AI challenges how businesses operate
Jonathan Sharp is CEO of Britannic Technologies
AI is challenging how businesses operate, as it becomes a strategic, core element of the operating model rather than a tactical add-on. Yet many businesses are not structurally or culturally ready, struggling to keep pace with the accelerating pace of technology and its demands. The challenges are not with the technology itself but with the requirements surrounding it, such as ownership and decision-making for the AI project.
Rethinking operations
AI forces businesses to rethink their entire operating models, challenging existing hierarchies, accountability structures, and decision-making processes. It produces enormous amounts of data, and it is critical that this data is acted upon to improve workflows across the organisation and enhance the customer and employee experience.
However, this is where a gap lies that isn’t capitalised upon. Deloitte recently revealed that 64% of businesses consider AI and decision-making important to success, but only 5% believe they are leading the way. This stat makes it clear that businesses must consolidate their operating models for true transformation.
Start with a strategy
Businesses need to go back to basics and ascertain why they need AI, what challenges they want to address, what areas they are trying to improve, and what their success criteria are. If businesses don’t take the time to answer these vital questions, they risk their AI project failing. Start with clear objectives so you can design and develop the AI solution around them.
AI requires a different mindset and way of thinking. To connect people, processes and data, you need to devise strategies, decision-making and systems that are integrated. You also need to ensure that you meet compliance and regulatory requirements to protect your corporate data as intellectual property.
From insight to action
It is vital that businesses interpret the intelligence garnered from AI data and turn those insights into action, so they succeed. It is surprising how many businesses have this data and intelligence but don’t utilise or act upon it, often because they are unsure what decisions need to be taken and who owns the project. So, it may sit in a dashboard, pile up, and go unused. This is where design matters most, not the technology itself, but the operational layer around it, because data without action is just noise.
The real opportunity is not AI itself, but when insight turns into intelligence and is acted upon. This is where the difference will be made
Put a name on it
Decide who owns the project and who is accountable so you can unlock the intelligence and make a difference. Break down the entire process by asking questions such as – Who will decide which challenges we are going to address? What improvements do we want to make? Who will interpret the data? What will trigger an action? What will the action be? Who owns the follow-through?
This is where AI quietly forces a redesign of how organisations operate; it breaks down the idea that insight sits in one place and action sits somewhere else, requiring clarity on ownership, accountability and decision-making.
Reassigning and rethinking roles
AI is already changing how decisions flow through organisations by pushing decision-making further down the organisation, to where data insights are generated. This naturally challenges traditional hierarchies, and the role of middle management is changing from control to interpretation, orchestration, and turning insights into action.
In many cases, this requires reskilling and, in some instances, redesigning roles entirely. The value is no longer in producing reports but in interpreting them and acting on them quickly.
Reassigning decision-making
Many organisations are rushing to deploy AI before they are structurally ready. AI demands agility and speed, shortening decision cycles. This means organisations must rethink how decisions are made in the first place, not just who makes them, but how they are triggered, validated and executed.
Increasingly, AI agents become part of that structure, raising new questions. What decisions are automated? What needs human oversight? Where is the escalation path when something goes wrong? This is where governance becomes critical, not as a control mechanism but as an enabler of trust and speed.
Trust in
You need to trust in the data, processes, people and AI. But the trust is not passive and needs to be designed accordingly. Deloitte revealed that employees who trust the AI agents they work with are 10 times more likely to see agents as creating critical value. This demonstrates the importance of getting the design right.
Measure and evaluate
AI is not a one-time deployment but a continuous cycle of measurement, evaluation and refinement. Ask the question why from the off-set and what for? Is it improving customer experience? Is it increasing efficiency? Is it changing outcomes? If not, why not? And what needs to change?
AI for transformation
Businesses need to rethink their operating models and devise new AI strategies to ensure successful transformation. It is not as simple as deploying AI and assuming that, now they have the technology, it will be a success. They have to take the time to rethink ownership, decision-making, and accountability. They need to move from historical hierarchies and siloed working to joined-up thinking for people, processes, and data.
It must also be viewed as an evolutionary core element of the business, not a one-off project, because real transformation only comes when you build capability step by step, learning as you go and evolving the organisation as confidence and knowledge build. The real opportunity is not AI itself, but when insight turns into intelligence and is acted upon. This is where the difference will be made.
