Many companies are using AI to automate tasks, reduce costs and speed up existing workflows, but this approach risks missing the much bigger opportunity.
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The first instinct for many companies adopting AI is to ask a simple question: Where can we save time or reduce costs?
This usually means automating day-to-day work, speeding up hiring, handling customer service queries or streamlining compliance. In practice, AI is often bolted onto existing workflows, with a chatbot here or an agent managing a narrow process there.
These are helpful steps. They can offer quick wins and visible short-term benefits. The danger is that they can also create a false sense of progress.
The real opportunity with AI is much bigger than making today’s business slightly faster, cheaper, or leaner. Companies that create lasting advantages will use AI to rethink how work is done, redesign customer experiences, and create business models that would have been impossible in the past.
The story offers a warning. When the Internet arrived, many newspapers and publishers simply digitized their existing products and put them online. Then search and social media companies redefined how people found news and how advertising money flowed. Brick-and-mortar retailers that moved online faced a similar shock when digitally native competitors like Amazon built their entire operation around e-commerce.
AI could follow the same pattern. Companies that focus only on efficiency may enjoy early gains, while more ambitious competitors use AI to reshape the market around them.
This is the AI efficiency trap and is one of the biggest strategic risks facing business leaders today.
Why is this a trap?
The AI efficiency trap begins when companies use AI primarily to streamline existing, siled workflows, rather than asking how it could create new value.
The result is often a business that becomes faster at routine tasks while missing out on the biggest opportunity to become more innovative, more differentiated and more helpful to customers.
Marketing is a good example. He was one of the earliest and most enthusiastic adopters of genetic artificial intelligence. However, many companies use it to produce more blogs, more ad copy, and more social media posts. The result is often volume rather than value, adding to the flood of generic AI-generated content already competing for attention.
Customer service tells a similar story. Many businesses now rely on chatbots to answer simple questions or escalate issues to human agents. This can speed up ticket handling, but it can also frustrate customers when the system doesn’t understand a more complex problem, gives irrelevant answers, or sends people in circles.
The biggest strategic problem is that these efficiency gains are easily copied. There is no permanent moat in using AI to automate the same basic tasks that your competitors can automate. At best, it creates a temporary advantage until everyone else catches up.
Meanwhile, more ambitious competitors may be using artificial intelligence to rethink how work is done. They may be redesigning workflows around AI agents that can handle complex, multi-step tasks, or finding better ways to understand, measure and improve the customer experience.
For me, the long-term opportunity is about building businesses that are AI-inherent at the core. This means using AI to create new ways of working, new forms of value and new customer experiences, rather than just making the old model work a little faster.
From Efficiency to Innovation
So where should business leaders start?
For me, it’s thinking beyond “what can we automate?” to “what would this business look like if it was built around AI from the ground up?”
Netflix is a great example of a business built around the opportunities of the previous generation of AI technology. Instead of just making its content available via streaming, it built AI recommendation engines and search functions that completely redefined online streaming.
The current generation of AI technology, based on generative AI models, will also redefine industries, and a new Netflix or Amazon is likely to emerge. But they won’t come from companies that aim solely for efficiency.
This could mean that AI assistants work proactively rather than waiting for instructions. They could anticipate needs, create personalized experiences and continuously learn from user behavior.
It could also mean products and services that are smart and adaptive by default. Health care systems could identify and reduce health risks before symptoms appear. Financial platforms could manage money smarter, helping people save, invest and plan with less friction.
The products and services we use 10 years from now are likely to look very different from what is available today. The businesses that shape this future will be the ones that use AI to solve real customer problems and drive meaningful transformation now.
Looking ahead: Your industry in 10 years
Serious AI requires serious investment, in terms of money as well as the effort required to build a culture where it can be used effectively and responsibly.
Why go to all that effort to create something that’s just a faster, cheaper, or leaner version of an already aging business model? Instead of something that just couldn’t be done before?
Addressing this means approaching AI as a leadership and cultural challenge as much, if not more, as a technique.
In many cases, the technology itself becomes easier to use. You can describe what you want an AI system to do, and it can often start producing useful work within seconds. The hardest challenge is knowing what to ask for, where to apply it, and how to make sure it creates genuine value.
This still requires human insight, judgment, oversight and long-term strategic thinking. AI can help companies move faster, but leaders still need to decide which direction is worth moving in.
