A 5-Step Road Map to Trustworthy AI Insights for Data Modernization in Retail

Retail consumers are relentlessly dynamic in their expectations. They not only expect organizations to provide personalized and seamless shopping experiences, but also intelligent anticipation of their needs across multiple channels. The challenge for retailers pivots on data in two dimensions. One is the deluge of data overload that confronts them. The other is the severe fragmentation of their data ecosystem. KPMG’s Global Tech Report 2024 reveals that while 54 percent of retail organizations experienced a minimum increase of 10 percent in profits by leveraging data and analytics, they face significant challenges in transforming data into actionable insights.

How can retailers make the leap from data overload to insights-driven value?

Artificial intelligence-driven data modernization realizes this objective. According to McKinsey, generative AI has the potential to unlock economic value of $240 billion to $390 billion for retailers. With its ability to streamline operations, enable faster and better-informed decisions, and deepen customer relationships, Gen AI can significantly augment the internal retail value chain. Gen AI tools can also be trained on proprietary data to create decisioning systems to achieve higher sales and profits. What retailers need isn't more models, but a unified, well-governed data foundation as well as production-grade MLOps that ensure accuracy, explainability and compliance in AI decisions.

A 5-Step Road Map to Trustworthy AI Insights

While Gen AI’s upside for retail is huge, value arrives only when trust is engineered into the data stack. Here’s a five-step and vendor-agnostic road map to building AI-driven insights that customers and regulators can believe in.

1. Take inventory, label and unify what you already own.

Customer data resides in multiple touchpoints — online, in-store, point-of-sale systems, mobile, loyalty programs, social media, and more. The first step is in identifying and integrating them to create a 360-degree view of both customers and products. Building a data lakehouse and standardizing business glossary and consent flags is important. Data lakes, specifically, can simultaneously perform business intelligence and advanced analytics operations, reduce data redundancy, improve data security, and reduce cost of storage.

AI-powered tools can build a comprehensive view. For example, Nordstrom uses Gen AI to leverage versatile sources of data to analyze its customers’ purchase histories, categorize them in a hyperpersonalized manner, and create predictive ensembles based on individual preferences.

Most importantly, AI systems can significantly reduce retail shrink — at the checkout, inventory and operational levels. In today’s retail landscape of razor-thin margins, this is a bonus for retailers.

2. Make trust measurable through governance-by-design practices.

The intention of achieving trust must translate into measurable actions. Two vital practices — data quality management and data security — can help retailers win the trust of customers, regulators and their employees.

When it comes to data quality management, retailers must establish the right protocols for data entry, validation and cleansing to eliminate the inefficiencies of duplication, information obsolescence and inaccuracy. Data quality service-level agreements for completeness and timeliness, plus automation of drift and anomaly checks are good practices to adopt.

Robust data privacy and security measures such as encryption, secure data storage, role-based access, and regular security audits must be put in place to protect consumer information from breaches and cyberattacks. These data governance practices should be strictly aligned to data protection regulations such as GDPR (General Data Protection Regulation) and CCPA (California Consumer Privacy Act). It's also critical for trust to be transparent to customers about how their data is used, and to give them control over their personal information.

AI-driven security systems accurately identify vulnerabilities in the digital infrastructure and also detect in real time abnormal patterns and potential threats. The National Institute of Standards and Technology (NIST) has created an effective AI risk management framework (RMF) for voluntary use, and retailers may leverage this to adopt model cards for transparency, bias checks, and human-override logging for pricing, fraud and loss prevention

3. Future-proof for change readiness in real time through multi-cloud optimization.

The ability to adapt and innovate is critical for retailers. AI enables swift and agile futureproofing through predictive insights that ensure hyperpersonalized customer experiences and dynamic responses to disruptive market shifts to create new revenue streams and business models. For example, H&M has effectively leveraged AI to understand data points across locations, geographies, seasons and product categories — and predict business drivers with granularity so it's well-prepared to offer future-forward product launches and hyperpersonalized experiences.

AI can achieve this objective by:

• Decoupling storage and compute, enabling streaming for demand and inventory signals.
• Leveraging multi-cloud options to avoid lock-in, preparing for GPU/accelerator workloads, and establishing cost guardrails.
• Establishing a feature store to reuse signals across forecasting, pricing and churn.
• Developing "ahead-of-the-curve" personalization initiatives at scale to sustain and grow customer engagement, loyalty and revenues.

Additionally, AI can future-proof cloud investments with intelligent automation for scalability and enhanced cybersecurity.

4. Industrialize AI from day one.

Every day, we find new ways in which AI is changing the retail arena. Playing catch-up with changes can be effective only if retailers are early birds in the adoption of AI across the gamut of data security, customer experience, inventory management, product cataloging, etc.

Existing data infrastructure must be modernized at speed to prepare for future integration of advanced AI applications. CI/CD for data and models, automated tests and rollback, monitoring of drift, fairness and large language model hallucinations should be in place. Prompt and RAG evaluation for Gen AI must be established, as must the human-in-the-loop imperative for high-stakes decisions. MLOps and AI governance need to be treated as complementary tracks. And, of course, people should be trained in data science and analytics, and this must be complemented by the right AI investments.

5. Continuously prove trust and confidence.

Trust has to be continuously validated and proved, especially in a rapidly evolving technology such as AI. NIST’s principles of map, measure, manage, and govern serve as a strong affirmation of trustworthy, transparent, responsible and ethical AI systems. This can be achieved by pairing business key performance indicators (e.g., sell-through, markdown reduction, NPS, etc.) with trust KPIs (e.g., explanation coverage, approval rate of AI recommendations, complaint rates, etc.). Running A/B tests that tie model outputs to outcomes is a great way of ascertaining transparency.

AI-driven consent-based analytics is a responsible and respectful approach of prioritizing user consent and transparently requesting permission from users. While taking a clear privacy-centric stance, it also complies with regulatory mandates.

In effect, trustworthy retail AI is disciplined data work and modern engineering in action. Start small, wire in guardrails, and measure trust along with revenue lift — that’s how you scale AI with belief and value.

Raman Awal is senior vice president and global practice head at Mastek, a trusted digital engineering and cloud transformation partner.