The delta: decoupling from linear benefits (the 10x, not 10%, mindset)
So why work so hard for these new business models? The reason is simple: it’s a new way to decouple inputs and outputs, and break through linear growth for exponential gains.
Businesses should be aiming to make 10X improvements—i.e., boost their business by a factor of 10, not 1 or 2. And AI, data and platform models offer those expansive new opportunities.
Example: a leading consumer packaged goods (CPG) company has created a platform play by turning to B2B ecommerce. The idea is: they provide the platform (a marketplace) to 1) sell directly to small retailers not covered by existing distribution networks (= more sales) and 2) make the platform available for other CPGs to sell their own products directly for a fee (= new revenues). This new strategy accounts for more than half the company’s incremental growth. And it’s a great example of economies of scale and new routes to market: not just B2B, but also B2B2C.
One key tool for decoupling inputs and outputs is cloud. It offers flexibility like never before for data storage and managing workloads; plus better server utilisation rates, and more energy-efficient infrastructure. And the delta is big, according to Accenture's research: Our analysis of the largest public cloud service providers shows average enterprise-owned-to-cloud migrations can lead to an impressive 65% energy reduction and 84% carbon reduction. And migrations to public cloud can result in up to 30-40% total cost of ownership (TCO) savings.
#3: Develop the flexible mindset to match
AI-fuelled business models behave differently, because they’re able to move faster. This changes the nature of how you go to market and the cadence of product/service development. But you have to get your head around it.
It’s about working differently, with a more flexible mindset. For instance: it may be better to pursue a series of smaller bets than fewer, bigger bets—even though many (or even most) of those small bets will fail. It’s about capturing insights fast, acting on the signals generated by those insights, and trying more new things, more frequently. And that type of approach is the determinant of whether businesses can adapt, not get left behind.
By way of example: over the last couple of years, Amazon has tested myriad financial service propositions for its customers, from payments to insurance. This constant experimentation enables Amazon to make its business better benefiting both the company and its customers. Contrast that with traditional financial services/payments companies: many will have a single project running at a time, spanning several years. If it does eventually go to market, it may already be obsolete by its launch date.
The fail fast culture is already making big players much more agile, with GlaxoSmithKiline (GSK) explicitly acknowledging that 95% of experiments would be likely to fail, but those that were successful would be worth it.
But it all relies on the right skills and the right teams. We found only 25% of employees feel able to use data effectively. It’s time to rethink the workforce, and many businesses already have, with upskilling, and reskilling high on the agenda.