2 Nov 2026
The Enterprise Application Reckoning: What Stays, What Goes, and Why It Matters | Paul Saunders, SAP
Enterprise application strategy is at a turning point. Paul Saunders, GVP Product Marketing at SAP, joins Julia Kloppenburg and Benedikt Gieger to explore what is actually changing in enterprise software and what will endure. From AI-first design principles to clean core strategy, from the three-gear model of transformation to a frank assessment of why best-of-breed architectures created more problems than they solved, Paul traces how the industry arrived at this moment and where it is heading. The conversation covers the gap between the vision of autonomous enterprise and the practical reality facing most customers, why trust in your underlying data is the prerequisite for any AI investment to pay off, and how the next generation of interfaces is already making today's UX look dated. A grounded, experience-rich conversation for anyone navigating enterprise technology decisions right now.
Enterprise applications are being redesigned from first principles, but the transformation is not moving at the same speed everywhere. In this episode, Paul Saunders, GVP Product Marketing at SAP, joins hosts Julia Kloppenburg and Benedikt Gieger for a wide-ranging conversation about what is genuinely changing in enterprise software and what will remain constant longer than most people expect.
Paul draws on 35 years in the industry, including time as a CIO and as an industry analyst before joining SAP, to explain why the shift from record-and-report systems to AI systems designed around sense, decide, and act is more profound than a technology upgrade. It requires rethinking process and organizational culture as much as the software itself.
The episode covers Paul's three-gear model for enterprise transformation: technology, process, and people. He argues that organizations trying to move all three at once typically stall, and that the most successful transformations sequence them deliberately. The banking analogy he uses throughout: a traditional bank installed ATMs and took tellers off the floor, reducing one form of friction while creating another. A mobile-first bank redesigned the experience entirely. Both happened, but at very different speeds and with very different results for customers.
On clean core strategy, Paul explains the underlying logic: separating what a business must standardize, what it can differentiate, and what it should treat as experimental innovation. He uses the BMW and Mercedes comparison to illustrate why two companies that look like direct competitors may need fundamentally different technology approaches based on where their competitive advantage actually lives.
The conversation also addresses the practical limits of AI adoption. Paul is direct about the trust problem: a CFO who walks into a board meeting and says "AI thinks revenue could be two and a half billion" will not stay employed long. Reliable AI outcomes require reliable underlying data, and most enterprises have not yet done that foundational work.
In the final section, Paul answers the question of what will look most outdated when viewed from 2030: best-of-breed architectures that gave each department the best tool for itself but left nobody accountable for how the pieces fit together or what the data actually meant across systems. What he expects we will be grateful for is simpler: an enduring focus on the customer's customer, and on technology as the stage crew that enables performance rather than the star of the show.
Topics covered in this episode:
AI-first enterprise design (sense, decide, act) · Clean core and pace layering · Three-gear transformation model · Trust and data quality as prerequisites for AI · Best-of-breed versus integrated platforms · Semantic data and the limits of API integration · Generational differences in UX expectations · The concert crew analogy for technology's role · What 2030 will reveal about today's enterprise architecture decisions
