficiency gains and productivity leaps for everyone. But the truth is less equivocal. For every company happy with their AI transformation, there are dozens struggling to gain any return on their investment. But more often than not, the problem lies not with the technology but the infrastructure that should be supporting it.
Why AI isn’t a standalone fix-all
The reason so many businesses feel cheated by their AI “transformation” is because AI isn’t plug-and-play. There’s an expectation that solutions should be box-ready, but AI can’t work in isolation; it requires clean data, organisational preparedness, and the tools to monitor and measure outcomes. Without that foundation, AI can be suboptimal. And this failure is about so much more than wasted investment; the current AI-first economy means that the businesses that fail to adapt effectively are at risk of losing both operational and strategic relevance.
This scenario isn’t being helped by the wider conversation either. While AI-related job losses remain at the top of the public agenda, most of the real disruption isn’t coming from automation itself, but from the corporate and cultural restructuring needed to make AI work. And businesses are stuck in the middle. They know change is inevitable, but not how to deliver it without backlash. And the technology is moving fast, making it even harder for businesses to keep up.
A lack of cohesion or fear of disruption
As well as predictive and generative systems, we’re now seeing agentic AI taking hold in businesses. Capable of making independent decisions and acting on them, it presents a paradigm shift, helping businesses to automate routine processes and remove the operational bottlenecks that have previously disrupted productivity. Employees are freed to focus on higher-value work and accelerate innovation, while AI provides the insights to guide decisions, improve forecasting, and personalise customer engagement. But the real value of agentic AI is only unlocked when it’s used as a cohesive whole. Without a unifying roadmap to guide adoption, organisations are left with multiple, uncoordinated initiatives and a fragmented ecosystem of tools that fail to deliver the business’ full potential.
Conversely, you have the businesses entrenched in outdated systems and too scared of disruption to change. Their legacy systems work – to a degree – so they try to stay current by retrofitting complementary tech, again resulting in an absence of cohesion, systems that don’t talk to each other, and an inability to achieve what would otherwise be possible. What’s needed is a complete behavioural shift.
Technology alone does not transform a business
The effective onboarding of AI is as much about an organisation’s culture as it is about the technology. For AI to deliver results worthy of the investment, companies also need to invest in the behavioural scaffolding to help teams learn how to work with rather than around it.
But more than that, you also need to track and measure your progress. When you track clearly defined metrics, such as productivity, efficiency, revenue growth, and customer satisfaction, you can understand where gains are really being made.
Scalability and replicability matter too. The more you’re able to replicate successful use cases across teams, the more value your investment can deliver. But it’s not something that can be ascertained instantly. The AI value curve grows over time, and it’s the failure to recognise this that is at the heart of so many businesses feeling that AI adoption has been little more than wasted capital. That and the issue of data.
Data is the big one, the critical weak point of AI that floors so many businesses. AI can only deliver optimum performance when your data is clean, accessible, and un-siloed. Fragmented and siloed information will undermine the performance of any AI system, so data must be addressed before AI adoption. If you don’t implement projects to restructure your data and bring fidelity to it before implementing AI, or work on your data concurrently, implementing systems to create a clean, cohesive data feed, AI will never deliver the results you need.
Then there’s the matter of tool selection. When adopting AI, you need to choose tools that address core business challenges. It’s fine to experiment with this, but it needs to be managed in a controlled way, closely monitoring results in one department before incremental roll out, alongside company-wide structured education. Allowing for controlled and appropriate investment that can provide worthwhile results.
AI can work wonders for all business models, but it’s not a fix-all. You can’t just plug-and-play and hope for the best. It takes discipline, planning, cultural alignment, and rigorous performance measurement. AI is nuanced and an equally nuanced approach is required if it’s going to reach its full potential for any business. So, it’s time to redefine our understanding of AI transformation. The aim is not to replace human capability, but to catalyse it, creating a working environment that is more intelligent, efficient, and adaptive, able to respond to the changing corporate environment.
About the author
David is the Chief Strategy Officer at &above, an AI product studio. A well-respected technologist and evangelist, his rich background spans senior product, product marketing, and innovation roles in the tech sector. He has driven strategy and creative innovation across various sectors including ad tech, AI, and cybersecurity.
