There is no shortage of excitement around artificial intelligence. Hardly a day goes by without a headline promising that AI will revolutionise everything from customer service to supply chains. And to be fair, the potential is genuinely extraordinary. McKinsey estimates that generative AI alone could generate between $2.6 trillion and $4.4 trillion annually across 63 identified use cases. They also suggest it could automate 60 to 70 percent of certain work activities. Those are staggering numbers.
But here is the question that too few business leaders are asking: where is that value actually showing up today?
The Gap Between Promise and Performance
When you look beyond the headlines, the picture becomes more nuanced. McKinsey’s own Global AI Survey found that only 44 percent of organisations reported cost savings from AI adoption. And while that sounds like a reasonable start, the distribution is wildly uneven. High-performing organisations — those that have invested properly in strategy, skills, and integration — are four times more likely to report cost reductions of 10 percent or more. Everyone else is largely treading water.
Thomson Reuters published research in June 2025 that painted a similarly revealing picture. Only around 22 percent of organisations have a defined AI strategy. Yet those with a strategy in place are twice as likely to see AI-driven revenue growth. The takeaway is not that AI does not work. It is that AI does not work by accident.
Meanwhile, professionals surveyed by Thomson Reuters expected to save roughly five hours per week through AI tools — equivalent to around $19,000 per person annually. That is a meaningful number when you multiply it across an organisation. But it only materialises if people are actually using the tools effectively, and if the tools are embedded into genuine workflows rather than bolted on as novelties.
Why Most Businesses Are Stuck
From my experience working with franchise networks and mid-market businesses, I see the same pattern repeatedly. Leaders know AI matters. They have probably experimented with ChatGPT or similar tools. Some have run pilot projects. But very few have moved from experimentation to integration.
The reasons are usually some combination of the following:
- No clear strategy. Teams are experimenting in isolation. Marketing is trying one tool, operations another, and nobody is joining the dots. Without a coherent plan, you get scattered efforts and scattered results.
- Underestimating the integration challenge. AI does not live in a vacuum. It needs clean data, well-defined processes, and systems that can talk to each other. In franchise businesses especially, where you may have dozens or hundreds of locations running slightly different setups, this is a real obstacle.
- Focusing on cost-cutting rather than capability-building. The businesses getting the most from AI are not just using it to replace headcount. They are using it to do things they could not do before — personalise at scale, respond faster, make better decisions with better data.
- Lack of skills and confidence. Many leaders are not sure what AI can realistically do, and they are wary of investing heavily in something they do not fully understand. That caution is understandable, but it becomes a problem when it leads to paralysis.
What the High Performers Do Differently
The McKinsey data tells us something important: the gap between leaders and laggards is not about budget. It is about approach.
Companies that are genuinely extracting value from AI tend to share a few characteristics:
They experiment with purpose. They do not just try things to see what happens. They identify specific business problems, test AI solutions against those problems, measure the results, and iterate. Every experiment has a hypothesis and a success metric.
They build a strategy before they scale. A defined AI strategy does not mean a 50-page document gathering dust. It means clarity on where AI fits into the business model, what data is needed, what skills are required, and how success will be measured. The Thomson Reuters finding — that organisations with a strategy are twice as likely to see revenue growth — is not a coincidence.
They integrate intelligently. They do not treat AI as a standalone project. They weave it into existing processes and systems. That might mean embedding AI-driven insights into a CRM, automating parts of a customer journey, or using AI to enhance reporting and decision-making at board level.
They invest in people. The best technology in the world delivers nothing if people do not use it, do not trust it, or do not know how to work alongside it. High-performing organisations invest in training, build internal champions, and create a culture where AI is seen as a tool that enhances capability rather than a threat.
The Franchise Opportunity
For franchise businesses, the AI opportunity is particularly significant — and particularly underexploited.
Franchise networks generate enormous volumes of data across locations, customers, and operations. That data, used well, is a goldmine. AI can help identify which locations are underperforming and why. It can predict customer demand patterns. It can automate routine communications and free up franchisees to focus on what they do best: serving customers and growing their business.
But franchises also face unique challenges. Technology decisions often need to work across a diverse network. Not every franchisee is equally tech-savvy. Systems need to be simple enough for front-line use while sophisticated enough to deliver genuine insight at the centre.
This is exactly the kind of challenge that Xpera helps franchise businesses navigate. We work with franchise leadership teams to cut through the noise, identify where AI and technology can make a genuine difference, and build practical strategies that deliver measurable results. Not hype. Not slideware. Real operational improvement.
So, Where Should You Start?
If you are a franchise leader or business owner wondering how to move from AI curiosity to AI value, here are three practical steps:
- Audit your current state. Where is your data? How clean is it? What processes are already ripe for automation? Where are the bottlenecks that technology could realistically address?
- Define a focused strategy. You do not need to transform everything overnight. Pick two or three areas where AI could have the highest impact, and build a plan to test and implement solutions in those areas first.
- Get the right support. AI strategy is not purely a technology question. It is a business question that requires understanding of your operations, your people, and your customers. Work with advisors who understand your industry and can bridge the gap between what AI promises and what it actually delivers.
The potential of AI is real. The McKinsey numbers are not fantasy. But the gap between potential and reality is wide, and it will only be closed by businesses that approach this with clarity, discipline, and a genuine focus on value.
If you would like to explore what AI could realistically do for your franchise or business, get in touch with Xpera. We would be happy to have the conversation.
Colin Rees is the founder of Xpera, a franchise technology and marketing consultancy. He works with franchise networks and mid-market businesses to build practical technology strategies that deliver measurable results.