It will not be too off the mark to say that retail and CPG organizations find themselves at a crossroads and a critical inflection point in decision-making for future growth. While the fundamentals of success, such as extreme customer-centricity, financial rigor and operational excellence through data-driven decisions remain firmly vital, there are some additional developments that must be considered.
For one, the value-seeking consumer has heightened expectations of personalization, speed, availability, and cost. A recent Deloitte study reveals that close to 40% of consumers regard a brand’s value on factors beyond price alone. Added to this rise in expectations is also the fact that many enterprises remain constrained by legacy applications and fragmented data ecosystems.
And this is what retail and CPG companies are realizing. While legacy solutions seem reliable on the surface, it has become increasingly challenging — if not impossible — to integrate modern technologies and applications that are critical for exceptional customer experience. Further, they carry strong disadvantages of expensive maintenance, operational inefficiencies and risks of weak security and noncompliance.
In such a scenario, AI-led application and data modernization have emerged as a pragmatic and high-impact approach to transform legacy systems and data together. This creates the foundation for agility, intelligence and future-ready digital commerce.
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Time to Integrate Data and Application Modernization as a Concurrent Exercise
Let’s look at the common challenges for retail and CPG enterprises. The most common is the proliferation of siloed data on customers, products and inventory across multiple channels. Add to this the sprawl of legacy ERP, merchandising and supply-chain platforms and it is not surprising that retailers and CPG companies are slow to respond to demand volatility and promotional cycles. Adding to their challenges is the huge gap in data quality, which limits their ability to operationalize AI use cases.
The need for data and application modernization cannot be more urgent, but here is what is critical. Historically, they have been pursued as independent initiatives. However, in today’s omnichannel and real-time environment, this separation severely limits business impact. Modernizing applications without data modernization accelerates systems but not insights. On the other hand, modernizing data without application modernization constrains adoption and execution. AI enables both to evolve in lockstep.
Data and app modernization in the retail and CPG industry unlocks tremendous opportunities. Modernized applications enable organizations to get the best of emerging technologies such as artificial intelligence and machine learning (AI/ML), augmented and virtual reality (AR and VR), Internet of Things (IoT) and more. The integration of machine learning, large language models (LLMs), generative AI and intelligent automation unlocks phenomenal outcomes in terms of
- Assessment and refactoring of legacy applications
- Modernization of enterprise data platforms and pipelines
- Enabling cloud-native, modular, and analytics-ready architectures
- Comprehensive omnichannel interactions and hyper-personalized consumer experiences.
The result is an extremely intelligent retail and CPG enterprise — one that is capable of sensing demand, responding faster and continuously optimizing performance.
Key Areas of AI-Led Retail App Modernization
AI-powered app modernization can enable intelligent code discovery and dependency mapping, effective refactoring and API enablement, and automated test generation and regression testing.
Let us look at some of the key core systems that retailers rely on and see how AI-led app modernization can help them.
- ERP systems are critical to retail organizations, but many still deploy legacy on-premises systems that suffer from lengthy batch processing times, rigid data models and problematic integrations. AI-led app modernization enhances their scalability, flexibility and cost-efficiency.
- Legacy order management systems (OMS) and warehouse management systems (WMS) suffer from limitations of system flexibility and scalability. App modernization unlocks the advantages of real-time data exchange for dynamic order and inventory management. Plus, cloud-native systems deliver scalable, AI-powered, omnichannel solutions that facilitate customization and innovation.
- When retail POS systems are integrated with AI apps and capabilities, they become powerful engines for inventory intelligence, customer insights, personalized offers and seamless shopping journeys.
What AI-Led Retail Data Modernization can Deliver
Modern commerce depends on trusted, real-time data across the value chain. Be it online browsing, in-store shopping or loyalty programs, every touch point creates significant volumes of data. As this ecosystem of data grows in size, so does its complexity. And legacy systems become a barrier in swift analysis and real-time insights for making data-driven decisions.
AI-led data modernization streamlines data flow, makes it available and accessible in real time across teams and functions to enhance business intelligence and decision-making. It unifies isolated data sources, predicts consumer behavior and demand, enables accurate inventory management and delivers hyper-personalized experiences to improve loyalty. In effect, AI achieves total harmonization of customer, product, inventory and supplier data. It facilitates seamless migration from legacy data warehouses to cloud Lakehouse platforms and enables real-time and event-driven data pipelines.
From a data integrity and governance perspective, data modernization with AI helps to automate de-duplication and anomaly detection, normalization of product and SKU attributes, and privacy, security and compliance measures.
Business Intelligence, the Winner in AI-Led Data and Application Modernization
AI achieves a synchronized modernization of applications and data and transforms reporting to embedded intelligence. It achieves intelligent orchestration of purchase behaviors, preferences and intent to deliver personalized omnichannel experiences, accurate demand forecasting, optimized inventory, merchandising and pricing, swift fulfillment and efficient supply chain risk management. In effect, AI pervades every layer of the retail value chain, creating intelligent and reinforcing networks of data, processes and decisions.
As application and data foundations mature, retail and CPG organizations will increasingly explore agent-based AI capabilities — where intelligent systems can monitor conditions, recommend actions and, in controlled scenarios, execute decisions within defined guardrails. It could be the optimization of intelligent order routing and fulfillment, or automated recommendations for replenishments — or even dynamic promotion tuning. One thing is certain — all these capabilities depend entirely on modernized applications, trusted data and strong governance. AI-driven modernization of data and applications thus becomes a critical pre-requisite for tomorrow’s autonomous commerce models.
With the future of retail resting in connected intelligence, AI-powered modernization steers retail and CPG players toward customer intent-centricity. The speed at which retailers can sense, decide and act on their customers’ intent will determine the competitive advantage they can gain. Enterprises that align agentic intelligence with modern architectures — enabling discover-to-purchase flows inside conversational interfaces, unified data-and-commerce platforms and seamless experiences — will not only optimize operations but also redefine customer experience in profound new ways.
For retail and CPG enterprises, AI-led application and data modernization is no longer a matter of IT transformation — it is the core of business transformation. By modernizing systems and data together, organizations unlock faster innovation, better decisions and the flexibility to adopt future capabilities in the event of an imminent agent-driven commerce scenario.
Those who act now will not only modernize their technology stack but also position themselves to lead in an increasingly intelligent and automated retail landscape. Retailers that modernize legacy applications and data with governance together will lead the next decade of trusted, intelligent, AI-enabled retail.
Mithun Shenoy is SVP and Head of the Retail and CPG Business Unit at Mastek, where he leads digital transformation initiatives for global retail and consumer brands. With over 20 years of experience in digital engineering, enterprise modernization, and digital commerce, he combines strategic leadership with hands-on execution.
