How generative AI enables sustainable transformation

Generative AI has reignited the discussion around digital transformation. While some organisations are still experimenting with individual use cases, others are already deploying the technology and achieving tangible results.

A recent McKinsey analysis shows that around 88 per cent of organisations now use generative AI in at least one business function. Deloitte has identified this trend too, describing generative AI as a central driver of new business models.

For operators of critical infrastructure, this means that AI can enable innovation, but it must also be verifiable, robust and compliant with regulatory requirements.

Many organisations start with pilot projects. These projects may involve testing service bots, internal knowledge systems or text-based automation. However, sustainable transformation only emerges when AI becomes an integral part of the IT and process landscape.

Three factors accelerate this step:

An IBM analysis highlights governance and clearly defined control mechanisms as key factors for the successful deployment of AI. Those who establish these foundations can leverage Generative AI as a reliable pillar of digital value creation.

Trust is particularly important in regulated sectors such as financial services, the public sector and telecommunications. The foundation for resilience and sustainable operations is formed by data sovereignty, traceability and technical independence.

Sovereign AI describes systems operated in accordance with European data sovereignty that are designed to be auditable and demonstrably compliant with regulatory requirements. The European Commission emphasises that the responsible deployment of AI is crucial for stable and secure digital infrastructures.

At 7P, we are committed to this approach. AI should only operate where it remains verifiable. This is why we use EU sovereign cloud environments and on-premises models combined with governance, monitoring and transparent documentation.

We support organisations on their journey towards productive, secure and compliant AI. This includes quality assurance, AI design sprints and managed AI operations.

Find out more: AI for Enterprise

Technological change always leads to organisational change. New roles, responsibilities and ways of working emerge. Therefore, it is necessary to build trust in the use of AI.

An analysis by Harvard Law School shows that clear decision pathways and transparent roles significantly reduce compliance risks.

Therefore, a responsible approach to AI involves regular audits, documented processes and traceable KPIs. ISO 27001, ISAE 3402 and continuous monitoring are integral to stable AI operations.

Many organisations have gained initial experience of using Generative AI. The next step is to translate this experience into sustainable operating models. A study by MLQ.ai reveals that over half of AI projects fail due to inadequate governance and process maturity.

In its latest AI study, the TÜV Association confirms that, although the use of GenAI is increasing rapidly, there is a lack of clear guidelines, security and direction. Sustainable value is created when architecture, governance and operations are considered together from the outset. This is how enterprise AI evolves to function reliably, withstand regulatory scrutiny and deliver measurable results.

Transforming with AI means digitising processes to make organisations resilient for the future. This involves establishing clear structures, transparent decision-making processes, and an operating model designed for long-term stability.

By investing in sovereign, auditable AI now, organisations can secure long-term strategic flexibility and strengthen their resilience in the face of regulatory and technological change.

Generative AI has the power to transform many things. The important thing is how it is designed and governed.

Further information on quality assurance can be found here: QA for AI: Quality Assurance for Generative Systems.