Rationalize R&D and
Take Charge of Your Destiny
When talking about Innovation and R&D, have you ever asked yourself: Why is the conversation about making big or small bets? Why am I making bets at all? Why can’t R&D be driven by clear objectives, achievable plans, and concrete, measurable business outcomes?
After decades leading innovation in start-ups, at the nation’s leading government R&D agency, and for a premier Fortune 100 R&D company, the team at IAG realized that the normal way we’ve all thought about innovation and R&D is all wrong. We’ve asked the wrong questions, measured the wrong outcomes, and too often, we’ve not generated the desired returns.
When we embrace risk, encourage failure, eschew metrics and gamble with our future, we are twisting ourselves into knots and advocating for things we know we don’t want. There has to be a better way — one that is based on metrics, delivers clear, measurable results and empowers leaders to have meaningful roles in charting their organization’s future.
A Clear Data-Driven Approach
Fortunately, now there is. IAG’s learning-based approach, developed over two decades, unwinds the convoluted logic of innovation into a clear, repeatable and measurable activity.
Three key observations motivate our approach:
1) Uncertainty is what stands between where you are today and the future you envision for tomorrow.
2) Retiring uncertainty is what gives you ‘special knowledge’ you can exploit in the marketplace. How efficiently you can retire uncertainty, and confidently take action based on the results determines your success and allows you to outperform your competitors.
3) The best way to achieve this is to identify and quantify the key uncertainties surrounding opportunities. Then, based upon that knowledge, you can define a Minimum Viable Plan (MVP), the plan that retires uncertainty most efficiently. The MVP is predictable, measurable, accountable, and generates actionable outcomes over timeframes relevant to your business.
IAG’s learning-based approach unifies the perspectives of operations, finance, engineering and technology teams into a single framework that can be understood and practiced by the entire organization.