Quantum Business Planning: Understanding Direct Effects
Quantum particles exist in multiple states simultaneously until observed. Similarly, organizations could in theory maintain multiple parallel planning scenarios until a decision point ‘collapses’ them into one reality. Planning requires effort, so most companies stop short of planning for every likely scenario, let alone every possible scenario. Modern AI will allow companies to map and understand the relationships between people, teams, functions and systems in their organization with far more detail. As a result, companies will be able to plan for all likely scenarios without having to consume their analytical capacity.
This will put companies in a position to understand all the direct effects of changes they are making prior to making them, giving them true comprehensive foresight. Decisions can be made faster, more confidently and with greater effectiveness as a result. Companies will be able to implement complex changes rapidly and attain goals previously thought impossible.
At any given moment, a company could go in many directions. The number of possibilities exceeds our ability to plan for those scenarios. So forecasts are made based on changes from baselines. Expectations form around how much a certain variable in the business model might change over a period of time, and then other assumptions flow from that assumption.
Often this means broad, high level, shallow scenario planning that accords cleanly with the calendar or reporting periods, like an annual or quarterly forecast of financial results. But that information doesn’t tell you much about the reality of the business beyond the kinds of things people share on a first date (dreams, hopes and aspirations). Mapping out direct effects is still opaque. It’s likely that desired results could come more quickly if the direct effect of changes were more clearly understood.
So what is a direct effect?
A direct effect is something that happens as a direct result of a change or decision being implemented. For example, if I decided to resign, then a direct effect would be that my role would become vacant, and a direct effect of that is we would need a new CEO. It is possible to know in advance that my resignation means we would need a new CEO practically, but a lot of the rich details of direct effects are not used in planning today. It doesn’t require a huge amount of imagination or probabilistic thinking to consider direct effects, but it does take intricate planning and the knowledge that comes from expertise.
In quantum planning, all possible knowable direct effects are brought into the present awareness. In practical terms that means if we are considering changing CRM providers, we are able to see a long list of TODO tasks that would result from them and what the effect would be. Your costs will change by $X. You will need to train this many people. The workflows will change for this many power users. You will lose these features, and gain these other features. You’ll need to decide who gets what access rights. You’ll need to form a team to plan the project, which requires capacity. Where will that capacity come from, and how will it affect your other goals? Without knowing the direct effects of a change, you can’t know how it will affect your other goals.
Much of the burden of understanding the direct effects of changes falls upon people making decisions. Executives make so called ‘high level’ decisions affecting the organization. These decisions are taken without necessarily understanding their full effects. Models are built to make the unknown known to an extent possible, for example, that reorganizations often lead to 1-2% of people quitting their roles. But if it were possible to know in advance exactly who might be adversely affected, without having to slow down the decision to reorganize, then companies could form specific plans to address retention. Having the direct effects at hand would change both the decisions that are made and the way they are implemented, and improve both.
How can I use direct effects?
Convictional has been researching using AI to predict the direct effects of decisions. A lot of energy is going into predicting distant outcomes in the future, like how much revenue Apple will have in Q3 of 2026. That is valuable information to have, both for Apple and its stakeholders, but it’s not the information that is best able to help Apple make decisions in the present. We believe that to make the best possible decisions in the present, decision makers need a powerful and scalable capacity to analyze the direct effect of changes, and then summarize those direct effects in business terms. Changing CRMs may have hundreds or thousands of direct effects. The true cost isn’t just the licensing fees, it’s the sum of the economics of all the direct effects. Potentially hundreds or thousands of highly paid people will have changed workflows, how do we account for that?
In our research, we have found that the problem requires breaking direct effects down into types. For example, the effect of decisions on people and the organizational chart might be one type of direct effect. Another would be the effect on expenses. Another would be the effect on how people’s time is being spent, the direct effect on people’s focus and energy. Modern AI is capable of enumerating these effects, and if given quantitative inputs, making predictions about all possible direct effects. With that information you can then do a simplified summary listing the most impactful effects across costs, focus, time and energy. And with that information, you can explore many possible scenarios and choose among them to progress your goals.
What’s next? Quantum planning
We are still early in researching how companies will be able to improve decision making and planning in pursuit of goals as a result of AI. Most companies are still in the stage of applying AI where the main use cases are focused on something along the lines of a ‘genius intern’ helping to prepare talk outlines and edit email drafts. But our research suggests that currently available commercial AI models are capable of predicting the direct effect of decisions exhaustively provided sufficient context.
We are continuing to explore how to bring all that context together such that we can provide powerful planning and decision support tools. This new Intelligence Age business planning capability will go far beyond what’s currently possible. It will give CEOs the ability to understand the exact effect of changes prior to implementing them, and business operations teams vast bandwidth to apply to quantum planning every likely or even possible future for a business. If you want to learn more, reach out.