Gartner Forecasts Major Shifts in Data & Analytics Landscape

Brickinfo News Agency – Gartner, Inc. has released its key predictions for data and analytics (D&A) through 2025 and beyond, highlighting a significant future for artificial intelligence (AI) in business operations. The forecast indicates that AI agents will augment or automate half of all business decisions, and that a focus on executive AI literacy will correlate with improved financial performance for organizations. Additionally, the report warns of potential critical failures in synthetic data management impacting AI governance, model accuracy, and compliance.

“Nearly everything today – from the way we work to how we make decisions – is directly or indirectly influenced by AI,” stated Carlie Idoine, VP Analyst at Gartner. “But it doesn’t deliver value on its own – AI needs to be tightly aligned with data, analytics and governance to enable intelligent, adaptive decisions and actions across the organization.”

Gartner advises organizations to incorporate several strategic assumptions into their planning for the next two to three years. By 2027, it’s projected that 50% of business decisions will be augmented or automated by AI agents for decision intelligence. Decision intelligence, which integrates data, analytics, and AI, aims to create decision flows that support and automate complex judgments. AI agents are expected to manage the complexities of data analysis and retrieval. Gartner suggests D&A leaders collaborate with business stakeholders to pinpoint critical decisions that could benefit from enhanced analytics and AI application. Idoine added, “AI agents for decision intelligence aren’t a panacea, nor are they infallible. They must be used collectively with effective governance and risk management. Human decisions still require proper knowledge, as well as data and AI literacy.”

Another key prediction is that by 2027, organizations prioritizing AI literacy for their executives are anticipated to achieve 20% higher financial performance. This is attributed to executives’ ability to make more informed decisions regarding AI investments, understanding both opportunities and risks. Gartner recommends D&A leaders implement experiential upskilling programs for executives, such as developing domain-specific prototypes to make AI concepts more tangible.

The report also addresses the rising use of synthetic data. By 2027, Gartner predicts that 60% of data and analytics leaders will encounter significant failures in managing synthetic data, posing risks to AI governance, model accuracy, and compliance. While synthetic data is crucial for enhancing privacy and generating diverse datasets for AI model training, challenges arise in ensuring its accurate representation of real-world scenarios, scalability, and integration with existing data pipelines. To mitigate these risks, Idoine emphasized, “organizations need effective metadata management. Metadata provides the context, lineage and governance needed to track, verify and manage synthetic data responsibly, which is essential to maintaining AI accuracy and meeting compliance standards.”

Looking further ahead, by 2028, Gartner anticipates that 30% of generative AI (GenAI) pilots transitioning to large-scale production will be built in-house rather than deployed using packaged applications, primarily to reduce costs and increase control. Building GenAI models internally offers greater flexibility and long-term value. Additionally, by 2027, organizations that emphasize semantics in AI-ready data are expected to improve their GenAI model accuracy by up to 80% and reduce costs by up to 60%. Poor semantics can lead to increased hallucinations and higher computational costs in GenAI models. Gartner suggests that rethinking data management to focus on active metadata can enhance model accuracy, efficiency, and AI data readiness. Finally, by 2029, the report projects that 10% of global boards will utilize AI guidance to challenge executive decisions critical to their business, underscoring the growing importance of strong data governance and clear policies for AI’s role in decision-making at the board level.