It’s not too far-fetched to say that retail and consumer goods organizations are at a crossroads and critical inflection point in their decision-making for future growth. While the fundamentals of success such as extreme customer centricity, financial rigor, and operational excellence through data-driven decision making remain important, there are also some additional developments that need to be considered.
First, value-hungry consumers have increased expectations for personalization, speed, availability, and cost. A recent Deloitte study found that nearly 40% of consumers value a brand in terms of more than just price. Add to this growing expectation the fact that many businesses remain constrained by legacy applications and fragmented data ecosystems.
And this is what retailers and consumer goods companies are doing. While traditional solutions may seem reliable on the surface, integrating the latest technologies and applications essential to a great customer experience is becoming increasingly difficult, if not impossible. Additionally, they have major drawbacks: expensive maintenance, operational inefficiencies, weak security and the risk of non-compliance.
In such scenarios, AI-driven application and data modernization has emerged as a practical and effective approach to transform legacy systems and data together. This creates a foundation for agile, intelligent, and future-proof digital commerce.
It’s time to integrate data and application modernization as concurrent executions
Let’s take a look at common challenges for retail and consumer goods companies. Most commonly, siled data about customers, products, and inventory proliferates across multiple channels. Add to this the sprawl of traditional ERP, merchandising, and supply chain platforms, and it’s no surprise that retailers and consumer goods companies are slow to respond to demand fluctuations and promotional cycles. Significant gaps in data quality add to the challenge and limit the ability to operationalize AI use cases.
The need to modernize data and applications could not be more urgent, but here’s the thing. Historically, they have been pursued as independent endeavors. However, in today’s omnichannel and real-time environments, this separation greatly limits business impact. Modernizing your application without modernizing your data will speed up your system, but it won’t provide any insight. On the other hand, modernizing data without modernizing applications limits deployment and execution. AI will enable both to evolve in lockstep.
The modernization of data and apps in the retail and consumer goods industries presents huge opportunities. Modernized applications enable organizations to take full advantage of emerging technologies such as artificial intelligence and machine learning (AI/ML), augmented reality and virtual reality (AR and VR), and the Internet of Things (IoT). The integration of machine learning, large-scale language models (LLM), generative AI, and intelligent automation delivers incredible results in:
- Assessing and refactoring legacy applications
- Modernize your enterprise data platform and pipeline
- Enabling a cloud-native, modular, analytics-enabled architecture
- Comprehensive omnichannel interactions and highly personalized consumer experiences.
The result is a highly intelligent retail and consumer goods company that can sense demand, respond faster, and continuously optimize performance.
Key areas for AI-driven retail app modernization
AI-powered app modernization enables intelligent code discovery and dependency mapping, effective refactoring and API enablement, and automated test generation and regression testing.
Let’s take a look at some of the key core systems that retailers rely on and see how AI-driven app modernization can help them.
- ERP systems are essential to retail organizations, but many businesses still have traditional on-premises systems that suffer from long batch processing times, rigid data models, and problematic integrations. AI-driven app modernization improves scalability, flexibility, and cost efficiency.
- Traditional order management systems (OMS) and warehouse management systems (WMS) have limitations in system flexibility and scalability. App modernization unlocks the benefits of real-time data exchange for dynamic ordering and inventory management. Additionally, cloud-native systems offer scalable, AI-powered omnichannel solutions that foster customization and innovation.
- When retail POS systems are integrated with AI apps and features, they become powerful engines for inventory intelligence, customer insights, personalized offers, and seamless shopping journeys.
What AI-driven retail data modernization can deliver
Modern commerce relies on reliable, real-time data across the value chain. Every touch point, including online browsing, in-store shopping, and loyalty programs, generates tons of data. As this data ecosystem grows in size, so does its complexity. Legacy systems also hinder rapid analysis and real-time insights to make data-driven decisions.
AI-driven data modernization streamlines data flows and provides real-time access to data across teams and departments to enhance business intelligence and decision-making. Integrate disparate data sources, predict consumer behavior and demand, enable accurate inventory management, and deliver highly personalized experiences to increase loyalty. In fact, AI enables complete harmonization of customer, product, inventory, and supplier data. Facilitate seamless migration from traditional data warehouses to the cloud Lakehouse platform and enable real-time, event-driven data pipelines.
From a data integrity and governance perspective, data modernization with AI helps with deduplication and anomaly detection, normalization of product and SKU attributes, and automation of privacy, security, and compliance measures.
Winners in business intelligence, AI-driven data and application modernization
AI enables synchronized modernization of applications and data and transforms reports into embedded intelligence. Enable intelligent orchestration of purchasing behavior, preferences, and intent to deliver personalized omnichannel experiences, accurate demand forecasting, optimized inventory, merchandising and pricing, rapid fulfillment, and efficient supply chain risk management. In fact, AI permeates every layer of the retail value chain, creating an intelligent and enhanced network of data, processes, and decisions.
As applications and data infrastructures mature, retail and consumer goods organizations will increasingly consider agent-based AI capabilities. This capability allows intelligent systems to monitor conditions, recommend actions, and, in controlled scenarios, make decisions within defined guardrails. That includes intelligent order routing and fulfillment optimization, automatic replenishment recommendations, and even dynamic promotion adjustments. One thing is for sure: all of these capabilities are completely dependent on modern applications, reliable data, and strong governance. AI-powered data and application modernization is therefore a key prerequisite for tomorrow’s autonomous commerce models.
The future of retail is about connected intelligence, and AI-powered modernization is helping retailers and consumer goods companies pivot around customer intent. The speed with which retailers can sense, determine, and act on customer intent determines the competitive advantage they can gain. Companies that align agent intelligence with modern architectures, discovery-to-purchase flows within conversational interfaces, integrated data and commerce platforms, and seamless experiences will not only optimize operations, but will redefine the customer experience in entirely new ways.
For retail and consumer goods companies, AI-driven application and data modernization is no longer just an IT transformation problem, but a core part of business transformation. By modernizing systems and data together, organizations can innovate faster, make better decisions, and have the flexibility to adopt future capabilities when agent-driven commerce scenarios are imminent.
Companies that act now will not only modernize their technology stacks but also be in a position to lead in an increasingly intelligent and automated retail industry. Retailers that leverage governance to modernize legacy applications and data will lead the next decade of trusted, intelligent, AI-enabled retail.
Mithun Shenoy is SVP and Head of Retail and CPG Business Unit. MASTECwhere 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.
