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At the most basic level, an organization’s technology investments can be divided into two categories: keeping the lights on and growing the business.
The operational role of technology—keeping the lights on—is fairly tactical. Guided by best practices, we work with CIO’s to prioritize efficiency over differentiation. We keep the strategic goals of the business in mind, but fundamentally we can rely upon specific turn-crank processes to optimize spending and deliver value.
But growth is a different story. Investing in any technology that is expected to drive value to customers—and increase revenue to the business—is inherently speculative. Whether it entails creating new software or deploying some other digital service, it demands a process of research and development, with uncertain results and outcomes. Executives tend to draw a single line between results. They believe that if you spend money on R&D, you make more money in revenue. If that were true, innovation would be straightforward and easy. But that belief ignores the actual details of execution, and the range of possibilities that go into successful investments. Technology R&D is a strategic challenge, which is compounded by the murkiness of the process itself. For many executives, building software can seem to happen in a black box. Even business leaders and executives who can articulate what they want will often feel the engineering execution as a kind of voodoo.
But the opacity of technology R&D isn’t a license for unaccountability. Technology R&D may be complex and strategic, but it is by no means unknowable. We have proven techniques and practices at our disposal to guide decisions and actions to achieve the business’s long-term goals—rather than merely complete some poorly bounded “project.” There are clear ways to make the process more visible and manageable. We can establish accountability that not only improves the efficiency of the initiative but also reveals broader and better outcomes.
Above all, clear leading indicators establish a fulcrum for course correction. It may seem obvious to say, but if the only outcome of a technology initiative is more revenue, then you will not know if you have succeeded until the initiative is complete—and by then you have already committed and spent all the investment. That is the definition of a lagging indicator of success, which means it’s too late to course correct. It doesn’t have to be this way. Many CTOs miss the opportunity to leverage leading KPI’s. Their goal should be to connect the details of what’s happening deep inside the development process to establish and validate strategy and execution, in a way that is legible to both leadership and developers.
As soon as the R&D function identifies and starts refining initiatives, they should already be mapping out the things they can do to measure the outcomes that validate the strategy. That means breaking down the process into a set of indicators that will build to the desired outcome. For example, a leading KPI might be, “I’m expecting more usage of my company’s app.” As changes begin to be made through the R&D process, usage should increase. If not, some expectation is not being realized, and the whole effort should be assessed before continuing to invest. Perhaps the premise of the initiative is shaky. With good KPIs, we can know that while there’s still time to change course. Ideally there are several iterations along the path from a new feature to the desired strategic outcome. Each of these iterations should include measurable KPI’s that build up the validation—or reveal its failure. We say that “all learnings are high value,” because it’s better to spend $1 million to know what isn’t working, than to spend $10 million on something that doesn’t work.
Fundamentally, effective leading KPIs improve business options because you are increasing confidence and mitigating risks by validating and invalidating options much sooner in the R&D cycle. The return on investment can be maximized through this approach because you are expanding the realized value. Instead of fully completing two ideas, you can start with four ideas and kill the two or three that can’t be validated—and therefore are much less likely to succeed. In the process, you are increasing your likelihood of landing on a successful strategy. You have bridged the divide between R&D strategy and execution by transforming a fundamentally speculative process into an efficient and carefully structured one.
