What does the future hold for AI and data? By 2026, organizations expect AI and data to face big changes, impacting people’s lives and work. As exciting as these changes are, they also come with ethical and regulatory challenges.
Imagine it’s May 2025, and you’re at the 10th anniversary of the Data Innovation Summit in Stockholm. You feel the excitement as over 250 sessions dive into the latest advancements in data and AI. Outside, the world grapples with climate, politics, and rapid tech changes. AI has become part of everyday life.
By 2026, the pace of technological change will only accelerate. Will we see fully autonomous AI? Self-sustaining data driving real-time decisions? AI transforming from tool to creator, blurring human-machine creativity?
To help answer these questions, we turned to some of the visionaries who will take part at the Data Innovation Summit 2025 (DIS25). We asked them to make bold predictions about the future of AI & data. Keep reading for a sneak peek at the trends set to dominate the next tech evolution!
1. AI Agents and Self-Managing Ecosystems Will Run the Show
Many experts at DIS25 agree that autonomous AI agents are becoming more common. These are smart systems that can work mostly on their own, without much help from people. They are changing the way we manage and use data and AI.
”Well, who is better to ask than AI itself?” says Trine Lundorf. She predicts the development of “truly autonomous data ecosystems” that will automatically manage, clean, and optimize data in real-time. This enables businesses to make data-driven decisions without human intervention. This vision points to AI systems that both analyze and govern data.
“By 2026, the biggest breakthrough will be the rise of fully autonomous AI-driven data ecosystems, where self-healing, self-optimizing data pipelines operate with minimal human intervention.” states Deepak Yadav. He adds that AI will detect and correct data quality issues in real-time and predict and optimize data workflows. Moreover, it will adjust the infrastructure based on usage. LLMs will empower business users to query, analyze, and interpret data without writing complex code. As a result, this will democratize data access and reduce engineering overhead.
Dmytro Basan says: “AI agents will move from experiments to everyday infrastructure, embedded in industries from finance and media to small businesses and individual workflows.” He highlights how voice AI will replace human phone operators. Agents will detect emotions, adjust tone, and even negotiate, moving from generalist AI models to hyper-specialized agents performing at 99.99% precision for mission-critical tasks. The most revolutionary shift will be that “AI will no longer just serve humans, it will serve other AI.“ Agents will autonomously negotiate, trade, and optimize workflows at scales impossible for humans to manage alone. He calls this the arrival of the AI-driven economy.
AI will become ubiquitous and seamless, fully integrated into everyday workflows. According to Jawad Saleemi, the rise of autonomous AI agents in customer and network domains will blur the lines between traditional and AI-driven applications.
Andrea Gioia expands on AI agents’ capabilities. He states that “AI agents will not only use external tools but will also independently develop their own, tailored to the tasks they are designed to solve,” showing an evolution toward AI creativity and adaptability.
Lotte Ansgaard Thomsen emphasizes the practical impact of AI agents. “We will see AI agents becoming more prevalent across various areas. Pre-trained models have significantly improved, and the tools for creating customized applications have matured rapidly. By allowing AI agents to solve larger tasks by breaking them down into smaller, manageable parts, we open up great opportunities.”
“The most transformative impact will come from AI’s ability to autonomously analyze, decide and act across complex systems, effectively becoming a self-sustaining intelligence layer that augments human potential at scale.” states Panos Tsilopoulos. He believes this will redefine the collaboration between humans and AI, reshaping entire industries.
Prayson W. Daniel says, “Agents powered by LLM will take self-taught decisions and create software/tools to complete their long-term objectives.” This indicates a future where AI autonomously drives innovation and problem-solving.
The future of enterprise data management will be shaped by self-owned knowledge graphs and custom AI agents, which Kristina Kondrashevich predicts will reduce reliance on SaaS solutions. She highlights AI’s growing role in decentralizing and personalizing data management within organizations.
Karianne Sundahl focuses on customer experience: “Practical use of AI agents—truly enhancing the customer experience,” pointing to AI’s role in improving service and engagement.
AI’s increasing personalization and contextual awareness will drive the rise of “personal agents for specific tasks tailored for your persona,” as predicted by Niko Kivelä.
2. AI Will Reshape Healthcare—For Good
Another major theme is AI’s growing impact in healthcare and life sciences. More specifically, in early detection, personalized medicine, and accelerating innovation.
AI is set to play a crucial role in cancer prevention, transforming early detection, risk assessment, and personalized intervention strategies. According to Dácil Hernández, advancements in AI-driven imaging, genomic analysis, and predictive modeling will shift healthcare from reactive treatment to proactive prevention.
“The future of work will feature well-deployed and governed AI agents at scale, as Giovanni Leoni envisions, with notable applications in healthcare where AI agents support clinical decision-making and research.
Jakob Thrane Mainz predicts “the first large-scale high-impact applications of AI agents in the life-science industry,” where “efficiency gains and time-to-market reductions will really become evident,” signaling AI’s role in speeding up drug discovery and clinical workflows.
Gautam Verma envisions conversational and agentic AI getting embedded in the IoT devices and robotics in the form of humanoids. This could extend to healthcare robotics, patient monitoring, and personalized care assistants.
3. Large Language Models Are Just the Beginning: AI Breaks Out of the LLM Box
While LLMs have dominated headlines, many experts anticipate a diversification and evolution of AI architectures. They focus on efficiency, transparency, and decision-making capabilities.
As AI continues to evolve, Judith Bütepage predicts, “We will enter the post-transformer area,” suggesting that new model architectures will emerge to overcome the current limitations of transformer-based models. This shift is expected to drive more efficient and scalable AI systems, addressing challenges like computational cost and model generalization.
Mats Stellwall sees LLMs as “just one tool in our toolbox.” He predicts new types of model architectures aiming to replace many classical ML algorithms, with more focus on decision making. He also hopes for “more transparency around data used for training models,” addressing ethical and interpretability challenges.
A reduction in the size of LLM models, along with improvements in their performance, will make AI more accessible and efficient. Rami Krispin anticipates this shift will enable quicker deployment, lower resource consumption, and maintain high levels of capability.
Yngvar Ugland shares a cautionary note on the hype around Artificial General Intelligence (AGI): “Some people will claim that we have reached AGI, forgetting that the human brain runs on 20 Watts,” reminding us that true AGI remains a distant goal.
A fusion of AI capabilities is on the horizon, where the reasoning power of AlphaGo will merge with the knowledge power of LLMs, creating highly powerful AI systems that can perceive, reason, and take action even on limited resources, such as in a mobile phone, according to Anders Arpteg. He also predicts that OpenAI has lost its dominance, with Sam Altman no longer with the company.
This category highlights a maturing AI field where innovation will focus on creating more specialized, efficient, and interpretable AI systems. Systems beyond the current LLM paradigm.
4. Data-Centric AI Will Reshape Power and Control
Many experts emphasize a fundamental shift toward data-centric strategies, improved data governance, and democratization of data access.
Javier Riesco Refoyo predicts “a fundamental shift in how organizations work with data by 2026, a transition from a model-centric approach to one that’s truly data-centric.” He envisions environments where teams “literally ‘talk’ to our data,” enabled by conversational interfaces and strong governance. He also foresees new hybrid roles blending technical expertise and domain knowledge, such as “knowledge engineers,” to make data usable and meaningful at scale.
“AI will take over much of the coding process, opening up a new era for data engineers,” according to Tulika Bhatt. She envisions a shift from writing code to crafting intelligent, dynamic data contracts. This will transform how data moves, integrates, and adapts. These contracts will support “self-sustaining data landscapes” that can automate optimizations and resolve conflicts in real time, creating evolving ecosystems. Tulika highlights the challenge of “shaping the contracts that dictate this future” and mastering “the art of data flow like never before.”
Stijn “Stan” Christiaens highlights how “AI will lower the barrier of entry and we’ll become data citizens thanks to it,” democratizing data usage beyond specialists. As AI continues to make complex data more accessible, how will this shift in accessibility impact the way we make decisions in business and society?
AI will make data interaction more intuitive and automated, offering insights and delivering reports instead of relying on predefined dashboards. Gabor Harsanyi predicts this shift will transform how we engage with data.
Increased investment in human & organization transformation is essential to unlock truly tangible transformative value, as technology alone isn’t enough. Minna Kärhä emphasizes that cultural and organizational change is just as crucial to achieving real progress.
Nela Chereja warns about the risks of “bad input data” skewing AI outputs, emphasizing the critical need for data quality and integrity. As we rely more on AI for decision-making, how can we ensure that the data we feed into these systems remains unbiased and representative of reality?
Data is crucial for AI, and while AI should support data management, we are still managing it manually. Olof Granberg highlights this challenge, raising the question: how long can we rely on manual data management before fully integrating AI-driven solutions to streamline and automate the process?
Together, these insights reveal a future where data is central, accessible, trustworthy, and governed by intelligent systems that empower all users to leverage data effectively.
5. AI Will Redefine Work, Automation, and Industry Itself
Ingo Paas explores the concept of AGI (Artificial General Intelligence), raising questions about its potential to match human cognitive abilities. As we edge closer to developing AGI, what safeguards will be necessary to ensure its alignment with human values and ethical considerations?
Damien Julliard emphasizes the importance of connecting data tools and platforms in simpler ways, making it easier for users to integrate and interact with complex systems. As data becomes more central to decision-making, how can these simplified connections help organizations unlock new insights and drive innovation?
Julia Flament-Wallin admits, “I can’t really wrap my head around developing on quantum computers, so we’ll see.” As quantum computing continues to advance, how will it reshape the development landscape? What kind of new challenges will arise for those tasked with harnessing its power?
These predictions highlight AI’s role as a catalyst for new ways of working and automating routine tasks. It also enables human creativity and strategic thinking at scale.
All quotes and predictions are directly attributed to the DIS25 speakers as provided.
The Future is Now: Don’t Miss Out on AI’s Next Big Leap!
In the next few years, AI will continue to revolutionize industries and reshape how we interact with data. By 2026, the future of AI looks exciting, so get ready for groundbreaking breakthroughs ahead!
Don’t miss your chance to hear from leading experts and thought leaders shaping the future of AI and data! The Data Innovation Summit 2025 is just 2 weeks away – secure your spot now and be part of the conversation that will define the next big AI & data breakthroughs!
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