Naturally, any business adopting any new technology needs the right key performance indicators (KPIs) and internal alignment of operations to ensure they get the results they want from the technology.
But there's a bigger, and often ignored, factor that will determine whether you'll reap lasting benefits rather than simply chasing big-ticket tech trends.
Upgrading old use cases and creating new use cases both constitute innovation, but only the former creates lasting economic and societal value.
This tension is now evident with generative AI. As Goldman Sachs noted this summer, companies are pouring $1 trillion into AI and still not seeing results. To maximize the return on their technology investments, business leaders need to think like architects, starting with a blank slate.
When digital cameras first came out a generation ago, consumers took memory cards to a brick-and-mortar store to print files; today, images can be shared instantly on mobile phones and social networks.
This evolution reflects a general 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 only to continue doing old things faster, easier, better, or at lower cost. Then we leverage the technology in new ways to create disruptive, lasting outcomes.
The leap from “skeuomorphic” thinking (where digital interfaces are designed to mimic traditional physical interfaces like a computer “desktop”) to native thinking takes time; for example, the journey from the first digital camera to the rise of Instagram lasted 15-20 years.
Businesses that adopt technology in skeuomorphic ways can improve their profit margins, such as using QR codes to replace printed restaurant menus. But those that invent new uses can create entirely new markets, as GrubHub did with its food delivery platform.
How can more companies make the leap to a native mindset that unlocks greater profits? One way is to look for friction. If you assume that the points of friction in your existing business model are fixed facts, you'll have a hard time breaking out of your old way of thinking. But if you identify and focus on the sources of friction, you'll often find that you can eliminate them.
Standard business imperatives like “faster, easier, cheaper” tend to lull us into skeuomorphic mode. They're so ingrained that we never question whether the product or process we're trying to improve should be sustained.
Amazon's approach to innovation at Whole Foods epitomizes this dynamic: In some stores, Amazon has sped up checkout by letting customers scan their palm instead of inserting a credit card. Some Amazon stores have even eliminated checkout altogether with “Dash Carts,” which count items as you shop.
There's a big difference between speeding up a step and eliminating a step. “How can we improve checkout?” is a skeuomorphic question. “Why do we still need checkout?” is a native question.
Friction points are what we call the “elephant in the room.” In our industry, financial technology, some of the friction points feel like permanent market features. When was the last time you waited three days and paid $6 to send a “cross-border email”? We're all sending messages instantly, around the world, and for free, so the idea itself is absurd.
Given that the internet financial system is now well established, cross-border payments can and should be just as seamless. But much of the industry is still stuck in a skeuomorphic mindset that takes fees, delays, and walled gardens for granted. Globally, the average remittance fee is 6%. It's like still printing photos in a brick-and-mortar store.
When it comes to the application of technology, users and functionality should take precedence over materials and properties. True innovation has its own unique power. Thinking natively requires identifying and leveraging that power. Digital photography's unique power wasn't high resolution, it was instant delivery. AI's power is pattern recognition, not truth-telling.
Using AI to enhance web searches is skeuomorphic. Using AI to scan medical images to detect anomalies that humans might miss is a great application. Additionally, AI can reduce or eliminate friction points throughout healthcare.
For example, AI-powered wearable devices could help detect illness before it becomes severe by monitoring changes in our basic health indicators. The US Department of Defense is already piloting a program to detect COVID-19 two and a half days before patients show symptoms.
Every business leader is looking for greater efficiency. But getting the most out of technology takes more than upgrading current products and processes. The key to success is questioning long-held assumptions about how things are done and inventing entirely new use cases. ©2024/Project Syndicate
