A recent executive roundtable hosted by the Executive Council for Leading Change (ECLC) and led by Jason Smith (AI Strategy Lead, Publicis Groupe), brought together leaders from diverse industries to discuss the strategies behind deploying generative AI. These leaders shared their experiences, offering a closer look at the challenges and opportunities that come with AI implementation, governance, and talent management. The key takeaways included how to integrate AI in a way that supports long-term organizational goals, while addressing the risks and impact on the workforce.
Strategic Considerations for Generative AI Deployment
1. Laying the Groundwork
Deploying generative AI successfully starts with laying a strong foundation. It’s crucial to align AI initiatives with the overall strategic goals of the organization. Leaders at the roundtable emphasized the importance of identifying AI use cases that bring tangible value to the business.
Several participants recommended partnering with AI technology providers or data experts early on. Collaborating with the right partners can help companies overcome the steep learning curve of implementing AI and speed up the adoption process. This partnership approach ensures that AI applications are customized to the organization’s needs and scale.
2. Governance, Ethics, and Risk Management
A big focus of the conversation was the importance of governance. With the power of AI comes significant responsibility, particularly when it comes to addressing ethical concerns and ensuring compliance with regulations. Generative AI can sometimes create biases or lead to unintended consequences, so it’s essential to build frameworks that manage these risks from the start.
Leadership commitment to generative AI initiatives varies significantly based on organizational context and use case. For instance:
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Customer-Facing Applications: Leaders are often more hesitant to invest in experiments involving customer data due to stringent concerns around data privacy, trust, and regulatory compliance. In industries like banking, where trust is paramount, this reluctance can slow innovation in outward-facing use cases.
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Internal Applications: In contrast, leadership tends to show stronger support for internal initiatives, such as improving developer productivity or streamlining operations, as these carry lower external risks and are perceived as safer entry points for AI experimentation.
This variability highlights the need for strategic clarity and robust governance to address leadership concerns and ensure alignment across different domains of AI application.
Leaders also suggested a phased rollout of AI projects, starting with smaller-scale pilots. This allows organizations to test AI systems, refine them, and build confidence before rolling them out on a larger scale. Clear, transparent communication about the risks of AI and establishing ground rules for its use is crucial to maintaining trust across the organization.
3. Talent and Change Management
Successful AI deployment isn’t just about technology—it’s about people. As AI reshapes workflows and decision-making processes, it requires an entirely new approach to talent management. Leaders agreed that addressing the talent gap is key. Employees need to be upskilled to work effectively with AI tools, so organizations must invest in training programs to ensure their workforce is ready for the changes ahead.
One leader shared how they created an ‘AI Academy’ to help employees at all levels gain expertise in using AI in their day-to-day roles. Leaders also noted the importance of addressing employees’ concerns about job displacement. Framing AI adoption as an opportunity to enhance productivity, rather than replace jobs, can help ease resistance to change and build excitement around new possibilities. Leaders should articulate benefits of AI to the organization and its people, helping employees see how AI can improve their work rather than disrupt it.
4. Data and Infrastructure
For AI to work, data is everything. But AI won’t be effective unless the data behind it is clean, structured, and properly governed. Many leaders at the roundtable emphasized that before deploying AI, organizations need to focus on data management—making sure they have high-quality data that aligns with business objectives.
In addition, AI tools should fit seamlessly into the company’s existing tech infrastructure. Rather than seeing AI as a standalone solution, it should enhance and integrate with current systems to drive better outcomes across various business functions.
5. Practical Frameworks for Implementing AI
Use Case Charter
To keep AI deployments on track, it’s helpful to follow structured frameworks that guide the decision-making process. One tool that was discussed at the roundtable was the Use Case Charter. This is a practical framework that helps leaders:
Identify business challenges AI can solve. Estimate the potential impact of those solutions. Determine the resources and technologies needed. Outline risks and set success metrics.
Prioritization
Another was to prioritize high-impact projects. Not all AI projects should be implemented at once. Leaders should prioritize projects that offer the highest ROI and focus on initiatives that are achievable in the short-term. For example, AI-powered chatbots for customer service or AI-driven analytics for forecasting were cited as quick wins that can show the potential of AI without overwhelming the organization.
Key Learnings and Challenges
Focus on Low-Hanging Fruit and Quick ROI
Organizations are feeling significant pressure to demonstrate results quickly when implementing generative AI. Identifying and pursuing low-hanging fruit—applications with clear and achievable outcomes—has proven to be a pragmatic starting point. This approach helps build momentum by showing tangible returns on investment early in the adoption process while creating a foundation for broader implementation.
Marketing and customer service have emerged as quick win areas for AI adoption, particularly for content generation, moderation, and augmenting customer service operations. However, privacy and regulatory considerations in sectors like financial services and MedTech introduce complexities.
Craft a Clear Top-down Strategy
A recurring theme across industries is the lack of a unified strategy for AI adoption. Instead, initial experiments are often driven by individual business units or department heads, following compliance and governance requirements.
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Organizations often rely on patchwork approaches driven by individual business units or markets, particularly for customer-facing applications.
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Across-the-board strategies are often perceived as too high-level, lacking actionable plans for actual implementation.
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The adoption of AI tools and technologies is influenced by market-specific factors, such as customer priorities and regional technological openness. For example, regions like the Nordics or Gulf countries may be more proactive in adopting proven technologies.
While this decentralized approach allows for experimentation and learning, it may result in fragmented efforts and missed opportunities. A more structured, top-down strategy could provide clear direction, align resources, and accelerate broader adoption.
Industry-Specific Challenges
Different industries face unique challenges in adopting generative AI. In banking, for example, the emphasis on risk management and compliance shapes the nature of AI initiatives. Regulatory requirements and the need to protect customer trust often dictate a cautious approach, particularly for customer-facing applications. Conversely, in technology or advisory sectors, there may be more flexibility and openness to experimentation, provided governance structures are in place.
Examples of successful AI applications in internal processes demonstrate potential but highlight challenges in scaling adoption:
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A healthcare organization used AI to evaluate tenders by analyzing data from past bids, customer feedback, and tender performance. The tool predicted the likelihood of winning tenders (high, medium, low), enabling teams to prioritize resources effectively.
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Despite initial success, broader adoption stalled due to lengthy feedback cycles (often over a year) that made it difficult to demonstrate clear value to leadership. As a result, these tools became localized successes within specific teams rather than organization-wide solutions.
Measuring Success and Learning from Examples
Participants emphasized the importance of defining business outcomes and measurement frameworks early in the adoption process. A case study of Klarna was highlighted as an example of a company that successfully aligned its AI strategy with customer service goals, setting clear metrics to assess effectiveness.
Without such deliberate planning, organizations risk fragmented efforts and difficulty demonstrating the value of their AI investments.
Conclusion
Generative AI deployment is not a technological challenge but an organizational transformation journey. Success requires a holistic approach that balances strategic vision, technological capability, talent development, and a culture of continuous innovation. By embracing these insights, organizations can not only deploy AI effectively but also ensure it aligns with their business goals, respects ethical standards, and enhances human capabilities within the workforce.
The Executive Council for Leading Change
The Executive Council for Leading Change (ECLC) is a global organization that brings executives together to redefine the landscape of organizational change and transformation. Our council's aim is to advance strategic leadership expertise in the realm of corporate change by connecting visionary leaders. It’s a place where leaders responsible for significant change initiatives can collaborate, plan, and create practical solutions for intricate challenges in leading large organizations through major shifts.
In a world where change is constant, we recognize its crucial role in driving business success. ECLC’s mission is to create a community where leaders can excel in guiding their organizations through these dynamic times.
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