History is littered with smart people making horribly wrong calls about nascent markets. In the 1940s, IBM’s Thomas Watson (supposedly) said there was a world market for only five computers. In the 1970s, Digital Equipment’s Ken Olson said there was no reason why people would want a computer in their home–versus their office. And it was Microsoft’s Bill Gates who was reported to have said in the early 1980s that 640K should be enough memory for anyone. Regardless of whether or not these stories are apocryphal, they ring true because we misjudge early-stage innovation so often.
It’s nobody’s fault. Data tends to accrue and become obvious only after people have already taken action. Most people make their first-mile decisions inside what I call the “fog of innovation.” It’s easy to get lost in the fog and never make any decision at all, because a risk that doesn’t pan out tends to have more negative repercussions on a person’s career than risks not taken. Problematically, if you never make a decision, that only creates more room for disruptive upstarts and hungry competitors.
Worse, companies often face a mismatch between their innovation plans and the overall strategy that should be in place to support those plans. I remember distinctly a large company that proudly told me about how it got all of its most important executives to sit on an all-powerful innovation board that met every 90 days. “What if,” I asked, “the day after a meeting, the team discovers its entire strategy needs a wholesale revision?” Silence.
It’s tempting to say that there shouldn’t be any control mechanisms for more uncertain efforts. Once you’ve decided to innovate, the argument goes, you should form a team, give them a check, and get out of their way. Letting chaos reign, however, carries substantial risks.
Most ideas emerge out of a process of trial-and-error experimentation. Without control mechanisms, teams can easily follow the wrong strategy for too long. Weak control systems also deny a company the opportunity to redirect resources to the most promising ideas or to find creative ways to combine ideas.
It takes discipline to launch new ventures.
Yes, there should be a discipline around managing ideas in the first mile. But it needs to be a different discipline from the mistake-minimizing systems that govern the core business.
Consider the discipline that venture capitalists impose on their investments. Venture capitalists are actively involved in the companies in which they invest–typically, a VC will sit on the company’s board and interact with management on a regular basis. If a decision needs to be made, the board will typically assemble in 24 hours. Venture capitalists carefully manage the funding process to focus entrepreneurs on the most critical issues early–tying future fund-raising rounds to achieving key milestones. It is a very different approach to funding than the typical annual budgeting cycle inside most companies.
Active stakeholder involvement, a scarcity mindset when it comes to available funds, and quick decision-making often ensure that VC-backed startups rarely get lost in the fog of innovation.
The military, too, faces the urgent need to make decisions when information isn’t clear. One doctrine taught to Marines is the so-called 70% rule. During the fog of war, the goal is to get enough data so that you are 70% confident in your decision, and then trust your instincts. If you have less data, you are making a close-to-random decision. If you wait until the data are perfect, the chance to make a critical decision has probably passed you by.
Follow these four principles for encouraging experimentation.
The uncertainty that characterizes the first mile of innovation requires an approach that encourages experimentation. Such an approach has four primary principles:
• Prioritize taking action over endless studying. That means viewing investments as strategic options that provide the right, but not the obligation, to invest more in the future. This way, action happens in steps rather than as “all or nothing” commitments.
• Review data from your venture frequently. Focus less on progress against goals that might change and more on learning–consider anticipated and unanticipated lessons. Let both quantitative and qualitative data inform decision-making.
• Diversify your team. Discussion and decision should not involve “business as usual” people and processes. For instance, when former Procter & Gamble chief technology officer Bruce Brown led R&D meetings, he would stop when it was time to review disruptive innovations to get a more diverse group of people into the room, including engineers, designers, marketers, and even customers–people who are typically alien to such gatherings.
• Take part in the experiment. Don’t just passively review details and data. The best executives will actively participate in the experiments and market tests whenever possible.
Practiced together, these principles should help you support the creation of breakthrough ideas.
Know whether you’re encouraging experiments or minimizing mistakes.
It’s useful to know what your goals are in terms of timing. Consider this study: In late 2010, three academics published a paper contrasting the impact of incentives on two institutions that give grants to promising life scientists. One program, led by the National Institutes of Health (NIH), features short review cycles, predefined deliverables, and tough penalties for missing milestones.
In contrast, financial support for scientists at the Howard Hughes Medical Institute (HHMI) takes a longer term focus, with a stated tolerance of early failure. Perhaps not surprisingly, HHMI scientists produce breakthrough ideas at a statistically significantly higher rate than do NIH grant recipients.
They also had more total output–yet also have more efforts that appear to be flops. One approach minimizes failures; the other maximizes breakthroughs. Is the HHMI system better than the NIH one? It’s a trick question–the answer depends on the strategic intent.
Experiment-encouraging systems aren’t necessarily better than mistake-minimizing ones. You just need to know what you’re after. Companies should have both systems running in parallel. Mistake-minimizing systems help to maximize resource efficiency in the core business; experiment-encouraging ones help to maximize learning in new businesses. But that’s exactly what you need to make it through the early-stage fog.