Why Most Firms Fail to Capitalize on New Technologies

BOSTON – From artificial intelligence and electric vehicles to blockchain and composites, we are in a golden age of innovation. To unlock value from these technologies, though, businesses must transform themselves, and, according to a McKinsey & Company study, over 70% of such efforts fall short.

Obviously, businesses adopting a new technology need the right key performance indicators (KPIs) and internal alignment of their operations to ensure they get what they want out of it. But there is a bigger, often neglected, factor that determines whether they are unlocking durable returns, rather than merely chasing expensive tech trends.

While upgrading old use cases and creating new ones both constitute innovation, only the latter creates lasting economic and social value.

This tension is playing out now with generative AI. As Goldman Sachs noted earlier this summer, companies have poured $1 trillion into AI without much to show for it yet. To maximize the return on investment in technology, business leaders should think like architects who are starting from a blank page.

When digital cameras emerged a generation ago, consumers still took memory cards to brick-and-mortar stores to print their files. Today, we share images instantly with our phones and social networks.

This evolution reflects a common pattern in technology adoption. As entrepreneur Chris Dixon notes in Read Write Own: Building the Next Era of the Internet, we initially use new technologies merely to continue old behaviors with greater speed, ease, or quality, or at lower cost. Only later do we leverage them in new ways to produce disruptive, lasting outcomes.

The leap from “skeuomorphic” thinking (when digital interfaces are designed to mimic traditional physical ones, like the “desktop” on your computer) to native thinking takes time. For example, the journey from the first digital cameras to the rise of Instagram lasted 15-20 years. Businesses that deploy technology in skeuomorphic ways can improve margins, such as by using QR codes instead of printed restaurant menus. But those who come up with new uses can create entirely new markets, like GrubHub did with its food-delivery platform.

How can more businesses make the leap to a native mindset that unlocks greater gains? One way is to look for friction. When you assume that points of friction in existing business models are fixed facts, you will struggle to escape older ways of thinking. But when you identify and focus on the sources of friction, you will often discover that they can be eliminated.

The standard business imperatives of “faster, easier, cheaper” tend to keep us mired in skeuomorphic mode. They are so ingrained that we don’t question whether the product or process we seek to improve should be preserved at all.

Amazon’s approach to innovation at Whole Foods epitomizes this dynamic. In some locations, it has made checkout faster by allowing customers to scan their palms instead of inserting a credit card. Some of its stores have eliminated checkout altogether via “dash carts” that tally goods as you shop.

There’s a profound difference between speeding up a step and eliminating it. “How can we improve checkout?” is a skeuomorphic question. “Why do we still need checkout?” is a native one.

Friction points are the proverbial elephants in the room. In our own industry, financial technology, some of them feel like permanent market features. When was the last time you waited three days and paid $6 to send a “cross-border email”? The very notion is ludicrous because we all transmit messages instantly, globally, and for free.

Sending money across borders can and should be just as seamless, given that the internet financial system is now well established. But much of the broader industry is still captive to skeuomorphic thinking that views fees, delays, and walled gardens as facts of life. Globally, the average fee on remittances is 6%. It’s as if we were still printing photos at a brick-and-mortar store.

When it comes to applying technology, users and functions should trump materials and attributes. Every genuine innovation has a unique power. To think natively, we must identify and tap into it. Digital photography’s unique power wasn’t high resolution; it was instant distribution. AI’s power is pattern recognition, not truth-telling.

Using AI to augment a web search is skeuomorphic. Using it to scan medical images for anomalies that humans may miss is a superior application. Moreover, AI can reduce or eliminate friction points across health care. By monitoring changes to our baseline health metrics, for example, AI-powered wearables could help us spot an illness before it becomes serious. The US Defense Department has already piloted such a program to detect COVID-19 two and a half days prior to patients becoming symptomatic.

All business leaders seek greater efficiency. When it comes to gaining the most from technology, however, upgrading current products and processes is not enough. Success lies in questioning longstanding assumptions about the way things are done, and devising entirely new use cases.