Causal Workflows: How AI & Agents Redefine Operating Models

What are the perfect business and operating models? I’ll let you in on something I tell every client right after the initial assessment. The goal of my AI product management and AI strategy frameworks is to help the business get as close to building the perfect business and operating models as possible. That’s the outcome V-Sqaured sells. But few realize the definitions (really the purposes) of each have changed.

Just as nuclear weapons changed the calculus of warfare, AI and agents have changed the calculus of business.

***Dario, skip down a few sections. This article answers your question, but everyone else needs the context you have to understand why the answer is relevant to them.***

Since most business leaders don’t know the outcome or business and operating model end states, I have information asymmetry, and that is the basis of my business model. But how do I know the end states before they actually happen? I turned futurism into an evidentiary workflow that delivers a reliable model of what’s next. The thesis is built on our best available evidence and continuously improved as new evidence emerges.

We monetize that thesis and its implementation, but it is not a sustainable competitive advantage. Other consulting companies are figuring it out and catching up. If V-Squared were standing still, that would be a huge problem.

V-Squared’s operating model engineers access to evidence or information no one has by building pieces of what’s next for clients and seeing if it does what my model predicts it will. That increases my information asymmetry, creating an advantage for my business that competitors don’t have access to. They would have had to have known what I knew, when I knew it, and built a flywheel that generates information as efficiently as mine does to be in the same place I am now.

Building what’s next and measuring business impact is my information flywheel and advantage accelerator. By the time they know what I know, I have had time to learn more. It’s not enough to build an operating model that delivers value. The operating model must also improve the business’s capabilities and create or sustain its advantages.

I just explained how to build a competitor to my business, and only a handful of people will be able to do it. Even if you’re one of the people who can, in the time it takes you to catch up to where I am now, the flywheel will advance V-Squared even further. Your flywheel would need to be faster than mine, but that’s a point for a different post.

This article is about the constraints to implementing a flywheel in the first place and how to overcome them. There’s no point in explaining how to compete against a company that’s built like this when only a few businesses are. It’s more valuable to explain all the barriers to building an operating model like mine and how to overcome them.

I’m a CEO, sole investor, and the only shareholder, so I have fewer constraints than you do. This is why I teach everything twice. Once how it should be if you have the perfect setup. Then again for how it probably is based on your constraints, optimizations, and objectives.

That’s why questions are such rich ground for me to teach with. A question is a statement of, ‘Here are my business realities. How do your frameworks work under those constraints, optimizations, and objectives?’ Students tell me that the live questions and answers during my instructor-led courses are the most valuable parts, followed closely by the office hours, which allow for long-term support through Q&A after the course ends.

My curriculum is structured around this core tenet. Essentially, the frameworks are just a cool story about cause and effect until I explain how they flex to fit your realities. Systems, models, and frameworks are the starting point. Questions give me context about your reality so I can explain how the frameworks flex to fit it.

It’s not enough to understand the structure. You must also understand the mechanics for any of this to be actionable. But for mechanics to inform actions that are relevant and personalized to your reality, they must be framed by the structures you operate in.

Hold that paragraph in your context window or short-term memory throughout this article. It’s a critical agentic architectural pattern that few have built enough to standardize.

Dario Morelli asked a question last week about the challenges of global outcomes because there’s a lag time between a completed workflow and the global outcome. For many workflows, that lag time is months or years. I don’t know if he realized it or not, but that’s a complex, dynamical systems question. I am expanding his original question a bit, but I promise not to answer 5 questions that weren’t asked…no matter how badly I want to.

I just need you to understand that transitioning the business from local to global outcomes introduces complexity and dynamism, so our solution must address that.

Complex because measuring the ‘causal’ (remember causal in the enterprise is the best available definition measured in terms of outcomes, not the scientific standard) chain from the workflow to the outcome has many steps and branches. In the recruiting example, the candidate gets hired, but the global outcome of employee value creation is only filled in after multiple years have passed from the time the hiring workflow finished.

Dynamical because the local and global outcomes and those steps are changing. In the recruiting example, the definition of employee value creation is changing as well. The way the business scores it changes. Business needs change. Employees upskill. Some are promoted into entirely new roles. As we have seen, a lot can happen in two years.

A couple of months ago, I said that AI scales access to information and that we are too early to be measuring intelligence. AI doesn’t know what to do with the information it has, so its agency is very limited. That is where this article and Dario’s question begin. This is novel ground because before agents, this was a difficult, expensive transformation.

AI and agents scale access to information about outcomes. The act of building them using my frameworks scales transparency into local and global outcomes, workflows, and so much more. That allows us to manage the business in a way that was never feasible before now.

AI and agents alone do not transform a business or operating model. However, if you align the technology’s architecture and implementation with the business and operating model transformation, they become massive enablers and accelerators. That is the goal of my frameworks and a much deeper rabbit hole that this article will step past.

Causal workflows were not feasible until recently. We needed AI and agents to scale access to information, so it became feasible to measure workflows and outcomes this way.