What is needed for a successfull AI adoption? Lessons from Gartner Data & Analytics Summit 2026 in London
02.06.2026
This year’s Gartner Data & Analytics Summit gathered data and AI leaders from across Europe at London’s ExCeL. Sii Poland Team was represented by Marcin Mosiolek, AI Offering Lead, and Malgorzata Wodzislawska, Data & Analytics Competency Center Director. They come back with one clear takeaway – the era of “let’s try AI” is over, and what matters now is who, and how, actually leads AI in their organization.
The most-cited figure was: “Four out of five companies increased their AI investment over the past year, but only one in five can point to a concrete return.” So, the question “should we invest in AI” has dropped off the agenda – the harder one took its place: How do we invest without throwing money away?
Leaders, not bystanders
One theme kept coming back in London – who actually leads AI transformation inside an organization. Gartner showed that companies that treat AI as part of their business strategy, rather than just another IT project, deliver noticeably better results.
If you don’t lead AI, AI will lead you. The people in your company are already using AI tools – or trying to build them. It is up to leaders whether that happens in a controlled and secure way – says Marcin Mosiolek, AI Offering Lead at Sii Poland.
The same point came through in every strategy session in London. Data and analytics leaders are no longer the technical owners of data – they are becoming the architects of key decisions. They are accountable not for the data itself, but for how the company makes the moves in a world powered by models.
No people, no AI
The second thread running through almost every session in London – the biggest barrier to scaling AI is no longer the models or the infrastructure; it is the teams. Deployments not paired with skill building, and a change in how decisions are made tend to end as expensive experiments with no follow-up.
Investing in AI without investing in people is throwing money away. Models will do exactly as much as our people allow them to – their skills, their habits, and their trust in the technology – says Marcin Mosiolek, AI Offering Lead at Sii Poland.
This mirrors our day-to-day work across financial services, energy, and manufacturing. The companies that treat AI adoption as a competence program from day one, not a tooling swap, deliver results visibly faster.
Natural ignorance before Artificial Intelligence
Day 2 of the summit was dominated by governance – particularly of unstructured data. The conclusion was simple: without a structured understanding of your own data and processes, AI has nothing to work with.
Before dealing with artificial intelligence, we need to deal with natural ignorance. Not understanding your own processes, data and strategic goals is a bigger risk today than any limitation of the models themselves – says Malgorzata Wodzislawska, Data & Analytics Director at Sii Poland.
Mature organizations are going back to the fundamentals – data catalogs, data quality, process mapping, and a shared business-IT language. Only on that foundation does AI start delivering value you can actually measure.
Will AI kill BI?
One of the most thought-provoking discussions at Gartner Data & Analytics Summit 2026 focused on the future relationship between AI and Business Intelligence. The key message was clear: AI is unlikely to replace BI; instead, the two are converging.
AI is transforming how users interact with data, but semantic consistency, data lineage, and a single source of truth remain non-negotiable. The future belongs to organizations that successfully combine AI-driven innovation with strong data foundations, ensuring that every insight is accurate, explainable, and business-ready – continues Malgorzata Wodzislawska, Data & Analytics Director at Sii Poland.
Gartner highlighted Agentic Analytics as the emerging model, combining AI-driven reasoning with BI-driven governance to deliver both agility and trust in decision-making.
AI and layoffs – the myth versus the numbers
Among the numbers cited in London was one that cuts through the media narrative. The story of “AI replacing people” is running well ahead of what the labour-market data actually shows.
For D&A leaders this points to two concrete moves: transparent internal communication and building hybrid teams in which people and models complement each other in specific decision processes.
Lessons from London
Three days at ExCeL leave us with a short list of principles.
- Start with strategy and decisions, not tools.
- Treat AI as a people-development program.
- Put data and processes in order before reaching for advanced models.
- And run an open conversation in your organization about what AI actually changes – and what it doesn’t.
Through its AI and Data & Analytics Competency Center, Sii Poland supports clients in AI strategy, data governance, machine learning development and responsible adoption of Artificial Intelligence.
