Strat & IoT supports organizations in structuring and unlocking the value of their data, from maturity assessment to the industrialization of artificial intelligence models. We establish the essential foundations (data governance, compliance, architecture) before identifying and prioritizing AI use cases with the greatest impact. Our approach ensures that each project moves beyond the pilot stage to become a driver of sustainable performance, integrated into operations and generating measurable value.
From experimentation to value creation.
At American Express, an AI travel advisory assistant initially struggled with unstructured data. A redesign of the data layer subsequently enabled its deployment to thousands of advisors across 19 markets. This case illustrates a broader trend: according to an MIT study, the majority of enterprise AI pilot projects fail to move beyond the demonstration stage due to a lack of governance and reliable data.
From Strategy to Scaled Value
Assessment
Diagnose ambition, data maturity, operating constraints, and value pools.
Strategy
Prioritize the highest-value opportunities, governance, and executive alignment.
Implementation
Industrialize architecture, workflows, capabilities, and controls.
Optimisation
Measure real value creation and achieve sustainable, continuous improvement.
Maintenance
Securing long-term performance through continuous and predictive monitoring
Where does the momentum come from?
Data maturity
Without reliable and structured data, no AI model can deliver sustainable results: data preparation is the foundation of every AI project at scale.
Use-Case prioritization
Use case prioritization combines two criteria: measurable business impact and feasibility: available data and existing systems.
Governance at scale
Controlling AI at scale means ensuring a unified governance framework (compliance, oversight, traceability) across all models in production.
What are your pain points?
- Fragmented and unreliable data
- Poorly prioritized AI use cases
- AI pilots that fail to move beyond the experimentation phase.
Where should we focus our efforts?
- Data & AI maturity assessment
- Data governance
- Value-driven prioritization of AI use cases
- Industrialization of AI models.
What are our tools?
The delivery model is built around a concrete set of platforms, operational signals, and transformation capabilities selected to address the specific realities of the mission.
with a financial, operational and/or strategic benefit
- Reduced operational costs
- Increased asset reliability
- Service continuity
- Faster decision-making
AI strategy aligns data, use cases and business priorities before mobilizing technology.
Governance ensures the compliance, traceability and trust required for any AI deployment in production.
Measuring AI performance means linking each model to a clear business metric, from deployment through to realized value.