AI for Leaders: From Strategy to Practice

Artificial intelligence (AI) is now an integral part of many aspects of our lives and business operations. For team and organizational leaders, it is crucial to use AI technologies strategically and responsibly. This seminar aims to present a practical approach for effectively integrating AI tools into the daily work of the modern leader, while simultaneously addressing potential challenges and opportunities.

During the seminar, we will seek answers to the following questions:
• What are the risks, and how can we manage them when using AI for managerial tasks?
• How can we save time with AI – through more efficient assimilation of information and building context?
• Which managerial tasks can we automate, and how?
• How can we help our teams select and implement the right AI tools for them?
• How is the role and the requirements of leaders evolving with the broader adoption of AI?

Each topic will be illustrated with real-life examples and demonstrations of AI tools that participants can immediately apply in their work.

Transcript

1. AI for leaders: from strategy to practice Nikolay Stoitsev Senior Engineering Manager at StorPool Storage
3. Caution, optimism, and skepticism
4. Privacy Inadvertent sharing of secrets
5. Accuracy Hallucinations or stale data
6. Intellectual property infringement Violating IP, copyright or trademark
7. You can’t ban AI
8. You lose competitive advantage
9. People will still use it
10. LLMs can’t think
11. The intelligence illusion is in the mind of the user
12. Deepseek R1 “While LLMs demonstrate behaviors that resemble reasoning in specific contexts, their capabilities differ fundamentally from human cognition”.
14. Crunching PDFs Demo
15. • ChatGPT • Gemini • Grok • DeepSeek • Perplexity Web search
16. Can provide feedback and different perspective Demo
18. Tasks that require quantity Demo
19. Tasks where you are an expert
20. Tasks to transform information
21. Tasks for data analysis Demo
22. Tasks that stretch you over many disciplines
23. Brainstorming with Live Mode Demo
25. • Team needs • Business value • Technical feasibility Where to start?
26. • Vendor’s expertise • Privacy/security measures • Integration with existing work fl ows How to pick a tool?
27. Each team has specific needs
28. Help them understand testing
29. Think about the end-to-end workflow
31. • Hallucinations • IP indemnity • Unknown failure modes Remind about limitations
32. Lead by example Use AI daily and share your experience
33. • What tools are allowed • What information should and should not go in • Verifying the output • Standard procedure Set the rules
34. Need to measure the RoI / efficiency gains
35. Managing degrading quality https://www.gitclear.com/ coding_on_copilot_data_shows_ais_down ward_pressure_on_code_quality
36. With more AI we need more managers Alekseeva, Liudmila and Azar, José and Gine, Mireia and Samila, Sampsa, AI Adoption and the Demand for Managerial Expertise (September 27, 2024). Available at SSRN: https://ssrn.com/abstract=4986379
37. Q&A Nikolay Stoitsev https://www.linkedin.com/in/nikolaystoitsev/