From Deficits to Strengths: Six Sigma from the AI Perspective

By David Shaked

Part One: Problem-Solving Experience Built, AI Discovered—Now What?

Over several years of experience, I had built a great track record in the corporate world. I was solving many business challenges and concentrating on improving inefficient and wasteful processes. I was using the well-tried business improvement approaches such as Six Sigma and Lean thinking. I also regularly used many analytical approaches such as gap, SWOT, and Force-Field Analysis to support the change efforts and balanced scorecard, KPIs and control charts to monitor the progress. Waste and defects were everywhere I turned! All my efforts to eliminate them impacted on our customers and the bottom line in a really positive way. At the same time, I was teaching others how to use these approaches so that they could hunt out waste.

Then one day, I discovered AI.

Being so used to identifying the problems in each situation, I was intrigued by the completely different focus AI had. The focus was on how to achieve our dreams by focusing on what was working well and exploring how good we could get? I found myself torn between two seemingly different worlds. While I knew how to methodically solve problems by the traditional approaches, I could see how the positive approach of AI unleashed an enormous potential within organizations. My big question at that stage was how to integrate this fantastic new approach with everything I was doing before. At that stage, I felt that my work with Six Sigma and Lean thinking was “bad” and that AI was “good.” I felt I had to throw away everything I had learned and experienced until then and re-start a new learning journey. All my experience to date seemed to fundamentally clash in style, language, process, and logic with AI. For example, how could I connect AI’s 5-D process with the DMAIC (Define, Measure, Analyze, Improve, and Control) process from Six Sigma and its specific emphasis on finding root causes for problems through analysis? How could I continue my efforts to eliminate waste while inquiring into what gives life to my organization? These questions kept confusing me for a while. On the one hand, I loved the energy and creativity AI brought by focusing on the strengths and high moments. On the other hand, I didn’t want to lose the familiar world of process mapping with Post-it Notes and deep statistical analysis.

Part Two: Appreciating All My Skills and Building Bridges Between Them

Over time I learned more about AI. The experience I gained offered a potential solution to my challenge. For example, I learned that the 5-D model, which is very solid and versatile, is not the only way to apply AI. Being driven by my desire to be a better AI practitioner, I gained a much deeper understanding and strong connection with the principles behind AI and their importance. I realized then how important it was to apply these principles to everything I did, both professionally and personally. The conversations I had around the AI principles changed the way I look at my work and my life. It made them both more meaningful and alive. I also started appreciating my strengths and best experiences to date. This included the strengths and best experiences I had while practicing deficit-based techniques. It meant that I started asking myself different questions. Instead of asking how to “fix” my problem-solving skills in order to create a bridge between my two internal worlds, I started exploring my own strengths. What do I do well when I work with Six Sigma and Lean thinking? What were the most powerful problem-solving experiences I had? What were the best insights I gained when analyzing data? What did I like the most? Which tools worked best? What did people I worked with like about these methodologies? What worked well for the organization when I applied them? What was so unique and attractive in my view with these methodologies? In other words, What gave life to my traditional way of solving problems?

The shift of my attention toward what gave life to my way of solving problems generated the breakthrough I was yearning for. All of a sudden, I could see potential bridges and new ways to work with the old methodologies people are so familiar with. At the same time, I also referred back to the guiding principles behind Six Sigma and Lean thinking. These principles were actually, to my surprise at the time, very strength-oriented. For example, the reason why Six Sigma is focused so much on defect identification and elimination is actually the pursuit of quality. The guiding principle behind Lean thinking is the desire to deliver the best value to the customer as quickly as possible. All of a sudden there didn’t seem to be such a dichotomy between the two worlds!

The next stage in this journey was to take the tools and techniques from Six Sigma that I liked the most and apply an appreciative approach (or a “lens”) and the principles of AI to them. I also had to rethink the questions I used as part of these approaches. For example, I used to enjoy facilitating groups through process mapping exercises to gain clarity around a given process. I realized that instead of focusing the group’s attention on the waste and issues in the process, I should apply the positive principle by focusing them on the parts of the process whereby value is created and good performance is achieved. Waste naturally disappears if people orient themselves (in other words, the anticipatory principle in action) toward ways of increasing the value they generate in any process. The questions I ask managers, employees, and customers as part of the Six Sigma Define stage are focused on trying to find when the process has worked well in the past or what they wish to see more of instead of reduce/eliminate.

Another example is the use of the powerful statistical tools and rigor that Six Sigma and the DMAIC model provide to identify root causes of success and amplifying them instead of studying defects. I also apply the principle of wholeness by involving a wider representation of the system I work with. Finally, I also realized that the value of data and statistics is not in the numbers or charts we produce but rather in the conversations we hold around them. We can choose to discuss them in any way we wish. Previously, in most cases I used to focus on the gaps, the weak performance points, the “red-colored” indicators and the data about customer complaints. This is not absolutely necessary! Being critical doesn’t mean being objective. Most of these critical observations were a result of my habits. There is a lot we can learn from any chart and spreadsheet if we seek the strong points and the cases of stellar performance. I choose to look at these data points and inquire about them not because I want to ignore the problems but rather because I am truly curious about them and believe they hold useful information. Bad data and weakness points are also very useful. However, their usefulness is not so much in understanding what caused the problem. Focusing on what we wish would be different or what we want to change is the key. This unique focus drives different conversations with other members of the organization and allow improvement teams to socially construct their desired future. (This means that one can apply the AI principle of social construction to analysis, data and statistics.)

These ideas may seem challenging to many successful problem solvers. We were trained under the assumption that in every organization and its various processes there are problems waiting to be identified and solved. What would happen if we approached our improvement efforts with an underlining assumption that every organization and process is a result of an originally great idea and that in every organization or process something works well and delivers value? After all, we can almost always point out areas in which our current problems were once a good solution to another problem. This cycle of problem solving results in laying the foundations of the next problem and is not necessary at all. If we dare to suspend our basic suspicion about every organization or process, we may find and access more creative ideas, greater motivation for change, and the innovation that is so essential for survival in the marketplace.

Part Three: Appreciative Lean Thinking and Problem Solving in Practice

Perhaps one of the best examples I have to date of the Simultaneity Principle in action was a recent client project I worked on. The client, a rail company, asked my colleague (Gill How of Buonacorsi Consulting) and me to facilitate a process improvement workshop to reduce the delays to rail services that occurred when faulty coaches were exchanged with serviced ones. The exchange, when not done correctly or in a timely fashion, causes delays to the rail service and a chain reaction of further delays to other services. At our first meeting, the head of the department in charge of rail performance provided us with plenty of data points about the delays, their frequency, root causes, and their great financial impact on the company. After a while, I asked our sponsor how often the organization changes coaches successfully and on time? A powerful moment of silence followed. The answer our sponsor provided was “I don’t know. I don’t think we ever measured it.” From that moment on, our conversation took a completely different direction. We were all curious to find out how often the process works well, what contributes to this success, and how we can do more of what already works well.

This single powerful question was the basis of the workshop we delivered. The workshop followed a new and innovative design following Lean thinking process improvement workshops (kaizen event) I have delivered in the past but run with an appreciative, strength, and value focus. We inquired about best experiences, mapping the process when it works, collecting stories and data about the process at its best, and asking participants what would make it even better. The questions asked, the evidence sought, and the analysis conducted were all different from the normal Lean approach and more powerful. The great ideas the participants came up with came from good practices they were already doing or had done in the past. It was an exciting process to facilitate and observe. It also felt very satisfying personally to reach this point in my own professional development and to be able to connect my ideas and knowledge in this approach. A new, more appreciative and life-giving way for Lean thinking process improvement was born!

To summarize my experience so far, I can offer other practitioners a wider and deeper look at AI and its implication on deficit-based approaches. As I learned from my own journey, there is no need to look at the two as opposites. AI can benefit from the variety and rigor of some of the deficit-based models that worked for us so well for such a long time. At the same time, successful practitioners of the various deficit-based models that have been developed during the 20th Century could bring a lot of energy and exciting new innovations by applying AI principles to their strengths and great experiences. If you need further advice how to apply this thinking to your particular situation or organization, get in touch.