AI at Work from Productivity Hacks to Organizational Transformation 2026

across different platforms and systems, fostering innovation and reducing barriers to entry. The consensus was that the spread of AI will not be uniform, but complementary. Large firms will continue to drive high-end development, while smaller organizations and emerging economies may showcase some of the most inventive applications. Extending AI’s reach, executives argued, is not about replicating the enterprise playbook everywhere, but about matching tools to context. In practice: SAP observes uneven AI adoption across its business units and lines of business. The pace of progress varies depending on factors such as underlying platforms, process interdependencies, data quality and organizational mindset. For instance, leveraging its own cloud-based HR solution, SuccessFactors, SAP integrated AI capabilities years before comparable advances were possible in areas with more complex data and system landscapes. Similarly, engineering functions have realized greater AI-driven efficiencies than business units with less standardized processes, underscoring that even within a single enterprise, AI adoption is far from uniform. Taken together, these realities show that AI’s impact inside firms is neither linear nor uniform. Adoption is progressing, but unevenly. Job hierarchies are being reshaped, albeit in unexpected ways. Workers are adapting with a mix of excitement and caution. And governance remains a moving target. The mood among C&T executives has been generally positive, with challenges seen as being solvable rather than paralysing. Yet their experiences make clear that embedding AI at scale will demand cultural adaptation and accountability as much as technical innovation. AI at Work: From Productivity Hacks to Organizational Transformation 15