July 26, 2021

By Joseph Byrum

History is filled with naysayers who doubted technology’s ability to lift society past hurdles once thought insurmountable. Thomas Malthus was the classic exponent of this philosophy, outlining in his great 1798 work how famine, disease, and war were the inevitable consequence of food production’s inability to keep pace with population growth in a prosperous society.

While this prediction failed to come true, his was, and is, no fringe belief. After more than two centuries, the idea that innovation has limits — that success cannot continue forever — remains strong. This tends to be less explicitly stated in the business context, but you can find it baked into the attitudes of risk-averse executives who fail to invest in the technologies that drive innovation and that hold on to the old way of doing things.

To avoid falling into this trap, decision makers need to adopt an innovation mindset.

The technologies that allowed food production to keep up with a global population that exploded from 1 to nearly 8 billion since Malthus were the product of concerted effort. The top seed companies, to take just one example of agricultural improvement, each spend $1 billion every year hunting for a small percentage increase in the bushels per acre extracted from their crop portfolio. As the development process for these products spans seven years on average, they have to think not in terms of what customers need now, but what they will need seven years from now. It’s a different mindset — an innovation mindset.

This mindset requires: (a) planning far ahead based on what’s likely to happen in the future; a (b) a willingness to make a substantial investment in time or resources to achieve goals; and, (c) the conviction that it’s always possible to achieve greater efficiency that will result in a competitive advantage.

It’s a constant process of evaluation and response. Many businesses have already embraced investment in artificial intelligence (AI) and data analytics. Properly developed tools provide an advantage to the companies that have deployed them, but we’ve only scratched the surface of what’s possible.  Executives should be asking, “What’s next?”

One might say, in the decade ahead, that all signs are pointing toward the development of quantum computing hardware that will smash through the limitations of conventional processors and bring even more capability to those AI algorithms.

The response comes from thinking about what the business should be doing right now to make the most of those capabilities when they become widely available. It’s no doubt daunting for a business outside the high-technology arena to consider investing in such an esoteric field. Internal resistance will take the form of, “we’ve never done anything like that — that would be a waste of money.” They might also point out that they have no expertise in those subjects.

This objection misses the point. The mathematics behind increased efficiency work the same, whether you’re manufacturing microchips or potato chips. Companies must judge for themselves the level of benefit they need, as there are different levels of sophistication. The corner bakery doesn’t have the same needs as a hedge fund. What data analytics can offer is  a straightforward approach to process optimization. AI can automate aspects of the decision-making process to ensure courses of action are selected based on the best available data, not suppositions and guesswork. Quantum computing hardware, once it’s powerful enough, should be able to run the most difficult “NP-hard” optimization calculations beyond the capabilities of current machines.

It’s possible to tackle projects involving any of these approaches without in-house expertise. Crowdsourcing platforms expand the pool of talent across industry and geographical boundaries. Tasks that a firm seeks to optimize just need to be broken down into discrete elements that crowdsourced teams can tackle as they work step-by-step to complete the project as a whole. The manager who wants to take advantage of this technique must have a clear vision of what needs to be done, and an openness to the new approaches and fresh perspectives that these outsiders will bring to the table.

Even with crowdsourced help, it would be easy to think quantum computing is outside the reach of anyone without a Manhattan Project-level of resource investment. That’s only the case for the true pioneers building quantum computing hardware from scratch — D-Wave, IBM, Rigetti, Microsoft, IonQ, and the like. There’s no need for that when the capabilities can be leased from cloud providers. Teams are free to experiment with these machines today to build the experience that can do useful things when the technology matures.

Quantum computers require specially crafted algorithms that take advantage of a different method of calculation that takes advantage of the quirky nature of subatomic particles. Already, some using “hybrid” solutions (quantum algorithms running on conventional machines) are discovering the benefit of looking at their own problems from a quantum perspective. The innovation mindset thrives on adopting fresh perspectives like this.

The pioneering companies developing the quantum hardware still face monumental engineering hurdles in taming the forces that allow these machines to exceed the processing power of conventional supercomputers. But the technology is poised to provide such a competitive advantage that companies without the capability to exploit it risk being left behind by those who can.

Nobody will want to be on the losing end of the quantum computing revolution, but being on the winning end takes the planning, investment, and conviction of the innovation mindset.