I champion the accountable use of generative AI for organisations. This means managing organisational knowledge - data, expertise, processes, and insights - in a way that ensures generative AI outputs are accurate, reliable, and aligned with organisational values.

How can trust in generative AI be achieved?

From an organisational perspective, creating trust in generative AI through de-risking the AI ecosystem means building confidence in Generative AI systems by reducing the potential negative impacts and uncertainties associated with their development and use.

“Trust” in this context means that people are more confident that generative AI systems are reliable, safe, ethical, and beneficial. They trust that the AI won't produce harmful outputs, invade their privacy, or be used maliciously. Generative AI has the transformative potential to reshape how organisations operate, but only when integrated thoughtfully and responsibly.

Yet, many organisations find themselves stuck at the generative AI proof-of-concept stage. Despite significant investments in pilot projects, the leap from pilot to operations remains challenging. This transition requires more than technological considerations - it demands cleaned data, alignment with organisational goals, governance structures, and operational realities. It needs trust built through transparency, capability, and reliability.

1. By promoting accountable Generative AI responsibility

That is, being aware of the limitations of generative AI and taking responsibility for the information it produces. This involves managing organisational knowledge - data, expertise, processes, and insights - to ensure AI outputs remain accurate, reliable, and aligned with ethical values. The focus extends beyond leveraging AI for efficiency or productivity to embedding AI into processes that reflect organisational values and meaningfully engage stakeholders and customers.

Through transparent practices and value alignment, organisations can build trust in their AI systems while ensuring they remain reliable and effective tools for driving innovation, improving processes, and delivering long-term value.

2. By de-risking generative-AI and building trust

Secondly, de-risking generative AI requires that organisations take a comprehensive approach to mitigate various risks, including financial losses, reputational damage, customer dissatisfaction, and regulatory non-compliance.

• Financial risks stem from significant investments into AI with uncertain returns, operational disruptions, or potential regulatory penalties for non-compliance.

• Reputational damage can arise from biased outputs, inaccurate outputs, misinformation or data leaks. Customer risk includes negative experiences due to inaccurate recommendations, data breaches, or service failures.

• Customer dissatisfaction from generative AI risks alienating customers through inaccurate results, privacy breaches, or lack of human oversight. Any failure of the AI to meet customer needs or any perceived ethical violation will damage the customer relationship.

• Regulatory risk means navigating the evolving regulatory landscape, particularly with regards to data privacy regulations like GDPR, and EU AI Act and the UK Online Services Act, to avoid legal repercussions and maintain compliance.

Scaling your PoC (or Pilot) into Operations

Many PoCs effectively showcase AI's capabilities in controlled environments but fail to scale due to misalignment with broader business needs. For example, in 2025, the UK’s Department for Work and Pensions cancelled six AI pilots. These were not minor initiatives - they were supposed to transform welfare services but were scrapped due to scalability, reliability, and testing issues. This reminds us that many organisations mistake the idea of innovation for actual execution. AI, often touted as a magic bullet for automation, rarely delivers on its hyped potential.

Common challenges include proof-of-concepts that:

• solve narrow problems without considering enterprise-wide applicability

• have complex data environments far removed from clean proof-of-concept datasets

• rely on insufficient governance frameworks

• have stakeholder misalignment

• do not take into account evolving regulatory requirements, e.g. the EU AI Act

• are not trusted by users due to lack of reliability and transparency

Scaling generative AI successfully requires a digital operating model that integrates AI into core business operations to address common challenges.

Engagement Approach

The path from AI potential to operational reality requires expertise, guidance, and a commitment to accountable implementation. I offer a tailored approach that helps to ensure that Generative AI supports your organisation’s growth without overshadowing or disrupting its priorities.

Whether you're scaling AI pilots or striving for responsible AI implementation, I can provide the expertise and guidance to help you succeed with generative AI.

Starting with:

• Alignment : Determine how/if generative AI can effectively support your organisational goals, identifying areas where it provides significant value and areas where it may not be the best fit. This will enable you to develop targeted strategies for achieving your desired outcomes.

• Complexity: Is it a true challenge or just a process issue? Does it need AI? This will help make that distinction, ensuring that AI is applied where it truly matters.

• Process: Uncover hidden bottlenecks in your service automation to see which tasks can be automated and where you need solutions or workarounds.

• Values & Consequences: Concerned about the long-term impact of AI on your organisation and society? This engagement helps you navigate AI's ethical and societal challenges, prioritise value-aligned initiatives, and develop a responsible AI strategy.