Five data and AI predictions that will define 2026

Why the next year won’t reward hype, but will reward foundations

If you consume enough B2B tech predictions content, it’s easy to believe that 2026 will be the year that AI finally “does it all.” Agents everywhere, cooperating seamlessly in multi-agent systems. Decisions and end-to-end processes that are fully automated. Data teams being quietly replaced by AI.

That narrative is convenient, but the sensationalism isn’t grounded in reality. The organizations that succeed in 2026 won’t be the ones chasing experimental AI breakthroughs; they’ll be the ones willing to get the basics right, invest into their data architecture, and be brutally honest about what’s actually working.

Based on what we’re seeing across enterprises today, here are five predictions from Ataccama leadership about what will really define data, AI, and enterprise decision making in 2026, and what data leaders should do now if they want to stay ahead.

1. The data trust layer becomes the backbone of enterprise-scale AI

AI initiatives won’t fail because models aren’t smart enough, but because the data that powers them is flawed. By 2026, AI adoption at scale will stall in organizations that treat data trust as an afterthought. To move faster and stay competitive, companies should invest early in a data trust layer that sits between raw systems of record and AI-driven decisions.

This layer is not a dashboard or even a catalog, but the architectural foundation that aligns data with how the business actually works.

When simple concepts like customer, revenue, policy eligibility, or other terms specific to your company and industry are defined clearly and consistently, models, agents, analysts, and business teams can all reason from the same baseline. Without that shared meaning, even the most advanced systems will produce conflicting, unreliable answers to the same questions and put major AI initiatives and investments in jeopardy.

Why it matters in 2026
As AI moves closer to operational decisions, inconsistencies stop being just another pain point and start becoming risky. Regulatory exposure, broken automations, and eroding trust quickly follow when systems (and people who use them) can’t agree on basic definitions. A trust layer that’s deeply embedded into enterprise data architecture makes insights more reproducible, agents more reliable, and compliance more achievable at scale.

What data leaders should do now

• Make it a priority to invest in a data trust layer that connects raw systems of record with AI-ready, human-readable meaning
• Standardize core business concepts across pipelines, analytics, and AI workflows
• Design for reuse and consistency, not one-off success stories from AI experiments

2. Natural language opens the door to data

Why have natural language interfaces failed in the past? In some cases, the interfaces just weren’t good enough, or users weren’t ready to engage with them. More often, they failed because they were fueled by low quality data and users couldn’t trust the output.

But there’s hope. We predict that In 2026, “just ask a question” will stop being a demo trope and start to be the norm. Text-to-SQL and conversational analytics will move into production because foundations like semantics, lineage, and quality will have finally caught up.

Enterprises should keep in mind that this shift makes analysts more valuable, not more replaceable.

By cutting down on time spent actioning vague or unclear requests, analysts will have more time to shape decisions that impact the business. At the same time, business users will gain direct access to governed answers rooted in high quality, trusted data.

Why it matters in 2026
Making decisions gets faster and easier when getting the answer you need is just a matter of asking the right question. Access to trusted data stops being a bottleneck, leaving more time for consideration, interpretation, and making data-backed decisions.

What data leaders should do now

• Invest in semantic consistency before scaling access to data
• Ensure lineage and quality signals are exposed to users, not hidden
• Redesign analyst roles around interpretation, not translation of vague requests

3. Unstructured data enters the production loop

Unstructured data isn’t new. What is new in 2026 is that enterprises can no longer afford to ignore it.

In 2026, documents, PDFs, transcripts, emails, and conversation logs will move out of digital purgatory and into real production workflows. Longer-context models and better retrieval pipelines will make this possible.

Alongside expanding capabilities of their AI models, organizations that succeed will apply the same discipline to unstructured content that they already apply to structured data, applying lineage, access controls, quality checks, and governance across all inputs.

Why it matters in 2026
Compared to structured data, unstructured data often contains more context and more risk. Bringing it into operational decisions without guardrails can create new points of failure that many organizations aren’t prepared to handle.

Enterprises that treat unstructured data as a priority while approaching it with the rigor and standards they have for structured data will gain access to more informed decision making, without having to sacrifice oversight.

What data leaders should do now

• Recognize that unstructured data is now too valuable (and too risky) to leave unmanaged
• Properly track the provenance and usage of unstructured sources by enforcing lineage, access controls, and quality across unstructured inputs

4. The ROI bar gets higher… and everything else gets cut

Enterprises will continue to experiment with AI, but returns will no longer be optional.

In 2026, boards and CFOs will stop funding AI projects based on an elusive standard of “potential.” Flashy pilots and perpetual proofs of concept will lose budget, and only initiatives that deliver measurable outcomes will survive and expand.

This is great news for teams that have invested into making sure they can trust their data by improving data quality, defining governance, and ensuring explainability. When data is trustworthy, AI becomes a durable asset with real, demonstrable value.

Why it matters in 2026
In a market that’s skeptical of black boxes and “AI-powered” claims, buyers want solutions that embed into daily operations. This is especially true in regulated industries where failure comes with a hefty price tag.

In a market where appealing dashboards and attractive front ends can no longer carry their weight, durable, sustainable solutions can be found in reduced costs, faster cycle times, and smarter automation.

What data leaders should do now

• Align goals and KPIs with the business to tie AI initiatives directly to operational metrics
• Retire AI pilots that don’t (or can’t) articulate business impact
• Demand explainability and traceability from vendors and internal teams to get answers about what’s working, where the impact is hitting, and why it matters now

5. Agentic AI expands the data workforce

AI won’t shrink data teams, but it will bring them into the spotlight. As Ataccama CEO Mike McKee puts it, data leaders and practitioners are now the “cool kids” in the enterprise.

We predict that as agents offload manual, repetitive tasks like querying, cleaning, documenting, and validating data from data teams, the cost of generating insight will fall dramatically (and demand will rise).

When it gets easier to ask questions, access data, and put it to use, more of the organization participates. Teams that once relied on instinct will start to use data for their decision making. Analysts embedded in the business will become more valuable, not less, and governance teams will grow because expanded usage means increasing oversight.

Why it matters in 2026
The number of data users will increase faster than most organizations expect, presenting both a major opportunity and a growing risk to businesses. Without strong foundations, data federation becomes ungoverned and chaotic; with them, it’s a catalyst for transformation.

What data leaders should do now

• Prepare data teams to service more users, handle more questions, and expect higher scrutiny
• Invest in data quality and governance as a growth enabler, rather than a constraint
• Upskill teams to work alongside agents

Webinar highlights

Data trust and AI: What will define 2026

With 2026 set to be a breakthrough year for enterprise AI, we brought together three data leaders to chat through the trends shaping the year ahead.

Speakers:

• Jay Limburn, Chief Product Officer at Ataccama
• Akhil Lalwani, Chief Data Officer at Allianz UK
• Shailen Mistry, Data & Analytics Senior Principal at Slalom

Watch the highlights below or head straight to the full webinar to better understand the major shifts data and AI leaders should expect in 2026.

What it takes to win in 2026

2026 won’t reward endless wild pursuits of AI experimentation. Only organizations willing to invest in data trust that breeds better, more sustainable, metric-backed decision making will succeed.

The future of AI is about systems built on data that can be trusted when the stakes are higher than ever. Data leaders who focus on foundations now will be the ones to define success for the rest of the market, ensuring data that feeds AI models can be trusted, preparing for a wave of new data users as natural language queries increase, attending to unstructured data in the organization, and pursuing radical alignment with business objectives.