Planning for multiple futures at once
Agentic AI is making real-time scenario planning accessible to any organization navigating uncertainty.
In this issue, we examine:
Why data alone isn’t enough
What continuous scenario planning looks like in practice
How AI agents are changing the way leaders model risk
Reading the Curve
There’s a tendency, in business and in life, to plan around what feels likely. The scenarios we outline and decisions we make are often built on patterns we’ve seen before. But the events that most profoundly influence our lives are often those we don’t fully prepare for, even when their consequences can be severe. In other terms it is critical to anticipate potential events with low probability and high impact.
Author Nassim Nicholas Taleb captured this idea in his book The Black Swan. He used the now famous example of a turkey that grows more confident in its safety with each passing day it’s fed and cared for. That is until it meets its Thanksgiving Day fate. His point was not just about unpredictability (from the bird’s point of view), but about how easily we rely on past experience to guide future decisions.
In today’s environment, profound or life-altering events feel less like exceptions and more like a constant backdrop.
Disruptions—whether geopolitical, economic or technological—can cascade quickly, showing up in ways that are both immediate and tangible. Consider that nearly 20% of the world’s energy supply and roughly 30% of global fertilizer trade move through the Strait of Hormuz. For a manufacturer in Europe or a farmer in the American Midwest, a conflict thousands of miles away can land directly on the bottom line.
My colleague Amir Bagherpour, Accenture’s head of agentic modeling and simulation, has developed Accenture’s proprietary agentic simulation and monitoring capabilities that can be leveraged to strengthen the resilience of organizations. Scenarios can be run at speed to assess not only first order effects but also second order and third order effects of potential events.
Advances in modeling and AI make it possible to build and test scenarios in real time, helping leaders understand how events might unfold and what they mean for the decisions in front of them.
As Amir put it to me recently, “It’s not about telling people exactly what’s going to happen. It’s about understanding the pathways and the scenarios on how things can happen.”
That shift from prediction to preparedness is where the curve is moving.
Good decisions in uncertain times require a different kind of intelligence
For business leaders navigating constant volatility, the challenge is not a lack of information. Data is plentiful. And geopolitical analysis is seemingly everywhere, 24/7.
The harder problem is translation.
When a major disruption hits, whether a global pandemic or a conflict that suddenly closes one of the world’s most critical energy corridors, the question that leaders pose is not “what’s happening?” It’s “what does this mean for us and what do we do about it?”
That gap between analysis and action is where most organizations have historically struggled.
For decades, scenario planning was the standard response. Leadership teams would gather periodically, map out a handful of plausible futures, assign rough probabilities and build contingency plans around the most likely outcomes. It was a serious and valuable exercise. It was also slow. Building a credible model of how a conflict might unfold or how a supply chain shock might ripple across the business required months of expert work and access to the kind of specialized talent that only the most sophisticated organizations could afford.
What’s changing now is the ability to course-correct continuously, sharpening the analysis as new information comes in.
A different kind of decision intelligence
Two colleagues who spend their days making sense of fast-moving, high-stakes information have shaped how I think about scenario planning. Tomas Castagnino has led Accenture Research’s global economic modeling capability for more than a decade. Svenja Falk heads our Accenture Research Geopolitical Center of Excellence and speaks with senior business leaders almost daily.
Both will tell you that what’s changed in recent years is more than the tools; it’s the nature of the work itself.
Castagnino describes the change through his own career. Early in his work as an economist, he estimates that the majority of his time went to manual data gathering: downloading records one by one, matching datasets and assembling the raw material that analysis requires. The intellectual work—the modeling and interpretation—accounted for 20% of the time in most projects.
“Now that’s flipped,” he said. “We’re spending more quality time trying to understand how to decode the noise in the data.”
This evolution in how economists spend their time reflects something larger. The data available to analysts today is fundamentally different from what existed even a decade ago.
Six years ago, as the pandemic unfolded in real time, modeling teams could track where people were spending their time, whether they were adopting new technologies and how consumer behavior was shifting week to week. Job postings, employee reviews, hiring patterns and technology adoption signals were all data sources that either didn’t exist a decade earlier or simply couldn’t be analyzed at scale. All of these new data streams made it possible to see things that had previously been invisible to analysts. And now agentic solutions are changing not only the speed and the scale of scenarios but the nature of scenarios building to prepare decisions of Executives.
Castagnino uses a black hole metaphor to describe what this data makes possible. Even something as difficult to measure, like pure gravity or how a virus could affect a global economy, can be inferred by observing the signals it creates.
“There are certain things you cannot truly measure (like a black hole), but you can actually get very close to understanding how they are moving just by observing all the data around it,” he said.
For Falk, who works directly with clients navigating such complexity, the challenge is keeping leaders focused on what matters for their business. “For a business leader, the most important thing is actually the decision itself,” she told me. “When I make a decision, what are the implications on my company and how do I take my leadership team along?”
Her team’s work starts with the questions that matter to a specific company. What will this mean for input costs? Pricing? Supply chain? That “translation,” from global signal to business decision, is where the real work happens. All the contextual information and insight is gathered and analyzed in real time through agents such as agents which run PESTLE (Political/Economic/Social/Technology/Legal/Environmental) analysis in seconds vs hours in the pre-agentic world.
A different way to make decisions
A few lessons stand out from my conversations with Tomas and Svenja:
The goal is focus. The volume of information available to leaders right now is staggering and it keeps growing. This is precisely where modeling earns its value, says Tomas. “Everything is data, but not everything is useful data,” he told me. “Use models to reduce the level of uncertainty. It’s like reducing the noise to signal ratio.” Asking the right questions is the first step. Scan available data and cluster them into a small number of drivers that shape outcomes is next. And then develop and pressure test a series of coherent potential futures.
Build agentic solutions combining specialized agents (say financial analysis, white space analysis), agents combining several specialized agents and orchestrator agents (coordinating the drafting of an overall analysis to prepare potential decisions). In the future, scenarios building will systematically include agentic simulations, under the supervision of humans, what we call “Humans in the lead”.
Prepare for more than one future simultaneously. In fast-moving situations, two scenarios can be equally plausible. A leader has to be equally prepared for both. That means understanding what might happen, as well as why and what each pathway would require of the business. We produce such research work regularly for clients and for Industries.
Use it to spot opportunity. A real-time modeling system is not only an early warning tool, it’s an early opportunity tool, says Svenja. “You can analyze intelligently so much faster,” she told me. “And you can match it with activities actually happening in the market.” Investments, competitive moves, shifts in demand: the same signals that flag risk can also reveal where others are placing their bets and where a window might be opening.
Treat scenario planning as a continuous practice. This is perhaps the biggest change. The tools now exist to monitor signals and update assumptions in real time. Organizations that build this into their regular operating rhythm, rather than revisiting it once a year, will be better positioned to act when conditions shift.
Learning Curve
Scenario planning with Agents
When you’re able to do things faster, then you’re also able to iterate more.
“It’s like your iPhone. The version you have now is significantly better than the one you had 10 years ago because there’s always updates and improvements,” says Amir Bagherpour, who leads Accenture’s agentic modeling and simulation.
Except in this metaphor, imagine that those updates occurred over the course of a week, not a decade.
What once required months of specialized effort, the kind of work that only the most elite research teams could pull off, can now be done in days. Sometimes hours. And that window keeps shrinking.
Amir spent his career building models that help decision-makers see around corners, including at the U.S. State Department, where he led this type of advanced decision analysis for the Secretary of State’s office.
Now, sophisticated analysis is becoming common practice in companies.
Amir walked me through the structure behind one of Accenture’s simulation platforms. The simulation itself starts with the kind of modeling that has long existed in academic and government circles, mapping stakeholder behavior, tracking who holds influence, charting the conditions under which a negotiation might hold or collapse.
What agents make possible is the next step: taking that complexity and making it accessible at speed.
Specialized agents handle distinct parts of the work. One continuously reads live data, tracking events and market signals in real time. Another translates that input into forecasts and simulated outcomes. An orchestrator coordinates the two.
Together, they form a system you can ask questions of directly.
Worth Your Attention
The Infinity Machine: Demis Hassabis, DeepMind, and the Quest for Superintelligence. The essential backstory to everything we explore in this issue. Based on more than 30 hours with the co-founder of Google DeepMind, author Sebastian Mallaby traces how one scientist’s audacious pursuit of artificial general intelligence produced the tools that are now reshaping how leaders model risk and act under uncertainty.
LLMs as Strategic Actors: Behavioral Alignment, Risk Calibration, and Argumentation Framing in Geopolitical Simulations. This paper, co-authored by Veronika Solopova, evaluates six leading AI models across four real-world crisis simulations, comparing how agents and humans navigate risk and escalation under pressure. Their finding is that models approximate human decision patterns in base simulation rounds but diverge over time, displaying distinct behavioral profiles and strategy updates.
Superforecasting: The Art and Science of Prediction. Co-author Philip Tetlock’s insight, that forecasting is a disciplined skill built on probabilistic thinking, intellectual humility and continuous updating, is the intellectual foundation of everything that Tomas and his team are building with real-time economic modeling. Taleb’s “black swan” tells us what we cannot know; Tetlock tells us what we can. Read them together.
Many years ago I learned how to be aware and careful about the influence of implicit assumptions in any model through studying Essence of Decision by Graham Allison and Philip Zelikow.
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