7 trends to shape data and AI in 2026

2026 promises to be a pivotal year for data management and AI. Businesses are looking to scale their AI programs to maximize value. They understand that these need to be built on the right data foundations. Meanwhile, the rise of agentic AI moves the technology from making predictions to carrying-out real-world actions, requiring increased context while enabling new ways of working.

All of these trends provide opportunities and challenges for Chief Data Officers (CDOs) and other data leaders. To help them effectively plan for the year ahead, we’ve collected key predictions from analysts and thought leaders, including Gartner, Forrester, IDC, and Constellation Research. Together, they demonstrate the central importance of data, and its seamless availability, to business competitiveness in 2026 and beyond.

A deep-dive into predictions research published by leading analysts and market commentators highlights 7 key trends around data and AI:

62% of organizations surveyed by McKinsey are already experimenting with AI agents. The consultancy predicts that in 2026 this will grow as existing pilots mature and new use cases develop. In its 2026 FutureScape report, IDC forecasts that by 2030, 45% of organizations will orchestrate AI agents at scale, embedding them across business functions.

Scaling AI agents brings three key challenges and opportunities to CDOs:

Data

Agents require access to relevant, contextualized data in order to make accurate decisions. The risk of poor AI decisions has been replaced by the risk of poor actions, which can be even more costly for organizations.

New roles

Agentic AI moves the CDO role from reactive data hygiene to become a workflow orchestrator. That means they are much more involved in the wider business. Snowflake’s Data + AI predictions 2026 highlights that success requires consistent processes, and robust data foundations and governance

Security

The use of data in agentic AI increases pressure to ensure governance, protection and compliance. Organizations need to be transparent and confident that confidential data is not being used unethically or being shared publicly by AI agents, requiring tighter governance and monitoring. IDC predicts that “By 2030, up to 20% of G1000 organizations will have faced lawsuits, substantial fines, and CIO dismissals due to high-profile disruptions stemming from inadequate controls and governance of AI agents.”

In its 2026 predictions, Forrester highlights widespread disappointment with the business benefits received so far from AI projects. Just 15% of AI decision-makers said that they’d seen a boost to the bottom line from their programs. This will lead enterprises to delay 25% of Al spend to 2027, focusing more heavily on projects that deliver clear ROI.

Allied to this, there will be a stronger focus on technologists (CDOs and CIOs) to get AI projects back on track. Forrester says that 25% of CIOs will be asked to turn around business-led AI failures, especially when it comes to putting in place the governance, skills and data capabilities to scale agentic AI.

Many AI projects to date have been based on general purpose large language models (LLMs), trained on vast, generic datasets. These may deliver benefits for common tasks, but don’t enable differentiation or specialization based on an organization’s specific context and industry.

By contrast, domain-specific language models (DSLMs) are trained or fine-tuned on specialized data, often from the organization itself. This gives much more accurate, focused results that deliver value. In its top 10 predictions, Gartner states that “by 2028, over half of the GenAI models used by enterprises will be domain-specific.”

Without accurate, reliable, consistent and easily accessible data neither AI models or agents will be able to operate effectively, removing benefits and increasing risks. It is also vital that data is comprehensive and from multiple sources to give the necessary context. That means bringing together multiple datasets into specific data products, and/or combining internal and external data (such as demographic or geographic information) to provide AI with a complete view. How data is used also must be managed and governed transparently, with a full, human-readable audit trail of its lineage across the AI journey. Explainable AI requires explainable data.

Without data readiness, organizations won’t generate ROI – and in fact will see their effectiveness worsen. IDC forecasts that “By 2027, companies that do not prioritize high-quality, AI-ready data will struggle scaling GenAI and agentic solutions, resulting in a 15% productivity loss.”

In the past, data was often shared with the business through dashboards and other reports. However, the volume and velocity of data that is now available leads to new issues for organizations. Essentially, they are drowning in insights, and find it difficult to translate these into meaningful operational action, according to Constellation Research. Effective decision-making is prevented by unclear guardrails, rules solely contained in employee’s heads, conflict over what metrics mean, and no clarity over what “good” looks like.

To overcome this, businesses need to move to decision loops that better guide humans and AI in driving better decisions. They need to be fed with both data and clear rules to govern how decisions are made, enabling consistency across the business.

Futurist and consultant Bernard Marr was one of the first people to popularize the term data democratization, i.e. sharing data in accessible, understandable, and usable ways with all relevant stakeholders. In his predictions for 2026 he highlights that data is becoming faster, smarter and more autonomous than ever before.

This requires new approaches from organizations, focusing on AI-ready data, deploying AI to automate data management, ensuring traceability through data provenance tools and compliance with growing regulations across the world.

Building on data democratization, Marr explains that 2026 will see the advent of Generative Data Democracy. Non-technical users will be able to use the simplicity of conversational Gen AI interfaces to gain answers from data through natural language queries, without needing to learn specialist skills or terminology. This will open up access to data much more widely and drive greater consumption.

2026 will see leading organizations adopt AI at scale, delivering benefits across the organizations. Analysts predict that agentic AI will be deployed in multiple departments, breaking down silos and reshaping the role of the CDO.

Writing in Harvard Business Review, industry experts Vipin Gopal, Professor Thomas Davenport and Randy Bean forecast that this expanded role requires a new title. Rather than different parts of the AI and data puzzle being handled by separate roles, they highlight the benefits of combining them into one, The Chief Data, Analytics, and AI Officer (CDAIO) will cover both strategy and operations, with a clear mandate to orchestrate enterprise value from AI and data while managing emerging risks. This will help companies to accelerate AI programs without creating over-complex hierarchies under a single, accountable leader.

Essentially all analyst predictions for 2026 and beyond show the importance of reliable, understandable and above all accessible data to power both AI and human decision-making. Information has to be available through self-service, with full context, in a highly consumable way in order to drive business value.

That requires solid foundations across the data journey, from collection to consumption, and can only be delivered through an AI-driven, self-service data product marketplace. This centralizes all data and makes it easily available through an e-commerce-style marketplace that uses AI to aid discovery and natural language queries in order to guide better informed decision-making, delivering benefits around productivity, innovation, and collaboration.

Planning for 2026? Talk to our experts to learn how our data product marketplace can underpin your strategy. Book your meeting now!