We are prepared to take risks, but intelligent risks. The policy of being too cautious is the greatest risk of all. - Jawaharial Nehru, Speech to Parliament, New Dehli, 18 Feb 1953.
Participants in new endeavors pour their hearts and souls into defining and developing solutions that will appeal to existing or emerging customer segments, while delivering tangible returns back to their investors. Yet any search for value consumes precious resources. When businesses make bets on vague problem statements, rely upon the leverage of immature technologies, or measure success using indicators that are only proxies for value, it is far too easy to waste those resources:
Product developers create a large number of proxy objectives: increase innovation, improve quality, conform to plan, shorten development cycles, eliminate waste, etc. Unfortunately, they rarely understand how these proxy objectives quantitatively influence profits. Without this quantitative mapping, they cannot evaluate decisions that affect multiple interacting objectives...
Life would be quite simple if only one thing changed at a time. We could ask easy questions like, "Who wants to eliminate waste?" and everyone would answer, "Me!" However, real-world decisions almost never affect only a single proxy variable. In reality, we must assess how much time and effort we are willing to trade for eliminating a specific amount of waste.
Navigating along the pathways over which value can be both discovered and realized requires sustained and open-minded commitments, and regular demonstrations that the way forward is affordable, feasible, and worthwhile. In order for businesses to make good investments, the investments must be based upon a clear set of goals that provide a big enough 'bang for the buck', must implement strategies that align with the organization's business model, and must be able to execute a credible plan that demonstrates progress incrementally. Such routes often require sifting through lots of things that sound attractive from selected viewpoints, in order to focus and synthesize product management activities on the challenges and business scenarios that will be most likely to deliver the highest return in the shortest period of time. Solution providers usually think of requested capabilities over longer time horizons, but are often pressured to over-commit to performance within shorter timeframes, since candidate customers with problems worth solving usually need answers sooner rather than later.
Abstract, notional project objectives are the predominant cause of failures in pursuing this value. This phenomenon occurs so frequently in new product development that it is woven into the fabric of solution providers, who nearly always adopt the classic commitment hedge "the devil is in the details'. These solution developers understandably want to provide their best face to decision-makers, and typically seek to offer enticing sound bites as bait for funding and sponsorship. Yet unless there are enough credible customers willing to roll up their sleeves and invest their own key resources in the necessary and messy details of solution development, organizations can waste precious time and resources solving the wrong problem, or delivering solutions with limited benefits, but which tie up precious resources that will only provide sub-optimal utility for emerging needs.
The velocity and direction required to navigate to an acceptable solution nearly always requires cycles of trial and error. This evolutionary development is called that because it is similar to the variation and selection we see in natural selection. The search for worthwhile pathways to high leverage solutions requires product managers and technical leaders to challenge the status quo, embrace change, and accept that required standards are usually tightly bound into the context in which they operate. This reality that may not fit will with the conflicting demands for speed and quality in businesses. This reality is not popular, since it means that simple and universal solutions may not always be appropriate or achievable, and requires us to embrace the variation which naturally arises, and often causes problems in fielding or using version one products. Ironically, it is this variation and refinement that is also the source for most really great ideas.
While many organizations attempt to embrace trial and error in evaluating business opportunities, the book Adapt: Why Success Always Starts with Failure describes the pitfall most fall into:
Variation is difficult because of ... grandiosity: politicians and corporate bosses both like large projects – anything from the reorganisation of a country’s entire healthcare system to a gigantic merger – because they win attention and show that the leader is a person who gets things done. Such flagship projects [fail] because errors are common and big projects leave little room to adapt. The other tendency emerges because we rarely like the idea of standards that are inconsistent and uneven from place to place. It seems neater and fairer to provide a consistent standard for everything, whether it’s education, the road network or the coffee at Starbucks.
If we are to take the ‘variation’ part of ‘variation and selection’ seriously, uniformly high standards are not only impossible but undesirable. When a problem is unsolved or continually changing, the best way to tackle it is to experiment with many different approaches. If nobody tries anything different, we will struggle to figure out new and better ways to do anything. But if we are to accept variation, we must also accept that some of these new approaches will not work well. That is not a tempting proposition for a politician or chief executive to try to sell.
Successful trial and error requires endeavors to substantiate that perceived problems are real and that solutions are achievable within available resources, and are capable of displacing incumbents. The path from problems to solutions is rarely straightforward. Small batch sizes, many experiments, and frequent demonstrations are essential before we should make major commitments, since such approaches can provide us with more immediate feedback that enables more frequent course corrections.
To visualize how this works, lets consider two separate efforts that are each in pursuit of the same target. Alternative strategies are depicted by two different sets of vectors on the diagram on the right. As in the real world, the diagram pursues targets that change with time. Such changes can be the result of changes in the environment, or alternatively, refinements in understanding these environments. Two different strategies are depicted over time that both pursue this target. The first, shown by the gray arrows, represent projects which utilize large batch sizes; such large batches usually take a long time to produce, and thus feedback takes longer to be incorporated, and is not available as frequently. The second approach depicts an effort which instead uses a smaller batch size, which are about one third of that used in the first approach. Both of these efforts, like real projects, have constraints on how much change can be incorporated into each iteration, but we'll let the large batch size project change its direction at twice the rate as the smaller batch size approach. This difference in agility highlights our tendency to over-compensate in subsequent batches, once you discover you are off course.
After the two approaches have both traversed the same distance (which involves 2 iterations for the gray arrows, and 6 iterations for the green ones), we can evaluate the magnitude of the gap remaining between each path's progress and the target. As the diagram indicates, the approach employing small batch size is 30% closer to the target than the large batch size approach. On a real project, progress is rarely as simple as that depicted in either of these approaches. Simplistic ideas offered up to solve complex problems may have broad appeal, but solutions are usually much more complicated than they were originally envisioned to be. The path which projects take from point A to point B is a voyage of discovery. While pursuing any one branch, a pitfall may be uncovered which blocks progress, causing one to retrace your steps to explore other alternatives. That is not the time to run out of energy, water, or air.
Embarking on hazardous endeavors without adequately planning for resources can expose the endeavor's broader scope to risks unless adequate verification has demonstrated that there will be sufficient resources to survive. Relying on many probabilistic outcomes to all line up with the best possible conditions makes for great entertainment, but is generally not the prudent way to converge on a moving target. Instead, iterative efforts can help enable parallel pursuits, each evolving and synthesizing maps of the terrain, so the gaps between perceived needs and economically realizable solutions can become a step of faith, rather than a leap.
Solution developers must realize that it is only by working together with potential customers that they can scout out this value, and chart out the terrain that must be confronted, so these gaps can be adequately understood, properly evaluated, and realistically traversed. Their partnership is critical as they work together to discover a route to their destination. Throughout this journey, it is helpful to use the following three questions to determine whether proposed solutions will credibly converge on the best value available within the business's landscape.