Maximising AI Success in Supply Chain with Clean Data

Clean data can maximise supply chain success, writes Mark Holmes, Senior Advisor for Supply Chain at InterSystems.

Artificial intelligence (AI), from traditional machine learning to recent generative AI, holds immense potential to revolutionise global supply chains by enabling adaptive decision-making. The approach refines predictions to meet evolving consumer demands, enhancing demand forecasting, streamlining fulfilment despite supply disruptions, and unlocking a range of innovative possibilities.

The global supply chain AI market is showing strong growth. According to Meticulous Research, it is expected to surge to US$58.55 billion by 2031, growing at a CAGR of 40.4% from 2024 to 2031. Yet, as more organisations implement AI across their supply chain operations, harnessing accurate, clean, and unified data remains a critical challenge. AI is only as effective as the data it processes. Inaccurate or fragmented data leads to flawed outcomes and erodes trust in AI-driven systems.

Achieving Data Quality

The good news is that regardless of where an organisation stands in its AI journey, there is still time to build the necessary data foundations, without the need for a risky rip-and-replace of legacy systems. However, ensuring data quality is not easy. Supply chain data streams originate from many disparate sources, including enterprise systems, IBP applications, suppliers, demand pattern changes, warehousing, and transportation systems. If these diverse sources are not harmonised, AI models may produce flawed outputs that prompt manual checks and redundant oversight, ultimately undermining efficiency.

Real-time data is equally critical. Supply chains involve numerous variables, from supplier availability to weather patterns, that can change rapidly. Analysing delays or disruptions in near-real time and acting swiftly on these insights can mean the difference between effectively managing an issue or missing a critical opportunity. Access to timely data is the first step toward harnessing AI for more accurate forecasts, adaptive planning, and proactive interventions.

The Role of Connective Technology

Creating the right data strategy requires modern solutions that act as a “connective tissue,” linking diverse data sources and formats. When deployed effectively, these solutions consolidate relational, non-relational, and streaming data without forcing a complete overhaul of core systems. This data unification enables immediate analysis, ensuring that AI models have a dependable, comprehensive picture of the supply chain at all times.

Beyond unification, connective technology cleans, standardises, and enriches data before any AI algorithms are applied. Such thorough preparation reduces the risk of inaccurate outputs and helps maintain confidence in AI-driven recommendations.

Evaluating Existing AI Implementations

Even as organisations begin deploying AI for use cases like demand sensing and order fulfilment, it is essential to access the right data in motion or at rest. Inconsistent or incomplete data might cause AI to overlook critical warning signs, produce skewed forecasts, or struggle to align inventory levels with real-world conditions.

Companies should regularly audit their data pipelines to pinpoint errors, such as missing entries or mismatched formats. Addressing any gaps, and ensuring the data remains fresh, can make AI models more robust and reduce long-term costs. By creating transparent feedback loops, supply chain leaders can monitor AI outcomes and measure them against established performance metrics. This approach helps determine whether refinements in data management or the AI models themselves are necessary.

From Foundational Data to Practical Results

After laying a strong data framework, organisations can more confidently progress toward advanced analytics, machine learning, and decision support tools that significantly improve supply chain efficiency. Predictive and prescriptive insights powered by both machine learning and latest genAI, can then be integrated directly into operational processes. Whether the goal is to handle demand fluctuations, optimise supplier networks, or accurately forecast inventory requirements, AI-driven analysis is most effective when powered by unified, trustworthy data.

Looking ahead, AI’s relevance in supply chain management will only grow as technologies evolve and businesses strive to stay flexible. AI can help with data availability, business insights, data-driven actions, etc. By making data integrity a priority, organisations establish a practical base for advanced solutions that yield real value. This means gathering, integrating, and using data in ways that support both present objectives and long-term growth.

Strong data practices ultimately open the door to AI-driven innovations that help supply chain leaders adapt at speed, reduce costs, and enhance customer satisfaction. With a clear focus on maintaining clean, unified information, businesses can transform daily operations and generate measurable returns.

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