Industry: Industrial manufacturing / product-driven organization
Function focus: R&D / Product Development leadership

Context

A global industrial organization struggled with fragmented product development systems (lab data, project tracking, financials).

Teams were constantly interrupted with ad-hoc questions:
“Are we on track for release readiness?”
“What turned red this month — and why?”
“Where are we burning budget without impact?”

Data preparation had already happened — but in silos:

• Product Development teams had prepared metadata and tagging for their own domains.
• Finance had done similar work two years earlier — only for finance use cases.
• Business units had never been involved in defining what data should support leadership decisions.
• There was no central data science function.
• AI topics were formally owned by IT.

The result: systems existed, data existed, tools existed — but leadership had no shared, trusted view.

Challenge

• Data and AI initiatives developed in functional silos
• Business was not involved in data preparation or tagging
• No shared leadership view of “what is true”
• Reporting cycles were slow and manual
• AI pilots existed, but did not change leadership routines
• Ownership split between PD, Finance, IT — with no integrator
• Low trust in data during decision-making

Approach

• Start from leadership decisions — not from tools
• Identify the few executive questions that actually matter
• Redesign leadership routines around a small set of business-critical KPIs
• Clarify ownership between business, data, and IT
• Align data preparation with leadership decision needs
• Embed governance and accountability into leadership steering formats
• Define a small number of funded pilot use cases tied to leadership decisions

A GenAI-supported PD Intelligence Assistant was defined as a future use case — not as the starting point — with clear leadership guardrails for traceability, sources, and confidence levels.

Outcome

• Significant reduction in ad-hoc reporting
• Faster leadership decisions based on shared data reality
• Clear accountability for KPI ownership
• Higher trust in AI-supported insights
• Foundation created to scale AI beyond isolated pilots

Leadership Insight

AI only creates value at the top when leadership routines and data ownership change.
If data is prepared in silos and leadership habits stay the same, AI remains a reporting gadget.