If You Can Conceive of It, You Can Calculate It

For months, it has seemed clear that we have been heading for something significant with AI, specifically Large Language Models (LLMs). Some say a revolution. Some say a bubble. Likely some of both.

Perhaps the “something significant” remains unclear, but something important did happen in February 2026. Anthropic released Claude Opus 4.6  and Sonnet 4.6. Interestingly, Sonnet, the less expensive model, is operating at near Opus performance. Google released Gemini 3.1, which put it at the top of the leaderboard for performance. Alibaba released Qwen 3.5, a massive 397B parameter model with 17B active parameters and native multimodal capabilities (text, image, video) designed for autonomous agents. There were many others.  What is remarkable is not the release of the next versions themselves (those releases have been happening for the last couple of years). What is remarkable is the capability jump of these models. They are jaw-dropping.

As an example, Ed put his MBA statistics and analytics problems through the models. They solved them! Not just the right answer, but the explanation and also clear guidance on the next steps. That was not true just a couple of months ago. It will be impossible to tell the difference between what the student has done and what the model has done unless the tests and homework revert to paper and pencil. If you can conceive of it, you can calculate it. This is a fork in the road. Down one side is panic and fear. Down the other is creativity and maybe even joy!

While AI handles the heavy computational lifting, it is the human connection that actually drives culture. From our research, Joy at Work is an expression of the 10 Dimensions of Joy at Work. None of those has anything to do with automating tasks. They have to do with connecting to people. Ed has focused on his students explaining the statistics in addition to doing the statistics. Now he can emphasize even more the explanation skills needed for business success. Perhaps even move his Statistics class to the Socratic method, something he would never have contemplated a year ago. The problems will get harder, but that is good too. Excel allowed the students to go further and do more in a course. LLMs will do the same.

Converting Data into Information

In our Data-Driven Change process, the third step is to Analyze the Data. The primary purpose of this step is to make the crucial conversion from raw data into actionable information. Data is simply a pile of numbers, like an Excel spreadsheet pasted onto a PowerPoint slide. Information, on the other hand, is defined by one single trait: you can make a decision based on it. AI is now the ultimate engine to speed up this exact conversion. This is also a path to avoiding Nassim Nicholas Taleb’s  "Narrative Fallacy," popularized in The Black Swan. The Narrative Fallacy is how insidious myths, like "70% of change initiatives fail," become accepted as fact simply because they sound like a good story. 

You don't need an MBA in statistics to do this anymore. With modern LLMs, you can upload a raw Excel spreadsheet or CSV file of your project data directly into the tool. Instead of memorizing formulas, you can simply ask the AI plain-language business questions. For instance, you could upload a dataset and ask: "Is there a correlation between the survey scores of the pilots and the aircraft operational usage?" (Often our examples are about flying!) The AI will write the code behind the scenes, crunch the numbers, and tell you exactly which stakeholder group has the highest impact on your success metrics.

The Catch: Thinking vs. Calculating

While this technology is incredibly powerful, there is a critical caveat: The AI does not "think". An LLM is a prediction engine; it calculates and predicts the next most likely word based on its training data. It cannot understand the unique cultural nuances of your organization. The AI does the calculating, but the human change leader must still do the thinking by providing the context, formulating the right questions, and ensuring the data is clean.

The implications for business are significant. To be sure, there will be job disruption, just like every other technology improvement in the history of humanity. But there is also an opportunity for each person to do even more. Because of the improvement in these tools, if you can conceive of it, you can calculate it. In the Neuroscience for Organizational Change, Hilary Scarlett explains that the brain seeks novelty, but when there is too much of it, then the threat response kicks in. For successful Change (and the introduction of AI is certainly a Change), the goal is to add novelty at a rate that people can handle. Part of that is providing an opportunity to Test & Learn, which is the third phase of our four-phase Change as Experienced process. 

Engage > Understand > Test & Learn >  Adopt

For the areas where The Change Decision engages, Change Management, Decision-Making, and Team Effectiveness, the implications for a significant increase in capability and success are energizing.

Change Management: Imagine a world in which anyone can tap into the knowledge that others have about leading Change. That means more can engage in leading Change successfully, but that also means that managing Change well becomes an expectation. Change Practitioners who lament that nobody knows what they do will see a decrease in the confusion around change management and an increase in the desire to do it. And for the difficult changes, to have help. That means Change Practitioners will need to increase their ability to guide more complex changes. The tools and templates that may have served well in the past will not do it. But their Change experience will be invaluable.

Decision-Making: Once a domain of mystery and gut-level intuition, decision-making can become a facilitated approach that is context-specific. Leaders need not work to convince or simply dictate. They can use these rapidly evolving tools to guide decision-making through highly complex situations. The LLM will help to bring out the elements of a decision, such that the choice of the alternative is clear. This process is anchored by a values-based decision-making, which has been studied for over 60 years and refined by the work Ed has done, beginning with his Ph.D. thesis and research.

Team Effectiveness: For managers, the powerful move is often one that needs to happen in the moment. What if there was a tool that could take in the context of a meeting or interaction and provide guidance for the manager just before? The wisdom of decades of research in practical management, industrial psychology, sociology, and influence can all come to bear on the situation. The manager can receive just-in-time guidance to create an effective interaction with their team.

What questions have you always wanted to know, but the tools and know-how were out of reach? Many of those questions can now be answered. It is time to get on an LLM and try!