Artificial intelligence
AI at the heart of executive committee strategy
Publiée le September 22, 2025
Publiée le September 22, 2025
In just a few investment cycles, artificial intelligence has gone from being a peripheral innovation to a strategic pillar. It now irrigates planning, operational excellence, customer relations, supply chain resilience and risk management. The interest is not just technological: AI is transforming the speed and quality of decision-making, which, on a group scale, converts into points of growth, EBITDA and cash. Against a backdrop of macroeconomic uncertainty, competitive pressure and frequent regulatory changes, executive committees are asking not so much “whether to go for it” as “how to institutionalize AI to make it a performance multiplier, while protecting the company and its stakeholders”.
The effective answer lies neither in a collection of POCs nor in obsession with the latest model. It requires a clear roadmap, clear business priorities, a robust data architecture, industrialization mechanisms, responsible governance and rigorous value measurement. This article proposes a framework for action for management bodies: why AI has become a priority, how to build the data foundations, where to integrate it to create value, and what governance rules to adopt to reconcile performance, compliance and trust.
Leading companies turn information into decisions faster and better than their competitors. AI makes it possible to merge heterogeneous signals (operational, commercial, financial, external) and generate dynamic trade-offs: prices, supplies, promotions, maintenance, capital allocation. Value is realized when decision loops are shortened, errors are reduced and resources are better directed. For an executive committee, this is a direct response to the three imperatives of the moment: qualitative growth, cost control and resilience.
The first deployments are costly and specific; subsequent deployments reuse datasets, software components, models and best practices. This “platform” effect lowers marginal costs and accelerates multi-country or multi-entity deployments. AI ceases to be a cost center and becomes a value factory, provided that a portfolio of initiatives is managed with investment criteria, value milestones and rapid shutdown decisions.
Customers, regulators, investors and employees demand transparency, fairness, security and sobriety. Poorly governed AI exposes the company to legal, reputational and cyber risks. Conversely, responsible governance strengthens trust and becomes an employer brand. It’s up to the executive committee to set the tone, assign responsibilities and measure overall economic and non-financial performance.
Move from a logic of one-off projects to “data products”: documented data sets, with an owner, quality SLAs (freshness, completeness, accuracy), access APIs and an accessible catalog. Each domain (customers, contracts, orders, assets, incidents, suppliers) becomes a reusable product. This approach reduces friction between business and IT, and accelerates industrialization.
Lakehouse architectures combine flexibility and governance; a feature store facilitates the reuse of variables in models; vector indexes and RAG (Retrieval Augmented Generation) pipelines connect generative AI to internal corpora (procedures, contracts, tickets, technical files). The MLOps/LLMOps factory ensures model and prompt versioning, non-regression testing, automated deployment, observability (performance, bias, drift) and fine-grained cost control. It’s the antidote to value-diluting POC-mania.
Quality is as much about engineering as it is about auditing: automatic ingestion checks, validation rules, anomaly detection, lineage for traceability, fine-tuned access policies, anonymization and masking. In sensitive cases, risk-proportionate explicability and human supervision are non-negotiable. Security covers data, models and flows (prevention of data poisoning, prompt injection, misuse), as well as the management of secrets and identities.
The Chief Data Officer steers data strategy and industrialization, in orchestration with the IT and business departments. A Data & AI Council reporting to the Executive Committee decides on priorities, examines sensitive cases, monitors incidents and publishes convergent indicators (usage, value, confidence). Data owners ensure quality on a day-to-day basis, while AI product owners guarantee usability, ergonomics and adoption. Together, they align digital transformation with operational reality.
Rolling forecasts and multi-factor scenarios
Detecting accounting anomalies
Automatic reconciliations
Optimize working capital with collection prediction and late payment scoring
Capital allocation guided by the probability of success of initiatives
Expected results: shorter budget cycles, reduced variability, smoother cash flow.
Recommendations and dynamic pricing
Omnichannel orchestration
Semantic analysis of verbatims and detection of irritants
Conversational assistants linked to internal knowledge bases via RAG
Typical impacts: higher average basket, improved conversion rates, lower AHT (Average Handle Time) and higher CSAT/NPS.
Predictive/prescriptive maintenance
Constrained planning and scheduling with continuous replanning
Inventory and flow optimization
Computer vision for online quality control
Effects: increased asset availability, simultaneous reduction in out-of-stocks and overstocks, less scrap.
Workforce planning, workforce calibration, schedule optimization
Skills matching and internal mobility
Customized training paths
In-house assistants who help with writing, summarizing and researching
Clause extraction, contract comparison, generative AI synthesis
Semantic regulatory monitoring and mapping of emerging risks
Prioritizing controls
Logging and traceability of automatic decisions
AIOps for earlier detection and correlation
Guided remediation
Detection of behavioral anomalies
Continuous curing
Accelerate development cycles with “guarded” code wizards
Purpose and proportionality
Fairness and non-discrimination
Adapted explicability
Human supervision
Data security and models
Decision traceability
The Executive Committee sponsors AI policy and is supported by an operational Data & AI Council. The Chief Data Officer coordinates data strategy and industrialization with the IT Department; business departments co-pilot utility and adoption; Risk & Compliance secure the sensitive perimeter; IT security covers AI-specific threats. Each sensitive use case is examined upstream, with test protocols, eligibility criteria and documentation.
An executive dashboard brings together technical metrics (precision, recall, drift), usage metrics (adoption, time saved), economic metrics (savings, revenue, cloud costs) and trust metrics (incidents, bias, complaints). Internally, a charter explains to employees how AI is used; externally, the company can spell out its commitments in a way that is proportionate to the stakes involved.
Skills development is differentiated: executives (use cases, AI economics, risks), managers (process integration, adoption), experts (MLOps/LLMOps, model security, explainability methods). The culture values responsible “test & learn” and the reporting of deviations.
Executive-level AI programs, backed by a solid data strategy and responsible governance, produce visible effects in 12 to 24 months.
The orders of magnitude observed are consistent:
10-30% cost reduction on targeted processes,
+3 to +7% in sales,
10% to 20% reduction in tied-up capital,
20 to 40% of losses avoided due to fraud and non-compliance,
+5 to +10 points of CSAT/NPS,
+2 to +5% EBITDA when adoption exceeds 70%.
These results are not automatic. They are the result of a disciplined sequence:
prioritize use cases linked to business objectives,
build reliable data products,
industrialize with MLOps/LLMOps,
measuring value and trust,
adjust quickly.
Anchoring AI in corporate strategy
Mandate and equip a strong Chief Data Officer
Institutionalizing industrialization
Turning responsible governance into a competitive advantage
Investing in adoption and skills
Control through convergent measurement
Mastering the economics of AI over the long term
Placing AI at the heart of executive committee strategy means making data a governed asset, decision a speed advantage, and transformation a continuous movement – a Digital transformation that is measurable, responsible and creates value for all stakeholders. Companies that embark on this process methodically will establish an advantage that will be hard to catch up with, as each new use case capitalizes on the infrastructure, culture and trust already built up.