Change Management and AI

IIA clients leverage analytics maturity assessments, battle-tested frameworks, and cross-industry collaboration to prioritize and execute strategic enterprise data and analytics initiatives. We regularly check the pulse of trending topics for our community and facilitate critical conversations in virtual roundtable format for peer-to-peer exchange.

In a recent IIA roundtable discussion, data and analytics leaders from diverse industries gathered to share their approach to change management and AI. The discussion covered a wide range of topics, from developing and communicating data and AI strategies to incentivizing AI adoption. Here are the key takeaways:

1. Developing and Communicating Data and AI Strategies

Participants at the roundtable discussed varied strategies in developing and communicating data and AI strategies within their organizations. One participant outlined their organization's decentralized approach, where strategic business units have defined strategies but lack a unifying enterprise-wide strategy. This results in fragmented analytics efforts across the organization. Efforts are being made to create a more integrated framework that utilizes hybrid and shared resources to achieve better synergy, although challenges persist in areas like security and privacy, which are managed inconsistently across units.

Another attendee described a shift in their organization towards a more integrated approach where data and AI strategies are woven into broader business initiatives. This approach has transitioned from a siloed playbook for major initiatives to one that is closely coordinated with business partner leadership, ensuring that AI and analytics are integral to business plans across various departments. However, the organization faces challenges in pushing beyond conservative, established practices to adopt more innovative and cutting-edge analytics solutions, especially given the reluctance in some quarters to fully embrace new technologies. This narrative underscores a common theme: the need for better alignment and integration of AI strategies with overall business goals to enhance effectiveness and drive innovation.

2. The Role of AI in Strategic and Operational Frameworks

In the discussion on the role of AI in strategic and operational frameworks, participants highlighted how AI is integrated within their organizations to drive specific business outcomes and operational efficiency. One participant detailed their strategy to employ AI for proactive, personalized solutions under a branded strategy, aiming to scale AI to improve outcomes and integrate it deeply within operational processes. This includes practical applications like an internal chat application developed on a major cloud platform, enhancing productivity by providing tailored functionalities such as coding assistance for developers and creative tools for marketers.

Another participant shared insights into their organization's comprehensive approach to establishing an AI ecosystem that supports various functions—service, marketing, product management—with intelligent systems. This ecosystem is not just about individual AI applications but involves a systematic alignment of these technologies with business objectives to optimize efficiency and enhance customer experiences. The focus is on clear communication of AI capabilities and benefits across the organization, ensuring that both technical and business teams are aligned in understanding and leveraging AI tools effectively. This structured approach emphasizes the importance of tailoring AI strategies to meet specific operational needs and strategically evaluating the economic impact of AI through diverse metrics like cost savings and operational improvements.

3. Formalizing Change Management Programs

In the discussion about formalizing change management programs, a participant described their role in fostering organizational change through an AI center of excellence. Initially tasked with creating a human-centered change function within the HR department, the leader now utilizes a broad range of expertise across departments such as talent and marketing to drive comprehensive organizational change. This underscores a collaborative approach to change management, which is not confined to a single department but is a shared responsibility across various segments of the organization.

The narrative also highlighted the challenges and strategies of implementing change management within global enterprises. For instance, while developing internal tools designed to optimize marketing strategies across diverse markets, it became evident that merely providing tools was insufficient without the corresponding knowledge on how to effectively utilize them. This led to a shift towards a more consultative approach to ensure that tools provided are complemented by adequate support and training, making them more beneficial and user-friendly for the teams involved.

Furthermore, the dialogue covered the evolution of training programs aimed at improving understanding of digital AI among senior leaders. This involved intensive sessions with experts that helped clarify the practical implications of AI integration, proving crucial for aligning technology with business strategies. Such educational initiatives are vital in ensuring that key decision-makers not only understand but can also advocate and drive the adoption of new technologies effectively within their organizations, thereby fostering a culture that is receptive to innovation and change.

4. Incentivizing AI Adoption through Compensation Alignment

The group ended the roundtable by focusing integrating AI into organizational structures, examining its implications on compensation, and aligning AI initiatives with specific business outcomes. While some participants suggested that linking compensation to AI adoption could boost stakeholder engagement, concerns were raised about potential challenges during a transitional phase where skepticism about new technologies is high. A more favored approach was aligning AI with clear, measurable outcomes, such as using evaluation frameworks that could later influence compensation decisions. The discussion underscored the importance of using AI to meet bonus targets and organizational goals through enhanced automation.

Participants also deliberated on measuring AI's impact on reducing workloads and improving operational efficiency, with suggestions to link these efficiencies to key performance indicators (KPIs) that reflect financial outcomes and overall organizational health. However, there was hesitancy due to uncertainties about AI's direct impact on business results like product sales and customer satisfaction. The broader conversation evolved into a discussion on organizational culture, emphasizing a shift from control to influence. This approach advocates for a collaborative dynamic that focuses on leading rather than commanding, aiming to foster a more inclusive and adaptable environment as AI technologies are implemented.

IIA virtual roundtables are exclusive, invite-only discussions designed to promote peer-to-peer exchange on pressing challenges in the data and analytics community. Seats are limited and reserved for C-suite data and analytics leaders or equivalent at mid- to large-sized enterprises. Conversations are geared toward non-digital native companies. If you meet these criteria, contact us for more information.