Gartner: Generative AI to power 75% of analytics by 2027

Gartner has forecast that by 2027, 75% of new analytics content will be contextualised for intelligent applications through generative AI, a shift expected to enable closer connections between insights and actions across business software and processes.

According to Gartner, this change signifies a move away from traditional analytic tools, instead ushering in an era where AI-driven analytics supports more dynamic and autonomous decision-making capabilities.

Georgia O'Callaghan, Director and Analyst at Gartner, outlined the implications of this anticipated evolution:

"We're moving from an era where analytic tools help business people make decisions, to a future where GenAI-powered analytics becomes perceptive and adaptive. This will enable dynamic and autonomous decisions that have the potential to transform enterprise and consumer software, business processes and models."

Backing this projection, Gartner cited results from a survey of 403 analytics or AI leaders, conducted from October to December 2024. The survey found that over half of organisations currently use AI tools for generating automated insights and for employing natural language queries within analytics and AI development. However, Gartner noted that the present systems are predominantly static in nature, often lacking the capacity for truly dynamic or automated analytics delivery.

Autonomous analytics platforms

Gartner further predicts that within the next two years, augmented analytics platforms will evolve to become autonomous. By 2027, these platforms are expected to fully manage and execute 20% of business processes, enabled by their ability to operate proactively, collaboratively, and within continuously updated contexts.

The next phase, as described by Gartner, will see the integration of AI agents and generative AI-driven technologies that can continuously monitor changing conditions and interpret environments such as market shifts, changes in customer behaviour, or supply chain disruptions.

O'Callaghan explained the benefits of this evolution:

"Perceptive analytics will use AI agents and other GenAI-fueled technologies to continuously monitor evolving conditions and perceive the target environment, such as market shifts, customer behaviour changes or supply chain disruptions."

She added, "Guidance and analysis can then be autonomously adjusted in response, creating a more resilient and responsive analytical infrastructure. As these capabilities emerge and be adopted by organisations, their potential to reshape business operations and drive growth will only continue to expand."

Managing risks

Despite these potential benefits, Gartner research also draws attention to the risks associated with increasing reliance on perceptive analytics, particularly regarding the level of autonomous action allowed without human validation. Such over-reliance could lead to negative consequences, including unforeseen errors, potential reputational damage, and regulatory scrutiny.

One of the key risks outlined is "agent drift," where AI systems gradually move away from intended goals due to evolving data or other interactions. To mitigate this, Gartner points to the emergence of guardian agents—systems specifically designed to monitor AI operations and enforce compliance with policies and rules, keeping analytics within safe and approved boundaries.

O'Callaghan highlighted the importance of governance, stating,

"Building guardian agents will need to be a key focal point of new governance initiatives for data and analytics leaders, as agentic and perceptive analytics become the standard way of insight delivery across platforms."

Industry response

Gartner analysts are continuing to provide analysis and recommendations on developments within data and analytics strategies, with a particular emphasis on driving business value and best practices for deploying AI responsibly within organisations.

The research underlines that as generative AI continues to influence analytics platforms, companies will need to adopt new governance models and develop mechanisms to prevent unintended system behaviours, ensuring that the promise of intelligent applications is realised safely and effectively.