Technology sprawl
Applications, vendors and data growing without common criteria, plus shadow IT and shadow AI.
Consulting · Governance
IT and AI governance: from principles to practice.
Technology and artificial intelligence are already strategy. We help you govern them with control, compliance and value: portfolio, budget, vendors, risks, data and models, all in one common language.
- IT + AIa single governance model
- ENS · GDPR · EU AI Actcompliance by design
- CIO · CAIO · CDOaccountable leaders, part-time
01Why now
The question is no longer whether to adopt technology, but how to govern it.
Regulatory pressure
GDPR, Spain’s National Security Framework (ENS) and the EU AI Act, which is being phased in.
Risk and continuity
Cyberattacks, vendor dependency and automated decisions with no traceability.
Investment with no visible return
IT and AI budgets that grow with no indicators of business value.
The missing step. Most organisations already have critical systems, AI principles and pilots under way. What they lack is not will but an operating model: moving from scattered initiatives to governance that is auditable, cross-functional and sustainable.
02IT governance
Six pillars for governing technology.
Every euro spent on technology should answer to a business priority, with risks contained and service you can rely on.
Strategy and alignment
An IT plan aligned with business objectives and reviewed with the leadership team.
Project portfolio
Prioritisation by value, cost and risk, with benefits tracking.
Budget and vendors
Visible IT costs, contracts under control and less dependence on third parties.
Risk, cybersecurity and continuity
IT and OT risk analysis, continuity plans and incident response.
Service and operations
Service levels, metrics and continuous improvement of what already works.
Compliance and audit
GDPR, ENS and sector regulations, with audit-ready evidence.
03AI governance
Governance doesn’t slow AI down: it lets it scale.
Six end-to-end pillars that connect business, IT and operations in one common language.
Strategy and portfolio
Use-case inventory and prioritisation by impact and risk.
Regulatory compliance
Risk classification and technical documentation under the EU AI Act.
Model lifecycle
MLOps, versioning, monitoring and explainability (XAI).
Data governance
Quality, catalogue, lineage and master data (MDM) as the foundation.
People and culture
Responsible use, training and control of shadow AI.
Risk and security
Model risk management and IT and OT cybersecurity.
04Ownership
Governance needs owners.
A fractional CIO, Chief AI Officer (CAIO) and Chief Data Officer (CDO), part-time or in phases, without adding permanent headcount.
CIOIT governanceStrategy, portfolio, budget, vendors and risk.
CAIOAI governanceAI policy, committee, portfolio and the EU AI Act.
CDOData governanceQuality, catalogue, lineage and master data.
- AI portfolio governance and prioritisation dashboard: impact versus cost and risk.
- Inventory and explainability of models in production (influencing factors and explainability).
- Governed conversational assistants over corporate documentation, with cited sources (RAG).
- Secure, audited data access through governed Text-to-SQL.
- Audits of cybersecurity and of integrations between IT and OT systems.
- Predictive models in operation with continuous monitoring.
05Next steps
Three steps to get off on the right foot.
- 01
Discovery session
We align sensitivities, scope and approach: business-led, IT-led or cross-functional.
- 02
Maturity assessment
An inventory of systems, data and AI, and a gap analysis against GDPR, ENS and the EU AI Act.
- 03
Governance roadmap
Operating model, committees and prioritised quick wins.
Contact
Let’s talk.
Tell us about your challenge by email or phone. In a first, no-strings session we align scope and approach, and tell you honestly whether we can help.
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