Human Judgment & Domain Expertise: Put AI to Use Responsibly

Описание: In this episode of The Shift Show, we’re joined by ⁠⁠Jelena Marjanovic, PhD⁠⁠ (https://jelenamarjanovic.com/ai-at-work) , a leader in responsible AI adoption and human-AI collaboration. Dive into what AI use really looks like in the workplace-beyond buzzwords and superficial stats, and focus on how humans can work with AI in ways that enhance judgment, trust, sustainable outcomes.



This episode matters now because AI isn’t just a tool anymore. It’s shaping how we think, how we work, and how organizations operate. Whether you’re leading a team, integrating AI into your workflow, or simply trying to understand how humans and AI can collaborate effectively, this conversation will give you clarity, frameworks, and real-world insights.



Jelena Marjanovic, PhD helps leaders, teams, and business owners navigate the rapidly shifting world of work accelerated by AI. She blends coaching psychology, adult learning science, and practical AI enablement to help professionals work smarter, lead with clarity, and adapt intelligently.



⁠⁠⁠LinkedIn⁠⁠⁠⁠ (  / marjanovicjelena  )



⁠⁠⁠JelenaMarjanovic.com⁠⁠⁠ (https://jelenamarjanovic.com/ai-at-work)



“What sets Jelena apart is her blend of practicality, emotional intelligence, and deep business experience. I love that she can shift seamlessly between coach and consultant depending on what’s needed.”



— Sian Bennett, Head of Content Marketing and Communication & Small Business Owner



“Her hands-on method helped our law firm rethink how we work. She turned a complex AI topic into a clear, practical path to higher efficiency.”



— Nenad Cvjetićanin, Managing Partner, C&P Law



TOPICS in this Episode:




•


What true AI adoption looks like vs. common misconceptions




•


How data and knowledge structures enable AI effectiveness




•


Importance of critical thinking as the foundation for AI use




•


Developing habits and questions that verify AI output




•


Stages of AI adoption from hesitation to over-reliance to balanced use




•


How teams and leaders navigate different comfort levels with AI




•


Shadow AI use and organizational impacts






10 Takeaways




•


Real organizational AI adoption is less than most industry stats suggest. Meaningful use is still emerging.




•


Using simple AI tools (like transcription) doesn’t equal strategic AI adoption.




•


Effective AI use starts with understanding your tasks, workflows, and desired outcomes.




•


Human judgment still matters — AI cannot yet fully replace nuanced reasoning or ethics.




•


Critical thinking is the most foundational skill for working with AI.




•


Good AI use involves repeating patterns of evaluation and verification.




•


Teams adopt AI at different rates, and leadership must recognize and support that.




•


Shadow AI use is widespread, and employees will use AI even without formal training.




•


Domain expertise, AI literacy, and discernment create the most effective human–AI integrators.






Chapters



00:00 – Intro: Human + AI Collaboration in 2026



01:31 – Reality of AI Adoption



04:00 – True AI Adoption vs. Simple Tools



06:18 – Practical Next Steps for AI Integration



08:22 – Future Data & Knowledge Structures



11:08 – Spectrum of Human–AI Interaction



14:38 – Critical Thinking in AI Workflows



17:48 – Training and Habits for Evaluating AI



24:04 – AI Adoption Curve Explained



27:16 – Balanced Collaboration with AI



30:12 – AI Adoption Across Teams



34:05 – Shadow AI Use in Workplaces



37:27 – Social Stigma and AI Use



40:16 – Work Displacement and Skill Redesign



41:27 – Human-AI Integrator Framework



46:56 – Workforce and Apprenticeship in the AI Era



49:27 – Academia, Regulation, and AI Adoption



Keywords



collaboration, adoption, AI, leadership, maturity curve, critical thinking, literacy for professionals, human-in-the-loop, balanced AI use, overreliance,
pitfalls, integration framework, human decision-making, automation,
metacognitive cues, management spectrum, teams, shadow AI use, future
of work, domain expertise, AI literacy, knowledge management, readiness, governance