Gartner published its top predictions for data and analytics in 2026 earlier this month, and the headlines are worth sitting with. The research firm, which sets the strategic benchmark for most enterprise technology decisions, frames 2026 as the year AI moves from experimentation to measurable return on investment in data and analytics.

Here are the predictions that matter most for business leaders making decisions now.

AI Agents Will Generate 10x More Data by 2029

By 2029, AI agents will generate ten times more data from physical environments than from all digital AI applications combined.

This is a significant forecast to absorb. Most of the current conversation around AI is focused on digital workflows — chatbots, document analysis, code generation, customer service automation. But Gartner is pointing to a coming wave of AI operating in the physical world: sensors, manufacturing floors, logistics networks, medical devices, building management systems.

The data generated by agents operating in those environments will dwarf what current enterprise AI produces. The implication is that businesses which invest in data infrastructure and data literacy now will be significantly better positioned to capture value from that wave. The businesses that do not will be managing a data volume they are not equipped to use.

75% of Hiring Will Require AI Proficiency by 2027

By 2027, Gartner predicts that 75% of hiring processes will require candidates to demonstrate AI proficiency certification.

That is less than 18 months away. The practical implication is straightforward: AI skill is shifting from a differentiator to a baseline expectation. Job descriptions that once listed “Excel” and “SQL” as requirements will increasingly list AI tools alongside data fundamentals.

For organisations, this creates two immediate pressures. First, the talent pool is bifurcating into those who have invested in AI skills and those who have not — and that gap will widen quickly. Second, businesses that upskill their current teams now will retain people who would otherwise leave for organisations that are further ahead.

For individuals, the path is clear. The question is not whether to develop AI and data skills. It is how quickly and in what sequence.

60% of Repetitive Data Management Tasks Will Be Automated

Gartner also predicts that by 2027, 60% of repetitive data management tasks will be automated — things like data entry, data cleaning, schema mapping, and routine reporting.

This does not mean data teams are shrinking. It means data teams will have capacity for work that currently does not get done: more rigorous analysis, more time spent on data quality strategy, more investment in data governance. The teams that automate well will not just save time — they will shift what they spend time on.

The risk is the reverse: teams that do not automate these tasks will find themselves falling behind on the work that matters, spending their capacity on work that AI should be doing.

2026 Is the ROI Inflection Point

The broader Gartner narrative is that 2026 is the year the “experimentation” phase of enterprise AI closes. For the past three years, it has been acceptable to pilot AI tools, run workshops, and declare that the organisation is “exploring” AI. That window is closing.

Organisations that have been in perpetual exploration mode will find themselves comparing their results against peers who committed to execution. The gap between leaders and laggards in AI adoption is already measurable in productivity terms. By 2027, it will likely be measurable in market share terms.

What This Means for Your Business

These predictions are directional, not guaranteed. But Gartner’s track record on enterprise technology shifts is strong, and these forecasts are consistent with what is already visible in the market.

A few practical implications to act on:

On AI proficiency: If 75% of hiring will require AI certification by 2027, your current team members should not wait for their next job search to develop those skills. Structured learning programmes — covering everything from data fundamentals to practical AI tool usage — create a competitive advantage for both the individual and the organisation.

On data infrastructure: The coming wave of AI-generated data from physical environments requires data infrastructure that can handle volume, variety, and velocity. Organisations investing in data architecture now are making a bet that will pay returns well beyond 2026.

On automation ROI: The automation of repetitive data tasks is not a distant horizon. Tools to do this exist today. The barrier is usually not technology — it is clarity about which tasks to automate first, and whether the team has the skills to implement and maintain those automations. Starting with high-volume, low-complexity tasks creates momentum and builds capability.

On leadership: Executives who are not personally developing a working understanding of AI and data tools are making a strategic error. Not because they need to write code, but because AI strategy decisions made without first-hand understanding of the tools will be poorly calibrated. This is exactly the scenario that fractional AI advisory is designed to address.

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Enterprise DNA’s learning platform covers Power BI, Python, SQL, Excel, and applied AI — giving data professionals and business teams the skills Gartner is predicting will be table stakes by 2027. Explore courses and certifications or talk to us about a team training programme.