In a recent meeting with a board of directors, the discussion about AI began in a familiar way: "We know it's important, but what do we actually do? Where do we start? And how do we avoid spending resources only on 'trendy trainings' that don't change anything in depth?"
The questions didn't come from a lack of openness. On the contrary, those leaders were already using artificial intelligence tools in their daily activities, and so were the people on their teams. The problem was different: how do you turn the occasional use of AI applications into an organizational capability able to generate tangible results, at scale?
This question, which keeps recurring in many organizations in Romania and the region, actually defines the moment we're in: between the global AI hype and the local reality of fragmented and uneven adoption.
The context: AI has become infrastructure, not an option
Artificial intelligence is no longer an experiment reserved for laboratories. In just a few years, transformer models have moved from academia into the mainstream, and generative platforms are now used by hundreds of millions of people. Global economic projections point to an impact of tens of trillions of dollars by 2030.
However, adoption within organizations is not linear. While at the individual level AI brings immediate productivity gains – drafting a report faster, analyzing complex data, or creating presentations – at the organizational level things get more complicated. AI becomes infrastructure: it affects business models, governance structures, decision-making processes, and even organizational culture.
Why we need new capabilities
In conversations with business and HR leaders, a few recurring themes emerge:
- Strategic alignment: AI is often seen as a set of isolated applications, not as a tool that supports the organization's goals.
- Lack of integrated skills: employees may learn to write prompts, but they don't understand how to connect AI outputs with business indicators.
- Governance and ethics: questions about data, confidentiality, and bias are often ignored, but they can generate major risks.
- Learning culture: many organizations invest in one-off trainings, without creating skill ecosystems and internal communities that support long-term adoption.
Without a coherent framework, AI technology risks remaining a "smart accessory" rather than a driver of transformation.
A fundamental point is that AI no longer belongs solely to IT specialists or data science teams. Any role in the organization can be augmented with AI: from the product manager testing market hypotheses, to the HR specialist creating engagement policies, or the recruiter screening CVs.
The critical skills taking shape are:
- Advanced prompting and critical thinking – the ability to formulate clear questions and evaluate the quality of answers.
- Integrating AI into decision-making processes – connecting AI deliverables with commercial or operational objectives.
- Governance and accountability – understanding the legal, ethical, and security implications.
- Redesigning workflows – the ability to reimagine processes with AI as a central element, not just as an extension.
These skills are not "nice to have", but defining factors for organizational competitiveness in the coming years.
Challenges for HR and L&D
This is where the essential tension arises: how do we turn AI from a sci-fi term into a scalable learning ecosystem?
Our interactions over the past 2 years with HR and L&D leaders have highlighted 4 major dilemmas:
- Which skills do we develop for each role? – A generic training is no longer enough.
- How do we measure ROI? – Organizations need tools that concretely show the impact on productivity, implementation speed, engagement or cost reduction.
- How do we manage resistance to change? – Organizational culture can be the biggest obstacle.
- How do we build internal champions? – Without AI leaders inside the organization, adoption remains superficial.
To address all these challenges, we developed together with Star Tech Team the first AI Academy in Romania, an initiative that goes beyond the classic training paradigm and proposes a learning and transformation architecture based on our proprietary D-BRAIN DT&AI Maturity Model. This model represents a validated scorecard of capability maturity covering 10 dimensions and integrating an organizational overview of leadership, culture, governance, operations, data and AI-readiness.
What makes AI Academy different?
- Sequential learning, from awareness to scale: the programs are structured in stages – Awareness, Adoption, Acceleration, Scale – so that organizations can follow a clear path, with measurable milestones.
- Real roles, not generic labels: participants take on roles such as Prompt Architect, GenAI Ops Engineer or AI Governance Advisor, with specific simulations and KPIs.
- Continuous diagnosis and measurement: each stage is accompanied by assessments through the D-BRAIN scorecard, which provides a clear picture of AI maturity and evolution over time.
- Learning through demonstration and applied projects: the modules conclude with business cases and AI prototypes relevant to the company.
- Building an internal community: through hackathons, AI Cafés and monthly knowledge-sharing sessions, the organization creates its own AI culture.
AI Academy is not a course, but a transformation process that aligns people's competencies with organizational ambitions.
The AI@Work Questionnaire – the first barometer in Romania on workforce perceptions of AI's impact
To understand where organizations in Romania stand in the face of this transformation, we launched the AI@Work Barometer – the first barometer of perceptions on AI adoption in companies.
This questionnaire works as a preliminary diagnosis: it measures the level of awareness, the level of use and expectations related to AI, giving HR and business leaders a solid starting point for discussions on investment decisions and for designing training programs.
Through AI@Work, organizations can understand where they stand on the AI maturity map and what steps are needed to transform AI from an abstract opportunity into a concrete competitive advantage.
Conclusion
AI is no longer an exercise in imagination, but the new infrastructure of competitiveness. The real question is no longer "are we preparing for AI?", but "how do we transform the organization to become AI-ready?".
For HR and L&D, the challenge is even bigger: if training remains just a delivery of information, organizations will fall behind. If, instead, training becomes a catalyst for new skills, redesigned work models and internal communities of practice, then AI can be the tool that accelerates not only productivity, but also innovation and adaptability.
The question we should ask ourselves is: can we still afford to treat training programs as simple "training modules", or do we need to reimagine them as true platforms for organizational transformation?