Descripción del algoritmo

It is time to face reality. And that reality is that the world has changed dramatically in the past decades and is continually changing at a rapid rate.

First, computer technology came along. Then Al Gore invented the Inter-net and the world started getting connected. All that connection created globalization. Globalization meant that anyone anywhere could affect our businesses and business rules. Combined with ever-increasing technology innovation, the world is now much more dynamic, much more competi-tive, and a whole bunch less certain.

Yet, we strive for certainty. Many of our traditional manage-ment processes—from financial analysis to project managemanage-ment—are designed to create certainty. But in this age of rapid change and the resulting ambiguity, do these management practices work? Or do they merely create the illusion of certainty?

We were recently asked by the chief information officer (CIO) of a large, global company to help him and his staff figure out how to use iterative methods in the face of a capital budgeting process that required a five-year, predictable planning horizon. The CIO explained,

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“We have to create a five-year cash flow projection for each of our major projects. We have no idea how to do that now that we are using iterative methods. We have been trying different things but can’t get any-thing that is acceptable.”

“Acceptable to whom?” we asked.

“To the CFO and the capital budgeting process. Before a project gets approved, we have to define the five-year costs and benefits for the project.

But using iterative methods, we don’t know what the final product will look like until we get past the approval stage. Prior to approval we can define an initial set of features and functions, but those might change during the project. If those change, the cash flows might also change.”

One of the CIO’s directors spoke up, “We are stuck. We cannot get approval without accurate cash flows, but we don’t know the actual cash flows until after the project is approved and we have done a fairly large number of iterations.”

What an interesting dilemma. The CFO requires a five-year cash flow so that the company can determine the net present value (NPV) of the IT projects. But using iterative methods, the final state of the IT project might be a bit (or more than a bit) different from what was initially planned, thus invalidating at least a portion of the five-year cash flow projection. Ideally, using such methods improves the NPV of the project as the project team adjusts to customer feedback and focuses on the features that generate the most value. But that is not the issue. The issue is that the CFO and company want to know, with certainty, what will be delivered and what the impact will be over the next five years. The CIO and his staff wanted to figure out how to deliver certainty while still using iterative methods.

We offered a different perspective and approach.

“When was the last time you gave a five-year cash flow projection that turned out to be accurate?” we asked.

The members of the team thought for a few moments and looked at each other. Finally, one brave person said, “We don’t know—we’ve never looked at it.”

“Has anyone ever looked at it? Does the CFO or his staff ever do a post-project analysis to see whether or not you got your five-year cash flow projections right?”

“I don’t think so—at least no one has ever asked us for the data they would need to make the assessment.”

“In that case, does it really matter what the impact is of using iterative methods on the CFO’s process? I am not trying to be critical or flippant,

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but does it really matter what numbers you use in your five-year cash flow projection at all? It seems to us that because no one ever looks at it, make the number whatever you want. Or why do it at all?”

This conversation was making the CIO very uncomfortable. He and his staff spent a lot of time refining estimates to generate “good” numbers for the capital budgeting process. This process was one of the company’s critical management methods. But was it doing any good? Did it make any difference in what the company did or did not do? Even worse, if there never was any real follow-through, how easy was it to game this process by inventing somewhat inflated cash flow projections in order to boost a project’s NPV?

Sensing the challenges we had just presented to the CIO and his staff, we attempted to work toward a solution.

We are not saying that your estimates and this process are invalid.

However, a useful process needs to recognize that the future is uncertain.

I suspect it is nearly impossible to define what will be happening in the marketplace in one year—let alone in five years. Think back on how tech-nology has changed in just the past year. Then in the past five years. When you proposed a project five years ago did you understand how data analyt-ics would change? Or mobile? Or social? When you proposed a project one year ago, how much certainty did you have about not only technology but the competition? How has the company’s focus changed in the past year? What ideas and processes did new employees or leaders bring with them? No, we are not saying that planning is not useful. Planning is very useful. Planning lets us sort through the range of things that might hap-pen and prepare ourselves for what might change. Rather, we are saying that the planning process needs to recognize the impossibility of certainty.

So instead of planning that anticipates no changes, accept ambiguity and incorporate it into your planning. In fact, if you recognize the harsh real-ity that almost nothing is certain, you break through to a higher level of organizational agility and leadership.

This is a strong and potentially unsettling concept—that by embrac-ing ambiguity, we can improve results. How is this possible? Let’s walk through the logic.

Ambiguity Is a Reality

It really is impossible to know the future. Some years ago, for one of my son’s elementary school science projects, we tracked the accuracy of the local weather forecast. Every morning, we opened the weather forecast

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and recorded what the forecast said about the high and low temperatures and sky conditions for that day and then three and five days in the future.

We then recorded the actual temperatures and sky conditions on those days. We considered the forecast accurate if the predicted temperatures were within three degrees of the actuals and if the sky conditions were within one grade of the actual sky conditions. (For example, if the forecast predicted sunny and the actual skies were partly cloudy, the forecast was accurate. But, if the actual skies were cloudy, the forecast was wrong.)

We conducted our experiment for an entire month. Over that time period, the same-day forecast was, as you might expect, reasonably accu-rate. The same-day forecast was on target over 70 percent of the time. The three-day accuracy was not as good, clocking in at around 50 percent accu-rate. The five-day forecast was the least accurate—barely over 30 percent.

Now, as a society, we have invested lots of resources to improve and refine our ability to forecast the weather—we make multiple decisions in our lives and in society based on the weather. We have lots of intri-cate, advanced systems to track weather and project the future weather.

In spite of this significant investment, the forecast for the same day was wrong nearly 30 percent of the time. Are you kidding me? Wrong nearly 30 percent of the time? To predict what is going to happen over the next 18 hours? And what about the three-day accuracy? We have weather satel-lites that can see what is looming three days away and yet we get it wrong almost 50 percent of the time? Admittedly, the weather is a difficult system to predict—there are many variables that come into play. But our business environment is also an incredibly difficult system to predict. Will a customer, today, make a decision to purchase or not to purchase? Will the critical resources on our most critical project decide to have a bad day?

Will that technology we are depending on not work? Will a human some-where along the critical decision path change his mind?

The harsh reality is that we are immersed in ambiguity and uncer-tainty. At a minimum, accept this. Even better, embrace this reality and operate accordingly. If we refuse to embrace ambiguity, we increase the likelihood that we will be wrong more often than we need to be.

Embrace Ambiguity and Incorporate It into All We Do We can build an appreciation for ambiguity into our leadership, plans, and processes. One of the most powerful tools for embracing ambiguity is using iterative methods. Agile software development is an example of such iterative methods. So is Stage-Gate for product development. Using these

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methods, we embrace the notion that we don’t know the future and will get to the future in a step-wise fashion, learning from customers and the market and correcting as we go. We make global plans that define a goal or an objective. But we follow a path that takes small steps toward that goal—

small steps so that we can pause, get feedback, learn, evaluate, and reorient ourselves after each step. This reduces our chances of being wrong about the product, the technology, the market, and everything because we incor-porate feedback and learn and adapt.

Learn as We Go

There is value in learning. If, in our plans, we assume we are correct and will go from point A to point B with no variations, we are also assuming that there is no chance to learn and act on what we learn until after we arrive at point B. But in an environment populated with innumerable variables and changes, we can learn critical lessons along the way and make corrections.

By embracing ambiguity, we embrace learning. Over time, individuals and organizations that learn and adapt get things right more often and things wrong less often.

All of this sounds fine, but in a world that seeks certainty, what can a leader or team do? We have found that a very good starting point is to experiment. Experiment with more frequent planning—and see if it improves results. Experiment with iterative methods and see if they make a difference. Experiment with delaying commitments until you know enough and see if that leads to success.

Dealing Honestly with Ambiguity

When I was in high school, we used to do physics experiments such as measuring how a spring extended when weights were added. When I wrote up the final report, I was expected to make estimates of the amount of error in the measurement or I was likely to receive an F. That was in high school and yet executives and managers running small and large businesses continually ask for certainty in their decision-making process.

Does the following sound familiar?

“How long will it take to deliver this?”

“ Hmm. I don’t know for sure as I haven’t looked at it in detail but I’d guess four to six weeks.”

“Okay, I’ll take the four weeks.”

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Or

“What revenue do you expect from this investment?”

“ Well, we don’t know for sure yet as we haven’t yet had any statistically valid customer feedback, but I’d guess around

$10 million plus or minus 20 percent.”

“Okay, we’ll accept your commitment of $12 million.”

Or

Some time ago I was discussing with the CFO how his company made investment decisions. I asked whether the company asked for uncertainties or error estimations in inputs to the decision.

“ Oh no. The engineers and business teams are always trying to add plus or minus figures to their numbers and dates, but I’ve told the staff to remove all of them. We need aggressive commitments.”

What that CFO and other leaders need to realize is that this approach is guaranteed to make the team set less aggressive targets. If the business will not honestly accept uncertainty, then teams and individuals will always adjust their estimates to give themselves some buffer.

Building an organization where honest acceptance of uncertainty is the normal behavior is one of the essential foundations of both good leader-ship and getting teams and individuals to take ownerleader-ship of the effective delivery of business value.

In an organization that practices honest acceptance of uncertainty you see the following behaviors:

■ Individuals and teams share as much information as possible. Open-ness is the norm.

■ Individuals and teams face up to the real situation and consequently can take the most effective action to deal with problems.

■ There is no game playing or manipulation.

■ Problems are discussed openly with a view to identifying the most effective solutions. There is no blame game.

■ Uncertainty is both acknowledged and accepted.

■ It is all right to identify and voice risks. It is also all right to take chances as long as they are visible and understood by all.

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The Leader’s Role

Effective leaders build this environment by example. They deal honestly with uncertainty and ambiguity in a way that is visible and obvious to their teams—and encourage their teams to do the same.

Effective leaders do the following:

■ Acknowledge the difference between opinion and fact. Many dis-cussions get heated because people tie themselves to opinions.

■ Accept, and practice, changes of position and opinion as new informa-tion arrives. This demonstrates that they are part of a learning culture.

■ Expect success, accept mistakes, and avoid blame.

■ Recognize that a plan is just a current outlook. The project will make the maximum progress that it can, and it is highly unlikely to exactly match any plan. Don’t force an aggressive plan. If one is needed, ask the teams to work out the best one they can. Join in the discussion and accept the risks.

■ Do not ask for certainty and commitment where it is not possible.

Otherwise, teams think they are being set up and will, at the very least, add large buffers to protect themselves.

■ Consciously consider risks. Effective leaders recognize they cannot prevent all risks. They accept risk where appropriate. If an accepted risk comes about, they accept the cost of rework.

■ Where firm dates or sizes are needed for interlocks between parts of the organization, they look for alternative mitigation rather than requiring dishonest uncertainty or spurious accuracy.

Please note that even when leaders embrace ambiguity it will take some time for the team culture to mirror the leader’s behavior. Even a single instance of negative behavior can do real damage and encourage the team to fall back to game playing.

In summary, actively embracing and leveraging ambiguity and uncer-tainty provide valuable competitive advantage.

If the future is uncertain (and it almost certainly is), plan big but take small steps.

Take a hard and honest look at your processes to determine which ones ask for certainty when certainty is not possible—these processes are likely time and resource wasters.

To honestly deal with ambiguity requires a deep culture change—and this starts with leaders and the examples they set.

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