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 (ROA)
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.
The 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.
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.
Check out the CALM Change Management Guide to find out how to easily integrate change management capabilities into your existing project management and analytics software development processes.
Organizations adopting generative AI solutions like ChatGPT adds an interesting twist to the ongoing debate among data and analytics (D&A) leaders about how best to organize and allocate data science, business analyst, and technical talent to scale advanced analytics in the enterprise. Do you centralize these resources and capabilities or distribute (Federate) them out to the business units?
The underlying assumption that AI starts to challenge is that capabilities across these groups are static, and it’s a matter of properly allocating them, like players on a football team, based on experience, size, skills, and ability. But what if your left tackle could suddenly run a 4.4-second 40-yard dash? Or your running back could throw a 60-yard missile into tight coverage? Does that change what you do, how you position the players, and the plays you run?
Building on what exists now, generative AI solutions will significantly impact your ability to enhance customer interactions, with more sophisticated chatbots changing the online experience. But what about the folks building and supporting these tools? What might be the impact on D&A capabilities internally if you imagine significant productivity improvements and capabilities brought on by new AI solutions like Microsoft’s “Co-pilot” enhancements for Office 365 and GitHub’s existing Co-pilot product?
The ability to query massive stores of unstructured data to extract insights or build applications with just natural language prompts changes what’s possible for individual output and should change our expectations about what people can accomplish and, potentially, their roles within the company.
One possible outcome is that the D&A organizational function morphs from a builder of analytics tools to an accelerator of AI-assisted knowledge work. In this scenario, knowledge worker productivity goes beyond software developers (GitHub Co-pilot) to include non-technical business analysts and decision-makers, where there will be greater capabilities in the hands of individuals across the organization (see Microsoft 365 Copilot). As more knowledge is accessible to the individual in the future, less coordination will be required between employees to get answers (the elusive self-service model). This will result in individuals interfacing more with the “big brain” than each other by training, maintaining, monitoring, and contributing to the large language models that will ultimately include the entire corpus of unstructured data inside an organization.
Given that they are on the front lines for new technologies, the D&A teams and their internal customers will likely be the early adopters of these new AI capabilities.
So, what are some tasks that could be automated or augmented by generative AI solutions now (or soon to be) commercially available? To answer this, let’s break down our analysis into two types of knowledge work: 1. organizational work and 2. project-based work. Organizational work involves strategy and operational decision-making, setting direction and priorities, and allocating people and capital. Project-based knowledge work involves building things like analytics solutions that enable better, faster decisions across the organization.
There is an obvious symbiotic relationship between the two groups as the analytics solutions built by the project teams enable better organizational decisions that lead to better allocation of resources and better prioritization. This is similar to what Jim Collins calls “Turning the Flywheel.”
Organizational Knowledge Work
Wharton professor, Ethan Mollick, has done extensive research into the capabilities of ChatGPT and, subsequently, BingGPT and came away with some compelling use cases. He has also mastered the art of prompt writing to produce extraordinary results.
Initially a skeptic of AI, his opinion changed with the release of ChatGPT in November 2022. After a deep dive into the capabilities of ChatGPT and the eye-opening rate of adoption (the fastest technology to reach 100 million users), he noted that:
“The current situation is truly unprecedented. We are seeing widespread adoption of a technology that has the potential to significantly boost individual productivity, but which is not yet being fully utilized by organizations. Recent research is starting to show us just how big a deal this is.”
Below are excerpts from his blog on this topic covering tasks that knowledge workers engage in, emphasizing writing and coming up with new ideas. So, why is writing important in a business context and how will it produce better thinking and decision-making? Because writing forces you to think.
As Wired founder Kevin Kelly notes, “I write primarily to find out what I’ve been thinking, and I don’t know until I write it.” Amazon’s now famous “six-pager” memo requirement for meetings uses narrative description in place of PowerPoint to produce clearer thinking and better decision-making. Bezos explained the move to a narrative form of communication and away from presentations by noting that “…writing a good 4-page memo is harder than ‘writing’ a 20-page PowerPoint is because the narrative structure of a good memo forces better thought and a better understanding of what’s more important than what, and how things are related."(1)
According to Mollick, generative AI will help knowledge workers with the following:
Project-based Knowledge Work
Let’s move on to project-based work and look at the latest AI-powered enhancements to one of the most popular technical project management tools and the leading code repository.
Jira and Confluence, products from Atlassian, are two ubiquitous solutions for technical project management. Jira is an agile project management tool teams use to plan, track, release, and support software. Confluence is a team collaboration and knowledge management tool to help drive productivity in the software dev process. These tools facilitate constant feedback on the software development process and allow teams to adjust based on this feedback, such as slower-than-anticipated progress on a particular feature or the emergence of an issue related to data access.
Atlassian just announced the release of Atlassian Intelligence, which will use Open AI’s GPT-4 to create a “virtual teammate” capability embedded in their products. In Confluence, the company claims that “workers will be able to click on terms they don’t recognize in documents and find automatically generated explanations and links to relevant documents. They will also be able to type in questions and receive automated answers based on information stored in documents.” The latter capability would minimize the back-and-forth between team members. For Jira, the new AI features will allow users to take natural language queries and turn them into JQL and SQL.
An interesting takeaway is not only the improvements in speed (productivity) but what contributed to these gains: the reduction in grunt work and the repetitive tasks that drain cognitive energy that could be applied to higher-level problem-solving.
Similarly, eliminating the constant interruptions (often the norm for organizational knowledge work) by querying your “copilot” rather than your colleague might result in similar productivity gains seen with software developers. But the real gains won’t be just producing more output but allowing knowledge workers to stay focused on difficult tasks, getting into a “flow” state that will increase the quality and creativity of the output. From the GitHub research:
“Developers reported that GitHub Copilot helped them stay in the flow (73%) and preserve mental effort during repetitive tasks (87%). That’s developer happiness right there, since we know from previous research that context switches and interruptions can ruin a developer’s day, and that certain types of work are draining.”
As generative AI solutions roll out in organizations, barriers to sharing information will recede as unstructured data is available org-wide and accessible through these large language models (LLMs). As a result, we will move from the current state of desktop search to find documents to desktop queries to find answers, and ultimately to desktop tasking – assigning tasks your co-pilot executes.
The result will be that technical and business staff will see significant gains in capabilities. And by loading these models with organization-specific data and continuously updating them with new learnings, the continuous interaction will help train and improve models. The staff gets smarter, and the Big Brain gets bigger.
To realize the full benefits of generative AI, organizational change will be required in two phases. The first phase involves helping automate and augment knowledge worker tasks to free up their time for higher-level problem-solving. The second phase maximizes knowledge workers’ newfound time to think and create. This will require mindset and skill set changes (see Quality Thinking).
The challenge for D&A leadership is prioritizing and aligning resources in ways that accelerate this knowledge worker revolution brought on by AI to set the learning, continuous improvement flywheel (smarter people < > smarter models) in motion.
(1) Chapter 4 of Working Backwards: Insights, Stories, and Secrets from Inside Amazon.
When we talk about data-driven cultures, what do we mean? What are the individual and organizational characteristics we look to cultivate? In this series of blogs, we will share three key strategies relating to people, process, and organizational change that lead to successful data-driven cultures.
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. [i] 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.”[ii]
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.
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, 1. Help combat our natural tendency towards self-deception that cognitive biases reinforce, and 2. 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
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.
A growth mindset, as researcher Carol Dweck discovered, “…is based on the belief that your basic qualities are things you can cultivate through your efforts.” [iii] 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.”[iv]
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.
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. [v]
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. [vi] 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.[vii] 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.
One method, called the “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. (For all you data scientists still reading, it should sound familiar, as it’s much like monitoring a model you’ve built!)
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.
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 by 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.
Editor's note: this post, co-authored by Rick Hinton and Lisa Targonski, originally appeared on the Elder Research blog
Read Part 2: Decision Process Improvement (DPI): Better, Faster Decisions
[v] Haber, Jonathan. Critical Thinking (The MIT Press Essential Knowledge series) (p. 166). MIT Press.
[vi] Parrish, Shane; Beaubien, Rhiannon. The Great Mental Models: General Thinking Concepts. Latticework Publishing Inc.
When we talk about data-driven cultures, what do we mean? What are the individual and organizational characteristics we look to cultivate? In this series of blogs, we will share three key strategies relating to people, process, and organizational change that lead to successful data-driven cultures.
Our prior post on data-driven cultures discussed how adopting the right mindsets can improve your decisions by combating the cognitive biases that can cloud your judgment. In this post, we examine why you need to systematize your decision-making processes to reinforce the behaviors found in data-driven cultures.
Examining your Decision Processes
As we noted, data-driven cultures are obsessed with an open-minded pursuit of the truth, supported by rigorous analysis. The combination of these two factors determines the quality of decisions. To accomplish this, you need to channel your obsession into creating a systematic process that can be learned, practiced, and improved daily. In other words, your daily behaviors and actions are guided by a clearly defined decision process.
There are three reasons for doing this:
Before we go any further, let’s define a decision process and how it differs from a business process. A decision process is a set of discrete steps individuals or teams use to evaluate and analyze information to create insights that fuel decisions. At a high level, it involves locating and evaluating evidence, structuring it for analysis, and making decisions based on this analysis.
Every decision involves predicting the future and, ideally, keeping score as to the accuracy of your predictions. Decision processes usually are embedded in a broader business process, with the distinction that the output of a decision process is an agreement to take some action. The output of a business process is typically a tangible thing (e.g., a product or service) resulting from many cumulative decisions.
Examples of decision processes include a consumer goods manufacturer engaging in demand planning to inform production schedules. Or the finance function of a fast-growing tech firm projecting cash flow and profitability to ensure hiring new staff doesn’t outpace financial realities.
A complete discussion of all the decision types is beyond the scope of this post. Still, a critical takeaway is that the mistake most leaders make is underestimating the complexity of decisions, which translates to overlooking opportunities for improvement. Furthermore, improvement doesn’t mean a single, significant change (which can be enticing but unattainable) but incremental progress over time and dozens of decisions.
DPI: Change and Continuous Improvement
To improve decision processes, teams must first understand how the process currently works, make assumptions about what might improve it, align stakeholders around the goals for improvement, and embed the new behaviors in the decision processes. This is straightforward -- even intuitive-- but is rarely done inside organizations. In their book, The Knowing-Doing Gap, two Stanford professors may have found the reason people too often know what to do but still do not follow through:
“…the answer to the knowing-doing problem is deceptively simple: Embed more of the process of acquiring new knowledge in the actual doing of the task.”
In other words, continuous improvement of your decision process is not an intellectual exercise; it’s a formal practice. We call this formal practice Decision Process Improvement or DPI. At its core, DPI is about improving operational performance by increasing the speed and quality of decisions. Let’s dive in to see how it works.
How DPI Works
Using DPI, teams start with a decision process (e.g., loan processing, sales or financial forecasting) and work backward, looking at all the inputs and outputs that informed the decision and identifying areas for improvement that will impact the speed and quality of decision-making.
The key elements of DPI include:
Common Decision Process Pitfalls
Over the years, we’ve worked on projects across industries building advanced analytics solutions for a wide variety of challenges, including fraud detection, manufacturing demand forecasting, pricing optimization, product defect rates, sales forecasting, and loan portfolio risk. While each of these use cases has slightly different underlying decision processes, we’ve seen a consistent pattern of decision process pitfalls.
The following are some of the contributing factors, organized by high-level decision process phases, that we’ve found lead to slower, lower quality, and more resource-intensive decisions.
Major Decision Process Phases and Common Pitfalls
Gathering and organizing data
Annie Duke, author, consultant, and accomplished professional poker player, attributes her success over the years in making rapid, high-stakes decisions with real money on the line to developing a high-quality decision process.
"Why is it so important to have a high-quality decision process? Because there are only two things that determine how your life turns out: luck and the quality of your decisions. You have control over only one of those two things. What you do have some control over, what you can improve, is the quality of your decisions."
Once you raise the visibility of your decision processes and commit to continuous decision process improvement, you will get closer to the data-driven culture you need to make faster, better decisions.
Editor's note: this post, co-authored by Rick Hinton and Lisa Targonski, originally appeared on the Elder Research blog
Read Part 3: Using the CALM Method to Sustain Change
When we talk about data-driven cultures, what do we mean? What are the individual and organizational characteristics we look to cultivate? In this series of blogs, we will share three key strategies relating to people, process, and organizational change that lead to successful data-driven cultures
In our prior post on data-driven cultures, we discussed why you need to systematize your decision-making processes so that they can be learned, practiced, and improved daily.
This post examines why adopting a formal change management process is essential for your transformation to a data-driven culture.
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:
Considerations for Data-Driven Cultural Change
Using the Heaths' 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: "…ultimately, all change efforts boil down to the same mission: Can you get people to start behaving in a new way?"
Three Key Strategies for Change:
Talking about change is one thing; making it happen is quite different. Simply put, it’s hard. 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 to ensure consistent execution over time.
Let’s start with some core principles derived from our experience and the research we have reviewed above:
Let’s start by defining change management as 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?
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 decision processes, and 3. decision processes and behaviors. Alignment, or engagement, requires 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 curriculum and the approach to learning; it:
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 the change 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 decide on the activities and behavioral changes that will drive the desired overall 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.
As you read through the description above regarding the CALM method, it's tempting to convince yourself that you are already doing these things. For example, you may 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, where 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 embrace change as synonymous with continuous improvement and embed it in the culture. To accomplish this, organizations must treat change management like other mature processes, ensuring it is clearly defined, disciplined, measured, and improved.
The CALM method is to knowledge worker productivity just like the Agile approach is to robust software development; both continuously improve the speed and quality of a specific output. In the case of Agile, it is a functioning software solution; with CALM, it is organizational decision-making. The change that CALM makes possible is faster and higher quality decisions throughout the organization -- the essence of a data-driven enterprise.
Editor's note: this post, co-authored by Rick Hinton and Lisa Targonski, originally appeared on the Elder Research blog
Quality Thinking: Essential skills for the 21st century (or, how to make friends with the “Big Brain”)
As technological progress makes our lives easier, we, paradoxically, need to work harder. Not harder in terms of effort and hours committed to work, but a greater effort towards something quite difficult and elusive: the ability to adapt and change in ways that improve the quality of our thinking.
The Coming Wave of AI/LLMs
The next wave of AI technologies, like ChatGPT, will profoundly affect those engaged in knowledge work. In the not-too-distant future, machines will be responsible for a significant share of the value embedded in any product or service. This includes knowledge worker tasks such as copywriting and coding that are already seeing substantial productivity improvements in the short time these new AI-powered solutions have been in the market.
Looking at Github’s Copilot solution, two interesting and early trends have emerged: 1. Developer productivity has jumped significantly, and 2. Non-technical users will be able to create software code on their own. So, in the first use case, a user increases productivity using an existing skill set; the other enables a user to learn new skills that were previously the domain of folks with technical competency (software development).
Impact on the Future of Knowledge Work
Let’s assume that because AI removes significant technical barriers to the software development process, a glut of AI-powered software applications may emerge, not only from freelance developers but also within companies with a substantial number of business analysts and developers. Also, with solutions like ChatGPT, barriers to information (dare we say “knowledge”) will continue to fall away as vast repositories of unstructured information transform into usable knowledge with minimal to no human intervention.
But the question remains, “usable” in what ways? How can we apply these capabilities to solve real problems and determine the right use cases to pursue? Uncovering and applying these use cases will require individuals, specifically those operating in larger organizations where the effects could be immediate and profound, to exercise better discernment or judgment. To effectively use the new “big brain” that is AI, people need to think differently, or more to the point, improve the quality of their thinking.
New Skills are Needed: The Rise of Quality Thinking
We need to tap into our higher cognitive abilities to create new things, build our critical thinking skills and unleash our creative potential. But, doing so requires a disciplined process and mindset dedicated to experimentation, testing, and learning. It’s about augmenting our thinking, not outsourcing it to the Big Brain.
Therefore, we need to adapt and change how we think and work, which requires developing the mindsets, skillsets, and habits of creative, critical thinkers. These new mindsets and skills apply to everyone in the organization, not just leaders, as AI will create greater autonomy when greater knowledge and more powerful tools are accessible to everyone in the organization.
We can unlock unimaginable human potential, but the limiting factor is not technology but us, our thinking abilities, and our capacity for change.
What is Quality Thinking?
Quality Thinking (QT) applies critical thinking concepts in an organizational context comprised primarily of knowledge workers exercising better judgment to improve decision-making. It’s taking an idea or process like critical thinking and applying it in the context of a business process, decision process, job function, or job role. It’s using a new way of thinking and working.
Let’s break out Quality Thinking and define the two terms separately. The “Thinking” aspect refers to the critical thinking methods and practices that are learned, refined, and applied in the workplace. It’s the process.
“Quality” refers to outcomes, where you can point to this thinking process producing better results. For example, new products launched, increased revenue, improved customer retention, better sales win rates, lower product defects, etc.
So, if improving the quality of our thinking is the end objective, how do we accomplish it? There are three core elements:
Preparing for Change: Overcoming Fear and Resistance
Before we go any deeper into this, I should clarify that the target audience for this discussion is primarily knowledge workers and their leaders. Of course, the impact of AI will be widespread, but the gatekeepers, if you will, will be those in technology and data science roles responsible for selecting, deploying, and managing the new technology. And they face a daunting task as resistance to the adoption of AI among the general employee population will be unlike anything they have seen in the past, as workers' fear of being replaced grows.
So, how does improving Quality Thinking (QT) help solve this problem? In two ways: 1. QT helps leaders exercise better judgment when selecting AI technologies and determining the use cases that will create the greatest value, and 2. QT helps create mindsets among employees that are receptive to change and open to new ways of solving problems, along with the skillsets for effectively working with the big brain. The combination of leaders being able to make a compelling case for adoption and employees more receptive to change creates a flywheel effect, accelerating adoption.
Thinking Independently, Acting Collectively
For large organizations, pushing through any change can be a significant challenge if leaders don't manage the change effort in a more deliberate, adaptive way that is responsive to our complex world.
Part of the complexity lies in the inherent tension in organizations that encourage independent thought leading to innovations and clear-eyed decision-making and collective action required to make things happen. Too often, the latter, in the spirit of just getting things done, leads to a culture of conformity laden with bureaucracy. By raising the level of QT across the organization, you become better equipped to manage the tension between independent thought and collective action.
Leaders and staff can operate more like networked teams rather than separate organizational units. The emphasis here is more on changing individual behaviors than organizational structures, as good information flow is critical to faster, better decision-making. And siloed org structures and turf battles restrict this flow.
Two pre-conditions are required for this to work: the mindsets that encourage independent thinking and the skillsets (communications, analysis) to act collectively. Like any networked system, the throughput and quality of information determine the system's effectiveness. To do this, you need to increase the number of Quality Thinkers in the organization. It's bottom-up, requiring a deliberate process of personal and professional development where small, individual, incremental improvements compound across the organization to produce non-linear results.
The Path to Quality Thinking
So, what's the journey look like for someone looking to improve the quality of their thinking? It includes both foundational and transformational elements.
Self-Awareness and Situational Awareness
These foundational mindsets, self, and situational awareness provide the baseline skills that enable everything that follows. Self-awareness is the ability to see, without blinders or bias, how you think and react to situations. Situational awareness involves understanding the external conditions in which you operate. In theory, you could lump the two together under self-awareness, but the distinction is important in this context as both these skills are cultivated and applied in an organizational environment. One way to think about this is self-awareness involves “zooming in,” focusing on self-improvement, while situational awareness means “zooming out,” understanding how the organizational systems and structures impact individual and corporate performance. In any organization, you will find the two (self and situational) intersect inside a decision process.
For example, let's say you missed your quarterly sales forecast and need to explain to your boss or colleagues why it happened and what you plan to do to fix the situation. The first issue, missing your forecast, requires you to zoom in and conduct an honest self-assessment that may reveal a consistent overconfidence that causes you to ignore clear warning signs. The second issue, your plan to fix it, requires zooming out and evaluating those warning signals that you perhaps deliberately overlooked or misunderstood how, or to what extent, they impact your forecast. Furthermore, are they even good leading indicators? Any plan to address the issues of inaccurate forecasts must first begin with an honest self-assessment and a clear situational understanding of the input metrics that inform your decisions.
There are two mindset and behavioral changes required: 1. the humility to say, “It’s on me; I was overconfident. I’m not 100% sure why I missed it,” and 2. the curiosity to find out how to fix the problem, seek out possible explanations that will improve your forecasting in the future.
Humility and Curiosity
A mindset that encourages humility and curiosity creates the conditions for clear-headed thinking as it combines two essential ingredients: recognizing what you don't know with the active pursuit of knowledge.
In their book, Humility is the New Smart, researchers Edward Hess and Katherine Ludwig talk about how our instincts for fight, flight, or freeze can inhibit our personal growth. They found in scientific research that two big inhibitors of learning and emotionally engaging with others are our ego and our fears. They further claim, "To mitigate ego and fear and excel at the highest levels of human thinking and emotional engagement requires a new mindset that embraces humility." Humility doesn't mean being weak or subservient but more open to new ideas, technologies, and ways of working.
While humility creates a mindset open to learning, curiosity is the catalyst for action. Curious people are motivated to learn more, and a learning culture provides fertile soil for personal growth. In addition, people that approach problems with a curious mindset tend to be less judgmental. As they try to understand and unpack a problem, they replace the tendency toward criticism with curiosity. Finding who's at fault is easier than understanding the complexity inherent in systems like large organizations operating in a highly dynamic market. Adopting Quality Thinking allows you to move from ego and blame, which damages trust and communication (and information flows across the organization), to better understanding and problem-solving.
Mental Models and Decision Processes
The simplest definition of mental models is that they describe the way the world works. They also influence how we think, understand, and form beliefs. Mental models can be employed to reveal flaws in our thinking as corrective measures against sloppy or biased thinking. Examples of commonly used mental models include first principles thinking, leading vs. lagging indicators, and probabilistic thinking (a more comprehensive discussion can be found here and here). These methods help expose assumptions underlying your thinking and challenge what you think you know about a problem. They also help you anticipate problems, intervene before it’s too late to fix them, and help maintain your outside view by constantly adding new information to your existing data to get closer to the truth. Finally, mental models provide the bridge from thinking about a problem by reframing or organizing your thoughts to making a decision, all of which happens inside a defined decision process.
So, what is a decision process, and how does it differ from a business process? A decision process is a set of discrete steps individuals or teams use to evaluate and analyze information to create insights that inform decisions. At a high level, it involves locating and evaluating evidence, structuring it for analysis, and making decisions based on this analysis (you can find a deeper dive into decision process improvement here).
Decision processes usually are embedded in a broader business process, with the distinction that the output of a decision process is an agreement to take some action. The output of a business process, however, is typically a tangible thing (e.g., a product or service) resulting from many cumulative decisions. The fact is that most decision processes are not well understood, documented, or objectively evaluated. Building Quality Thinking skills enables leaders and staff to apply the right mindsets and skillsets to improve decision processes and, ultimately, organizational performance. It requires a commitment to continuous improvement with people operating inside a process that closely examines decision processes, determines where there are deficiencies, makes adjustments, and measures results.
What should we be looking for in evaluating decision processes? There are three priorities or objectives, 1. Exposing gaps in decision processes related to throughput, quality, and cost, 2. Documenting behavioral changes within the process necessary for improvement, and 3. capturing potential use cases for the application of new technology to support decisions, in particular, looking at AI/LLMs to determine how to incorporate advances like ChatGPT into existing decision processes.
As noted earlier, we can unlock unimaginable human potential to creatively solve our hardest problems. Still, the limiting factor is not technology but us, our thinking abilities, and our capacity for change.
Quality Thinking provides a framework for individuals to focus their personal and professional development in ways that prepare them for what’s next. And what’s next is hard to predict. Hence the central idea with QT is that “preparing” means honing the skills of observing, adjusting, and adapting, which means open-mindedness combined with disciplined practice.
A critical takeaway is this: discipline and creativity are not incompatible but complementary and self-reinforcing. Throughout history, we’ve seen this pattern across domains that include art, literature, architecture, science, philosophy, and technological innovation, where creativity combined with disciplined effort produced “genius” breakthroughs in scientific discovery or timeless art.
Below are quotes from people across disciplines (and time periods) to drive home the point. The first from Chopra speaks to a mindset of openness, be it to new ideas or information, that is essential to improving the quality of your thinking. The rest of the quotes reinforce the universality of rigor, discipline, and creative output.
"Be comfortable with and embrace paradox, contradiction, and ambiguity. It is the womb of creativity.”
Deepak Chopra, physician and best-selling author on alternative medicine.
"I was always conscious of the constructed aspect of the writing process, and that art appears natural and elegant only as a result of constant practice and awareness of its formal structures."
Toni Morrison, award winning novelist
"What made Leonardo [Da Vinci] a genius, what set him apart from people who are merely extraordinarily smart, was creativity, the ability to apply imagination to intellect."
Walter Isaacson, professor, journalist, and best-selling author
"Imitation and mastery of form or skills must come before major creativity."
Oliver Sacks, neurologist, naturalist, historian of science
"The really rare skill is the ability to marry discipline to creativity in such a way that the discipline amplifies your creativity rather than destroying it."
Jim Collins, researcher, author, speaker and consultant
So, what’s next? What should we be working on every day? The answer is ourselves. We must work systematically to improve the quality of our thinking. And the future is coming at us fast—time to get started.
Image credit: engineered by Eddie Hinton using Dall-e