ARTIFICIAL intelligence technologies have transformed many businesses in the UK over recent years, but there’s one glaring mistake that too many are still making.
Tools such Claude, ChatGPT and Gemini have turbocharged productivity and efficiency for many, but their success is not always measured in the correct currency.
Companies are leaving money on the table by failing to sell artificial intelligence to boards in terms of the financial benefit, not just the hours saved.
It’s great that hundreds or thousands of employee hours can be saved each year, but how exactly does that affect the bottom line?
Many business cases are often expressed in a currency that the board does not actually spend.
Saving time does not equal creating value or making money.
The financial benefit appears when the extra capacity directly creates additional revenue, boosts growth without additional headcount, or eradicates a traditional cost.
Without achieving one of these outcomes, the business has improved productivity but not financial performance.
This is why many AI projects are stalling after a successful pilot, because it doesn’t quite make a compelling case for more expenditure on the technology.
It’s time to stop seeing efficiency as enough on its own and start questioning where the financial benefits appear.
The two most important measures are what the business earns and what the business is worth. That’s what boardrooms really care about.
AI investment should make a clear and quantifiable difference to one, if not both.
Increasing revenue
Let’s take earnings first. AI can unlock revenue that businesses are already entitled to but fail to capture because critical information remains trapped in a system nobody queries.
It can also turn previously untapped commercial opportunities into viable revenue streams by bringing fragmented data together and making it usable for decision-making.
There’s also an opportunity in supporting growth without increasing headcount. Increased productivity and efficiencies play an important role here.
Making employees faster or freeing up more of their time doesn’t automatically increase profitability. This increased capacity must be translated into reduced expenditure or commercial output.
Increasing enterprise value
This is the larger prize which often gets forgotten about when making a case for AI.
Gains made compound against each pound of earnings, they don’t just add to them.
Lenders, investors and potential buyers want predictability and visible recurring revenue. They want evidence and data infrastructure that stands up to scrutiny without having to be rebuilt.
They want resilient operations that are held in systems rather than a small number of employees with years of institutional knowledge. And they want provable scalability, not just buzzwords and ambition.
AI tools can help remove structural weaknesses or risks that might be hindering these four goals.
The financial value lies in the effect these improvements can have on perceived risk and, ultimately, valuation.
It might be that you currently have two roles dedicated to admin or processing that are
costing £60,000 per year.
AI tools eliminate the need for that labour and the salaries leave the business.
If you get that back in EBITDA (Earnings Before Interest, Taxes, Depreciation, and Amortisation) on a five times multiple, that’s hundreds of thousands of pounds in value that you have achieved.
The value is not being demonstrated
Too many vendors approach businesses with a product, not a solution. They’re there to sell the features and benefits of the technology, but don’t quite know where or how to implement it.
While it’s great to be able to streamline processes and increase efficiency, it’s also important to look at the potential of adding capabilities that didn’t exist before.
On a practical level, that could be building a new product or improving the customer experience to encourage revenue growth.
On a wider level, it’s about empowering your business to reach your goals faster and clearer.
My advice is to consider how an AI digital transformation program could unlock new capabilities or revenue streams that simply weren’t possible before.
It should be either growing what the business earns or what it is worth. That’s where AI’s value makes the most difference.
Businesses need to move past judging AI on efficiency, productivity and hours saved. The real measure is how much the business earns and how much it is worth.
If it’s revenue, then it should be demonstrated how AI creates commercial growth. If it’s cost reduction, then it should be demonstrated that the cost leaves the business and isn’t simply moved around.
If enterprise value is the goal, then it’s important to show the risks, be it operational or commercial, that are removed. This is what’s needed to explain how the changes affect how investors see or value the company.
There is a huge difference between proving that AI works and proving that it is worth spending money on.
Ultimately, the financial case determines whether the concept deserves to scale.
