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As these powerful tools proliferate, insights alone are no longer a source of competitive advantage. With more companies and competitors building AI capabilities in strategy, what will set them apart is how effectively they integrate AI into decision-making and act on what they learn. The payoff promises to be tremendous, giving organizations clarity to move faster and with greater confidence.
To unlock this full potential, the strategy function itself must evolve. Most strategy teams still follow annual planning cycles, disconnected from day-to-day decisions. That approach can’t keep up with the speed at which AI generates insight or markets shift. To compete, strategy teams must become more dynamic and advance alongside the technology to elevate the quality of decisions for organizations.
How does an organization rewire its strategy function for an AI-first world? It begins by re-engineering how strategy works, embedding AI as a core capability across the full arc of strategic work. This means rethinking who makes decisions, how they are made, and how quickly they are acted upon. We detail four fundamental shifts toward an AI-driven strategy function with supporting examples from leading companies.
1. Build technology and data infrastructure bespoke to strategy.
Strategy has lagged in digital technology adoption compared to functions like finance or operations. Classic strategic activities like competitive analysis and opportunity search remain analog, with critical data still siloed. This slows the generation of insights and limits the quality and timeliness of strategic decisions.
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The luxury retail conglomerate LVMH illustrates how companies can begin to make this transformation. The company’s operating model is designed to provide group-level support to its more than 75 luxury brands while preserving their creative independence. The result was fragmentation across data and systems, making it difficult to apply AI at scale. Yet doing so became a strategic priority, to strengthen execution across the group and unlock opportunities brands couldn’t capture on their own.
This was resolved by designing a centralized platform in partnership with Google Cloud, with a modular architecture bringing together internal ERP and CRM data without forcing uniformity. The company launched an AI factory to develop customizable algorithms deployed across the group. This allowed it to scale tools like a generative AI assistant to more than 40,000 employees, while enabling brands to apply AI in ways consistent with its own structure and needs.
The application of AI across operations and strategy promoted cross-brand learning and visibility into previously siloed insights. LVMH’s AI assistant handles more than 2 million employee queries a month, speeding access to internal knowledge and building a shared base for future strategy decisions. Brands like Dior use predictive tools to refine customer engagement. Corporate teams apply AI to support strategic initiatives such as repositioning toward ultra-luxury, protecting margins through dynamic pricing, and safeguarding brand value through supply chain and anti-counterfeiting efforts.
2. Make strategy a dynamic and distributed capability.
AI is transforming how companies make decisions, shifting from static planning cycles to systems that adapt continuously. Instead of relying on episodic reviews, organizations are building decision-making models that respond to real-time conditions, using AI to detect signals and trigger action closer to the front lines.
This shift depends on feedback loops where insights and recommendations are generated in real time, with AI agents helping teams simulate scenarios, generate options, and adjust plans dynamically. As organizations embed AI into smaller, cross-functional teams, strategy functions can become more distributed, shifting from overly centralized planning to enable faster, coordinated moves as conditions change.
PepsiCo shows how companies are developing a more distributed strategic capability. The company lanched a digital transformation in partnership with AWS, reimagining its enterprise model, which includes one of the world’s largest distribution networks, spanning 23 brands and more than 200 countries. It is embedding AI across its commercial network, working with Salesforce to deploy Agentforce, a platform that pairs its frontline sales and promotion representatives with AI agents.
This platform enables the company’s vast frontline to feed real-time data back into the system, creating a more robust AI engine and enabling continuous feedback loops that help it advance its innovation capabilities and strategic decision-making. The aim is to close the gap between strategy and execution, allowing decisions to reflect real-world conditions without waiting for formal strategic planning cycles to catch up.
To support this shift, PepsiCo is adapting how its organization operates. Decision architectures need to be redesigned. New performance metrics are focused on speed of responsiveness and service outcomes based on AI-generated insights. As the system scales, PepsiCo leaders say it will help the company adjust more quickly to changing conditions and better connect day-to-day execution with enterprise-level priorities.
3. Reshape strategy skillsets and roles.
AI will not make strategists obsolete, but it will raise the bar and redefine the skills and composition of effective strategy teams. Strategists will shape how AI operates, from setting decision rules to defining how it competes and learns.
Many companies are already acting on this shift. LVMH is training teams in prompt engineering to help them retrieve and apply institutional knowledge more effectively through the company’s AI assistant. PepsiCo is preparing frontline and corporate teams to work alongside AI agents, while rethinking governance to support faster, more distributed decisions. In both cases, the goal is creating new ways of working that link AI output to strategic action.
Shell provides a blueprint for reshaping strategy teams through a systematic, layered talent development approach. Its Shell.ai residency offers rotational experiences that give participants both technical AI depth and strategic perspective. Over 60% of residents have transitioned into permanent roles focused on combining AI expertise with strategic decision-making.
In parallel, Shell has launched company-wide training, enrolling thousands of employees in customized programs covering topics like machine learning and natural language processing. This is helping the energy company embed AI across its value chain, from exploration to trading, as part of a broader strategic pivot aimed at enabling new business models and cost transformation.
A key outcome of these efforts is the emergence of hybrid roles: strategy technologists, AI product owners, and domain experts who translate model outputs into strategic action. Shell now deploys over 10,000 machine learning models daily, reflecting the scale and impact of AI fluency in the strategy workforce.
4. Differentiate through better decisions and faster execution.
Once the foundations are in place, AI-driven strategy operates differently. When every competitor has access to similarly powerful tools, advantages from traditional sources of strategic insight collapse. It’s no longer just about strategy functions generating insights to decide where to play or how to win. Instead, the edge goes to companies with hybrid decision-making models, using AI to evaluate more options while combining it with human ingenuity to act decisively.
As AI systems become more powerful and autonomous, decision quality and execution speed matter more. Companies can design playbooks with distinct rules and guidelines that enhance how AI makes decisions, building systems and operating models that retrain and redeploy models quickly, and tracking how competitors are using AI. As systems learn and adapt in real time, the focus of human talent evolves. For strategists, that means clarifying which outcomes matter most and embedding those priorities into how AI makes decisions.
While this capability is still nascent in strategy functions, the financial services industry offers a glimpse of where it could head. Financial services has been reshaped by real-time infrastructure, automating high-frequency, high-stakes processes like trade execution, risk management, compliance checks, and settlement. Firms compete not just on what they do but on how fast and how precisely they do it.
At Goldman Sachs and UBS, AI assistants support decision-making by delivering faster interpretations of market shifts and enabling more consistent responses. Hedge funds and proprietary trading firms are running autonomous agents that adjust continuously in response to competitor behavior. The result is a market shaped by systems interacting in real time, each one adapting to the others.
RBC Capital Markets’ Aiden trading platform shows how firms are orchestrating this shift. Aiden uses reinforcement learning to process hundreds of real-time signals and execute millions of calculations per trade. Human traders define the objectives and constraints, and Aiden adjusts during execution, learning from market behavior and refining its actions in the moment. RBC is applying Aiden across new asset classes to improve execution speed and decision precision, building a system that competes directly on adaptability.
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When a strategy function is effective, it serves as the nerve center for consequential decisions that drive long-term growth and value creation. AI promises to be a force multiplier for that critical role in organizations.
About the Authors
Freddy Solis is a Senior Director at Innosight in Boston. fsolis@innosight.com
Ned Calder is a Managing Director at Innosight in Boston. ncalder@innosight.com
Shahriar Parvarandeh is a Senior Director at Innosight in London. sparvarandeh@innosight.com
