Gartner tips AI to upend work tools, hiring & data

Gartner has published a set of data and analytics predictions that put AI at the centre of changes in workplace software, governance, hiring and the volume of data generated by machines in the physical world.

It expects AI to reshape leadership, governance, talent and market dynamics in the coming years. Gartner also points to a shift beyond text-focused models, with more systems operating in real environments and producing new classes of data.

"The pace of change in data and artificial intelligence is so rapid that each year feels like stepping into a new chapter of a science-fiction novel," said Rita Sallam, Distinguished VP Analyst at Gartner.

The predictions include a claim that AI agents will generate far more information from physical environments than from digital AI applications. Gartner also forecasts changes in how organisations assess job candidates and a shake-up in office productivity tools.

Productivity challenge

One near-term prediction focuses on the software used for everyday work. Through 2027, Gartner expects generative AI and AI agents to pose the first true challenge to mainstream productivity tools in 30 years, triggering a USD $58 billion market shake-up by the end of 2027.

It also describes a shift in how people create and edit content. Content creation is increasingly starting with generative AI synthesising existing material rather than beginning from scratch. Editing is moving toward iterative AI-generated rewrites instead of manual redrafting by a human author.

Gartner also expects new competition for productivity suites as value shifts toward agentic AI experiences. That change would reshape how users interact with tools and what they expect them to do. Data and analytics leaders, Gartner says, will need to demand tools that reflect these newer patterns of work, including different user interfaces, plug-ins, document types and formats.

Physical-world data

Gartner also forecasts a major shift in the scale and source of future data. By 2029, it projects that AI agents will generate 10 times more data from physical environments than from all digital AI applications combined.

The forecast reflects the spread of systems that act in physical settings and record their behaviour. Gartner points to "trajectory data" produced across logical, spatial and multiagent scenarios as these systems interact with their environments. This, it says, could allow "world models" to learn patterns from such data and run predictions and simulations.

The prediction also suggests a growing operational and technical burden for organisations that collect, store and manage this information. It implies a need for stronger approaches to data quality, lineage and controls as data volume and variety increase.

Hiring and skills

On workforce planning, Gartner predicts that by 2027, 75% of hiring processes will include certifications and testing for workplace AI proficiency during recruiting. It frames this as a response to rapid AI innovation and the need for clearer evidence of practical competence.

"D&A leaders should encourage rigorous, data-driven measurement of skills to surface deficits that stand between their AI-ambition and IT workforce readiness," said Sallam.

The prediction points to a more formal approach to AI skills and a shift in recruitment toward validated assessments over self-reported experience.

Governance enforcement

Gartner expects governance to move from policy documents to machine-checkable controls. By 2030, it predicts that 50% of organisations will use autonomous AI agents to translate governance policies and technical standards into machine-verifiable data contracts, automating compliance and policy enforcement.

In a related forecast, Gartner predicts that by 2030, 50% of AI agent deployment failures will stem from insufficient runtime enforcement by AI governance platforms, as well as issues tied to multisystem interoperability. It also warns that, in the near term, ungoverned decisions using large language models will cause financial or reputational loss for enterprises.

"D&A leaders should experiment with data governance agents in low-risk pipelines to orchestrate and automate negotiation processes," said Sallam. "They'll need to validate that agents can correctly interpret context and protocols in a controlled environment before trying to scale further. Analytic workflows should also be redesigned to include a required evaluation stage."

Efficiency and roles

Beyond governance and tools, Gartner predicts that by 2030 a new wave of unicorns will emerge, with USD $2 million in annual recurring revenue per employee and billion-dollar-plus valuations. It expects those valuations to be driven by capital efficiency and performance rather than investor capital.

By 2030, Gartner also predicts that 60% of organisations that achieve successful differentiation with AI will be led by executives who prioritise mastery of human relational skills. Separately, it forecasts that by 2028, 50% of content risk roles will shift from legal and cybersecurity to AI engineering as controls become more embedded in development processes.

Gartner analysts are due to discuss AI trends and data and analytics themes at its Data & Analytics Summit events in several cities, including Sydney, London and Tokyo.