Retail 4.0 borrowed heavily from Industry 4.0, focusing on areas like automation and digitization across production, stores and supply chain. Retail 5.0 goes a step further: it ties formerly siloed functions, including sourcing, logistics, merchandising, store ops, ecommerce and marketing, to the single north star of customer experience. In this environment, every upstream choice is made with downstream service in mind.
In retail, this shift is being operationalized through smarter supply chains and AI-enabled materials tracking, giving retailers real-time visibility, faster decisions and fewer points of failure.
As Debraj Bhattacharya, Senior Industry Specialist, Retail and CPG, at HCLTech put it during a recent conversation on the HCLTech Trends and Insights podcast: “Everything aligns with one ubiquitous goal, which is how better we can add experience to the customer…this is the transition from Retail 4.0 to Retail 5.0.”
HCLTech’s latest research, The Blueprint to AI-Led Operating Model, underscores why an AI-led, product-aligned operating model is the backbone of that shift. Product-aligned firms are 4x more likely to maximize ROI on AI investments than traditional organizations, and 51% of product-aligned operating model firms are already investing in AI, with 45% reporting ROI compared to 11% who haven’t embraced this way of working.
Why product-aligned models accelerate smart materials tracking
When asked about how product alignment enhances materials tracking in Retail 5.0, Bhattacharya contrasted product-led versus project/services models: “Measuring each of the modules within the entire material tracking system is important…rather than waiting for the entire step to get over…the inherent agility of a product-led model — fail fast, move forward and keep on iterating — ties in very well with a complex solution as smart material tracking.”
This lines up with the research’s broader finding: companies that structure around enduring products and value streams can track what matters continuously via OKRs, not episodically, and break silos that block data flow end-to-end. The report also flags systemic barriers with nine in 10 organizations lacking the leadership muscle to drive cultural change. One-third are stuck integrating modern apps with legacy estates and 22% lack reliable metrics, which are precisely the pitfalls a product-aligned model is designed to address.
The tech trifecta: IoT, edge AI and blockchain
Lalit Kumar Singh, Senior Solutions Director at HCLTech, explained the enabling stack during the podcast: “IoT adds a sensor stack to materials, pallets and containers and allows continuous tracking with conditions…Edge AI processes data right at the source…For temperature-sensitive goods [for example], edge AI can trigger an alert immediately…and blockchain puts in a trust or transparency layer by creating a chain of custody across the ecosystem.”
For retailers, this is Retail 5.0 in practice: condition-aware, real-time tracking at the edge, low-latency interventions that prevent spoilage and shrink and verifiable provenance to strengthen cross-partner trust. It also complements the observability and control tower pattern now emerging in retail, where business-flow observability platforms give real-time visualization of processes, integrations and systems and “proactive alerts to prevent business process failures.”
Sustainability by design across the chain
Smart tracking isn’t just operational; it’s environmental. Bhattacharya emphasized that the supply chain “is amenable to a lot of emissions…trucking and all of that,” and that robust tracking underpins “waste reduction,” “improved energy efficiency” and “greater regulatory compliance.” In perishables, “Farm-to-Fork…is dependent on freshness” and tracking pinpoints the levers to reduce waste.
Pair this with product-aligned operating model discipline with clear value metrics and real-time visibility, and retailers can quantify carbon reductions per lane, cut food waste via FEFO (First-Expired, First-Out) and FIFO (First-In, First-Out) and prove compliance through system-of-record events. Retail control towers add the missing live context: anomaly detection, KPI thresholds by time, drilldowns to impacted clients and business calendars by region, so sustainability actions are triggered where they matter.
Integration reality check and how product-aligned operating models help
Scaling AI-enabled tracking often stalls on architecture. “Data silos, inconsistent protocols and security and compliance across geographies” are three big blockers, said Singh.
The answer to overcome them, he argued, is product alignment: “Shift the whole focus from functional silos to end-to-end ownership of the outcome…build the integrated platform so that all the data can come on a single platform…to provide a unified business outcome.”
The research echoes this: one third of firms are trapped in a legacy maze, struggling to connect new systems with outdated infrastructure, which raises costs and slows innovation. Separately, 66% of large firms are pouring money into experimentation and cultural programs but struggle to connect spending to value delivery, which is another symptom of fragmentation and weak value governance. A product-aligned operating model reframes the mandate: integrate around the product value stream, tie funding to measurable outcomes, and use shared metrics to coordinate decisions across functions.
Prove the value early: The metrics that matter
Before committing, leaders require a scoreboard. Bhattacharya recommended foundational KPIs informed by HCLTech implementation experience:
- “Reduction of effort to locate assets…as high as 80–85%”
- “Reduction in inventory holding cost…roughly 10–15%”
- “Improvement in inventory accuracy…roughly 60–75%”
- “Improvement in cycle count time”
- “Reduction in wastage…50–60%,” using FEFO/FIFO
- “Improvement in production capacity by ensuring timely availability of the right materials”
Tie these operational metrics to Retail 5.0 outcomes, including availability, freshness, on-time delivery and Net Promoter Score, and to the AI-led research benchmarks, such as value flow measurement and ROI realization. The research’s warning that 22% are “navigating blind” without reliable metrics is a call to embed these measures in day-one design. Ideally, this should be realised through a retail control tower, so executives and operators see the same truth.
From visibility to decisions: faster loops, better CX
Once visibility is in place, Singh noted that decisions speed up: “You will have the visibility of all your assets…you can reroute the complete material shipment…track expiry…utilize space the right way…and AI models can learn and give you better insight for improvement, reducing cost and increasing productivity.”
This is the product-aligned operating model virtuous cycle: the product team responsible for last-mile experience also owns the upstream data, models and interventions. Observability makes status and impact transparent; edge AI compresses detection-to-action and shared outcomes focus across every function on the same customer event.
Culture: The operating system for AI
Change anxiety is real. Bhattacharya addressed the common fear: “We are not really taking away jobs, we are enabling the person to perform a lot better…providing inputs around greater visibility.” Singh added that with adoption, legacy systems and on-the-ground processes mean leaders must “see how this technology can be adopted by the existing resources.”
The research quantifies that barrier: nine in 10 organizations lack the leadership muscle to drive cultural change. Retailers should over-invest in product-team enablement, change communications and role design, so frontline staff move from manual monitoring to exception handling, customer recovery and continuous improvement, which are exactly the higher-value tasks Retail 5.0 promises.
Wire Retail 5.0 into the operating system
Retail 5.0 isn’t a slogan; it’s an operating choice. The retailers winning with AI-enabled materials tracking are the ones that:
- Organize around products and value streams
- Deploy edge-ready IoT and AI with verifiable provenance
- Stand up business-flow observability and a control tower to make value, and risk, visible in real time
- Define and socialize the few metrics that truly matter
Do that, and retail organizations can convert AI from proof-of-concepts into durable performance at scale. As the research concludes, product-aligned models are now a key differentiator, and in Retail 5.0, the differentiator that customers can actually feel.
