Andjelka Djukic
Head of Sales at Eclincher and a Co-Founder of Innovative Women of Serbia
Belgrade, Serbia
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Andjelka Djukic is a SaaS and digital platform professional specialising in AI, technology adoption, and human decision-making. She currently serves as Head of Sales at Eclincher, with prior experience in the edtech sector. She has contributed sessions at DevLearn, ACHE, and Learntec Karlsruhe, and is Co-Founder of Innovative Women of Serbia, where she explores leadership, credibility, and professional perception in modern work environments.
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Topics
The Human Side of AI at Work: Why Modern Work Needs Better Adoption Design
AI is quickly becoming part of the modern workplace, from productivity tools and collaboration platforms to automation, knowledge management, and Copilot-style assistants. But access to AI does not automatically create better work. Many organizations introduce new tools and expect teams to become more productive, without redesigning the workflows, habits, communication, and trust needed for adoption.
This session explores the human side of AI in modern work environments. It looks at why AI initiatives often stall after the initial excitement: unclear ownership, poor change management, weak onboarding, fear of automation, disconnected workflows, and teams that are asked to use tools without understanding where they create real value.
Drawing from experience in SaaS, EdTech, digital marketing, and international go-to-market work, the session will offer a practical perspective on how organizations can move from AI implementation to AI adoption. Attendees will learn how to build trust, connect AI to daily workflows, define human oversight, and design modern work systems that support people instead of overwhelming them.
The goal is to help organizations think beyond “adding AI” and toward creating smarter, more human-centered ways of working.
AI Adoption Is Not a Tech Problem — It’s a Human Systems Problem
AI adoption is often presented as a question of tools, automation, and productivity. But in real organizations, the biggest challenges are usually much more human: unclear ownership, low trust, messy workflows, poor communication, and teams that are expected to adapt faster than they are supported.
This session explores what it actually takes to move from AI experimentation to meaningful business impact. It will look at how AI changes the way teams learn, communicate, sell, market, support customers, and make decisions — and why many initiatives fail when organizations focus only on the technology, not the systems around it.
The session will offer a practical framework for identifying useful AI use cases, preparing teams for adoption, building trust in AI-assisted workflows, and avoiding “productivity theatre” — the illusion of progress created by more tools, more outputs, and more speed, without better decisions or outcomes.
Attendees will leave with a clearer way to evaluate AI adoption: what problem it solves, who owns it, how workflows need to evolve, what success should look like, and how to make AI useful without making work noisier.
AI Adoption Is Not a Tech Problem — It’s a Human Systems Problem
AI adoption is often presented as a question of tools, automation, and productivity. But in real organizations, the biggest challenges are usually much more human: unclear ownership, low trust, messy workflows, poor communication, and teams that are expected to adapt faster than they are supported.
This session explores what it actually takes to move from AI experimentation to meaningful business impact. It will look at how AI changes the way teams learn, communicate, sell, market, support customers, and make decisions — and why many initiatives fail when organizations focus only on the technology, not the systems around it.
The session will offer a practical framework for identifying useful AI use cases, preparing teams for adoption, building trust in AI-assisted workflows, and avoiding “productivity theatre” — the illusion of progress created by more tools, more outputs, and more speed, without better decisions or outcomes.
Attendees will leave with a clearer way to evaluate AI adoption: what problem it solves, who owns it, how workflows need to evolve, what success should look like, and how to make AI useful without making work noisier.
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