Predictive Sensing

In my role as an orchestrator helping cross-functional team leaders enable transformational change, it’s been interesting to reflect on how often the topic of complexity comes up. And yet, when I ask ‘Tell me how you are defining complexity? — and later —- What are you doing about it? – The answer is usually crickets, crickets, and more crickets. With so many aspects of our personal, professional, and political worlds currenttly upside down, perhaps this might be a good time to share some of my current thinking on the topic. It’s a ‘way of working’ that I call Predictive Sensing, designed to help cross-functional team leads more effectively manage complexity.

Merriam-Webster defines complexity as “the quality or condition of being difficult to understand or of lacking simplicity”, which is a pretty good way of making clear that complexity is less of a thing and more of a ‘feeling.’ Complexity usually brings increased levels of anxiety and often a ‘hard to pin down’ awareness that complexity isn’t just ‘complication on steroids’. But isn’t complexity just another word for complicated?

For me, the best clarification of the difference between complicated and complex problem solving can be found in Associate Professor Rick Nason’s book It’s Not Complicated – The Art and Science of Complexity in Business. Not only do I think it is a ’ must-read, but it also complements much of the Attentive Leadership© mindset that I’ve been promoting these last few years. Nason argues that most leaders have mastered complicated thinking. It’s an approach to management problem solving that all of us were taught in Management 101. It works really well in situations where success can be defined; the factors contributing to success are known, as are the exact steps needed to achieve that success. But, Nason says, “complicated thinking only works for complicated problems.”

What are the Attributes of Complex Problems?

Many problems these days aren’t complicated, they are complex with a unique set of defining attributes as paraphrased from Nason:

• First, they have lots of stakeholders who often are highly segmented, not just ‘suppliers who provide inputs’ and ‘customers to whom they deliver outputs’ that a typical SIPOC suggests.
• Second, these stakeholders are all connected in a wide variety of ways (Think Facebook, LinkedIn, What’s App, Slack, or Webex Teams) with both strong and weak linkages, any of which can influence how the individual and the collective behave.
• Third, all have the potential to change and adapt their behaviors and decisions at any moment based on trends, perceptions, and other influences.
• Fourth, when they do change or adapt, feedback loops get created that either reinforce the status quo or can cause destabilization (aka ‘going viral’ in current vernacular).

Of course when a system gets destabilized all bets are off as to what will happen next, or where the team will end up either in terms of what it collectively thinks and feels or of what it does. Applying additional sales incentives, when what is needed is better training in helping the customer understand the business value of the new solution, is a case in point I’ve seen repeated many times. What is also interesting about this sort of destabilization is that it is usually leaderless. It does though always involves people and therefore the system can’t be managed in a rational way because people don’t always behave rationally as we all know.

So how does a leader lead in this kind of environment?

My current viewpoint after many scraped knees and lots of headaches is that in order to manage in our current environment of extreme complexity, leaders need to look at their world far more holistically than they do today and enable two different but complementary dynamics. The first is to build and tap into what I call Predictive Sensing Networks. The second is to empower teams to learn how to become a Sensing Assessment Engine that uses actionable intelligence derived from these Predictive Sensing Networks. Before I share my thoughts as to how one exactly does this, let’s step back for a moment and better understand my definition of a Predictive Sensing Network.

What are predictive sensing networks?

My basic premise is that most complex business problems have a set of core sensing networks that operate in the same way as the human body organ systems that the brain monitors (e.g. nervous, circulatory/cardiovascular, endocrine/lymphatic, digestive systems, respiratory) If a team can predictively tap into these networks rather than looking at their results in hindsight, complexity management can be simplified. The six core sensing networks that I’ve identified are as follows:

1. Technical Performance Sensing Network – the underlying set of processes or connections that define what the team offers to its ‘customers.’
2. Opportunity Performance Sensing Network – the underlying set of processes or connections that define how the team identifies, sells to, and closes those ‘customers either directly or through partnering relationships.’
3. Customer/Partner Experience Journey and Value Realization Sensing Network – that defines how customer stakeholders are engaged and realize value from what they ‘bought’ from the team.
4. Team Member Journey Sensing Network– that defines how team stakeholders are engaged, what they experience, how leaders build a sense of belonging, and how do all feel that they are contributing in a meaningful way to the collective team purpose.
5. Operating Infrastructure Effectiveness Sensing Network – that defines how well the Team’s underlying day-to-day operating model works.
6. Listening and Learning Capabilities Sensing Network – that defines how well the team captures and interprets feedback and translates that into learnings that can be applied to future situations.

How do you tap into these predictive sensing networks?

Taping into these sensing networks in a predictive way requires identifying a specific set of sensors that are directly tied to the related execution strategies that the team can track over time. Most sensors should be leading indicators with a few lagging indicators to represent either the desired end state or specific achievement milestones. For example, I’ve learned that a good predictor of technical performance is the number and type of complaints received and how long it takes (cycle time) to resolve them. Once identified, the progress of each sensor must be tracked with targets identified, otherwise, there is no way of knowing whether or not the patterns that emerge are good or bad. For example, if a predictive indicator for value realization is the number of customers that start on the adoption journey, it’s important to agree on what the target number of customers starting should be. Otherwise, there is no way of knowing whether or not the ten who did start is good news or bad.

There was a time that I thought that monitoring a few sensors was better, now I’m not so sure. Now I think what matters is ensuring that the sensors are generating data that is actionable and useful not just interesting. It also needs to be displayed in an open and transparent dashboard that is made available to any and all stakeholders that are interested. The more diverse the group looking at the dashboard is the more likely that breakthrough insights will be generated. These insights otherwise known as ‘actionable intelligence can then be plugged into a virtual assessment engine of people who can then make the needed judgment calls as to whether or not the resulting patterns and connections require immediate action or should just be monitored or perhaps even ignored for the time being.

How does this actionable intelligence assessment engine actually work?

First, it’s important to remember that managing complexity requires CONSCIOUSLY recognizing that the team is dealing with a complex, not a complicated situation. This is because there is a tendency for most of us to lean towards complicated thinking without realizing it. Alas, the recent belief in the infallibility of ‘big data analytics’ has made this tendency even worse. Complex thinking isn’t easy and requires focusing on trying, learning, adapting, and making new choices. In specific this means team members must:

• Acknowledge that the way forward may be untidy and unorganized.
• Initiate more in-the-moment thinking based on whatever actionable intelligence is available at that moment in time.
• Help team members become more comfortable with ambiguity and uncertainty.
• Model a willingness to open oneself to new ideas and influence and accept that two people can look at the same patterns and see completely different things.
• Proactively introducing diversity both organizationally and in one’s thought patterns.
• Build cross-team and cross-organizational connections and linkages, both weak and strong, with colleagues who are the same and who are different who can weigh in easily.
• Be willing to abandon rigid operating frameworks and strategies that don’t allow the team to quickly change course.
• Recognize that making mistakes will occur but will likely be in such incremental degrees that they can be course-corrected without self-destructing (career or otherwise).

This process of continuously observing the trends and patterns that appear amongst a collection of sensors, across the key sensor networks in different ways usually results in the harvesting of different, and hopefully diverse, perspectives. This enables the setting of a general direction in response to those trends and patterns. In other words, the idea is to empathetically ‘sense’ the general mood of how the collection of sensor patterns (this actionable intelligence) is speaking to each team member and ‘groking’ where they are now, where they are likely to be headed and why they might it be changing?

This ‘complexity mindset’ as so well articulated by Rick Nason, or team empowerment process as I call it, involves recognizing and accepting that it’s impossible to create an environment that enables ‘solving’. At best, all the team can do is create a system that ‘manages’ what is really ongoing continuous change and nudge it in directions – some of which may result in the outcomes desired and in other cases not but that allows for speedy course corrections.

What are the benefits of this approach?

My belief, based on many years engaged in transformational change of many kinds is that this approach to complexity management also allows the introduction of what Nason calls random learning – another important feature. Random learning in managing complexity can take the form of ‘Deep Dives’ to deconstruct the viability of current ways of doing business, ‘Sprints’ for group ideation and brainstorming, or individual self-directed ‘Calls-to-Action’ to try something experimental and see what happens. The resulting learning may have little to do with the problems at hand but does plant seeds and new perspectives that might prove valuable in the future.

By learning how to monitor the various sensors embedded in the predictive sensing networks, and the resulting feedback loops, using transparent ‘Proactive Sensing and Listening Dashboards, teams can ‘see’ the impacts of the changes initiated immediately (good or bad or even unrelated). This builds awareness of, and the ability to effectively respond to, what Nason describes as ‘the shifting nature of rationality.’ His case study of the defeat in the market of ‘New Coke’ in the mid-1980s is a fascinating read.

Of course, much of this is impossible without trust and as discussed in my last article, Is it a Great Checkout —- Or a Great Trust Out? https://www.linkedin.com/pulse/great-check-out-trust-gaye-clemson/ trust doesn’t just happen, it has to be proactively cultivated and maintained.

As always, thoughts and further insights are always welcome! I can be reached at gclemson@globalinkage.net.