Why AI Is Becoming Interim Management’s Greatest Strategic Ally

By Prof. Guido Stein and María Belén del Campo

AI is transforming interim management by shifting talent strategies from reactive to predictive models, enhancing the strategic value of human expertise through AI-driven capabilities.

Traditional approaches to talent management have proven insufficient to address new challenges arising from automation, digitalization, and the growing need for organizational agility. In this context, GenAI and strategic foresight are emerging as tools for modernizing HR models. Here, Guido Stein and María Belén del Campo of IESE consider the benefits, and the challenges, of the phenomenon from an interim management perspective.

A time of transformation

Traditionally, companies have managed talent by responding to needs only after they have emerged. However, in an environment characterized by uncertainty, the rapid pace of technological change, and the constant emergence of new skills, organizations would do well to use forward-looking models based on trend analysis and the ability to anticipate future scenarios. This means not only reacting to the market but also staying ahead of it through strategic talent planning aligned with business objectives.

In this regard, talent management models play an important role. These models are structured frameworks for managing an organization’s human resources, aimed at identifying and developing employee skills and competencies in alignment with organizational needs, thereby increasing their effectiveness at work. However, traditional approaches to talent management have proven insufficient to address new challenges arising from automation, digitalization, and the growing need for organizational agility. This limitation reflects an even deeper transformation: the shift in talent management from an administrative function to a key strategic element of business competitiveness.

In this context, generative artificial intelligence (GenAI) and strategic foresight  (see Figure 1) are emerging as fundamental tools for modernizing HR models as part of an approach that combines strategic vision with the use of AI. This approach allows organizations to anticipate needs, adapt to changing environments, and drive innovation in talent management.

AI as a driver of change

The use of AI-based tools enables organizations to adopt more forward-looking approaches. Used alongside strategic analysis methodologies, these tools facilitate the exploration of future scenarios, the identification of trends, and the design of organizational strategies aimed at anticipating and managing change. Consequently, decision-making is shifting away from relying solely on historical data and toward using predictive models to improve organizations’ ability to anticipate and adapt.

AI is profoundly transforming the traditional functions of HR departments. Its application makes it possible to automate administrative processes, optimize talent management through predictive analytics, and personalize employee learning and development experiences. In addition to increasing process efficiency, this improves the employee experience and the effectiveness of talent policies. Evidence shows that the combination of AI and strategic foresight has significant effects on talent management, particularly by enhancing decision-making, increasing organizational efficiency, and fostering business innovation.

Interim management in the age of AI

The relationship between AI and interim management is especially important in the context of today’s evolving labor market. Several studies show that AI technologies, particularly GenAI, have been gradually integrated into administrative and office-based roles. These technologies have redefined the nature of these roles, as automated tools have become increasingly embedded in how work is performed.

However, Hosseini and Lichtinger (2025) highlight a particularly significant phenomenon: AI’s impact is not uniform across levels of professional seniority. Specifically, while employment among junior profiles has declined—with a drop of up to 7.7 percent following the adoption of tools such as ChatGPT-3.5—demand for senior profiles has continued to grow.

AI is driving a shift toward work models in which experience and professional judgment become more valuable. This suggests that tasks performed by senior professionals—such as strategic decision-making, oversight of results, interpretation of complex information, and management of client relationships, among others—are not easily replaced by AI systems.

This shift has direct implications for interim management. In an environment where companies increasingly demand flexibility, adaptability, and specialization to address specific projects—especially digital transformation initiatives—interim managers take on a strategic role. Their experience, leadership ability, and results orientation enable them to manage contexts of change while integrating AI tools to support decision-making and project execution.

When an organization is able to structure and systematize its accumulated experience—comparable cases, lessons learned, methodologies, or playbooks—and make that experience available to the executive, the interim manager no longer relies solely on individual experience but begins to operate on the basis of collective intelligence. This transforms interim management from individual expertise into an organizational capability.

By combining the strategic experience of senior professionals with the potential of AI, interim management is establishing itself as an attractive solution in the new technology landscape. This symbiosis enables organizations to carry out their transformation processes more effectively and optimize both management and the achievement of their business objectives.

Applications of AI in interim management

AI can add value to interim management at different stages of the management cycle—from identifying the most suitable talent to supporting decision-making, strategic capability planning, and managing the knowledge generated during projects. The main applications of AI in this context are:

1. Identification and selection of talent

In interim management, AI plays a fundamental role in the processes used to identify and select interim managers. The use of analytics tools and data models enables companies to assess their existing competencies more precisely and to identify talent gaps that could limit their ability to move new projects or strategic initiatives forward. AI facilitates comparison of internal capabilities with those required for particular projects, helping organizations select suitable professionals to take on interim executive roles in specific transformation or growth contexts.

AI helps improve the strategic allocation of talent and ensure that decisions related to bringing in interim managers are aligned with the organization’s strategic priorities.

The use of industry talent maps and competency databases makes it possible to compare the human capital of an organization with that of other companies in the same sector or with that of leading organizations. This type of analysis provides a broader perspective and facilitates the identification of professionals with relevant experience.

AI helps improve the strategic allocation of talent and ensure that decisions related to bringing in interim managers are aligned with the organization’s strategic priorities.

2. Decision support

Interim managers must make strategic decisions about which areas to transform first, how to reorganize work processes, and how to adapt operating models to an increasingly digital environment. In this context, AI systems can provide advanced analysis of how economic sectors are evolving, potential changes in value chains, and where new business opportunities may emerge.

With access to data-driven tools that enable them to analyze complex scenarios, interim managers can make better-supported strategic decisions. The integration of AI into management processes requires redefining professional roles and leadership competencies.

3. Strategic talent planning

Strategic workforce planning is another area in which AI can add significant value to interim management. AI facilitates this process through advanced analysis of data on skills, labor market trends, and industry trends, enabling more informed and forward-looking decision-making. Building on this approach, organizations can develop a solid knowledge base about their human capital, enabling them to anticipate future needs and adjust the size and composition of their workforces more efficiently to address potential talent gaps.

Interim managers represent an agile, high-value solution for meeting specific talent needs in the short and medium term. Bringing them in enables organizations to respond quickly to key capability gaps, lead transformation processes, or manage complex projects without compromising their permanent workforce structure. In this way, interim management acts not only as an adjustment mechanism but also as a strategic tool that, supported by AI-driven predictive analytics, helps align talent planning with business objectives and strengthen organizational adaptability.

4. Knowledge management

The integration of data-driven tools into talent management enables organizations to capture, analyze, and reuse the knowledge generated during strategic projects, thereby reducing the risk of information loss when interim managers complete their engagements.

To make knowledge management effective, organizations must integrate AI into their core processes, enhance employee interaction with digital tools, and redesign talent strategies around data-driven insights. Together, these efforts strengthen the organization’s ability to capture, transfer, and apply knowledge more systematically, ensuring that strategic learning is retained and aligned with long-term business objectives.

This strategic approach depends on three interconnected dimensions: understanding a changing environment by recognizing shifts in industry, technology, and workforce dynamics; identifying new sources of value by exploring opportunities to address emerging needs and challenges through AI; and developing distinctive competencies that allow organizations to translate knowledge into sustainable competitive advantage.

5. Organizational adaptation and transformation

AI supports the analysis of future scenarios and workforce planning based on different scenarios for market evolution or technology adoption. Using models that forecast talent supply and demand, companies can anticipate potential capability gaps and design targeted strategies to address them. In addition, scenario analysis makes it possible to assess how the adoption of new technologies—such as GenAI—can transform certain organizational functions and require new competencies. In this context, interim management can play a key role as a mechanism for transferring specialized knowledge and helping organizations adapt to new technological and organizational models.

Overall, it is essential to understand AI not only as a means of storing or analyzing information—comparable cases, lessons learned, methodologies, or patterns of success and failure—but also as a tool that makes it possible to structure and activate the knowledge accumulated by an organization.

Challenges and risks of AI in interim management

Although AI opens up new opportunities to strengthen interim management, its adoption also poses significant challenges. Its impact on employment, system reliability, and organizational culture change calls for a gradual, critical approach to implementation that is aligned with business strategy.

1. AI’s uneven impact on employment and organizational structure

Automation tends to affect more junior professionals to a greater extent, reducing opportunities to enter the labor market, while senior roles maintain—and even strengthen—their strategic relevance. This phenomenon can create an imbalance in the talent pyramid, making it more difficult to build an internal talent pipeline and increasing dependence on experienced external professionals, such as interim managers.

Interim management may come under pressure from two dynamics. On the one hand, demand for experienced interim managers capable of leading AI-supported transformation processes is increasing; on the other, a smaller pool of mid-level and junior talent may limit the capacity to execute these projects, creating operational bottlenecks and making it more difficult to transfer knowledge within the organization.

2. Reliability and bias in AI systems

AI models depend heavily on the data used to train them. This can cause the models to reproduce or even amplify existing biases in talent selection and management processes. In the context of interim management, this can lead to poor decisions in the identification and selection of executive-level talent, compromising the quality of interim leadership and, consequently, the success of strategic projects.

For interim managers, whose distinctive contribution lies precisely in their experience, judgment, and adaptability, excessive automation could limit their latitude to act and their strategic value within the organization.

3. Cultural and organizational transformation

The integration of AI into talent management requires a deep cultural and organizational transformation. It is not simply a matter of adopting new technological tools, but of redefining processes, roles, and leadership models. Resistance to change may arise, especially in environments where AI adoption is not accompanied by a clear strategy or appropriate change management. For interim managers, this means leading technical projects while also acting as change agents, managing uncertainty, and helping teams adapt to new ways of working.

These challenges show that, although AI offers significant opportunities to improve interim management, its implementation must be approached critically and strategically, taking into account both its benefits and its potential risks.

It is also important to note that not all interim management models are affected in the same way. While companies that operate primarily as intermediaries—essentially, as people providers— may see the value they add decline as AI tools become increasingly effective at sourcing, matching, and assessing talent profiles, firms specializing in high-value-added projects—grounded in rigorous selection, a proprietary methodology, ongoing support, and the ability to capitalize on knowledge—show greater resilience. This is because their value proposition is not centered on intermediation, but on execution, trust, and delivering demonstrable results.

Concluding remarks

AI is fundamentally reshaping labor market dynamics and talent management, driving a transition from reactive models toward strategic and predictive approaches. This new environment requires organizations to anticipate needs, adapt their structures, and rethink the value of human capital as a key factor in competitiveness.

In this context, interim management emerges as a strategic partner in organizational transformation efforts. Interim managers combine deep experience and strategic vision with a high degree of adaptability. They can further strengthen their contributions by using AI tools.

The integration of AI into interim management opens up new opportunities in areas such as talent identification and selection, data-driven decision-making, strategic workforce planning, and knowledge management. This combination enables organizations to improve efficiency, optimize resource allocation, and navigate complex, changing environments with greater agility.

However, these advances also bring significant challenges: the uneven impact of AI across levels of professional seniority, the risks associated with bias and system reliability, and the need for deep cultural and organizational transformation all require careful, strategic implementation. It is also likely that the competitive advantage of leading firms will depend not only on their network of executives but also on the combination of senior talent, a proven methodology, and accumulated knowledge enhanced by AI.

Ultimately, the competitive advantage of organizations will depend on their ability to combine AI-driven capabilities with human judgment, experience,
and leadership.

About the Authors

Prof. Guido Stein is Professor of Managing People in Organizations and Director of the Negotiation Unit at IESE. He has a PhD in Philosophy (Management) and an MBA from IESE. He is also a Partner at Inicia Corporate, specialized in M&A and Corporate Finance.

María Belén del Campo is a Sociologist specialized in Human Resources. Currently a Researcher at IESE Business School alongside Professor Guido Stein.