Every board wants AI. Every executive slide mentions it. Every vendor promises it will “transform your operations.” And yes, AI can be transformational. But here is the uncomfortable truth: AI will not save you if your data is a mess.
After designing AI and machine learning solutions for network planning, sales optimization, and capacity scheduling in complex manufacturing operations and supply chains, one pattern stands out to me: the organizations that win with AI are not the ones with the most advanced models, but the ones with the most reliable data.
The AI Hype Meets Operational Reality
The conversation usually starts the same way. A leader points to a competitor using AI for demand forecasting and says, “We need that.” IT estimates a six‑month implementation. Vendors reassure everyone that their “AI‑ready platform” will optimize the network end to end.Months later, the model is live – and nobody trusts it. Forecasts look off, planners keep working in Excel, and the expensive AI license quietly becomes shelfware. The issue is almost never the algorithm. It is the data feeding it.
Why AI Projects Really Fail
AI models learn from historical data. When that data is inconsistent, incomplete, inaccurate, or scattered across systems, the model will simply learn those flaws and reproduce them at scale. In supply chains, that often means product codes that changed without proper mapping, returns and test orders mixed into demand history, promotions not flagged, and channel or network data blended together. The result is forecasts and optimization outputs that look “smart,” but are detached from operational reality.
Use Cases That Only Work on Solid Data
In practice, the same pattern shows up across different supply chain domains:
- Network planning: AI can optimize routes, capacity, and costs across a complex network – but only when route data, constraints, and external factors are captured consistently.
- Demand forecasting: AI can combine history, seasonality, promotions, and external signals to improve accuracy – but only when those elements are correctly recorded and distinguishable in the data.
- Inventory optimization and capacity scheduling: AI can balance stock levels and capacity across locations – but only when lead times, capacities, and constraints are reliable.
These are great use cases where many leaders start with optimizing their supply chains. They are also the use cases where poor data quality is exposed first.
What “AI‑Ready” Really Looks Like
In practice, being AI‑ready means a few concrete things.
Your data quality is trustworthy: definitions are consistent, key fields are complete, and errors are detected and corrected as part of normal operations. If people do not trust the data for basic reporting, they will not trust it for AI‑driven decisions.
Your data is integrated: demand, orders, inventory, capacity, lead times, promotions, and external drivers can be brought together reliably for the same products, customers, and time periods. If critical data lives in disconnected systems, no amount of modelling can compensate.
Your data is structured for learning: time‑series, context, and outcomes are captured so patterns can actually be discovered. If you only store what happened, but not when, why, or with which result, models have very little to learn from.
And finally, your teams understand what AI is good at and where human judgment remains essential. AI performs well on pattern recognition, optimization, and prediction where there is enough stable history; it performs poorly in brand‑new situations, rapidly shifting markets, or decisions that hinge on nuance and relationships.
How Twin Transformation Helps You Deliver These Use Cases
With a twin transformation lens, AI use cases in supply chain are no longer “just” data science projects. They become integrated journeys across three dimensions: digital, sustainability, and human/operations.
- For network planning, this means using AI not only to optimize cost and service, but also to reduce emissions and design more sustainable routes – while involving planners and logistics teams in co‑creating the new way of working.
- For demand forecasting, this means designing the data model and process so commercial teams, supply planners, and sustainability leads can see the same truth and understand how promotions, portfolio choices, and waste reduction interact.
- For inventory and capacity, this means aligning operations, finance, and sustainability on what “optimal” actually means: not just working capital, but also waste, service, and footprint – and building governance so trade‑offs are transparent.
Twin transformation gives you a frame to design AI use cases so they support both business performance and sustainability goals, with people at the center of how those solutions are adopted.
How to Get Truly Ready for AI
The paradox is simple: if you want AI to work, do not start with AI. Start with data – and with the way people and processes work around that data.
- Step 1: Assess your data reality
Run basic quality checks, map how data flows through your manufacturing and supply chain systems and identify gaps that would make AI blind. Involve both data experts and planners so you capture technical issues and real‑world pain points. - Step 2: Prioritize AI use cases through a twin lens
Choose network planning, forecasting, or inventory cases where data is relatively clean, business impact is high, sustainability impact is tangible, and the teams involved are willing to adopt model‑based recommendations. - Step 3: Fix data before deploying AI
For the chosen use case, clean the relevant data domains, integrate the required sources, and put governance and monitoring in place so quality does not degrade over time. This is where a cross‑functional “business data” team, combining operations, IT, and sustainability, becomes crucial. - Step 4: Start with a focused twin pilot
Work with one use case, one team, and one segment of your network to prove that the combination of better data, AI, and new ways of working actually improves cost, service, and footprint. Capture lessons learned and refine both data and processes before scaling. - Step 5: Build capability, not dependency
Develop internal skills so supply chain teams can interpret AI outputs, give feedback, and understand how models are updated, while leaders learn to use AI insights in their decision forums. AI that only external experts understand will not be sustainable in daily operations.
The Bottom Line
AI is powerful, but it is not magic. If your data is messy, AI will amplify the mess; if your data is clean and well‑structured, AI will amplify the value. The fastest way to get more from AI in your supply chain is not buying more sophisticated models, but investing in the quality, integration, and governance of the data they depend on – and using twin transformation to design use cases that serve business, people, and planet at the same time.