Driving business forward with AI-powered analytics

Rita Sallam at Gartner explains how perceptive and GenAI-powered analytics are quietly revolutionising business operations

 

In 2025, businesses operate at a relentless pace. Geopolitical tensions, from trade disputes to data sovereignty laws, disrupt supply chains overnight. Customer expectations evolve in hours, fuelled by artificial intelligence, digital platforms and economic shifts. Regulations, like the EU’s Cyber Resilience Act or the US Cyber Incident Reporting Act, demand instant compliance.

 

Traditional analytics, with its static dashboards and human-dependent decisions, cannot match this speed. The delay between insight and action isn’t just a workflow issue; it’s a competitive weakness.

 

Perceptive, generative AI (GenAI) and agentic-driven analytics platforms are emerging as the autonomous and semi-autonomous solution, quietly reshaping how businesses function. Gartner predicts that by 2027, one in five business processes will be fully managed by these AI-powered systems, which detect patterns, decide, and act without human intervention. Unlike GenAI’s headline-grabbing chatbots, perceptive analytics embeds itself into the core of operations—logistics, finance, customer service—driving resilience in a volatile world. Here’s why this matters now, how it works, and what it means for leaders.

 

 

From insight to instant action

Traditional analytics is reactive: a report flags a supply chain issue, but by the time a manager acts, the market has already moved on. A fraud alert reaches a team, but the transaction is already lost. Perceptive analytics platforms erase this lag. They don’t just identify trends or suggest actions; they act semi-autonomously or autonomously to execute them, rerouting shipments, adjusting prices, or freezing accounts in real time, based on pre-set rules and continuous data analysis.

 

For instance, in logistics, a platform might detect a port closure due to geopolitical unrest and instantly reroute shipments, balancing cost and speed. In e-commerce, it could tweak pricing based on competitor moves or demand surges. These systems act as digital operations managers: always monitoring, always deciding, always acting. In 2025, where every second counts, this immediacy is a game-changer, turning data into outcomes before opportunities slip away.

 

 

Why now? The new edge in a volatile world

Recent Gartner predictions indicate that by 2027, 75% of analytics content will leverage GenAI including agentic AI, making perceptive capabilities in analytics platforms foundational to transforming how businesses operate in real time. What sets perceptive capabilities in analytics platforms apart isn’t automation, businesses have automated tasks for years. It’s the leap from analytics as a support function to the driver of operations. In 2025, geopolitical volatility demands instant adaptation; regulations require compliance at scale and customers tolerate no delays. Perceptive analytics enabling autonomous processes meet these demands by embedding decision-making into workflows, acting without human bottlenecks.

 

The selling point is resilience: the ability to anticipate and respond to disruptions before they escalate. A retailer using perceptive analytics can autonomously adjust inventory across regions during a supply chain crisis, outpacing rivals stuck in manual processes. This agility, less flashy than generative AI, is quietly redefining competitive advantage, making it critical in 2025’s unpredictable landscape.

 

 

The catch: trust and governance

Handing over 20% of processes to machines by 2027 raises a critical question: can we trust systems to decide and act autonomously? Many business leaders and CIOs cling to human oversight, assuming it guarantees safety. Yet, well-designed perceptive and autonomous analytics often outperforms humans—reacting faster, scaling seamlessly, and minimising bias in routine tasks. A platform managing inventory doesn’t miss subtle patterns or get distracted.

 

But trust demands rigour. Poor data quality, model drift, or weak ethical guardrails can lead to errors or regulatory violations, especially in sectors like finance or healthcare. Governance frameworks are essential to ensure transparency in decision-making and enable human intervention when needed. Synthetic data, which mimics real-world patterns while protecting privacy, can enhance these systems but requires metadata management to maintain accuracy. This isn’t about sidelining humans; it’s about focusing them on strategy and exceptions.

 

 

The leadership challenge: rethinking control

The technology is ready. Leadership mindsets are not. Embracing perceptive analytics requires more than tech adoption, it demands a cultural pivot. Ceding control to systems forces leaders to redefine their roles: from decision-makers to enablers of intelligent automation.

 

This doesn’t mean learning to code. It means building executive AI literacy, understanding where autonomy drives value, where human oversight remains essential, and how to manage the trade-offs. Leadership must evolve from managing process to orchestrating intelligence: fostering trust in data-driven decisions, guiding teams through uncertainty, and creating space for experimentation. Experiential upskilling, like piloting AI prototypes, lets leaders engage with potential, not just theory.

 

Those clinging to legacy dashboards risk irrelevance in a real-time world. The imperative now is to build trust, through measurable outcomes, responsible design, and adaptive governance, before the competitive gap widens.

 

 

The bigger picture: a new operational blueprint

Perceptive and autonomous analytics isn’t an incremental improvement; it’s a foundational shift in how businesses operate. By 2027, Gartner predicts one in five processes will run without human input, spanning everything from inventory balancing to customer query routing.

 

This autonomy liberates human potential. With systems handling routine execution, teams can shift focus to creativity, resilience, and long-range planning. For a retailer, it means logistics that self-correct during disruptions. For insurers, it’s real-time claims triage. In manufacturing, it’s predictive maintenance preventing costly downtime.

 

These changes are urgent because today’s environment, marked by geopolitical instability, shifting regulations, and digital-native customers, demands systems that act, not just inform. Perceptive analytics makes it possible. It’s not about replacing people, but amplifying their impact in organisations built for speed, insight, and adaptability.

 

 

The next step — or the next leap?

Business leaders and CIOs now face a critical choice: passively observe the shift or actively lead it. The path forward starts with clarity, identifying where speed, accuracy, and scale matter most, whether in logistics, regulatory reporting, or customer personalisation.

 

Equally vital is embedding governance from the start. As AI systems become more autonomous, businesses must ensure their data practices are ethical, compliant, and aligned with evolving regulations. Clear accountability and transparent metrics are no longer optional; they’re prerequisites for trust.

 

This isn’t a one-time transformation. It’s an operational evolution. Business leaders and CIOs who embrace experimentation, guide cultural change, and invest in adaptive systems today will shape the competitive frontier of tomorrow.

 

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Rita Sallam is Distinguished VP Analyst at Gartner. Gartner analysts will further explore how to harness AI for successful digital transformation at the Gartner IT Symposium/Xpo, from 10-13 November 2025, in Barcelona

 

Main image courtesy of iStockPhoto.com and da-kuk