Kjell Carlsson

AI agents can upend traditional AI use cases and unlock transformative potential, but the real danger isn’t overhyping their power — it’s overestimating your organization’s readiness. D&A leaders must see past the hype, sidestep common pitfalls, and build the foundation needed for true AI success.

Agentic AI is a hot topic, but its true investment cost remains unclear due to a lack of best practices. How is your organization handling this? How detailed is your cost tracking? When do you implement controls to manage spending without hindering innovation? Join us to explore what works—and what doesn’t—in understanding and managing Agentic AI costs. Through active participation, we’ll co-create practical tools like input factor lists, value equations, and cost management principles. To kickstart our discussion, please reflect on the questions below before the session. Your insights will shape our conversation, and you’ll receive a customized resource afterward to support your continued success. 1. Why is this so hard? What do you think is at the heart of this challenge? 2. In what ways have you succeeded in measuring the true cost of Agentic AI? 3. What would you like to learn from others?

GenAI solutions include several choices and trade-offs. A critical decision is: should you build custom AI solutions in-house or buy off-the-shelf products? This session brings together a debate on the trade-offs, risk and rewards of each approach. The session will be based on scenarios and use-cases to highlight key considerations such as cost, reliability , flexibility and speed for different decisions such as LLMs vs. SLMs, RAG vs. AI agents, packaged platform capability vs. bespoke custom solution, packaged vs. open-source.

Join this session to share your questions, challenges and experiences with peers. Have you scaled the deployment of AI agents? Have you used technologies or practices that proved successful, or challenging? Whether your AI agents are customer-facing, employee-facing or back-office automation, attend this analyst-moderated session to learn from and share with your peers.

Organizations now have more options to build effective RAG systems, and those options come with confusion. Many organizations are looking to capitalize on new innovations such as long context windows, knowledge graphs, reasoning models, multi agent systems, and beyond. Attend this session to learn about seven challenges with RAG systems, their associated architectural choices, and best practices to improve their performance.

AI agents can upend traditional AI use cases and unlock transformative potential, but the real danger isn’t overhyping their power — it’s overestimating your organization’s readiness. D&A leaders must see past the hype, sidestep common pitfalls, and build the foundation needed for true AI success.

GenAI solutions include several choices and trade-offs. A critical decision is: should you build custom AI solutions in-house or buy off-the-shelf products? This session brings together a debate on the trade-offs, risk and rewards of each approach. The session will be based on scenarios and use-cases to highlight key considerations such as cost, reliability , flexibility and speed for different decisions such as LLMs vs. SLMs, RAG vs. AI agents, packaged platform capability vs. bespoke custom solution, packaged vs. open-source.

Analytics and BI platforms and data Science and machine learning platforms are important technologies that drive insight-driven decision making and allow AI systems to be built and operationalized throughout the enterprise. This session unpacks the Magic Quadrants of both markets and gives insight on the trends that you should be aware of.

Data is not the strategic resource of the 21st century, data scientists are, but they come from all walks of life with an exceptionally wide ranges of skills, experience and training.