2024 Digital Awards Winner – IBM, Plan-to-Results – AI-Driven Insights for Enterprise Performance Management

In this episode of the “Business Excelleration^™ Podcast,” Vin Kumar speaks with IBM about their new transformative strategy. Learn how data is the “secret sauce” behind insights powered by artificial intelligence (AI) and how to get it right for meaningful outcomes. The conversation also explores strategies like fostering cultural adoption, empowering teams with self-service tools, and co-creating with IBM’s “Client Zero” approach of using its own technology in-house to ensure it’s scalable and ready for clients. Looking ahead, IBM is shifting how businesses consume insights – from static reports to dynamic AI-driven conversations. This episode offers valuable insights for anyone navigating challenges with enterprise data or enhancing their data strategy.

Welcome to The Hackett Group’s “Business Excelleration^™ Podcast,” where week after week we hear from experts on how to avoid obstacles, manage detours and celebrate milestones on the journey to world-class performance. This episode is hosted by Vin Kumar, a principal at The Hackett Group, leading the AI and Digital Operations practice. He is joined by two esteemed guests from IBM – Ed Lovely, vice president and chief data officer, and Lyndal Davis, director of engineering and chief data officer. Together, they discuss IBM’s enterprise performance management (EPM) solution, which won a digital award from The Hackett Group in 2024.

To begin, Ed outlines IBM’s commitment to data as the “secret sauce” for artificial intelligence (AI) success. The company focused on organizing, aligning, and integrating its data into a trusted single source of truth, enabling faster and more accurate insights and scalability for AI use cases. Leveraging tools like watsonx, IBM achieved rapid deployment of AI solutions, showcasing the critical role of clean, current, and trusted data. IBM’s transformation journey began with siloed efforts across departments like finance, human resources, and marketing. Recognizing that business operations flow holistically from demand generation to accounts receivable, IBM reoriented its focus on aligning workflows and integrating data into a cohesive model. The shift to an end-to-end approach allowed IBM to improve operational efficiencies and create a unified platform for AI-driven decision-making.

IBM’s “client zero” approach involves using its own products and services, co-developing solutions to ensure they meet both internal and external needs. This methodology aligns IBM’s internal transformation with client offerings, ensuring delightful user experiences for both IBM employees and clients. By applying these solutions internally, IBM validates their effectiveness, scaling them to meet the needs of large, complex organizations. The discussion shifts to IBM’s EPM platform, which integrates transactional source systems, cloud object storage and relational databases (like DB2), with a business intelligence solution layered on top. This scalable architecture supports 30,000 users and vast datasets without performance degradation.

To successfully transition into the generative AI (Gen AI) space, the IBM team first had to establish a foundation of trusted, integrated data. However, the greatest challenge lay in the engineering team’s operational processes. Despite having investment and organizational support, the team’s progress was hindered by dependency issues across multiple development squads. The team restructured its workflow, with each squad owning a specific business requirement. While this helped focus on outcomes, overlapping data object changes created further challenges. As part of an ongoing evolution, the team has since shifted to a value stream approach, grouping work into larger deliverables while still managing dependencies effectively. This evolution highlights the importance of continuously refining workflows to maximize productivity.

To further promote adoption of the platform, the team employed various change management techniques, including lunch-and-learn sessions, brief instructional videos and personalized “white glove treatment” sessions with executives. These efforts, combined with leadership advocacy and a requirement for senior executives to use the platform during operational meetings, reinforced the cultural transformation. Unlike traditional project-based approaches, IBM’s EPM team emphasized the importance of operational sustainability. To manage the growing operational workload, the team standardized services and frameworks, enabling repeatable tasks and cross-team collaboration, and embraced automation.

In closing, hear the transformational impact of the EPM platform on IBM’s decision-making processes. The move to self-service analytics empowered users at all levels to access and analyze data instantly, eliminating the delays associated with traditional “data concierge” models. By integrating watsonx’s Gen AI capabilities with EPM, users can now ask conversational questions and receive instant, actionable insights. IBM’s journey serves as a testament to the power of integrated data, user-centric design and AI-driven insights. By overcoming technical and cultural hurdles, the company has redefined how data is used across the organization, setting a new standard for enterprise decision-making.

Time stamps:

• 0:12 – Welcome to this episode hosted by Vin Kumar.
• 1:09 – IBM’s AI-driven transformation philosophy.
• 3:10 – Transition from silos to end-to-end workflow integration.
• 7:34 – The “client zero” approach: co-development and internal testing.
• 10:14 – Enterprise performance management platform.
• 18:05 – Engineering challenges and overcoming dependencies.
• 21:45 – Driving user adoption and cultural transformation.
• 26:31 – Maintaining operational excellence and scaling automation.
• 28:32 – Transforming decision-making with AI and self-service analytics.