How Retail and Consumer Goods Companies Achieve Like-for-Like Growth During Macroeconomic Volatility
Market trends: Uncertainty is a new normal
In 2026, retail has shifted from navigating uncertainty to a new normal of permanent disruption. The era of cost-optimized global production is over in the face of rising tariffs. In its wake, a nimble-beats-optimal approach has emerged, forcing retailers to diversify into hubs outside of Asia and nearshoring to mitigate geopolitical risk, even as 95% of executives anticipate higher operating costs.
In this constrained environment, with consumers feeling stretched to the end of their means, retailers' and brands' strategic focus has shifted to the disciplined pursuit of like-for-like (LFL) volume growth through productivity gains rather than inflationary markups.
At the same time, while capital is more expensive and inward investment is stifled by geopolitical instability, retailers and CPGs are prioritizing margin management and cost discipline over speculative expansion. Growth is also decoupling from hiring; investors now expect productivity gains driven by technology and process redesign rather than headcount increases.
The winners in this environment will be those who can navigate tariff turbulence while pivoting from price-led growth to volume-led resilience, securing market share in the process.
Data implications: From static planning to dynamic modeling
Operational agility is now the primary engine of like-for-like volume growth. To achieve this, data must transition from periodic reporting to a continuous intelligence loop that bridges the gap between the boardroom, shop floor and shelf, both physical and digital.
Intelligent store operations begin with unified visibility across POS, foot traffic, inventory levels, shrinkage and local demand signals. AI models help optimize store layouts, replenishment cycles and task prioritization, while anomaly detection can help identify emerging risks before they impact revenue.
Workforce productivity starts with effective AI-powered on-the-job training. Intelligent assistants empower business users with decision support embedded directly in workflows. Intelligent agents — autonomous digital workers — can help automate routine processes while maintaining human oversight.
Across the supply chain, predictive and prescriptive analytics drive resilience. As tariff turbulence redraws global sourcing maps, static forecasts are liabilities. Scenario simulation and stress testing allow leaders to model cost shocks or supply disruptions before they occur. For example, predictive cold-chain monitoring can help reduce the risk of spoilage, while inventory-balancing algorithms help improve inventory allocation and working-capital efficiency. Ultimately, self-healing supply chain networks are designed to aid in disruption detection, simulate alternatives and support mitigation actions — transforming volatility from a threat into a manageable variable.
Enabling technology: Accessible data and trusted intelligence
Intelligent store operations
Store Associate Assist: Store associates are a retailer's front line, and they deserve front-line intelligence. Mobile-first "talk-to-X" tools put contextual guidance directly in associates' hands, enabling them to ask questions, surface answers and receive dynamic task reprioritization in real time. When foot traffic surges or demand patterns shift unexpectedly, the system recalibrates, enabling your team to work on what matters most, when it matters most — which is right now.
Merchandising 360: The shelf is where sales are happening. By connecting shelf gap detection to planogram analysis, retailers can close the loop between what should be on the shelf and what actually is. Layer in loyalty data to identify personalization opportunities at the point of decision, and use geospatial analysis to power smarter replenishment recommendations and get the right product to the right store at the right time.
Predictive maintenance: Phydigital store assets — self-checkouts, smart signage, refrigeration units — can't afford downtime. By ingesting IoT sensor data and applying predictive analytics, retailers can better detect equipment degradation before it becomes a failure. Maintenance tickets and reports are triggered automatically for store managers, shifting the operating model from reactive repair to supporting a more proactive maintenance approach.
Workforce productivity
Digital trainer: Standard operating procedures and training manuals are only useful if people can actually find what they need. By ingesting these documents, building knowledge graphs and enabling "talk-to-doc" capabilities, retailers can turn static content into an interactive, on-the-job training assistant — giving employees faster conversational access to the knowledge they need, when they need it.
Line-of-business copilots: Not every business user needs to be a data analyst, but every business user needs data. Domain-specific AI copilots bring augmented decision support directly into the workflows of merchandisers, planners and operations managers, making data accessible and actionable without requiring a SQL query or a ticket to the analytics team.
Agentic digital workers: Routine operations consume a disproportionate amount of time and attention. Autonomous AI agents can take on repetitive, rules-based tasks, such as shift-swapping, compliance logging or back-office HR processes, and execute them end to end with speed and consistency. The result is a human workforce that’s freed to focus on higher-value work that actually moves the business forward.
Agile and self-healing supply chain
Supply chain digital twins: A digital twin of the supply chain is more than a dashboard; it's a living simulation. By integrating data from across your ecosystem and ingesting real-time feeds from IoT sensors, truck GPS, POS systems and warehouse management platforms, you can build a comprehensive model of how your supply chain behaves. Deploy AI models on top to understand the underlying rules, run what-if scenarios and stress test the system before disruption hits — not after.
Cold chain monitoring: For perishable goods, every minute matters. Real-time IoT telemetry combined with computer vision enables continuous monitoring of temperature, humidity and product quality throughout the cold chain. This can result in reduced waste, improved compliance and fresher products reaching the shelf.
Dynamic inventory balancing: Demand doesn't distribute itself evenly, so neither should your inventory. AI agents can continuously rebalance stock between distribution centers and stores, drawing on predicted demand spikes informed by both first-party and third-party marketing signals and agentic interactions. This will help inventory flow to where it's needed, not where it's always been.
Self-healing supply networks: This is the end goal: an autonomous ecosystem where AI agents detect a shortage, identify alternative suppliers, negotiate pricing and place orders, all with human-in-the-loop oversight at critical decision points. It's not about removing people from the supply chain; it's about giving them an intelligent network that can sense, respond and recover on its own.
Executive takeaway
With the current tariff turbulence and stagnant pricing power, retail and consumer goods success depends on financial fortitude and true like-for-like growth. Data is increasingly used as a risk-management tool, while AI matures into a core driver of efficiency and savings across store and supply chain operations, as well as employee productivity.
Ready to unlock like-for-like growth? Talk to our retail & consumer goods industry experts to build your roadmap.
