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The retail AI inflection point
AI is transforming retail at a rapid pace compared to previous waves of innovation. The conversation has shifted decisively from exploring AI to envisioning enterprises where AI supports value creation. Retail leaders now imagine stores that sense demand in real time, supply chains that self-correct, and teams empowered by intelligent agents that help improve decision-making.
Genpact's Autonomy by design research includes responses from 65 senior retail executives. Their insights suggest that success requires more than technology. While retailers are optimistic and already seeing early value, many still face challenges – especially in integrating AI into workflows, skill gaps, and fragmented ownership – which slow down the leap from experimentation to enterprise-wide impact.
In this study, we explore their views on moving from siloed use cases to collective intelligence that creates long-term value. These perspectives are designed to help retail leaders translate ambition into action and shape what their autonomous retail enterprise can become.
Four levels of AI maturity
By assessing respondents across six dimensions – adoption effectiveness, decision autonomy, governance rigor, operational enablement, regulatory compliance, and technical capability – we break down AI maturity across four levels.
Only 11% of retail organizations are considered leaders
Leaders demonstrate strategic foresight and executional rigor as they enhance processes, build architecture for scale with cohesive implementation strategies, and systematically resolve legacy technology constraints.
Retail is the largest industry in the scaling level, suggesting that many retailers are still in the early phases of expanding AI capabilities beyond pilots. A key factor constraining faster progression is persistent AI skill gaps, which remain the most significant barrier to effective AI implementation in the retail sector. Cross-functional AI coordination councils are being set up as part of the evolving organizational structure to support more distributed, AI-assisted decision-making.
The state of AI in the retail industry
Technology-centric ownership with broad team involvement
AI ownership in retail is primarily anchored within technology or specialized AI/data leadership, reflecting the sector's reliance on managing large volumes of complex, fast-moving data. Retail reports the highest concentration of specialized AI/data leaders driving AI initiatives, positioning AI as a distinct, standalone strategic capability rather than an embedded extension of existing business or technology functions.
At the same time, the sector shows strong functional-level momentum, with more leaders planning or implementing AI within their own teams than in other industries. However, the lowest levels of sponsorship for AI strategy and decision-making suggest that AI in the retail industry is more often advanced through function-led initiatives than through a coordinated enterprise-wide transformation agenda.
AI is making a tangible difference to retail operations
Three out of four retailers report satisfaction with their current AI investments, indicating tangible outcomes delivered to date. The highest effectiveness is reported for generative AI applications, followed by predictive analytics and monitoring, then quality control, demonstrating the value emerging across insight generation and operational execution. As retailers adopt more distributed, AI-assisted decision making, real-time AI insights can enable faster decisions, greater innovation, and increased localization of AI-driven actions to specific markets and data contexts.
Human-anchored decision models with growing participation of AI in retail
The operating model for AI in retail remains human-anchored, with decision authority largely retained by people. The adoption of agentic AI in the retail industry remains nascent. None of the retailers surveyed are actively implementing agentic orchestration, with only 11% testing or planning deployment. Similarly, just 2% are actively implementing autonomous business units, and 11% are in the testing or planning phase, highlighting that autonomous retail is still some way off, despite progress on data and AI foundations.
People-led approaches continue to dominate critical stages such as problem-framing and final approvals. Respondents show growing adoption of hybrid execution models, particularly AI-assisted, but human-directed or joint human-AI for decisions on resource allocation, anomaly escalation, and outcome evaluation. For generating recommendations, an AI-led model with human oversight is on par with other hybrid models. In parallel, most retail respondents expect their governance structures to evolve to support agentic AI, with most anticipating minor adjustments to moderate restructuring rather than major redesign or complete transformation.
Powering advanced AI deployments
In our study, retail leads other industries in the adoption of most AI technical foundations, reflecting a strong focus on scalable, interoperable architectures for production-grade deployment. Real-time data infrastructure, MLOps or AIOps, modular or API-first integration, zero-trust security, and responsible AI frameworks show the highest levels of adoption in retail relative to other industries. More than half of technical respondents report fully embedding responsible AI frameworks into the development lifecycle. Exceptions persist in federated data or AI collaboration across teams (70%) and cloud-native AI platforms (80%), indicating that while execution-critical foundations are mature, broader architectural and collaboration models are still developing.
The AI of now: Barriers to scaling AI in the retail industry
The progress toward autonomous retail is constrained by a set of persistent, interrelated barriers:
Skills gaps are the dominant constraint: 72% of respondents cite talent availability as the primary bottleneck to capability building and scaling AI
Regulatory and compliance challenges are structurally significant: Over half of respondents (51%) cite regulatory or compliance hurdles
Integration challenges limit value realization: Difficulty in integrating AI into workflows is more acute than in other industries, suggesting AI pilots often struggle to scale into production-grade operations
Fragmented ownership and accountability hinder progress: A substantial share (45%) reports fragmented ownership as a barrier to scaling AI in retail. This can lead to slow execution, unclear decision rights, and inconsistencies
The rise of the autonomous retail enterprise
Leaders are making real progress, unearthing four enabling themes that offer a working model of autonomous retail in practice:
A symphony of agents
As an enterprise deploys more AI agents, it risks having them work at cross-purposes without coordination across functions.
Pricing in retail is inherently complex, shaped by volatile demand, competitive dynamics, and margin pressures. A symphony of AI agents can help retailers navigate this complexity more effectively. One agent can continuously analyze market signals such as market pricing, demand shifts, and inventory positions. A second agent can simulate pricing scenarios and assess their potential impact on sales and margins. A third agent can translate these recommendations into brand-compliant promotions and bundles and activate them across stores and channels. Together, these agents convert insights into recommendations and next best actions – helping retailers move pricing decisions from strategy to shelf faster, with greater consistency and confidence.
The universal AI practitioner
Democratizing AI fluency and adoption across roles and functions can empower employees to act as AI practitioners. Leaders are reframing how humans and AI collaborate by managing change and reskilling.
Enterprise architecture redux
Technology complexity is a significant challenge. To scale autonomous AI in retail, companies need a strong data-centric foundation, but this architectural backbone has yet to emerge for many firms. While many retailers are adopting the core AI technical foundations, full maturity across these capabilities remains limited. And although 90% of retailers report adopting real-time data infrastructure, only 43% have fully adopted it. This gap between broad adoption and full operational maturity underscores why, despite strong foundational progress, many retailers may not have been able to scale AI reliably into production-grade autonomous retail operations.
Governing at the speed of AI
Governance sets the pace and limits of AI innovation. But 99% of executives indicate that their governance models are not yet adequate for autonomous or agentic AI systems. And 35% now operate under a federated structure.
Our research highlights clear patterns in the way governance is structured and deployed. Centralized governance offers consistency, control, and streamlined decision-making, but it can be slow to adapt and less responsive to local needs. In contrast, federated governance enables agility and contextual responsiveness across domains yet requires strong coordination between central oversight and decentralized units to prevent fragmentation. Almost a third of retailers still rely on decentralized or ad hoc governance, highlighting a clear opportunity to standardize guardrails while preserving business unit speed.
The leaders' playbook
Translating vision into action
Our playbook shares practical actions across the enabling themes for organizations at any stage of their AI journeys:
Redefine processes to lead with AI. Embed AI into end-to-end retail processes such as merchandising, replenishment, and store operations by extending process intelligence into a unified orchestration layer. Retailers with decentralized or ad hoc governance can start with a lightweight orchestration layer that connects local innovation to enterprise priorities
Elevate AI fluency across the organization and redesign roles to be AI-first. Position employees as coauthors of AI change – making sure local autonomy is paired with a clear understanding of model limits, guardrails, and risk to drive confident, responsible adoption
Build a coherent data and integration backbone to innovate successfully. And use an agentic development lifecycle to guide AI design decisions
Establish centralized governance before federating. With retail split almost evenly between centralized and federated models, the priority is sequencing: define what "safe to scale" means first, then federate execution to business units. Then embed automated monitoring and policy enforcement into your AI architecture
Anchor AI impact on retail to measurable commercial outcomes. Shift focus from use case proliferation to a small set of value-backed metrics tied to retail economics, such as margin uplift, inventory turns, conversion, and labor productivity. Use rapid test and scale cycles to identify models that demonstrably move profit and loss (P&L)
Mastering the five enablers can help your business transition into an autonomous retail enterprise and turn AI into a lasting competitive advantage.
