Executive Summary
The GenAI boom has forced enterprises to rethink the data architecture they will need to run their AI-powered next-generation applications. This creates a massive demand for that architecture to be able to run more efficiently and reliably and offers scalability that was never before demanded from data platforms. That is particularly critical for what matters most for enterprises—transactions. Consequentially, relational/transactional databases need to scale and run more efficiently than ever. While traditional workloads (ERP, custom, and so forth) could all be understood and sized, and therefore optimized to run to required performance levels, the demands on transactional database systems in the AI era are practically infinite, requiring all offerings in the space to reach new efficiency, reliability, and performance levels.
The good news for enterprises: AI not only writes the new performance playbook but can also be used to power databases toward that goal. Practically, AI is helping enterprises to get ready for AI. IBM’s flagship relational database offering, Db2, runs critical workloads across a variety of industries and use cases. With IBM’s track record of innovation, it is no surprise that IBM is turbocharging Db2 with AI now, thus providing critical innovation for its customers to keep operating and investing further into Db2.
This report presents the relevant trends for databases in the AI era, shares the key IBM innovations with a focus on the differentiation of the Db2 offering, and concludes with hands-on recommendations for the CxO audience.
