The rapid rise of AI has prompted businesses to rethink their operational models. It’s no longer just a question of ‘if’ but ‘how’ to integrate AI effectively. Falling behind means losing relevance, but rushing in without direction increases the risk of failure. Yet, despite the potential gains of AI, organizations struggle with aligning AI initiatives to business goals, overcoming resistance, ensuring ethical governance, and measuring success.

This executive brief shares the key strategies from industry leaders on navigating the complexities of AI transformation. Gain practical insights into navigating potential challenges and seizing the opportunities AI presents to drive meaningful and sustainable transformation.

This roundtable was held on February 19th, 2025.

Roundtable Participants

Co-led by Nellie Wartoft (CEO of Tigerhall and Chair of the ECLC) and Brittany Gunter (Chapter Lead of the ECLC)

• Abigail Ward, Elevance Health - Head of Change Management
• Alix Jules, Broadridge - AI Business Transformation Leader
• Allison Drobniak, former SVP, Chief of Staff Office of the COO
• Andrew Spector, Paramount - Senior Director, Change Management
• Angie Woodruff, TTI, Inc. - Head of Change Management
• Carey Sealy, JLL - Managing Director, Global Head of Operational Excellence
• Christina Han, Estée Lauder - Chief of Staff to EVP of Global Communications & Global Public Affairs
• Eleazar Orellana, Former Northwestern Mutual - Head of Field Planning, Strategy & Business Operations | Chief of Staff, Engineering
• Emanuele Cimica, Shell - General Manager - Business Transformation
• Eric Orlaska, Mastercard - VP, Commercial Business Operations
• Gayatri Ohri, AWS - Sr. Manager, Gen AI for Operations
• Gregoire De Chevron Villette, Former RONA - Head of Transformation
• Holly Hon, Entertainment Partners - Senior Vice President, Operations
• Iris McQuillan-Grace, OLIVER Agency - Head of Employee Experience
• Jeanette Cimino, The AES Corporation - VP of Global Change Management
• Jennifer Pawlowski, The Estée Lauder Companies Inc. - Chief of Staff / Executive Director, Strategic Initiatives & Operations
• Jennifer Blank Hecht, Symetra - VP Change Leadership
• Karen Paff, Viatris - VP, PR & Communications | Corporate Branding, M&A Integration, Data Analysis & AI Campaigns
• Kristi Bulnes, Gap - Director, Change Management | TMO
• Laurie Ditch, iMerit - Sr. Director, Organizational Strategy and Operations
• Nan Li, Condé Nast - SVP, Head of Global Transformation
• Nicholas Mudd, Cummins - Global Service Transformation Director
• Paul Ruggier, Helios Towers - Group Head of Business Excellence
• Rahul Trivedi, Transunion - VP, Business Transformation
• Raj Yelamanchili, Synopsys - Head of People Technology
• Robert Westwood, Barclays - Head of Technology Strategy and Transformation
• Roger Kenyanya, Lowe's - VP AI Strategy & Transformation, Governance, and Data Science
• Rosaline Hester, Former The Coca Cola Company - Global Change Leadership Director, Marketing Transformation Office
• Sara Jetta, Purolator Inc. - Vice President of Strategic Enablement
• Shefali Shah, JP Morgan - Managing Director, Operational Excellence
• Stacey Jo Bonenberger, JLL - Global Lead, Change and Knowledge Enablement
• Sujan Pailwan, Tech Mahindra - Vice President AI & Automation
• Sundeep Thusoo, Philips - Director, Business Transformation
• Susan Hardy, CDW - Head of Change Management & Communications, Transformation & Technology
• Tami Beheler, Eli Lily - Sr Director, Process Effectiveness + Change
• Traci Spero, Haven Tech - Chief Human Resources Officer & Strategic Communications
• Vanessa McDonald, WNS Global - SVP Change Management
• Wendy Wheeler, Koch Industries - Sr. Director, Strategy & Legal Services Innovation

Beyond the Buzz: Aligning AI Transformation with the Business Strategy

AI initiatives often get caught up in the buzz, but real success comes from aligning your initiatives with your company’s strategic objectives. Without this focus, AI risks becoming just another trendy initiative that fails to deliver on its promise.

1. Identify the Value Proposition

✔ Clarify the ‘Why’ of Change

AI isn’t a ‘nice-to-have’—it’s a means to drive results. Begin with a clear value proposition that answers why change is necessary. By identifying specific business problems that AI can solve, organizations ensure that the transformation is rooted in meaningful outcomes.

✔ Leverage Use Cases & Success Stories

Prioritize actionable AI use cases focusing on high-impact areas to avoid overloading the organization with too many initiatives at once. AI efforts should be grounded in value creation and real-world applications. Identify specific problems or inefficiencies where AI can deliver measurable impact.

“Hone in on specific use cases and success stories to help prevent AI from becoming mere buzzwords. Create alignment with overall transformation programs and look where AI can help drive value statements and communicate them broadly. ”

Carey Sealy, JLL - Managing Director, Global Head of Operational Excellence

2. Define Measurable Outcomes

Define ROI metrics to make it easier to track success and prove AI transformation values to the business, especially in elevating productivity and performance. Benchmark metrics based on industry targets and leverage them to manage executive expectations. Without these, AI efforts can quickly stray off course and become lost in the shuffle of digital initiatives.

“Establish clear, measurable AI transformation outcomes that tie directly to broader company objectives. For example, increase in revenue, improving operational inefficiencies, or enhancing the customer experience.”

Rahul Trivedi, Transunion - VP, Business Transformation

3. Prioritize and Pilot Before Scaling

Start small, scale smart, and avoid burnout. Don’t get distracted by the ‘bright and shiny’ part of AI that is irrelevant to the business. Ruthlessly prioritize and test the most impactful use cases first. Run pilots before scaling to allow teams to fine-tune processes and build momentum before committing to large-scale implementation.

“Adopt a 3-pillar framework and run AI transformation in phases—starting with building foundational knowledge, identifying top use cases, and finally, creating a community of champions.”

Gayatri Ohri, AWS - Sr. Manager, Gen AI for Operations

4. Ensure Executive Alignment

The success of AI transformation relies heavily on executive alignment. Without early buy-in from leadership, AI initiatives risk losing focus and momentum. Provide regular updates to promote a top-down understanding of AI’s impact, ensuring that AI transformation is seen as a strategic business priority, not just another tech project.

"In the Global Marketing Transformation Office, a robust plan was implemented to ensure Executive Alignment, including regular updates to global leadership, globally coordinated communications sharing the strategy and pilots, all supported by the Change Leadership orchestrators in each operating unit."

Rosaline Hester, Former Global Change Leadership Senior Director - The Coca-Cola Company

Are We Losing Our Jobs? Strategies to Overcome Resistance to AI Adoption

1. Establish Positive Narrative Early

Despite its benefits, AI often faces resistance, especially when fear of job loss is in play. Use storytelling in change communications to establish positive narratives around AI from the beginning.

✔ Frame AI as a Career Investment

Reframe AI adoption as a career investment to generate enthusiasm and mitigate resistance. Offer upskilling programs, host boot camps, provide certifications, and create other learning opportunities to equip employees with the tools they need for the future.

✔ Pivot Messaging to Intelligence Augmentation

Rather than positioning AI as a competitor to human intelligence, highlight how it augments capabilities and simplifies tasks. Emphasize productivity metrics, showcasing how AI boosts efficiency without replacing jobs.

“Develop case studies or testimonials that illustrate how AI has freed up time for higher-value work, such as strategic thinking, creativity, and collaboration.”

Roger Kenyanya, Lowe's - VP AI Strategy & Transformation, Governance, and Data Science

“Demonstrate how AI can enhance their roles by exploring practical applications in their daily tasks. For instance, using AI for note-taking in virtual meetings or leveraging AI to assess future plans against the SMART framework.”

Nicholas Mudd, Cummins - Global Service Transformation Director

2. Maintain Engagement through Education

✔ Conduct Grassroots Education & Workshops

Launch educational sessions from the ground up to establish a strong foundational understanding and foster a shared vision of AI. It’s essential that everyone, from the C-suite to entry-level employees have a good grasp of the benefits and relevance of AI in their day-to-day work.

✔ Diversify Engagement Strategies

Use interactive approaches such as gamification, design thinking workshops, and hands-on participation to boost engagement and drive adoption.

“We ran a gamified session where teams were divided into groups, each given ‘fake money’ to invest in AI opportunities for the organization. By making idea generation a competitive process, we identified three practical implementations—one of which is Robotic Process Automation (RPA).”

Sara Jetta, Purolator Inc. - Vice President of Strategic Enablement

✔ Leverage Change Champions

Empower early adopters to lead the AI conversation. Equip them with the right resources and encourage them to advocate for AI within their teams. Demonstrate real-world benefits and lead by example to alleviate concerns and drive faster adoption.

“Meet people where they are. Gather use cases through listening circles, one-on-one interviews, and in-depth conversations. Then, identify opportunities to engage an informal network of change champions to amplify excitement around AI transformation—whether through peer-to-peer sharing, small group discussions, or other collaborative forums”

Gayatri Ohri, AWS - Sr. Manager, Gen AI for Operations Addressing AI Ethics & Challenges on Data Quality, Security, and Governance

✔ Establish AI Strategies, Ethics, and Policies Early

Establish clear AI policies and ethical guidelines early in the process to help address concerns around data privacy, security concerns, and regulatory compliance. Conduct regular reviews to ensure adherence to ethical standards and address potential biases. Setting expectations from the start will help mitigate risks and foster employees' confidence in adopting AI solutions.

“Look at AI transformation from an ethical standpoint first. It’s best to align ethical AI practices with organizational values before diving into other strategic aspects of the transformation. This foundation helps guide responsible AI implementation and fosters trust across the organization.”

Laurie Ditch, iMerit - Sr. Director, Organizational Strategy and Operations

Collaborate with legal to address risks of free AI tools and conduct training to educate employees on the importance of using sanctioned platforms to mitigate security and compliance issues.

“The loudest voices may express hesitation about AI, but we often overlook those quietly using unsanctioned tools in their day-to-day, putting sensitive data at risk. It’s crucial to highlight the dangers of public AI platforms and emphasize why licensed solutions are essential for safeguarding security and compliance.”

Angie Woodruff, TTI, Inc. - Head of Change Management

✔ Reassess AI & Data Governance

Setting clear frameworks for both AI and data governance is critical for ensuring data security, quality, and availability are not compromised within the organization. It’s important to establish governance for AI outputs by modeling results and creating detailed regulations over data sources used as inputs.

“AI governance isn’t just an extension of data governance—it operates on a different spectrum. While data governance manages input quality and availability, AI governance ensures the responsible use of AI outputs. Both are integral yet distinct, working together to enable ethical and effective AI implementation. ”

Holly Hon, Entertainment Partners - Senior Vice President, Operations

✔ Address Legal & Compliance Challenges

In highly regulated industries like pharma and healthcare, it is critical to address legal and compliance challenges upfront. Executives are often eager to discuss AI and explore use cases, but the enthusiasm often meets resistance from legal, compliance, and regulatory teams. The pushbacks often stemmed from concerns around data privacy, risk, bias, and proprietary information, eventually halting AI initiatives before they began.

“AI has proven value in automation and optimization, predictive analytics, and even drug discovery—but adoption depends on tackling these barriers first. Ultimately, AI use cases are only as strong as the organization’s ability to navigate the regulatory and ethical concerns that stand in the way.”

Karen Paff, Viatris - VP, PR & Communications | Corporate Branding, M&A Integration, Data Analysis & AI Campaigns

✔ Adopt Risk-Based Quality Control

AI is a complement, not a replacement. Maintain a balance between efficiency and safety when driving AI adoption to minimize potential issues. Define clear structures to evaluate where AI can be leveraged with minimal oversight, for example, for automating tedious, repetitive tasks. Meanwhile, employees remain critical for focusing on high-value work.

“Differentiate AI application with risk-based quality control—for instance, optimizing lower-stakes tasks like analyzing contracts under $2 million with 80% accuracy, while leaving high-risk, legal decisions human-led within the legal department.”

Wendy Wheeler, Koch Industries - Sr. Director, Strategy & Legal Services Innovation

✔ Implement Continuous Feedback Loops

A continuous, human-in-the-loop feedback system drives significant success, particularly in data quality, accuracy, and reliability. This approach helps pinpoint areas for improvement, enabling continuous model refinement and ensuring that outcomes consistently align with initial expectations.

“In industries like financial services where regulatory and client-facing concerns are critical— it’s vital to test, validate, and refine. By tracking model performance and identifying areas for improvement, you create a feedback loop that drives ongoing enhancement. As confidence in the AI model grows, gradually reduce human oversight and shift towards more systematic validation.”

Shefali Shah, JP Morgan - Managing Director, Operational Excellence

Measuring KPIs & Impact: How to Demonstrate Tangible Business Value of AI Transformation

Create relevant dashboards to capture data and track metrics that align AI transformation with your broader business objectives. Start by identifying the pain points you’re addressing or the key questions to answer. Measure various aspects of the AI transformation and pinpoint where data is needed to showcase tangible results.

✔ Linking AI Training to Business Impact

AI training should be tied to clear business outcomes. Without a direct link to impact, it’s hard for employees to see its value and relevance. Use frameworks like the Jack Phillips ROI or 70:20:10 models to show how AI skills drive the bottom line.

For example, the Jack Phillips ROI model helps to establish benchmarks for AI training efforts. The model tracks overall satisfaction and real-world application, focusing on how knowledge is implemented, adopted, and applied. This way, you can connect training impact to business outcomes and assign a tangible value to the results.

“When developing AI training programs, start by clearly defining measurable learning outcomes that tie directly to business impact. Establish specific behavioral changes and skill applications you expect to see, and create concrete metrics to track how these new capabilities translate into business value before training launches. This approach ensures training isn't just about knowledge transfer, but about driving meaningful organizational transformation that can be measured and demonstrated to stakeholders..”

Iris McQuillan-Grace, OLIVER Agency - Head of Employee Experience

✔ Prioritize Use Cases with Direct Revenue Impact

Focus on use cases with direct revenue impact. For example, integrating sales automation in manufacturing helps reduce manual, repetitive work; which subsequently improves overall productivity.

However, it’s hard to demonstrate direct revenue impact by relying solely on productivity metrics such as time saved or completion rate. Instead, bring it up a notch and measure productivity’s impact itself—identify how productivity boost has resulted in broader business outcomes such as revenue growth and cost savings.

✔ Leveraging Leading vs. Lagging Indicators

Establish clear KPIs and baselines for measuring AI impact by leveraging both leading and lagging indicators. Ensure these metrics align with business goals and are easily communicable to stakeholders.

• Adoption vs Re-Engagement Rate: Track adoption rate and re-engagement metrics to assess the long-term impact of AI. Measure how frequently users return to AI-driven tools and how their engagement evolves to drive sustained usage. A dedicated change activation platform like Tigerhall can provide real-time insights needed to track adoption, engagement, and sentiment, helping to refine AI implementation over time.

• Model Accuracy: Evaluate how well AI models perform in real-world scenarios. This includes measuring precision, recall, and overall reliability to ensure AI is delivering meaningful outcomes.

• Efficiencies & Cycle Time Reduction: AI-driven automation can significantly cut down time spent on repetitive tasks. For instance, reducing post-call documentation from two minutes to 30 seconds in a call center directly improves productivity and cost savings. Similarly, AI-powered data retrieval can simplify complex searches, saving valuable time.

• Behavioral Change: AI’s impact extends beyond efficiency, it drives behavioral shifts. Track behavioral changes through indicators such as collaboration, empowerment, and willingness to adopt new processes. Use sentiment analysis, surveys, and feedback tools to assess whether AI helps employees focus more on high-value tasks.

Conclusion

AI transformation is about strategy, people, and execution. Success depends on aligning AI with business priorities, tackling challenges with ethical governance, and equipping teams with the right skills and mindset. Leaders who take a focused, value-driven approach will ensure AI becomes more than a passing trend and instead a sustainable competitive advantage. The key is to start small, scale thoughtfully, and continuously measure impact to refine strategies and drive meaningful business outcomes.

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 aims 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.

Interested in joining ECLC? Learn the membership criteria and sign-up below.

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