Chat will be the interface, but trust will be the real differentiator
Chat interfaces are becoming the natural way people will work with data and AI. That in itself is not surprising. However, the difference between a functional solution and a dangerous illusion will not be how easily you can ask a question, but how much you can trust the answer.
AI responses must be transparent
One of the most important messages for 2026: AI must show how it arrived at an answer.
Not just the result, but also:
- what data it is based on
- where the limits of the answer are
- what assumptions are being used
Verifiability must be immediate and directly connected to the data layer
One of the strongest messages repeatedly highlighted throughout the presentations and discussions at Qlik SKO was simple yet fundamental: an AI response only makes sense if it can be immediately verified at the data level. It is not enough for the system to sound convincing or formulate an answer confidently. For real decision-making, it is essential to see what data the answer is based on, what assumptions were applied, and in what context it is valid.
And that is exactly why so much attention at Qlik SKO was devoted to topics that until recently were considered merely “technical background”:
- data integration
- data governance
- system interoperability
In the context of AI, these are no longer just supporting areas. They are decisive success factors.
Agentic AI: an accelerator of decision-making or an expensive experiment?
AI development is changing, and it is no longer just about generating answers or supporting insights, but about systems that independently plan, decide, and execute tasks within business processes. Such agentic AI goes beyond traditional assistants or chatbot models and moves AI closer to what many leaders call a “digital employee.”
For companies, this means two fundamental things:
- the use of agentic AI can significantly accelerate performance and automation
- at the same time, it introduces new risks if it is not supported by a high-quality data and governance layer
Agentic AI is not meant to replace people, but to free up their capacity for decision-making and innovation.
How agentic AI is applied in practice
In business, agentic systems are already being used today for the complex automation of tasks that previously required human decision-making:
- optimization of operational scenarios
- end-to-end process automation
- collaboration with enterprise applications
For decision-makers, this represents a strategic shift in how human expertise and business processes are utilized. 
Practical takeaways
- In 2026, AI will not compete through interfaces, but through the trustworthiness of its answers.
- Without transparency, context, and the ability to verify, AI will not become a management tool.
- Verifiability must be immediate and directly connected to the data layer.
- Data integration, governance, and interoperability are becoming key prerequisites for AI success.
- Agentic AI can significantly accelerate decision-making and automation if it is built on high-quality and governed data.
- Companies that master this foundation will move AI from experimentation into everyday management.
