How AI is transforming strategy work – perspectives from Eyal Steinitz and Julie Møller Lønhart

AI is not only expanding the possibilities for analysis and decision-making, but also fundamentally changing how strategy work is structured and executed, including the need for external advisors.

Speed and quality can be significantly enhanced with AI
The traditional approach to strategy, where the process is divided into analysis, formulation, implementation, and follow-up in fixed intervals, often with one or more years between cycles — is now under significant pressure and is no longer sufficient for most companies.

Increasing complexity, the pace of change, and a substantial growth in the volume of available data mean that the value of strategic work now lies in the ability to rapidly translate validated data into insight—and insight into action.
“Value creation lies in driving the right change,” says Eyal Steinitz.

The analysis phase: Data collection is automated, focus shifts to validation and prioritization
Traditionally, analytical work has been a resource-intensive discipline focused on collecting and structuring information. With AI agents, this data collection and processing is significantly automated. AI can efficiently combine internal and external data points, identify patterns, monitor competitors, and analyze complex parameters such as customer behavior and market trends.

As a result, the effort required for pure data collection moves towards zero. Instead, the focus shifts to validating, interpreting, and prioritizing. Because AI can produce convincing outputs that may contain factual errors or be based on biased sources, human quality assurance is critical. Moreover, AI systems often lack critical judgment and have a tendency to confirm rather than challenge. The real value is therefore created through the ability to critically assess AI-generated outputs and translate them into action.

Strategy formulation: More scenarios and a stronger basis for decision-making
Where strategic scenarios were traditionally developed based on experience and intuition, AI enables a significant expansion of the decision space through a much broader set of scenarios and simulations. AI can rapidly surface risks, dependencies, and test the assumptions underpinning the strategy.

However, AI cannot replace strategic judgment or the ability to make difficult decisions under uncertainty. Strategic work still requires human capacity to understand the specific context, distinguish what matters most, and prioritize with discipline and conviction.

Execution: AI supports communication effectively, but does not ensure ownership and behavioral change

The greatest challenge in strategy work is rarely formulation – it is execution and organizational anchoring. Strategy is often overtaken by day-to-day operations, where immediate priorities dominate.

Here, AI can support the translation of strategy into concrete day-to-day actions and make it relevant for individual leaders and employees. AI also enables greater accessibility, for example by allowing employees to interact with an internal AI assistant to receive guidance aligned with their role and tasks.

However, technology alone does not solve the challenge of execution. At its core, executing strategy is a change management process. AI can support communication, but it cannot replace the leadership required to ensure understanding, ownership, and behavioral change.

“A critical factor in making strategy succeed is that it is understood across the organization and that employees can translate it into their daily work. AI makes this translation easier” according to Julie Møller Lønhart.

Follow-up: AI enables continuous monitoring and adjustment of strategy

Strategic follow-up has traditionally been tied to fixed, often rigid intervals, such as quarterly or annual reviews.

Today, AI agents enable fully continuous and automated follow-up. Performance can be monitored in real time, deviations identified immediately, and the decision base continuously updated. At the same time, strategic scenarios and options can be quickly adjusted in response to sudden market changes.

Follow-up should no longer be seen as a periodic activity, but as an integrated, dynamic, and ongoing part of how organizations manage performance. It becomes anchored in real-time data and directly linked to leadership decision processes.

“With AI, strategy can become a continuous process, where it is constantly assessed and adjusted,”
adds Eyal Steinitz.

External Advisors: From analysis to domain expertise and change leadership

The role of external strategy consultants is fundamentally changing with the increasing use of AI. Formerly, a significant part of consulting firms’ value proposition have been in their access to data and extensive analytical capacity. Today, organizations can generate complex analyses and robust decision foundations themselves using AI – tasks that previously required teams of consultants.

This shifts the consultant role towards areas that technology cannot address. As traditional analytical work diminishes, the focus changes significantly. In the future, value will lie less in choosing the right consulting firm and more in selecting the right individual consultants.

Value no longer resides in large analytical capacity, but in identifying the right change agent and domain expert – someone who can move organizations from insight to decision, and from strategy formulation to real organizational anchoring. It lies in personal experience, industry knowledge, and the ability to challenge leadership assumptions and facilitate difficult trade-offs.

AI raises the baseline of strategy work, but it also introduces new risks

The greatest danger is confusing speed with quality and being misled by AI-generated outputs that appear well-argued but lack contextual relevance. More data and simulated scenarios do not, in themselves, lead to better decisions. Human judgment – the ability to cut through noise and focus on what matters – remains critical to achieving success in strategy work.