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How business leaders can upgrade strategic planning with multi-agent platforms

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Even in the best of times, strategic planning is a challenge for business leaders. When global uncertainty and volatility rise, the task gets even more difficult—and more integral to business success. When the world is in flux, an organization’s optionality grows in importance, as does organizational resilience. In fact, as research has repeatedly shown, resilience is now a substantial driver of corporate outperformance. 

But building resiliency and optionality into a strategic plan challenges humans’ cognitive (and financial) bandwidth. The seemingly endless array of future scenarios, coupled with our own human biases, conspires to anchor our understanding of the future in what we’ve seen in the past. Generative AI (GenAI) can help overcome this common organizational tendency for entrenched thinking, and mitigate the challenges of being human, while exploiting LLMs’ creativity as well as their ability to mirror human behavioral patterns.

One potent approach to incorporating GenAI into strategic planning is coupling it with agent-based modeling (ABM) that is already in use to simulate complex, dynamic scenarios. Instead of relying on ABM’s deterministically coded agents of yore—still constrained by the boundaries of human imagination—modern LLMs can make simulations much more flexible, human-like, and (fruitfully) unpredictable. What’s more, they can achieve this at a fraction of the time and cost of in-person planning workshops, providing a powerful tool to explore a wider range of futures and prepare for the unexpected with greater agility.

The promise of LLM-powered behavioral simulations

GenAI has the potential to supercharge the strategic arsenal that is (or at least should be) commonplace among large corporations, including war gaming and scenario planning. The key is using the technology to simulate interpersonal or inter-institutional dynamics, from boardroom discussions to international competition and engagement with regulators. 

Software has long been deployed in similar ways in the social and natural sciences using agent-based modelling in which heterogenous, independent agents interact over time. ABM has been used to simulate the spread of infectious diseases or even the emergence of behavioral norms in human societies. The use of ABM typically involves the time-intensive design of each of the heterogenous agents, traditionally comprised of a series of hard-coded rules that determine how they respond to inputs and interact with other agents. Now, with the ability to use dynamic LLMs as agents, ABM is within reach for most companies. (This use of LLMs is not to be confused with 2025’s buzzword, agentic AI.) 

Having this sort of flexible, cheap, scalable aid for strategy makes it much easier for businesses of all sizes to put in practice the OODA loop often used in military contexts: “Observe in order to adjust the reference scenario based on the cockpit indicators; orient by identifying the strategic options according to the company’s starting point; decide on the most effective option; and act quickly and accordingly.” The OODA loop makes organizations better, faster learners.

In fact, our argument reflects our own experience using a multi-agent LLM simulation platform built by the BCG Henderson Institute. We’ve used this platform to mirror actual war games and scenario planning sessions we’ve led with clients in the past. As we’ve seen firsthand, what makes an LLM multi-agent simulation so powerful is the possibility of exploiting two unique features of GenAI—its anthropomorphism, or ability to mimic human behavior, and its stochasticity, or creativity. 

LLMs can role-play in remarkably human-like fashion: Research by Stanford and Google published earlier this year suggests that LLMs are able to simulate individual personalities closely enough to respond to certain types of surveys with 85% accuracy as the individuals themselves. Other studies show that, when appropriately prompted, LLMs can replicate human decision-making patterns in economic experiments, or can accurately reproduce linguistic patterns and political inclinations observed in actual social media user behavior. 

In addition, LLMs are not deterministic models, meaning that the same inputs won’t always result in the same output. They are instead stochastic, endowed with an inherent degree of randomness in the way they generate outputs. Stochasticity is the root cause of GenAI’s so-called “hallucinations,” or the generation of false answers to factual queries, but it is also what gives them such creativity-enhancing potential. When it comes to creative simulations, and expanding one’s conception of possible futures, these “hallucinations” can be plus.

By using a modelling platform that incorporates multiple LLMs, it becomes possible to have a range of agents, such as regulators, customers, and competitors, each with their own set of idiosyncrasies and agendas, all of which more closely resemble real-life, dynamic interactions The use of multiple LLMs in a simulation also helps minimize the role of human biases as expressed in the prompting of a single model. Researchers at Columbia University found that using a multi-agent approach over a single-agent approach improves the accuracy of simulations of human behavior by roughly 75%.

New fixes for old problems

So, how can LLM-powered ABM help leaders develop and augment their strategic planning efforts? 

Blind-spot detection

The very experience that makes high-level executives valuable—deep industry knowledge, pattern recognition from past crises, and relationships built over years—can also limit their ability to envision truly disruptive or just unconventional moves by others in the marketplace. This, in effect, creates blind spots for executives and their organizations, leaving them vulnerable to disruption and closed off to creative solutions. Amazon’s surprise entry into grocery retail with the 2017 acquisition of Whole Foods, for example, must’ve seemed implausible to incumbent retailers at the time and likely wasn’t considered as a scenario to anticipate. Such unlikely, but highly consequential possibilities are hardest to foresee due to human bias, but grappling with them can expand strategic imagination and foster more adaptive, option-rich planning.

These biases and constraints can manifest at all levels of business, not just in individuals. Similar constraints can also impact a group or team, where the innate collective drift towards groupthink limits idea diversity. At an organizational level, the prevailing culture can also entrench certain paths, sidelining new ideas, and leading to institutional inertia. The use of AI can help overcome these constraints, in part, by allowing organizations to better identify unknown unknowns. Once transformed into known unknowns, it becomes easier to get ahead of such possibilities by building flexibility into plans. 

Extending the reach of strategic planning tactics

The cost associated with strategic planning can also make it infeasible to gather all of the stakeholders needed to game out dozens of possible scenarios. Such costs can affect the frequency with which companies assemble their brain trust, or make it less likely that these exercises are conducted organization-wide. Yet the more frequently organizations can engage in scenario planning and war gaming, the better prepared they can be for navigating unforeseen, exogenous shocks to their business. 

As complements to live strategy sessions, LLM-powered ABMs are easy to use, cheap to deploy, and scalable. As a result, they can facilitate the spread of the successful strategic planning tactics to a wider range of teams within a business (and beyond to new organizations) as well as more frequent reassessments of strategic plans and decisions.

Facilitating convergence

LLM-powered ABM can’t and shouldn’t replace classic, in-person strategy sessions because these face-to-face interactions help leaders unite around a shared strategic direction. But these new tools can help make that happen by building confidence in an organization’s strategic decisions. Simulations can be run repeatedly, eliciting patterns where a sort of Venn diagram of commonalities across scenarios emerges. While frequency doesn’t necessarily correspond to probability, it can nudge attention toward previously overlooked paths and help alignment on new strategies.

When we compared the output of our LLM-powered, multi-agent ABM with strategy workshops held with a top life insurance company, for instance, our simulations arrived at three of the same strategic recommendations that the human-led workshops produced, allowing the company’s leaders to go forward with additional confidence in those decisions. The LLM-powered ABM simulations also pointed to two new options that hadn’t come up during in-person workshops, including the strengthening of workforce training in emotional intelligence and AI literacy. 

How to start, and why do it now

For organizations that want to get started, the first step is to augment strategic decision-making processes with multi-agent GenAI platforms. This doesn’t require starting from scratch; leaders should prioritize using existing frameworks to help establish inter-agent goals, context, and dynamics. It’s important to use the platform before and alongside existing strategic planning sessions, and to run it at scale in order to find the Venn diagram of most resilient strategic steps. Those results can then be used to build consensus on strategic decisions, to harness and maximize the potential of the future rather than fearing the uncertainty that comes with it.

Starting now can help make AI a routine input, helping organizations acclimate to an environment of higher-frequency change and adaptation informed by real-time learning. Evidence shows that resilience and optionality are now more important than they have been in decades. The sooner that a company can upgrade its strategic planning and foresight capabilities, the more likely it’ll be to thrive in a world of heightened uncertainty.

***

Read other Fortune columns by François Candelon.

François Candelon is a partner at private equity firm Seven2 and the former global director of the BCG Henderson Institute.

Leonid Zhukov is the director of the BCG X AI Science Institute and is based in BCG’s Dubai office.

Max Struever is a principal engineer at BCG X and an ambassador at the BCG Henderson Institute.

Alan Iny is a partner at the Boston Consulting Group and director of Creativity & Scenarios, and is the co-author of Thinking in New Boxes.

Elton Parker is a partner at the Boston Consulting Group and associate director of its Uncertainty Advantage team.

The authors would like to thank Nick D’Intino for his contributions to this article.

Some of the companies mentioned in this column are past or present clients of the authors’ employers.



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Senate Dems’ plan to fix Obamacare premiums adds nearly $300 billion to deficit, CRFB says

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December 5, 2025

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The Committee for a Responsible Federal Budget (CRFB) is a nonpartisan watchdog that regularly estimates how much the U.S. Congress is adding to the $38 trillion national debt.

With enhanced Affordable Care Act (ACA) subsidies due to expire within days, some Senate Democrats are scrambling to protect millions of Americans from getting the unpleasant holiday gift of spiking health insurance premiums. The CRFB says there’s just one problem with the plan: It’s not funded.

“With the national debt as large as the economy and interest payments costing $1 trillion annually, it is absurd to suggest adding hundreds of billions more to the debt,” CRFB President Maya MacGuineas wrote in a statement on Friday afternoon.

The proposal, backed by members of the Senate Democratic caucus, would fully extend the enhanced ACA subsidies for three years, from 2026 through 2028, with no additional income limits on who can qualify. Those subsidies, originally boosted during the pandemic and later renewed, were designed to lower premiums and prevent coverage losses for middle‑ and lower‑income households purchasing insurance on the ACA exchanges.

CRFB estimated that even this three‑year extension alone would add roughly $300 billion to federal deficits over the next decade, largely because the federal government would continue to shoulder a larger share of premium costs while enrollment and subsidy amounts remain elevated. If Congress ultimately moves to make the enhanced subsidies permanent—as many advocates have urged—the total cost could swell to nearly $550 billion in additional borrowing over the next decade.

Reversing recent guardrails

MacGuineas called the Senate bill “far worse than even a debt-financed extension” as it would roll back several “program integrity” measures that were enacted as part of a 2025 reconciliation law and were intended to tighten oversight of ACA subsidies. On top of that, it would be funded by borrowing even more. “This is a bad idea made worse,” MacGuineas added.

The watchdog group’s central critique is that the new Senate plan does not attempt to offset its costs through spending cuts or new revenue and, in their view, goes beyond a simple extension by expanding the underlying subsidy structure.

The legislation would permanently repeal restrictions that eliminated subsidies for certain groups enrolling during special enrollment periods and would scrap rules requiring full repayment of excess advance subsidies and stricter verification of eligibility and tax reconciliation. The bill would also nullify portions of a 2025 federal regulation that loosened limits on the actuarial value of exchange plans and altered how subsidies are calculated, effectively reshaping how generous plans can be and how federal support is determined. CRFB warned these reversals would increase costs further while weakening safeguards designed to reduce misuse and error in the subsidy system.

MacGuineas said that any subsidy extension should be paired with broader reforms to curb health spending and reduce overall borrowing. In her view, lawmakers are missing a chance to redesign ACA support in a way that lowers premiums while also improving the long‑term budget outlook.

The debate over ACA subsidies recently contributed to a government funding standoff, and CRFB argued that the new Senate bill reflects a political compromise that prioritizes short‑term relief over long‑term fiscal responsibility.

“After a pointless government shutdown over this issue, it is beyond disappointing that this is the preferred solution to such an important issue,” MacGuineas wrote.

The off-year elections cast the government shutdown and cost-of-living arguments in a different light. Democrats made stunning gains and almost flipped a deep-red district in Tennessee as politicians from the far left and center coalesced around “affordability.”

Senate Minority Leader Chuck Schumer is reportedly smelling blood in the water and doubling down on the theme heading into the pivotal midterm elections of 2026. President Donald Trump is scheduled to visit Pennsylvania soon to discuss pocketbook anxieties. But he is repeating predecessor Joe Biden’s habit of dismissing inflation, despite widespread evidence to the contrary.

“We fixed inflation, and we fixed almost everything,” Trump said in a Tuesday cabinet meeting, in which he also dismissed affordability as a “hoax” pushed by Democrats.​

Lawmakers on both sides of the aisle now face a politically fraught choice: allow premiums to jump sharply—including in swing states like Pennsylvania where ACA enrollees face double‑digit increases—or pass an expensive subsidy extension that would, as CRFB calculates, explode the deficit without addressing underlying health care costs.



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Netflix–Warner Bros. deal sets up $72 billion antitrust test

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December 5, 2025

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Netflix Inc. has won the heated takeover battle for Warner Bros. Discovery Inc. Now it must convince global antitrust regulators that the deal won’t give it an illegal advantage in the streaming market. 

The $72 billion tie-up joins the world’s dominant paid streaming service with one of Hollywood’s most iconic movie studios. It would reshape the market for online video content by combining the No. 1 streaming player with the No. 4 service HBO Max and its blockbuster hits such as Game Of Thrones, Friends, and the DC Universe comics characters franchise.  

That could raise red flags for global antitrust regulators over concerns that Netflix would have too much control over the streaming market. The company faces a lengthy Justice Department review and a possible US lawsuit seeking to block the deal if it doesn’t adopt some remedies to get it cleared, analysts said.

“Netflix will have an uphill climb unless it agrees to divest HBO Max as well as additional behavioral commitments — particularly on licensing content,” said Bloomberg Intelligence analyst Jennifer Rie. “The streaming overlap is significant,” she added, saying the argument that “the market should be viewed more broadly is a tough one to win.”

By choosing Netflix, Warner Bros. has jilted another bidder, Paramount Skydance Corp., a move that risks touching off a political battle in Washington. Paramount is backed by the world’s second-richest man, Larry Ellison, and his son, David Ellison, and the company has touted their longstanding close ties to President Donald Trump. Their acquisition of Paramount, which closed in August, has won public praise from Trump. 

Comcast Corp. also made a bid for Warner Bros., looking to merge it with its NBCUniversal division.

The Justice Department’s antitrust division, which would review the transaction in the US, could argue that the deal is illegal on its face because the combined market share would put Netflix well over a 30% threshold.

The White House, the Justice Department and Comcast didn’t immediately respond to requests for comment. 

US lawmakers from both parties, including Republican Representative Darrell Issa and Democratic Senator Elizabeth Warren have already faulted the transaction — which would create a global streaming giant with 450 million users — as harmful to consumers.

“This deal looks like an anti-monopoly nightmare,” Warren said after the Netflix announcement. Utah Senator Mike Lee, a Republican, said in a social media post earlier this week that a Warner Bros.-Netflix tie-up would raise more serious competition questions “than any transaction I’ve seen in about a decade.”

European Union regulators are also likely to subject the Netflix proposal to an intensive review amid pressure from legislators. In the UK, the deal has already drawn scrutiny before the announcement, with House of Lords member Baroness Luciana Berger pressing the government on how the transaction would impact competition and consumer prices.

The combined company could raise prices and broadly impact “culture, film, cinemas and theater releases,”said Andreas Schwab, a leading member of the European Parliament on competition issues, after the announcement.

Paramount has sought to frame the Netflix deal as a non-starter. “The simple truth is that a deal with Netflix as the buyer likely will never close, due to antitrust and regulatory challenges in the United States and in most jurisdictions abroad,” Paramount’s antitrust lawyers wrote to their counterparts at Warner Bros. on Dec. 1.

Appealing directly to Trump could help Netflix avoid intense antitrust scrutiny, New Street Research’s Blair Levin wrote in a note on Friday. Levin said it’s possible that Trump could come to see the benefit of switching from a pro-Paramount position to a pro-Netflix position. “And if he does so, we believe the DOJ will follow suit,” Levin wrote.

Netflix co-Chief Executive Officer Ted Sarandos had dinner with Trump at the president’s Mar-a-Lago resort in Florida last December, a move other CEOs made after the election in order to win over the administration. In a call with investors Friday morning, Sarandos said that he’s “highly confident in the regulatory process,” contending the deal favors consumers, workers and innovation. 

“Our plans here are to work really closely with all the appropriate governments and regulators, but really confident that we’re going to get all the necessary approvals that we need,” he said.

Netflix will likely argue to regulators that other video services such as Google’s YouTube and ByteDance Ltd.’s TikTok should be included in any analysis of the market, which would dramatically shrink the company’s perceived dominance.

The US Federal Communications Commission, which regulates the transfer of broadcast-TV licenses, isn’t expected to play a role in the deal, as neither hold such licenses. Warner Bros. plans to spin off its cable TV division, which includes channels such as CNN, TBS and TNT, before the sale.

Even if antitrust reviews just focus on streaming, Netflix believes it will ultimately prevail, pointing to Amazon.com Inc.’s Prime and Walt Disney Co. as other major competitors, according to people familiar with the company’s thinking. 

Netflix is expected to argue that more than 75% of HBO Max subscribers already subscribe to Netflix, making them complementary offerings rather than competitors, said the people, who asked not to be named discussing confidential deliberations. The company is expected to make the case that reducing its content costs through owning Warner Bros., eliminating redundant back-end technology and bundling Netflix with Max will yield lower prices.



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The rise of AI reasoning models comes with a big energy tradeoff

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December 5, 2025

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Nearly all leading artificial intelligence developers are focused on building AI models that mimic the way humans reason, but new research shows these cutting-edge systems can be far more energy intensive, adding to concerns about AI’s strain on power grids.

AI reasoning models used 30 times more power on average to respond to 1,000 written prompts than alternatives without this reasoning capability or which had it disabled, according to a study released Thursday. The work was carried out by the AI Energy Score project, led by Hugging Face research scientist Sasha Luccioni and Salesforce Inc. head of AI sustainability Boris Gamazaychikov.

The researchers evaluated 40 open, freely available AI models, including software from OpenAI, Alphabet Inc.’s Google and Microsoft Corp. Some models were found to have a much wider disparity in energy consumption, including one from Chinese upstart DeepSeek. A slimmed-down version of DeepSeek’s R1 model used just 50 watt hours to respond to the prompts when reasoning was turned off, or about as much power as is needed to run a 50 watt lightbulb for an hour. With the reasoning feature enabled, the same model required 7,626 watt hours to complete the tasks.

The soaring energy needs of AI have increasingly come under scrutiny. As tech companies race to build more and bigger data centers to support AI, industry watchers have raised concerns about straining power grids and raising energy costs for consumers. A Bloomberg investigation in September found that wholesale electricity prices rose as much as 267% over the past five years in areas near data centers. There are also environmental drawbacks, as Microsoft, Google and Amazon.com Inc. have previously acknowledged the data center buildout could complicate their long-term climate objectives. 

More than a year ago, OpenAI released its first reasoning model, called o1. Where its prior software replied almost instantly to queries, o1 spent more time computing an answer before responding. Many other AI companies have since released similar systems, with the goal of solving more complex multistep problems for fields like science, math and coding.

Though reasoning systems have quickly become the industry norm for carrying out more complicated tasks, there has been little research into their energy demands. Much of the increase in power consumption is due to reasoning models generating much more text when responding, the researchers said. 

The new report aims to better understand how AI energy needs are evolving, Luccioni said. She also hopes it helps people better understand that there are different types of AI models suited to different actions. Not every query requires tapping the most computationally intensive AI reasoning systems.

“We should be smarter about the way that we use AI,” Luccioni said. “Choosing the right model for the right task is important.”

To test the difference in power use, the researchers ran all the models on the same computer hardware. They used the same prompts for each, ranging from simple questions — such as asking which team won the Super Bowl in a particular year — to more complex math problems. They also used a software tool called CodeCarbon to track how much energy was being consumed in real time.

The results varied considerably. The researchers found one of Microsoft’s Phi 4 reasoning models used 9,462 watt hours with reasoning turned on, compared with about 18 watt hours with it off. OpenAI’s largest gpt-oss model, meanwhile, had a less stark difference. It used 8,504 watt hours with reasoning on the most computationally intensive “high” setting and 5,313 watt hours with the setting turned down to “low.” 

OpenAI, Microsoft, Google and DeepSeek did not immediately respond to a request for comment.

Google released internal research in August that estimated the median text prompt for its Gemini AI service used 0.24 watt-hours of energy, roughly equal to watching TV for less than nine seconds. Google said that figure was “substantially lower than many public estimates.” 

Much of the discussion about AI power consumption has focused on large-scale facilities set up to train artificial intelligence systems. Increasingly, however, tech firms are shifting more resources to inference, or the process of running AI systems after they’ve been trained. The push toward reasoning models is a big piece of that as these systems are more reliant on inference.

Recently, some tech leaders have acknowledged that AI’s power draw needs to be reckoned with. Microsoft CEO Satya Nadella said the industry must earn the “social permission to consume energy” for AI data centers in a November interview. To do that, he argued tech must use AI to do good and foster broad economic growth.



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