Your Competitors Can Buy the Same AI Models – But Not Your Business Processes
Artificial intelligence has become one of the most accessible technologies in business history. A mid-sized company today can license roughly the same foundation models, copilots, and AI platforms as a global enterprise. What was once a technological differentiator is turning into a line item. So the question worth asking isn't "which AI should we buy?" - it's "once everyone can buy the same AI, where does the competitive advantage and growth actually come from?"
Having worked with organisations on business and technology transformations across industries like retail and manufacturing, one observation stands out consistently: sustainable competitive advantage rarely comes from technology alone. Organisations may have access to the same AI models, but they do not have access to the same operating model, processes or organisational know-how.
AI enables transformation, but it is the way an organisation operates that ultimately determines whether it creates measurable business value. This gap between access and impact shows up clearly in the data: according to State of AI in Business report by MIT, only 5% of respondents see significant ROI from AI efforts - despite widespread access to the technology itself, with 80% of the respondents exploring AI.
AI Amplifies the Way Your Business Operates
Many organisations approach AI with the mindset of finding as many use cases as possible. While this often generates enthusiasm and a long list of promising ideas, it can also lead to a common mistake: automating work without questioning whether the work itself should be done differently.
If responsibilities are clear, and data is trustworthy and moves cleanly between teams, AI can meaningfully improve speed and output. If work is fragmented, approvals are ambiguous, or people spend their days coordinating rather than deciding, AI will automate that fragmentation just as efficiently. It doesn't discriminate between good process and bad process - it just runs whatever it's given, faster.
“AI doesn’t discriminate between good process and bad process - it just runs whatever it’s given, faster.”
This is why organisations that achieve the greatest value from AI rarely start with the technology. Instead, they start by examining how work flows across the business to recognise bottlenecks and opportunities for improvement. They ask where decisions are delayed, where responsibilities overlap, where employees spend time on repetitive coordination, and where customers experience unnecessary waiting. AI then becomes an enabler of a better operating model rather than a layer added on top of existing complexity.
The Hidden Cost of Operational Debt
Many businesses have accumulated what could be described as operational debt. It's not the same as technical debt - outdated systems and brittle infrastructure. Operational debt builds up through years of reorganisations, one-off fixes, and local optimisations that made sense individually at the time: a spreadsheet built to cover a gap in a system, an extra approval added after one bad incident, a handoff between two teams that was supposed to be temporary.
Cases like this rarely show up on a balance sheet, which is exactly why they survive. Employees route around them quietly, and the workarounds calcify into "how we've always done it." Nobody designed the resulting complexity on purpose - it simply accumulated, one reasonable decision at a time.
This is also why AI doesn't pay the debt down, but exposes it. An assistant can complete a single task faster, but it can't remove undocumented duplicate manual checks, decide who should own a decision, or simplify a process no one has looked at in five years. Those are business design questions, not technology ones.
“Operational debt builds up through years of reorganisations, one-off fixes, and local optimisations that made sense individually at the time. AI doesn’t pay the debt down, but exposes it.”
What Changes When You Redesign the Work First
The organisations that get lasting value from AI tend to ask a different first question. Instead of "where can AI replace manual work," they ask "how would we design this process if we were building it today, with no legacy constraints" – and only then look at where AI accelerates that redesigned version.
This same pattern shows up in how organisations choose to build. MIT’s State of AI In Business report found that AI pilots built through external vendor partnerships were twice as likely to reach full deployment as those built in-house, with roughly 67% success versus 33%. Buying in expertise rather than reinventing it internally is, in itself, part of getting the operating model right and accelerating value delivery.
This is how you can get started:
Measure AI by business outcomes, not activity. Track whether the business actually runs better - not how many pilots you've shipped.
Simplify the customer journey before you automate a step in it. Automating a broken process just moves the bottleneck elsewhere.
Clarify who owns a decision before building a tool to speed that decision up. Ownership gaps don't disappear with automation.
Bring in external partners for the build if the required expertise does not yet exist in-house. MIT's data shows pilots built through vendor partnerships succeed roughly twice as often as those built purely in-house.
Your Operating Model Is the Harder Thing to Copy
Software can be licensed. Implementation partners can be hired. Foundation models are, increasingly, a commodity available to anyone with a budget.
What can't be bought off the shelf is an operating model where people know who owns what, data moves without friction, and decisions get made instead of routed. These capabilities are built over time, and are far harder for a competitor to replicate than any platform.
The organisations that generate measurable ROI and growth with AI over the coming years will not necessarily be those with the most advanced models or the largest number of pilots. They will be the ones that use AI as an opportunity to rethink how work gets done, systematically reduce operational debt and build processes that are simpler, faster and more customer-centric.
“The organisations that generate measurable ROI and growth with AI will be the ones that use AI as an opportunity to rethink how work gets done.”
About the Author
Noora Tanska is a Business Engineer motivated by creating business value through better processes, data and AI. She excels at analysing complex challenges and clarifying needs across multiple stakeholders. Having worked with large-scale organisations on both the business and technical aspects of successful data-driven and digital transformations, she combines deep insight with the practical skills.