A white paper by Accenture in collaboration with Stibo Systems
If you are responsible for AI delivery across a large enterprise, you have probably watched a successful pilot struggle to survive contact with the rest of the organization.
The models were not the problem. The data was – fragmented across systems and never designed to support AI operating beyond a controlled environment.
In this white paper, produced by Accenture in collaboration with Stibo Systems, you learn exactly why that happens and what it takes to address it.
It also makes the case for why the window to address this – without a separate, costly initiative – is narrower than most organizations assume.
What you will find inside:
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Why a successful pilot is a poor indicator of enterprise readiness – and what the data conditions behind it hide
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Where AI breaks down at scale, and why it is an architecture problem rather than a model problem
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What the explainability gap means for internal trust and your regulatory exposure under the EU AI Act
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Why conventional data approaches were never designed to support AI operating across the full enterprise
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The case for multidomain master data management as foundational AI infrastructure rather than a data management project
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What a governed, multidomain data foundation needs to look like – and how to build one without a separate transformation initiative
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What an operating model for AI at scale needs to address that most governance frameworks currently ignore
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