Organisations that report successful AI initiatives invest up to four times more (as a percentage of revenue) in foundational areas such as data quality, governance, AI-ready people and change management compared to those that experience poor outcomes from AI, according to Gartner.
However, a global survey of 353 data and analytics (D&A) and AI leaders from November through December 2025 also found that only 39% of technology leaders are confident that their enterprise’s current AI investments will have a positive impact on financial performance.
“D&A leaders play a central role in achieving their organisation’s AI value ambition,” says Rita Sallam, distinguished VP analyst, Gartner Fellow and chief of Research at Gartner. “Through 2030, the D&A leader’s mandate is to deliver foundational areas including new trusted data, context foundations, and perceptive intelligence. Responding to this mandate will require shifts in how the D&A team organises and works, builds and scales, and creates value.”
Shift 1: Build toward AI-first D&A
This shift starts and ends with an AI ambition for leveraging AI to transform, not tweak, business and operating models aligned to achieve audacious business objectives. Pioneering leadership is required to apply new technology in high value, innovative ways.
Shift 2: Redesign the D&A organisation for human-agent collaboration
“The future is not about replacing humans, but amplifying their ingenuity,” says Sallam. “Because AI will create extra capacity, D&A teams will shrink in size and expand in impact. AI-first D&A organisations will have smaller, ‘tiny’ teams organised as decision pods of broad-skilled talent augmented by AI and AI agents specialists focused on business outcomes. We see pacesetting companies experimenting with teams as small as one ‘technical’ person and one ‘business’ person.”
Shift 3: Establish context as critical infrastructure
Gartner found that organisations with the highest maturity of AI-ready D&A capabilities are achieving up to 65% greater business outcomes – including revenue growth and cost optimisation. D&A success in 2030 is not about better models – it is about giving agents governed, contextual access to the right data.
Agents cannot function autonomously without high-quality context and absolute trust. Context capabilities act as the brain for AI. Therefore, context – including semantics and metadata – are now mission-critical for D&A. D&A leaders must redesign the D&A architecture to make the context layer the central brain for AI agents to deliver trusted intelligence.
Shift 4: D&A organisations should scale connected engineering practices
Realising AI ambition at scale requires new, deeply integrated engineering practices. Siloed practices for data, AI, context and software engineering will fail to realise an AI-first ambition.
D&A organisations should shift from an endless loop of proof-of-concept cycles to enterprise scale by building interconnected data, AI, software, and context engineering practices and skills.
Shift 5: Establish trust as a catalyst of value and innovation
Governance is becoming the foundation for realising value and driving innovation. However, a Gartner survey of 360 IT leaders in the second quarter of 2025 found that only 23% said they are very confident in their organisations’ ability to manage security and governance when deploying GenAI tools.
“Traditional control should be overhauled to prioritise trust-based governance models for AI agents by building dynamic governance to embed automated context and checks for bias, privacy, and compliance directly into workflows,” says Sallam. “Without trust in the data, outputs and decisions of AI models and agents, there is no value from AI.”