Create Generative AI Value at Scale
Kevin Schmitt, Gregory Vial, and Ivo Blohm
Key Insight: Organizations are expanding their GenAI use by implementing coordinated cross-functional structures that draw on domain expertise and user innovation.
Top Takeaways: Companies that establish a new kind of internal AI organization that researchers have dubbed the “AI spine” are better positioned to expand the scope of use cases, continually improve them, and identify the ones that will improve processes and create real value for the business. The spine model facilitates greater sharing of knowledge and innovative ideas across business units by connecting resources — including users and cross-functional experts — to a flexible technical core. Disciplined project governance keeps resources focused on the areas where generative AI is most likely to have a positive impact.
Scaling AI With Adaptive Governance
Gianvito Lanzolla, Margherita Pagani, and Christopher L. Tucci
Key Insight: Organizations must implement a new approach to AI governance across a system’s life cycle to manage risks at scale.
Top Takeaways: As organizations adopt AI systems across business functions, they need to manage increasingly complex risks not only during the development process but also after deployment. Leaders should start by identifying the risks their organization faces and the controls needed to manage them. Then, by adopting adaptive AI governance practices, they can continually realign AI with organizational needs as those systems scale. Organizations that embed risk controls into operations, overcome cross-domain barriers, and institutionalize continuous learning and improvement will have an advantage over those that don’t.
Why AI Isn’t Transforming Finance Yet
Stijn Viaene, Kristof Stouthuysen, and Bjorn Cumps
Key Insight: CFOs must adapt their leadership approach to balance finance’s traditional role with the use of AI to help shape organizational strategy.
Top Takeaways: Finance offices have been slow to meaningfully adopt artificial intelligence, often due to a narrow perception of the function’s role as a steward of discipline and consistency. When finance leaders and their teams realize how AI can help them stay alert to changes in the business environment, experiment in the course of their work, think differently about the future, and embed new practices in their everyday processes, they will begin to see opportunities for using AI as a tool that supports broader organizational change.
Why Businesses Should Experiment With Quantum Computing Now
Avi Goldfarb and Florenta Teodoridis
Key Insight: Quantum’s benefits won’t materialize overnight. Companies that start experimenting today can gain a competitive edge.
Top Takeaways: Companies shouldn’t wait until quantum computing technologies have reached maturity to invest in them. As an enabling technology, quantum requires hands-on experimentation, feedback loops that support incremental learning, and co-invention cycles between producers and users — over time — to identify practical use cases. Investments in quantum today may see near-term payoffs, but the focus should be on active learning and the potential for breakthrough innovations over the longer term.
Level Up Your Crisis Management Skills
Rick Aalbers, Killian McCarthy, and Arjan Groen
Key Insight: Leaders can become more adept at responding to crises by developing stronger skills in seven critical practice areas.
Top Takeaways: People who have successfully managed crises in governments and large organizations aren’t innately better at it. They’ve learned to apply critical crisis management practices. Interviews with high-level leaders in a variety of industries found that organizations with strong crisis management capabilities have invested time and effort to develop maturity in seven key areas researchers have dubbed the “7C’s”: contingency planning, cross-functional coordination, transparent communication, compassion, confrontation of hard truths, control, and continuity.
Data Transformation Is the CEO’s Business
Barbara Wixom, Ogi Redzic, Brandon Hootman, Joaquin Rodriguez, Gabriele Piccoli, and Cynthia Beath
Key Insight: Caterpillar’s data overhaul shows the essential transformation work that CEOs and senior leaders must commit to for AI readiness.
Top Takeaways: A multiyear data transformation project at Caterpillar that made the heavy-equipment manufacturer AI-ready provides an exemplary case for what leadership commitment to such a technology project involves. CEOs must go beyond communicating abstract intentions by setting a tangible, strategic business goal that the transformation will support; giving teams realistic time horizons and adequate resources; and assigning meaningful, instrumental roles to members of the leadership team.
What It Takes to Scale Value-Based Industrial Solutions
Johan Frishammar and Vinit Parida
Key Insight: Manufacturers can successfully build upon value-based sales pilots by using a framework centered on six core capabilities.
Top Takeaways: Industrial equipment manufacturers moving to a value-based sales model often find that delivering initial solutions on a one-off basis is relatively straightforward. The real challenge lies in scaling those solutions to more customers, which requires structured, repeatable processes and strong, entrenched capabilities. New research points to two important phases of capability building — scaling prerequisites and scaling execution — and identifies the organizational skills,
