Adaptive Change: Stay CALM and Carry On

Organizations feel pressure to adapt and change as they scale and operationalize advanced analytics solutions, but they often don’t have a formal process to help sustain the change over time. This problem grows as the number of advanced analytics projects proliferates, magnifying the impact of change by introducing new ways of communicating, working, and making decisions.

Leaders need to understand the conditions under which change is likely to occur and implement a clear methodology for helping people plan for and adapt to the change necessary to transform into a data-driven culture.

Why and How People Change

In their bestselling book, Switch: How to Change Things When Change is Hard, authors and academics Chip and Dan Heath tapped into decades of research from psychology, sociology, and other disciplines to uncover the keys to effective change. What they found is that successful change efforts share a common pattern requiring change leaders to address three realities at once:

1. If you want people to change, you must provide clear direction. What might look like employee resistance to change is often a lack of clarity; ambiguity and confusion can cause people to revert to the status quo. Therefore, leaders must establish a clear and compelling reason for the change, the essential “why,” and provide explicit direction for what is expected. Without clear direction, those charged with implementing change are wracked by decision paralysis, which “can be deadly for change — because the most familiar path is always the status quo.”
   
   

2. Change is hard because people wear themselves out. Deep in the throes of a change effort, what looks like laziness or indifference is often exhaustion. This is because change requires tapping into your reservoir of self-control which, unsurprisingly, is a finite resource. Similar to what humans experience with physical endurance, there are limits to the mental load that one can bear during times of continual change. The more significant the change, the more it will sap people’s energy and motivation. Clear direction and a formal process for change help minimize the grind that bogs down change efforts.
   
   

3. To change someone’s behavior, you have to change their situation or environment. What looks like a people problem in organizational change is often a situation problem. In other words, look at what changes can be made to the work environment to make it easier for people to make the right decisions. Furthermore, try to understand how someone’s situation (including culture, incentives, and social pressure) will likely influence their “bad” behavior, knowing that that behavior does not necessarily indicate who they are now or what they can become.

Considerations for Data-driven Cultural Change

Using the Heath's framework as a baseline for change, what are the implications in a business context? How should leaders approach change? They recommend three core strategies to enable change in any organizational context. But first, leaders need to recognize a simple truth drawn from the research: "…ultimately, all change efforts boil down to the same mission: Can you get people to start behaving in a new way?"

The Key Strategies for Change:

1. Point to the destination, bright spots, and critical moves. You need to be clear as to the ultimate destination. Strategic clarity and a compelling “why” are essential to engaging and motivating stakeholders. To hold their interest, you must highlight what the Heaths refer to as “bright spots.” This means highlighting what’s working and replicating it wherever possible. Finally, the “critical moves” refer to setting clear direction and expectations for the desired behaviors and actions. Clear, consistent communication is essential to this process.
   
   

2. Motivate, Shrink the change, and grow people. You are not starting from scratch. There have been successes, and you want to build on them. The Heaths remind us, "rather than focusing solely on what's new and different about the change to come, make an effort to remind people what's already been conquered." This is part of making the change more manageable, "shrinking the change" in ways that maintain motivation. When employees see change as hard but doable, they become more motivated and more likely to change themselves, seeking personal and professional development opportunities.
   
   

3. Tweak the environment and build habits. We often witness someone’s behavior and automatically assume that is who they are. Research has shown, however, that the environment in which someone operates can affect their behavior. Many incentives, motivations, cultural norms, or social pressures in a work environment can influence how people respond to change, adversity, or uncertainty. You need to look at the environment where people work to determine whether it is change-ready and where there are impediments to change. In other words, what work environment changes should you consider that would allow the new behaviors to stick? How do you make them habits?

The C.A.L.M. Method for Analytics Transformation

Talking about change is one thing; making it happen is quite a different story. Simply put, it’s hard. Therefore, like any difficult problem or challenge, you must approach the task with an understanding of underlying core principles that help frame your thinking about the issue and adopt a systematic approach that ensures consistent execution over time.

Let’s start with some core principles derived from our experience and the research discussed above:

• Change is hard. There is no quick fix or easy tactics.

• People are complicated, and motivations and behaviors can be difficult to unpack, including factors relating to the work environment.

• Lasting behavioral change is synonymous with habit formation, turning desired behaviors into daily actions. As historian Will Durant observed, “We are what we repeatedly do. Excellence, then, is not an act but a habit.”

Finally, since change is not short-term, you need a way to sustain effort while applying the principles just outlined. The CALM method is a straightforward approach to consider. C.A.L.M. stands for communications, alignment, learning, and measurement.

Before diving into the CALM method, let's start with a definition of change management. Change management is a systematic process helping individuals, teams, and organizations plan for and adapt to change. An important point to note is that change is continuous and accelerating. Therefore, leaders should think of change management as continuous learning or improvement. This reframing of change management is critical for organizations working to create more adaptive organizations as the practices outlined below become the foundation for a new way of working.

How does CALM Work?

Communications

The first job for leaders is to establish a clear and compelling rationale for the change--the “why.” This requires paying close attention to the context for the change and explicitly defining the problem using clear communication. Leaders often gloss over this and assume broader stakeholders have the same understanding of the problem. A good rule of thumb: spend at least 50% of your initial communications defining the problem using a written narrative rather than a slide deck. For communication to stick, it must be clear, concise, consistent, and continuous.

It’s not about launching and moving on; it’s about constantly communicating, getting feedback, learning, and adjusting. Communication assets like executive summaries, FAQs, and case studies are some ways to reinforce the message. Roadshows, town halls, and workshops are effective vehicles for delivering the message. Leaders must be forthright in sharing that uncertainty is inherent in everything they discuss and clear about the assumptions underlying the strategy. This level of openness helps build trust, an essential precondition for getting people moving in the same direction. Effective communications build the foundation for better alignment, the next step in the process.

Alignment

Alignment activities keep stakeholders on the same page, gauging where there is a misunderstanding, lack of support, or resistance. It happens at three levels: 1. aligning analytics strategy and people, 2. people and processes, and 3. processes and behaviors. Alignment, or engagement, is about conversations, listening to concerns, and processing feedback.

Unlike communications, which tend to be one-to-many, gaining alignment is more of a one-to-one approach that typically includes coaching, workshops, roadshows, and counseling. However, just as with communications, transparency is essential to building trust. Alignment creates the conditions for effective learning by providing the context for behavioral change -- aligning the work people do with the required behaviors.

Learning

Learning is the critical enabler for transforming change into continuous improvement. It combines traditional instructor-led and self-directed learning, with three clear distinctions related to the approach to learning and the curriculum: 1. It combines mindset training and technical skill sets, and 2. It is highly contextual, tied to specific processes and the requisite behaviors supporting those processes, and 3. It is continuously reinforced with the intent of turning practices into habits.

In addition, continuous learning leads to mastery, a crucial element of human motivation. As employees learn and grow, mastering the essential skills and concepts, they experience positive reinforcement that triggers a flywheel effect, driving more of the desired behaviors of data-driven organizations, which at their core are committed to learning and adapting.

Measurement

The primary goal of measurement is to track the degree to which the change effort impacts predetermined success criteria. In other words, is what you’re doing concerning change activities having the desired impact? While setting and measuring goals related to company performance is important, they are not sufficient for driving change. Instead, organizations need to make assumptions about the activities and behavioral changes that will drive the change and measure those.

These activities serve as the “leading indicators,” or inputs of change, with the goals (performance metrics) serving as the lagging indicators or outputs. Tools like a Change Scorecard track change activities, employee sentiment, and behavior change metrics that leaders review to gauge progress and determine where they may need to intervene and modify activities or communications.

Conclusion

As you read through the description above regarding the CALM method, it's tempting to convince yourself you are already doing these things. For example, you communicate effectively, have all-hands meetings, provide training, and track employee sentiment. These are all necessary preconditions for a change-ready organization. Still, they are insufficient in today's competitive environment. Data-driven, people-centered organizations that learn and adapt will experience a significant competitive advantage.

Leaders must start by reframing the problem and focusing more on preparing the workforce for change. This requires continuous effort, adapting with smaller course corrections, rather than rewarding people for surviving disruptive change through heroic efforts when it does arrive. Leaders must also embrace the idea of change as synonymous with continuous improvement and embed it in the culture. Organizations must treat change management like other mature processes to accomplish this they must applying greater rigor by clearly defining, integrating, measuring, and constantly improving the change process.

Another way to think about this is how Agile is to software development; the CALM method is to the continuous improvement of knowledge worker productivity. They are both designed to improve the speed and quality of a specific output. In the case of Agile, it is a functioning software solution. The CALM method improves the speed and quality of organizational processes and decision-making by enabling faster and higher quality decisions throughout the organization -- the essence of a data-driven enterprise.

Data-Driven Change: Essential Mindsets

Data-driven cultures are obsessed with an open-minded pursuit of the truth, supported by rigorous analysis to drive faster and better decisions. Leaders frequently assume it works the other way: that engaging in rigorous analysis will automatically lead to this open-minded pursuit of the truth. Instead, human nature often gets in the way.

Research from McKinsey revealed that only 28 percent of executives said the quality of strategic decisions in their companies was generally good. The root cause of these poor results was primarily managers struggling with cognitive biases such as overconfidence, confirmation bias, or groupthink. When the team examined what led to superior decisions, the quality of the decision process mattered more than that of analysis by a factor of six.

A Path to Better Decisions

The quality of a decision process is determined by the degree to which you can objectively evaluate the evidence before you, explore alternative hypotheses, and engage in open debate. Analysis, however sophisticated and tech-enabled, is often used to reinforce what we believe rather than seek the truth. Cognitive biases hardwired into our brains can lead us to make poor decisions in life and work.

The use of mindsets can bring more objectivity to decisions by forcing you to reframe or rethink your approach to decision-making. Mindsets can provide a fresh perspective and take what psychologist and Nobel Prize laureate Daniel Kahneman calls the “outside view.”

By mindsets, we mean our state of mind when faced with a decision. They are a way of reframing the problem and seeing the situation through a different lens or perspective. However, simply understanding mindsets and how they work is not enough. You need to use them.

Why Mindsets?

Clear-eyed analysis often competes with bureaucracy, agendas, egos, and risk-averse, consensus-driven cultures. Mindsets help you take an outside view — outside yourself and your specific circumstances. Leadership and frontline staff can apply this thinking whether making big strategic bets or unit-level investments and operational improvements.

Mindsets, if applied systematically, help reinforce the rigor of analysis and establish consistent behaviors that translate into daily habits. They have two categories: foundational and transformational:

Foundational Mindsets include self-awareness and a growth mindset. These are foundational because they help combat our natural tendency towards self-deception that cognitive biases reinforce, and support the notion that we can change and improve ourselves through concerted effort and a systematic process.

Transformational mindsets include “think-like” and mental models. These move you closer to mindset mastery, where you can easily reframe and rethink situations using multiple lenses to see problems more clearly. Understanding your role in the decision-making process and the additional context of the task at hand will help you assemble the best set of mindsets for each decision.

The Four Essential Mindsets

Self-awareness

As humans, we are too often overconfident or dismissive of contrary opinions, and we rationalize past failures instead of learning from them. Author Julia Galef sees the solution to this problem in what she calls the ”Scout Mindset.” Fundamentally, it is the motivation “to see things as they are, not as you wish they were,” which leads to better judgment and decision-making. After years of research, she concluded that understanding how to act rationally doesn’t mean that you will actually do so.

“Being able to rattle off a list of biases and fallacies doesn’t help you unless you’re willing to acknowledge those biases and fallacies in your own thinking. The biggest lesson I learned is something that’s since been corroborated by researchers…our judgment isn’t limited by knowledge nearly as much as it’s limited by attitude.”

Self-delusion commonly hijacks self-awareness when you attempt to explain to yourself and others past decisions that led to bad outcomes or failures. The best antidote to self-deception is cultivating a mindset of self-discovery and self-awareness. In its most basic form, it is paying attention, observing, and noticing how you think, act, and behave.

Growth Mindset

Researcher Carol Dweck defines a growth mindset as a belief that your basic qualities are things you can cultivate through your efforts. In her research, she found that a growth mindset creates a powerful passion for learning, as self-improvement is within your control, given the right effort and strategies. People with a growth mindset demonstrate grit. They stretch themselves, take chances, and stay engaged despite setbacks. A growth mindset allows people to thrive during challenging times and, critically, reveals a motivation to learn. As organizational psychologist Adam Grant found in his research, there is a self-reinforcing passion for learning, and he noted that “the highest form of self-confidence is believing in your ability to learn.”

Dweck’s growth mindset framework is an important tool for raising awareness of how mindsets impact behaviors, and perhaps the most important is discovering the willingness to engage in highly self-directed learning.

Organizations can (and should) create conditions conducive to learning through formalized training and education, aligned incentives, and a culture that encourages personal growth, but change is ultimately up to individuals. Healthy individual mindsets coupled with cultures that cultivate and nurture growth mindsets create professional communities of lifetime learners focused on continuous improvement.

“Think-like” Mindset

As organizations build data-driven cultures, it’s helpful to draw attention to core principles of critical thinking from other people and professions. For example, scientists in all fields use the scientific method to experiment and learn. Its disciplined processes allow them to test theories about how complex systems work, be it how drugs react in the human body, or breakthroughs in materials that enable reusable rockets for space travel.

In his book, Critical Thinking, Jonathan Haber talks about the importance of science serving as a model for systematic reasoning.

“With all its successes, science is often held up as a model for systematic reasoning. Yet if you look at science not as a unique activity engaged in only by special people, but rather as a cultural approach designed to slightly diminish the confirmation biases that tend to make all people (including scientists) believe untrue things, you can begin to see the huge payoffs that come from small improvements in how we think.”

This “think-like” mentality is the gateway to changing mindsets as it raises awareness and encourages growth but doesn’t threaten someone’s current identity. It’s different than searching for best practices, which involves finding situations as close to your own and copying those practices. Instead, the “think-like” mindset encourages searching for principles wherever they can be found in different professions, industries, cultures, or the natural world.

Think-like means trying to understand how others go about problem-solving, and adopting any of their principles and practices that could be readily adapted to your situation. We are firmly in the analytics age, and the “think-like” requirement now is to think more like a data scientist and be more analytical in your approach to problem-solving. Using this effectively doesn’t require you to actually be a data scientist any more than understanding the scientific method requires you to be a nuclear physicist.

Mental Models Mindset

The simplest definition of mental models is that they describe the way the world works. They influence how we think, understand, and form beliefs. Let’s look at a few examples of mental models commonly employed when using advanced analytics, how they reveal flaws in our thinking, and how they can be used as corrective measures.

• First principles thinking: First principles reasoning helps clarify complicated problems by separating the underlying ideas or facts from any assumptions based on them. In other words, it’s a way to expose assumptions underlying your thinking and challenge what you think you know about a problem. This process requires you to keep digging, sweeping away unproven assumptions until you arrive at the facts.

• “Five whys” requires challenging each outcome with the simple question, “Why?” This technique, first formally used by Toyota as part of their Lean manufacturing process, is now a standard method for getting to cause and effect relationships.
  

• Leading vs. lagging indicators: One way to think about leading indicator metrics is they measure the activities that lead to results. Amazon, a leader in using analytics to drive decisions, refers to them as “controllable input metrics.” By identifying, defining, measuring, and monitoring leading indicators, you can anticipate problems and intervene before it’s too late to fix them. You rely less on postmortem processes like the “five whys” and more on real-time monitoring, intervening, and implementing course corrections. This method is an excellent way to operationalize a mental model. Rather than periodically sitting down and challenging assumptions underlying past decisions (e.g., postmortem), you set up metrics that challenge assumptions continuously. In other words, the metrics you set up constantly answer the “what” questions (and perhaps the “why” questions) in near real-time. And to the extent they don’t, modify what you are measuring or how you measure it.

Probabilistic thinking

Probabilistic thinking is the process by which you determine the likelihood of any specific outcome happening in the future. We engage in this thinking whenever we check the weather to see if it will rain or speculate about the next Super Bowl winner.

But we are not particularly good at understanding probabilities in our personal or professional lives. We tend to use imprecise language to describe the likelihood of an outcome and are overly optimistic about our future predictions. Being right and making correct predictions is important, but knowing why you were right is essential. Adopting practices like Bayesian updating can help maintain your outside view by constantly adding new information to your existing data to get closer to the ground truth.

Conclusion

Mindsets offer a systematic way to ensure your thinking processes are more disciplined and consistent, enabling quality decisions across your organization. Mindsets help get you out of autopilot mode to stop and think. You can start by learning and applying the initial foundational and transformational mindsets discussed here and embedding them in your decision processes.

Ultimately, realizing the data-driven culture depends on changing individuals’ daily behaviors. Adopting mindsets that encourage taking the outside view will help to systemize your thinking processes and establish consistent behaviors that translate into powerful daily habits.

Moving Beyond ROI: Return on Adoption

With generative AI showing early promise of substantial productivity gains, organizations should start to think not just of traditional metrics like ROI but ROA, Return on Adoption as a critical measure of success in the future.

The reason for this is the widespread impact that generative AI solutions will have. It’s not about rolling out a new application in a single department but vastly improving the productivity of your workforce comprising technical and non-technical knowledge workers. The strategy for the tech behemoths like Microsoft (w/ OpenAI) that are driving the adoption of AI is to tightly integrate AI into existing applications (See Office 365 Co-pilot, GitHub Co-pilot). For companies, widespread adoption is now feasible, and focusing on ROA highlights the opportunity cost of moving too slowly.

Return on Adoption

Typical ROI calculations include the investment in technology, including startup and ongoing maintenance and subscription licensing, while using some return assumptions related to cost savings, margin improvement, or top-line growth. While prior application deployments would have people cost components associated with, for example, training employees on the new solution, most expenditures would be technical (licensing, support).

The variables differ with AI, as rollout and adoption will require greater investment in people than we’ve seen with other technologies in the past. It goes beyond upskilling, which will be necessary, requiring new mindsets and accepting new ways of working to take advantage of these powerful and rapidly advancing capabilities.

The productivity potential is there, but the degree to which employees will adopt these new AI capabilities is very much a question mark.

Adoption Challenge

In the past, when new technology was deployed in an organization, users were accustomed to learning a set of functionality and becoming proficient over time in applying this knowledge to their daily tasks. They would use a tool to accomplish a specific task (e.g., Salesforce to track pipeline or Excel to produce a dashboard). They were in control and responsible for the output resulting from their efforts.

With AI, the idea of a smart assistant comes into play, where users can now delegate entire tasks to their “assistant,” which begs the question of who is responsible for the output and introduces additional questions related to which tasks to delegate and to what degree does one trust the veracity of the work product coming from the AI.

In short, with AI, users will not only give up some control but could begin to dissociate or feel less ownership over the work product. As a result, they may feel less pride in authorship which could make them resistant to adopting AI, believing it is just one more step to being replaced.

However, some promising early research (with the caveat that it’s not coming from an entirely impartial source in Microsoft) reveals attitudes from leaders and employees that may help spur adoption. For example, Microsoft’s recent Workforce survey found that:

“While 49% of people say they’re worried AI will replace their jobs, even more—70%—would delegate as much work as possible to AI to lessen their workloads.”

In addition, leaders are more focused on increasing productivity than reducing headcount:

“Amid fears of AI job loss, business leaders are 2x more likely to choose ‘increasing employee productivity’ than ‘reducing headcount’ when asked what they would most value about AI in the workplace.”

The power and pervasiveness of AI make its potential impact orders of magnitude higher than anything we have experienced. In the past, new solutions impacted certain functional areas, like SaaS software for enterprise applications or AWS for infrastructure. Both had a substantial impact, but nothing has come along that could realistically deliver up to 50% or more productivity gains across an employee population. Wharton professor Ethan Mollick found that recent research indicates the gains could be real and substantial.

“This suggests that the productivity gains that can be achieved through the use of general-purpose AI tools like ChatGPT seem to be truly large. In fact, anecdotal evidence has suggested that productivity improvements of 30%-80% are not uncommon across a wide variety of fields, from game design to HR.”

Therein lies the “Return” related to ROA and the significant upside potential for organizations. But the return is predicated on adoption, and realizing widespread adoption will require a dedicated effort by organizations to create the conditions for change. So, what will be needed to transition to this new way of working?

Making the Transition

There are two core elements of the transition. The first is shedding low-value tasks, which will drive productivity gains. If we use the standard definition of productivity as more output per hour, employees will produce more stuff (e.g., docs, presentations, reports, analysis). However, the concern leaders are likely to have with this is that more is not necessarily better. Focusing on the output volume doesn’t say anything about quality, which is essential to delivering real results.

The second element, working on higher-value creative and analytical work, is where the real value will be realized by organizations and where employees will likely have the most significant challenge making the transition. They will be asking themselves (and their bosses!) what it means to be “more creative” or “more analytical,” particularly in the context of their current job.

Mindsets and Skill Sets

Developing new mindsets and skill sets will be critical to break free from old work habits. But developing new work habits requires new behaviors, and making them stick will require anchoring these new behaviors to defined processes. Further, AI will automate not only workflows but also any number of decisions, and that will require a new discipline: decision process improvement supported by a disciplined change process.

So, transitioning to an AI-ready organization focused on adoption will require leaders to execute across three dimensions: 1. Focusing on decision process improvement, 2. Developing the right mindsets and skill sets, and 3. Using an adaptive change process that keeps stakeholders aligned and engaged.

Conclusion

Return on Adoption of AI will be fully realized when employees successfully leverage AI to improve productivity, become more creative, and develop greater analytic rigor. The latter two components will help create a culture of continuous improvement. Staff armed with the tools and the time will be better able to evaluate and improve business processes, discover new customers/markets, and create new business models. All of which will lead to growth, profitability, and market share without adding headcount. The critical first step for organizations is prioritizing AI adoption.