RCG tech priorities: Cloud still rules, but GenAI is re-drawing the map

Retail and consumer goods (RCG) firms are voting with their wallets—and the ballot box is firmly skewed toward cloud and data analytics. A fresh cut of HFS Research data shows that the lion’s share of tech budgets remains anchored in traditional strongholds: cloud computing (26%) and analytics (21%) collectively command nearly half of all enterprise tech spending. But the real surprise lies in the swelling appetite for new-age AI: generative AI (10%) and agentic AI (7%), which now outpace traditional AI (6%), underscoring a dramatic pivot in enterprise AI adoption narratives. RPA and intelligent automation are still very much alive (9%). Meanwhile, emerging tools such as blockchain and digital twins hover at the margins, but their moment may be approaching.

Exhibit 1: Generative AI surpasses RPA as the third-top focus area for RCG tech initiatives

Source: 35 RCG firms; HFS Horizons – Exploring the Future of Intelligent Retail and CPG Ecosystems, 2025

Cloud and analytics: The old guard still holds court

It is tempting to label cloud and analytics as yesterday’s news. Yet, for RCG firms grappling with omnichannel integration, supply chain volatility, and customer personalization, these pillars remain mission-critical. Most global retail leaders still struggle to derive actionable insights from customer data, making analytics investments indispensable. Snowflake, Databricks, and Microsoft Azure are still preferred by enterprises seeking scalable, composable data architectures. On the analytics front, customer 360 solutions, demand forecasting models, and merchandising optimization platforms are the biggest draws. These projects are less about moonshots and more about stitching together fragmented digital cores. RCG tech leaders are prioritizing agility, not overengineering, focusing on modular deployments and outcomes tied to specific business functions.

GenAI and agentic AI leapfrog traditional AI

GenAI (10%) and agentic AI (7%) now outpace traditional AI (6%), signaling a pivot in enterprise AI narratives—from prediction to creation and decision-making. With combined spending of 17%, GenAI and agentic AI are no longer experimental sidelines; they are front-row investments. GenAI is being deployed to automate planogram design, simulate customer personas, and power synthetic data generation for testing marketing strategies. Agentic AI—the next wave where models not only generate but also act with autonomy—is finding use in supply chain incident response and inventory decision-making. While traditional AI continues to underpin pricing engines and churn prediction models, its growth curve has flattened. Enterprises are shifting their bets from deterministic to probabilistic systems, betting that adaptability will trump accuracy in fast-moving consumer landscapes.

Automation is quietly reinviting itself for the GenAI era

RPA is not just alive; it has evolved. Intelligent automation now encapsulates workflows that span document understanding, process orchestration, and AI-powered exception handling. UiPath, Automation Anywhere, and Microsoft Power Automate are being embraced not for cost-cutting but for agility—helping store managers, merchandisers, and planners offload repetitive, rules-based tasks. In the age of GenAI, RPA is shedding its image as yesterday’s tech and integrating with AI to offer cognitive task automation—a crucial enabler in hybrid digital workforces.

Blockchain and digital twins are fringe tech with outsized potential

Blockchain and digital twins together account for a mere 2% of current spending, but to dismiss them would be short-sighted. These technologies are being deployed selectively—in high-value, high-complexity scenarios. Digital twins, for example, are enabling supply chain simulation and store layout optimization. Walmart and Unilever have explored digital twin applications to simulate demand shocks and warehouse flows. Blockchain, too, has gained traction in ethical sourcing and food provenance tracking. These technologies are quietly building the scaffolding for next-gen RCG ecosystems, even if they aren’t grabbing headlines (yet).

The Bottom Line: CTOs and other functional leaders in RCG firms must balance reinvention with integration and resilience, pacing themselves for the next leg of growth.

Cloud and analytics remain indispensable, but they should be wired into new AI-driven engines of value. GenAI and agentic AI will define customer engagement and operational responsiveness, not in the distant future but now. Automation, far from irrelevant, is becoming more cognitive and context-aware. RCG firms that can harmonize foundational platforms with intelligent autonomy will lead.