Written by Lina Malaskeviciene
AI is advancing quickly, but understanding what that progress means for people is just as important as understanding the technology itself. At World Summit AI 2026, discussions ranged from new applications of AI in medicine to questions about human learning, oversight, and the practical skills needed to use AI effectively.
Held on October 7–8 at Taets Art & Event Park in Zaandam, near Amsterdam, the 10th-anniversary summit brought together researchers, startup founders, investors, technology companies, business leaders, and policymakers as part of World AI Week. AI4ALL joined as a community partner, attending sessions and speaking with startups, entrepreneurs, established companies, and partners. Across the program, one theme stood out: as AI capabilities grow, people need practical knowledge to understand, question, and use them well.
Daphne Koller, founder and CEO of insitro, explored AI’s role in understanding disease and developing treatments in “From Data to Targets: Causal AI for Better Medicines.” Her session highlighted how AI can help researchers identify patterns in biological data, investigate disease mechanisms, and potentially improve how treatments are developed.
In related interviews, Koller has described research using tumor biopsy images to infer molecular information that could eventually support treatment selection. She has also outlined a five- to ten-year outlook for AI’s growing contribution to drug discovery and clinical development. These possibilities, however, depend on research, clinical testing, and validation.
This distinction matters beyond healthcare. A promising research result, a medicine entering clinical trials, and a treatment available to patients are different stages of progress. As AI develops, understanding where a technology stands — and what evidence supports its use — is an important part of AI literacy.
The panel “Smart Machines, Dumber Minds? Navigating the Brain Drain in the Age of AI” examined what technological progress means for human learning and judgment. Trena Minudri, Coursera’s Chief Learning Officer, emphasized that investment in human capability needs to keep pace with investment in AI.
For organizations, this means employees need more than access to AI tools. They need to understand where a tool is useful, how to evaluate its results, and when to question its recommendations. Someone using AI to summarize a report, for example, still needs to recognize missing context and check the sources.
Developing these skills requires practice. Training that allows people to work through relevant problems, receive feedback, and learn from mistakes can help build the judgment needed to use AI effectively. It can also give employees greater confidence to participate in changes that AI is bringing to their work.
The summit’s healthcare program included “AI in Healthcare: How Safe Is Safe Enough?” In a preview published ahead of the session, the organizers asked how the effectiveness of human oversight should be tested. They highlighted the need for people to have the information, tools, and authority to challenge an AI system, as well as evidence that their involvement improves outcomes.
The principle applies beyond healthcare. When people review AI-generated results, they need to know what to check, what context matters, and what action to take if something appears wrong. The World Health Organization’s guidance on generative AI in health similarly emphasizes transparency, risk management, and appropriate governance.
For organizations adopting AI, effective oversight therefore requires more than assigning a person to “check the AI.” It requires clear review processes, appropriate training, and defined responsibility.
During World AI Week, AI4ALL’s AI Workflow Builder Workshop, led by Oliviana Bailey, gave participants an opportunity to design and build workflows around problems from their own work.
One principle from the workshop was 80% planning, 20% building.
Planning involves understanding the process, identifying the information it needs, and deciding where human judgment is essential. It also helps establish how the finished workflow should be tested.
This approach provides a useful starting point when exploring AI tools or product demonstrations:
– What problem does the system solve?
– What information does it need?
– How can its results be checked?
– Where does a person need to make a decision?
Professionals can begin with their knowledge of the task, even without a technical background. A clear understanding of the problem helps them evaluate whether a tool fits their needs.
World Summit AI’s discussions on medicine, learning, and oversight illustrate the range of skills involved in putting AI to use.
Scientific advances need testing and clear explanations. Workplace adoption requires training relevant to the decisions employees make. Review processes depend on people understanding the systems they are responsible for checking.
For AI4ALL, these are not abstract questions. They are exactly the skills people need to develop if AI is going to become part of their everyday work and lives.
As applications develop, people need opportunities to understand the technology, explore its uses, and ask informed questions. AI literacy is not about turning everyone into an AI engineer. It is about giving people enough understanding to ask better questions, make informed decisions, and use AI with confidence and responsibility.
Building these opportunities into education and workplace training can help more people participate meaningfully in decisions about AI — and in shaping the future it creates.
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