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Artificial intelligence is rapidly shifting supply chain operations from reactive to predictive. However, the true value of AI doesn’t lie in the algorithms itself, but in the quality, transparency, authenticity, and governance of the data that fuels it.
AI-driven trade analytics are offering supply chain leaders levels of foresight and inter-connectiveness they’ve previously not had access to, and this shift is happening at pace. According to Dun & Bradstreet’s recent Q4 Global Business Optimism Insight Report, 73% of businesses globally are either piloting or exploring generative AI use cases.
Early adopters report measurable gains, including reduced logistics costs and improved operational efficiencies. However, despite this strong interest, full AI integration remains rare, with just 13% of businesses globally having embedded generative AI across their operations currently, making foundational digital capabilities the real differentiator in 2026.
The biggest barrier to successful AI deployment is not the model but the data behind it. Many organizations still struggle with inconsistent, incomplete or duplicate data. An AI model built on this shaky foundation will not correct errors, it will magnify them, making it more susceptible to flawed predictions and strategies. To deploy reliable risk engines, a crucial step is engaging a data partner to standardize, validate and link raw, messy global data into consistent, high-quality and actionable datasets. This process transforms chaos into machine-ready intelligence, ensuring that every data point is clean, complete and contextually linked before it ever touches a model.
Even with strong data foundations, two huge challenges remain: systemic bias and model opacity. Because AI learns from historical data, it can inadvertently perpetuate existing human and organizational biases, which may result in missed opportunities, unfair outcomes, or even legal liabilities.
At the same time, many robust deep learning systems suffer from the “black-box” problem. Their sheer complexity prevents human users from understanding precisely how a specific decision or prediction was reached. This opacity doesn’t just present a technical hurdle; it fundamentally erodes trust. When leaders can’t validate the logic behind a high-stakes prediction, they cannot grant it authority. They are left with a choice between blind acceptance of an unverified output or setting back operations with a costly rejection.
Leaders should embrace AI, but this adoption must be grounded in robust governance. They need to acknowledge that data quality is a business issue, not just an IT issue. An explicit, C-level-sponsored data governance strategy is a critical mechanism to ensure data is accurately collected, validated, and linked at the point of entry.
For high-stakes decisions, human accountability must remain in the loop. Leaders need to push for inherently explainable models or demand tools that provide sufficient interpretability for human validation of black-box decisions. While AI can automate repetitive tasks, strategic responsibilities — such as assessing essential data, auditing for bias, breaking down silos and ensuring compliance — cannot be delegated to machines.
AI-driven trade analytics offer a transformational leap in foresight, agility, and efficiency. But, the promise of AI will only be realized by organizations that prioritize high-quality data, validated models and clear governance frameworks. Sustainable value lies not in deploying AI quickly, but in deploying it responsibly.
Businesses that invest now in the foundations of trusted data and human-led oversight will be best positioned to harness AI’s full potential, turning global complexity into competitive advantage.
Andy Crisp is senior vice president, global data owner at Dun & Bradstreet.
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