Gartner’s top 5 data & analytics predictions for new wave data teams in 2026

Did you know that Gartner estimates that by 2026, 90% of current analytics content consumers will become content creators enabled by AI?

Leading analytics, research, and expert guidance firm, Gartner, recently shared their thoughts on the future of the data and analytics industry, including understanding how to work with emerging AI tools while ensuring high-quality data management.

Keep reading to discover Gartner’s top predictions for the future of data & analytics as AI tools grow in adoption and the amount of data collected grows in quantity.

More and more, data and analytics leaders worldwide are seeking ways to better work with leading AI tools, invest in their data products, and continue using their data as an organizational asset rather than a setback.

Gartner’s top predictions for data & analytics in 2026

1. CDAOs must step up or step out & deliver data products with measurable ROI

2025 view

Gartner emphasized that CDAOs must shift from operational guardians to strategic value creators.

They must align data initiatives tightly with business outcomes, influence executive peers, and champion a unified data vision.

2026 integration

Gartner’s 2026 predictions increase the pressure:

• Over 25% of Fortune 500 CDAOs will be accountable for a top-earning D&A product.
• CDAO performance will be tied to product revenue, AI readiness, and governance maturity, not just data quality.
• CDAOs are expected to own AI-driven risk mitigation, including sovereignty, ethics, and explainability.

This reinforces the need for platforms like DataGalaxy that support data product lifecycle management with embedded governance, lineage, and usage metrics.

This means CDAOs will need to not only focus on data governance and quality but also become key strategists and communicators who drive the organization’s data agenda forward.

They must bridge the gap between technical teams and business leaders, ensuring data initiatives align with broader business goals and deliver measurable value.

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2. Augmented analytics will become mainstream & AI fluency will be mandatory

Augmented analytics, which leverages machine learning and AI to automate data preparation, insight generation, and insight explanation, will become more widespread.

2025 view

Gartner predicted most analytics workflows would be augmented by machine learning and natural language interfaces.

Insight generation would democratize, empowering non-technical users.

2026 integration

Gartner’s 2026 forecast adds a workforce dimension:

• 75% of hiring processes will require AI proficiency assessments or certifications.
• Organizations will introduce “AI-free” competency tests to ensure employees maintain critical thinking and analytical reasoning skills.
• Augmented analytics tools will increasingly include autonomous agentic behaviors, such as goal-directed analysis or anomaly detection, requiring minimal human intervention.

This means organizations need AI-literate employees and trusted data products, or augmented analytics may amplify bad data instead of insights.

3. Data fabric will be the foundation of work & multi-agent AI systems rely on it

Data fabric, a design concept that serves as an integrated layer of data and connecting processes, will become the foundation for managing an increasingly complex data environment.

2025 view

Data fabric enables connected, metadata-driven access across fragmented environments, supporting scalable AI and real-time analytics.

2026 integration

Gartner’s top technology trends for 2026 introduce multi-agent systems and AI supercomputing platforms, both of which require strong metadata and unified architecture:

• Multi-agent AI workflows depend on consistent schemas, lineage, and semantic metadata to collaborate across tasks.
• AI supercomputing platforms elevate the need for high-quality, connected data pipelines.
• Data fabric becomes a prerequisite for sovereign AI, where provenance, location, and compliance matter.

This shifts data fabric from “helpful” to mission-critical infrastructure for AI autonomy.

As data sources proliferate and data volumes grow, organizations will adopt data fabric architectures to ensure seamless data integration, accessibility, and governance.

This approach will enable a more agile and resilient data management strategy, supporting real-time analytics and decision-making.

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4. More AI means more investment in people & human judgment becomes a strategic asset

As AI technologies become increasingly integrated into business operations, organizations must prioritize investment in their people.

2025 view

Organizations needed to build data literacy and critical thinking to safely adopt AI tools. This includes developing skills in understanding, interpreting, and acting upon AI-generated insights.

Critical thinking and data literacy training will become essential for all team members, ensuring they can effectively leverage AI tools and the insights they provide.

2026 integration

Gartner warns that reliance on AI could reduce human critical-thinking capacity:

• Through 2026, 50% of organizations will require AI-free assessments to ensure employees retain strong reasoning skills.
• Workplace learning budgets will shift toward dual fluency: AI skill-building + human cognitive skill protection.
• AI copilots will handle more routine tasks, pushing employees to focus on higher-order decision-making.

Companies must design human-centered AI adoption strategies, or risk skill atrophy.

Developing skills in understanding, interpreting, and acting upon AI-generated insights, critical thinking, and data literacy training for all team members will help plan for the future of using AI tools and their implementation.

5. Data literacy as a core competency & AI literacy becomes the new baseline

Data literacy, or the ability to read, understand, create, and communicate data as information, will become a core competency for employees at all levels.

2025 view

Organizations were expected to launch enterprise-wide literacy programs as data-driven decision-making became the norm.

2026 integration

By 2026, literacy expands beyond data:

• AI Literacy = understanding how AI works, where it fails, how to validate outputs, and how to use it responsibly.
• Gartner predicts that AI literacy programs will become mandatory onboarding in large organizations.
• “Decision-centric literacy” will emerge: training employees not just to interpret data, but to direct autonomous AI systems.

Platforms like DataGalaxy will play a key role by making data relationships transparent through active metadata and explainable lineage.

Gartner predicts that organizations will invest heavily in data literacy programs to ensure that their workforce can effectively use data to drive decision-making and innovation.

Centralize all your data assets in one unified platform, automatically build and maintain lineage across systems, and enrich every asset with AI-powered context. With DataGalaxy, teams can quickly search, discover, and understand the data they need, while ensuring full traceability and trust.

This shift will help bridge the gap between data specialists and business users, fostering a more collaborative and data-driven organizational culture.

Looking Ahead to 2026 & beyond: What’s next in D&A and AI

In addition to the previously discussed 2025-level predictions,

Gartner has now published insights for 2026 and beyond that signal deeper structural shifts in how data, analytics, and AI will work together.

Here are key takeaways relevant to data & AI product governance.

Talent, skills, and the CDAO’s evolving role

• By 2026, Gartner predicts a sharp focus on AI proficiency in hiring: 75% of hiring processes will include certifications and assessments for workplace AI skills.
• At the same time, there is an anticipated push toward ensuring human critical-thinking skills remain strong: through 2026, Gartner forecasts that 50% of global organisations will require “AI-free” skills assessments to counter talent atrophy due to over-reliance on generative AI.
• For the CDAO (or equivalent leader), the expectation escalates: those who cannot deliver measurable business impact and align data & AI strategy with business goals risk being reassigned. (From earlier D&A forecasts: by 2026, more than a quarter of Fortune 500 CDAOs will be responsible for at least one top-earning data & analytics product.)

Risk, governance, and sovereignty in AI

• A big theme in the 2026 predictions is sovereign AI — the idea that control, provenance, and responsibility around AI systems become strategic competitive levers.
• Gartner warns of AI governance risk: by the end of 2026, legal claims tied to AI decision automation may exceed 2,000 due to insufficient guardrails.
• The implication for data & AI product governance platforms like DataGalaxy is clear: strong metadata, lineage, data product governance, and AI-model observability become non-negotiable.

Architecture, scale, & agentic workflows

• Gartner identifies “AI Supercomputing Platforms” as a top strategic technology trend for 2026 — integrating CPUs, GPUs, AI-specific ASICs, neuromorphic hardware, and orchestration to unlock new performance levels.
• Also singled out: “Multi-agent systems” — collections of AI agents that interact to achieve shared goals and automate complex workflows.
• For data architecture & product governance, this means: moving beyond single-platform analytics to intelligent, autonomous decision flows that rely on trusted data products, active metadata, and governance baked into every layer.

Implications for data-driven organizations

• Organisations must evolve from thinking of data as a by-product to treating data as embedded in AI-driven products and outcomes.
• The shift from “data-driven” to “decision-centric” is becoming more than a slogan — it’s a core strategic shift.
• Building an operating model which treats each data product as a governed, measurable entity—complete with lifecycle, ROI metrics, usage tracking, and AI-readiness—is critical. Platforms like DataGalaxy become the backbone of this model.

Conclusion

Gartner’s forward-leaning predictions for 2026 and beyond emphasize that the next wave of value from data & analytics will not just come from more data or more AI — but from intelligent orchestration at scale, governed autonomy, and human-machine symbiosis.

To thrive in this new era, organisations must:

• Ensure their data products are governance-ready and AI-enabled
• Build a workforce fluent in AI and human judgment
• Embed architecture and platforms that can support multi-agent workflows and behaviour at scale
• Treat governance not as a back-office function but as a strategic capability tied to measurable business outcomes

By aligning your strategy today with the 2025 and 2026 forecast horizons, you position your organisation as one ready for the decision-centric future — not just the data-centric present.

In conclusion, the integration of AI into business operations necessitates a significant investment in people. By focusing on skill development, strategic planning, continuous learning, collaboration, and strong leadership, organizations can ensure they are well-prepared to harness the power of AI.

This holistic approach will enable businesses to make the most of AI-generated insights, driving smarter decisions and achieving sustainable competitive advantages.

About the author

Jessica Sandifer

With a passion for turning data complexity into clarity, Jessica Sandifer is an experienced content manager who crafts stories that resonate across technical and business audiences. At DataGalaxy, she creates content and product marketing messages that demystify data governance and make AI-readiness actionable.