The AI Adoption Framework is built around seven interconnected pillars, each representing a critical domain for responsible, effective and scalable AI integration. They capture the concrete capabilities that organisations must develop to implement AI that creates value while staying in control.
Together, the pillars reflect not only practical enterprise needs but also a distinctly European perspective. One that emphasises human-centred innovation, legal and ethical accountability and long-term public value. In contrast to the “move fast and break things” mentality of dominant global tech players.
STRATEGIC INTENT & PRIORITISATION
Align AI initiatives with your enterprise goals and industry context. This pillar ensures that every AI effort supports long-term value creation and competitive relevance.
How does AI align with our business strategy?
– What is our AI vision and strategic objective?
– What market, sectoral or geopolitical drivers apply?
DATA & ARCHITECTURE READINESS
Lay the data foundation needed for scalable and secure AI adoption. It covers data quality, governance, architecture and compliance-by-design.
Is our data foundation ready for AI?
– How do we ensure data quality?
– Do we have the right infrastructure in place?
RESPONSIBLE AI, ETHICS & SECURITY
Ensure your AI is transparent, fair, and aligned with EU and global regulations. This pillar embeds privacy, oversight, and accountability into every model.
Is our AI transparent, fair, and compliant?
– Are we aligned with the AI Act, GDPR, and ISO 42001?
– Do we implement model cards, bias checks and DPIAs?
GOVERNANCE & RISK MANAGEMENT
Establish clear controls and ownership over AI use across the organization. From decision rights to risk monitoring, it ensures safe and auditable AI deployment.
How do we safeguard control and accountability?
– Who decides on AI deployment and use?
– Are responsibilities, oversight and escalation well defined?
PLATFORM, TOOLING & INTEGRATION
Standardize how AI is built, deployed and integrated into your IT landscape. This includes MLOps practices, reference architectures, and cloud-native patterns.
How do we scale and standardize AI implementation?
– What cloud, MLOps, or architecture patterns do we use?
– Are security and deployment standards established?
PEOPLE, SKILLS & CHANGE ADOPTION
Empower your teams with the mindset and skills to adopt AI effectively. From leadership to workforce training, this pillar ensures AI succeeds beyond the pilot phase.
Are people prepared for AI-driven transformation?
– Do we have a skills matrix and leadership development plan?
– How do we manage adoption and reduce technostress?
MEASUREMENT & VALUE REALISATION
Track impact, prove ROI, and adapt AI solutions post-deployment. It turns experimentation into measurable outcomes through KPIs, dashboards and feedback loops.
How do we track and realise value from AI?
– What KPIs do we follow (efficiency, ROI, risk)?
– Are dashboards and feedback loops in place for optimisation?
The AI Adoption Playbook translates the AI Adoption Framework into concrete steps.
It is the definitive guide for AI leaders and project managers to ensure compliant, enterprise-wide implementation.
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