Author: Bart A. De Muynck
In boardrooms across the globe, Artificial Intelligence (AI) is being lauded as the single greatest transformation engine for efficiency, resilience, and competitive advantage in a generation in supply chain. Yet, for every success story, the reality is stark: 95% of enterprise AI projects are a complete failure, representing billions of dollars being “lit on fire” with no measurable return on investment (The GenAI Divide STATE OF AI IN BUSINESS 2025 – MIT). This profound gap between promise and reality has one single, non-negotiable cause: a broken foundation rooted in low-quality data.
To move past this crisis, supply chain leaders must shift their focus from the complex AI algorithms to the simple truth: data is the lifeblood of the modern supply chain. The question is not if you will use AI – you must or be left behind – but whether the data running your engine is clean enough to move your business forward.
The Structural Hurdle: Data Silos and the 4% Gap
The last two decades have delivered a data eruption propelled by enterprise applications and, especially by the Internet of Things (IoT) revolution, which embedded sensors, GPS trackers, and network connectivity into countless devices. This explosion in volume has only exacerbated the crisis of data quality.
As we attempt to evolve from using business intelligence (BI) to merely look back at what happened, toward using advanced analytics to predict and prescribe actions, most of the data remains siloed and disconnected. Data is fragmented across disparate systems—ERPs, SCP, TMS, WMS, and many other solutions—creating blind spots that prevent a holistic view of end-to-end processes and make it impossible for AI to connect planning data with execution data.
This fragmentation leads directly to the single most alarming statistic confronting executives today: Industry research, including survey data often used by Gartner, reveals that “only 4% of enterprise data is considered AI-ready”. This staggering gap is the fundamental barrier to unlocking any AI potential.
The problem is simple: just like bad petrol will ruin your engine, bad data will provide more harm than good in your supply chain. When the core data is inaccurate or inconsistent, the output of the AI’s predictive model will be incorrect, leading to poor decisions, increased risk, and a wasted investment. It’s like a GPS with poor data: it leads you to the wrong place, with potential for a dangerous outcome.
The Blueprint for Success: What the 5% Do Differently
The success of the minority, the 5% of companies that are making billions from AI, proves that the solution is not simply more technology, but better strategy, enabled by AI technology.
These successful adopters have created a clear blueprint by focusing on the foundational groundwork:
- Picking the Right Problem: Success starts by identifying one expensive problem that moves the profit-and-loss (P&L) statement, rather than funding fifty random pilots. Gartner notes that more than 80% of AI value comes from solving narrow, well-defined business problems, not broad experimentation.
- Doing the Data Groundwork:
- Conduct a Data Inventory and Assessment: Map all existing data sources that touch the problem domain. Gartner calls this “data source discovery,” and warns that most organizations underestimate the fragmentation of their operational data.
- Improve Data Quality: Commit to having high-quality data access before scaling any project. This involves a strategic focus on data cleansing, ensuring their platform can handle the transformation of raw, low-quality inputs into a high-fidelity information resource. Gartner estimates that up to 40% of AI project time is lost to fixing data quality issues that should have been addressed earlier. Establish clear data governance and ownership.
- Establish Data Governance and Ownership: Gartner stresses that without governance, models drift, lose trust, or become unmaintainable. We see more companies creating a Chief Data Officer (CDO) role, but without this CDO the data ownership is unclear or there isn’t a single owner for all the different enterprise data. In companies without a CDO, the CIO often temporarily assumes the governance mandate.
- Build a Unified, Accessible Data Layer: Data needs to be consolidated, normalized, accessible via APIs or data lakes and updated in near real time. Gartner notes that companies with modern data architectures achieve AI deployment three times faster due to data availability and scalability.
- Label, Enrich, and Engineer Data for AI: This may include creating target labels, engineering predictive features, enriching with external data, and structuring event sequences for time-series models.
- Ensure Ethical, Compliant, and Secure Data Use: This is particularly important in logistics, where data flows across shippers, carriers, brokers, suppliers, and platforms.
- Create a “Ready for AI” Data Sandbox: This derisks early experimentation.
- Proving ROI Early: These leaders understand that proof of value kills skepticism. This approach fuels momentum and internal buy-in.
- Starting Narrow, Then Scaling: Successful companies roll out AI in a phased approach. This measured approach ensures the organizational structure and data quality are stable before scaling the project globally.
The Path to the Future: Data Quality
The promise of a future defined by better analytics, smarter forecasting, and greater agility is immense. The path to that resilient, profitable future is clear: it starts with a relentless, strategic focus on data quality, seamless end-to-end integration, and a commitment to empowering the workforce through the human-AI partnership. Investing in high-quality data is not a cost; it is the essential foundation for turning AI into a competitive advantage.
The AI revolution in the supply chain is ultimately a people problem. Successfully leveraging AI requires not just technical skill, but a shift in organizational culture and leadership strategy. The focus and effort surrounding data quality is key to the success of this new strategy.
