For the past few years, it has felt as though every conversation about AI started with the model. Which one is more powerful? Which one is faster? Which one can reason betteror achieve the highest benchmark score?
That focus made sense during the early stages of the AI boom. Businesses were exploring what was possible and trying to understand how emerging technologies could create value. But, as organisations move from experimentation to implementation, I believe we’re entering a different phase of the journey.
The conversations I’m having with customers today are less about capability and more about outcomes. They’re less focused on what the latest model can do and more about the problem they’re trying to solve; less about adopting AI for its own sake and more about applying technology in a way that delivers measurable business value.
That shift is important because it signals a growing maturity in how organisations think about AI. Recent reporting has highlighted how businesses are becoming increasingly selective in their use of advanced models, with many organisations choosing lower-cost alternatives for a significant proportion of workloads as they focus on efficiency and return on investment rather than capability alone. Even some of the world’s largest technology organisations are taking a more pragmatic approach to deploying AI at scale.
To me, this isn’t a sign that enthusiasm for AI is fading. It’s evidence that organisations are starting to ask better questions.
Start with the problem, not the technology
Too often, businesses begin their AI journey by looking at the technology. They see a powerful new capability and immediately start asking where they can deploy it. The result is that projects become centred on the tool rather than the outcome. Teams are encouraged to “use AI” rather than solve a specific challenge.
I’ve always believed the better approach is to start with the problem. What are we trying to improve? What process is creating friction? Where are people spending time on tasks that could be simplified, automated, or supported? What experience are we trying to create for a customer, employee, or citizen?
Only once those questions have been answered should organisations decide which technology is most appropriate. Sometimes the answer will be a large language model. Sometimes it will be automation. Sometimes it will be workflow software. Sometimes it will be a combination of all three.
The most effective technology strategy isn’t driven by what’s fashionable. It’s driven by what works.
The value organisations already have
One of the most interesting things AI has done is to force organisations to reassess where value actually sits within their business.
For years, many companies viewed data as their biggest untapped asset. While that’s certainly true, the AI era has revealed something broader. The real value lies not just in the data itself, but in the combination of data, processes, rules, and expertise that allow organisations to operate effectively.
Every business has developed ways of working over time. They have accumulated knowledge, built specialist processes, established governance models, and created operating frameworks that reflect the realities of their sector. Yet these assets are often fragmented across departments, systems, and teams.
AI has exposed how much intelligence already exists within organisations. The challenge is that this intelligence is frequently disconnected.
Data sits in one system. Workflow rules sit in another. Critical operational knowledge exists in documents or in the minds of experienced employees. Bringing these elements together often unlocks more value than simply introducing a new technology layer on top.
Why expertise matters more in the age of AI
One of the assumptions people sometimes make about AI is that it reduces the importance of expertise. I believe the opposite is true.
As AI becomes increasingly accessible, domain knowledge becomes even more important because context matters. Understanding how work happens in practice, what regulations apply, where risks exist, and what outcomes are required cannot be separated from the technology itself.
At OneAdvanced, we work with organisations across sectors including healthcare, education, legal, and workforce management. While the challenges differ, one thing remains consistent: success depends on understanding the environment in which decisions are made.
Technology can help process information, identify patterns, and support decision-making. But it doesn’t automatically understand the operational realities that sit behind those decisions.
The organisations that create the greatest value from AI will be those that combine technological capability with deep sector knowledge. They will understand not only what AI can do, but how it should be applied within the context of a specific workflow, process, or industry. In my view, this is where the real competitive advantage is emerging.
Building intelligent systems, not isolated tools
When I speak to business leaders, I often encourage them to think beyond AI as a standalone capability. The future won’t be built on individual technologies working in isolation. It will be built on intelligent systems.
An intelligent system combines multiple capabilities to deliver a better outcome. It brings together software, workflow automation, data, governance, business rules, machine learning, and AI in a way that feels seamless to the user.
The best examples of innovation rarely come from replacing everything that came before. They come from orchestrating technologies so each one performs the role it’s best suited to perform.
Traditional software remains incredibly effective at managing structured processes. Automation excels at reducing repetitive manual effort. Machine learning is powerful when identifying patterns and generating predictions. Generative AI provides new opportunities for reasoning, summarisation, communication, and interaction.
The real opportunity lies in bringing those capabilities together. At OneAdvanced, that’s increasingly how we think about the role of AI. Not as a technology that sits separately from the flow of work, but as one component within a broader system designed to help people work more effectively, make better decisions, and achieve better outcomes.
When organisations focus exclusively on the latest model, they risk overlooking the broader system that ultimately delivers value.
Looking beyond the model race
Much of the public discussion around AI still resembles a technology arms race. The headlines focus on benchmark scores, processing power, token volumes, and the next frontier model. While those developments are important, they don’t necessarily reflect the reality of how businesses create value.
Most organisations succeed because they improve operational performance. They reduce friction, increase productivity, improve experiences, strengthen compliance, and make better decisions. Those outcomes are rarely delivered by technology alone.
They come from the combination of technology, people, processes, and expertise. That’s why I believe the most important question for business leaders isn’t, “Which AI model should we choose?” It’s, “What outcome are we trying to achieve, and what combination of capabilities will help us achieve it?”
That question leads to better decisions, stronger business cases, and more sustainable results.
A more mature future for enterprise AI
The next chapter of AI adoption will look very different from the first. Rather than a rush to implement the newest technology, I expect we’ll see a greater focus on precision, governance, efficiency, and value creation. Organisations will become far more deliberate about where advanced AI is used and where alternative technologies can achieve the same outcome more effectively.
The winners won’t necessarily be those spending the most on AI or using the most advanced models. They will be the organisations that understand how to combine the right technologies with their unique expertise, workflows, and data assets.
In other words, the future belongs to organisations that move from AI-first thinking to outcome-first thinking.
Because ultimately, customers don’t buy technology for technology’s sake. They invest in results. They want to solve problems, improve experiences, increase productivity, and create value.
Amanda Grant
Amanda Grant is Chief of Strategic Ventures (Data & AI) at OneAdvanced. With more than two decades in enterprise software, Amanda is focused on unlocking value from data and AI through strategic partnerships and joint ventures spanning industry and academia.
