When Abilities to Execute Are Secondary

It’s a great idea, but can we do it?  Can we make it happen?  We are going to boil the oceans and then provide everybody gourmet seafood dinners.  Ok for those who eat seafood, but how is this going to be accomplished?  Making the elements of a solution happen – executing — tends to be an enormous challenge.

What if everyone in the world had smart phones, state of the art laptops, and high performance broadband connectivity?  Wouldn’t everything be ok then, at least eventually?  These capabilities would help, but life involves much more than technology.  What about economic opportunities and access to food, healthcare, and education?

I have been involved in seemingly endless conversations about digitally transforming an enterprise.  The idea is to eliminate paper, become totally data driven, and to embrace evidence-based decision making.  Sounds great if data are available and curated.  Let’s reflect on what success will require.

If one wants data-driven, evidence-based strategy discussions, there are a few precursors:

  • Does one have the requisite data over a meaningful period of time?
  • Have these data sets been curated to assure that they represents a valid corpus?
  • Have inconsistencies and incompatibilities across data sets been identified?
  • Have models been identified that can provide valid projections of future outcomes?

Assuring appropriate answers to these questions is difficult work.  In my experience, many organizations treat these questions much too lightly.  They want to avoid the difficult work and “install” solutions quickly.  Two examples provide good illustrations. 

A large aerospace company asked me to help them determine what knowledge management solution to acquire.  I asked them, “Where is poor knowledge management hurting you?”  After much discussion, they chose foreign military sales.  We then proceeded to address that one specific problem to learn what knowledge management really meant and how it could help them.

The CEO of a large appliance company asked me what knowledge management solution they should acquire.  I asked him the same leading question.  His team concluded that production plans often resulted in appliances being produced that no one had ordered and appliances not being produced that Walmart, in particular, had ordered.  They noted Walmart because this customer charged a penalty for undelivered products, i.e., the profit they would have made had the products been delivered.

We tracked down how production forecasts were developed.  Field representatives provided projections of appliance sales, by product, in their region.  These forecasts were compiled and resulted in production plans.  We talked with field representatives about how they came up with their forecasts.  A common answer was, “I look at last quarter’s orders and decide where to increment them up or down.  By the way, what do you do with those numbers?”

The knowledge management problem was that key participants in the production planning process did not know how their inputs affected the process.  Beyond managing knowledge, the company needed to do a much better job at sharing it.  This led the company to create an initiative focused on who needs to know what and how this knowledge is shared.

Once one gets past the above hurdles of data access, curation, and modeling, several new questions become central:

  • What economic, social, and political forces are likely to affect the future?
  • How are these forces likely to impact our projections of future revenues?
  • Are our competencies well positioned for this competition?
  • Where will we experience challenges – performance, cost, customer satisfaction?

To address these types of questions, we need to move beyond “what is” to address “what if.”  Questions associated with “what is” can be addressed with the data sets noted above.  These data sets are inherently about was has happened, not what will or might happen.

“What if” questions can be informed by but not answered by empirical data.  This is simply because the future has not yet happened.  Nevertheless, execution happens in the future.  A digital strategy, or equivalent, that is limited to examining the past will be very much inadequate.  Emerging forces, their impacts, and abilities to compete are all about executing in the future, a future that is quite likely to be significantly different than the past.

Abilities to execute are usually addressed within the context of the incumbent enterprise’s abilities to scale what it has long been doing.  This perspective has merits – unfortunately, often only briefly.  A company’s ability to steadily increase quality and decrease costs can sustain and perhaps grow revenues for existing offerings.  This worked for Henry Ford’s Model T for almost 20 years, but the competition came to offer better models in more than one color.

How can an organization catch up with what will be needed?  How should they plan to execute in the future?  These questions tend to be major challenges, often insurmountable challenges.  Many, perhaps most, organizations think they are doing their best to execute their processes today.  Their processes may be outmoded and inefficient, but they have little time to think about this possibility.

Consider briefly the domains of healthcare, education, and energy.  Technologies will potentially impact all three domains.  Telehealth and artificial intelligence will change key elements of healthcare.  Online learning, including the unbundling of learning, will profoundly affect the economics of post-secondary education.  Renewable, yet intermittent, energy sources will challenge reliable and resilient provision of energy.

These three domains are likely to execute in the future much differently than they execute today.  Those who wait to see what happens are unlikely to be tomorrow’s leaders.  In contrast, those who see execution in the future as primary, and consequently play central roles in designing these futures, will undoubtedly lead their domains into those futures.

Several years ago, I was engaged with a large information technology company in developing an R&D strategic plan.  They had an agreed upon social norm that surprised me.  Apparently, marketing had a tendency to dream up wild ideas.  Engineering and manufacturing would explain the difficulties of executing their visions.  Someone from marketing would invariably say, “Oh, come on.  How difficult could that really be?”  The technical folks complained to top management.  This execution-oriented challenge was henceforth banned.

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