As AI continues to dominate headlines and boardroom agendas, techUK’s latest AI Campaign Week set out to separate substance from hype. Usman Ikhlaq, programme manager for Artificial Intelligence at techUK, reflects on how UK industry leaders are tackling trust, infrastructure, and talent to scale AI responsibly and what it really means to lead in a fast-evolving global landscape.
Artificial Intelligence has dominated the headlines in recent years – from the rise of generative AI platforms like ChatGPT to questions around safety and the implications of AI, there has not been a day where this technology has not been talked about in the media, business planning, and even in day-to-day conversations.
But, and perhaps because of this proliferation of talks around AI, it is sometimes difficult to understand what AI actually is achieving, to the point that some of the debate is shifting and some are asking themselves “is AI actually promising or is it just fluff?”.
In May, techUK ran its most ambitious AI Campaign Week yet, bringing together industry leaders, research institutes, and technology experts under the theme of seizing the AI opportunity to help make sense of what AI can actually do for people, society, economy, and the planet. Our aim was to showcase the UK’s position as a global AI powerhouse while addressing the critical challenges and opportunities that lie ahead in our nation’s AI journey.
With AI poised to boost UK GDP by an estimated £550 billion by 2035, the urgency of widespread AI adoption has never been clearer. Our AI Campaign Week shone a spotlight on visionary ideas and expertise from across out membership to propel the AI sector forward through informed discussion and collaborative ambition.
Industry engagement and thought leadership
Throughout the campaign week, it has clearly emerged that the UK has a huge breadth of AI expertise – not that it’s surprising. Companies like Holistic AI and KPMG contributed their expertise on how to implement trustworthy AI frameworks, demonstrating that building trust is a strategic imperative, not just a technical one. Holistic AI has pioneered a risk-based approach to AI governance, using a platform to continuously monitor systems for potential bias, robustness issues, and transparency gaps throughout the entire AI lifecycle. Similarly, KPMG has championed a principles-based framework for responsible AI, guiding businesses to design, build, and deploy AI solutions with a clear focus on accountability, fairness, and explainability. Together, these approaches illustrate how organisations can move beyond theoretical ethical principles to establish practical governance and monitoring systems that build stakeholder confidence and enable the safe, scalable adoption of AI.
What does it mean to be an AI leader?
The UK currently holds the position of having the third-largest AI sector globally, a testament to its robust research institutions, innovative start-ups and scale-ups, and more established players. However, maintaining this leadership requires more than just current capabilities – it demands a forward-thinking approach to research, development, and deployment.
That is why we wanted to ask our members to explore what it truly means to be a leader in AI.
Industry experts from across the technology spectrum contributed to these discussions, with established players like Kyndryl and Atkins Realis sharing insights alongside innovative SMEs such as Teraflow, Puritan AI, and emerging AI specialists.
To truly lead in AI, the UK must focus on three critical areas: fostering world-class research and development capabilities, creating an enabling regulatory environment that balances innovation with responsible deployment, and building strategic partnerships between academia, industry, and government. This means investing in cutting-edge research facilities, supporting AI talent development from universities through to industry, and establishing clear frameworks that give businesses confidence to innovate while protecting citizens and maintaining ethical standards.
The UK can maintain its AI leadership by leveraging its unique strengths: world-class universities producing cutting-edge research, a robust financial services sector driving AI adoption, and a regulatory approach that balances innovation with responsibility. Our competitive advantage lies in creating an ecosystem where established corporations can collaborate with agile start-ups, where academic research translates rapidly into commercial applications, and where government policy supports both technological advancement and ethical deployment.
Scaling AI from pilot to production
Perhaps the most critical challenge facing organisations today is moving AI initiatives from promising pilot projects to full-scale deployment.
Through our engagement with industry, including educational technology leaders like JISC and innovative AI platforms such as UBDS and Autogen, it’s emerged that data readiness, stakeholder alignment, and integration challenges are factors that often determine whether AI projects succeed or fail. For example, poor data quality can derail even the most promising initiatives – we’ve seen organisations invest significantly in AI models only to discover their underlying data infrastructure couldn’t support reliable, consistent outputs at scale, forcing them to abandon deployment plans.
These factors also continue to shape the development of AI. As the conversations shifts from large language models and generative AI, we are now seeing new transformative technologies that are reshaping the AI landscape, including agentic AI systems that can operate with greater autonomy, small language models that offer efficiency benefits, and industrial AI applications that are revolutionising manufacturing and production processes. These emerging technologies represent the next frontier of AI adoption, moving beyond traditional applications to more sophisticated and autonomous systems.
Building trust through AI assurance
Trust remains the cornerstone of successful AI adoption. This is a theme that resonates strongly with both industry practitioners and policymakers, reflecting growing awareness that public and private sector confidence in AI systems is essential for widespread adoption.
We wanted to explore practical approaches to AI assurance, including model testing and validation procedures, comprehensive auditing frameworks, system cards that provide transparency about AI capabilities and limitations, and red teaming exercises that stress-test systems against potential risks and failures.
Holistic AI demonstrates how comprehensive model testing puts trust at the centre of their approach - they implement rigorous validation procedures that test AI systems across multiple scenarios and edge cases before deployment, ensuring consistent performance and identifying potential bias or failure points. Similarly, GSK’s approach to AI governance in heavily regulated healthcare environments shows how systematic auditing frameworks can build stakeholder confidence by providing clear documentation of AI decision-making processes and regular assessment of model performance against real-world outcomes.
Industry contributors shared best practices for implementing trustworthy AI frameworks, demonstrating how organisations can move beyond theoretical ethical principles to practical governance and monitoring systems. These approaches enable companies to deploy AI with confidence while maintaining accountability to stakeholders and society.
Infrastructure and compute foundations – the building blocks for AI
However, none of what I have touched on in this article so far can be realised without a robust infrastructure supporting AI’s development and deployment.
As computational demands of modern AI systems continue to increase, we must address head-on how we can face this challenge.
Major infrastructure provider Snowflake suggested the UK can build sustainable, scalable data infrastructure that supports both national AI initiatives and regional innovation hubs. British AI companies such as Braidr also showcased innovative approaches to AI-powered infrastructure solutions, while cloud and enterprise software specialists like Zoho UK demonstrated how companies of all sizes can access enterprise-grade AI capabilities.
The role of various stakeholders – from established data platform providers to innovative startups developing next-generation AI infrastructure solutions – was examined in detail. This ecosystem approach demonstrated how both multinational corporations and agile SMEs contribute essential components to the UK’s AI infrastructure landscape.
However, none of what I have touched on in this article so far can be realised without a robust infrastructure supporting AI’s development and deployment.
As computational demands of modern AI systems continue to increase, we must address head-on how we can face this challenge.
Major infrastructure provider Snowflake suggested the UK can build sustainable, scalable data infrastructure that supports both national AI initiatives and regional innovation hubs. British AI companies such as Braidr also showcased innovative approaches to AI-powered infrastructure solutions, while cloud and enterprise software specialists like Zoho UK demonstrated how companies of all sizes can access enterprise-grade AI capabilities.
The role of various stakeholders – from established data platform providers to innovative startups developing next-generation AI infrastructure solutions – was examined in detail. This ecosystem approach demonstrated how both multinational corporations and agile SMEs contribute essential components to the UK’s AI infrastructure landscape.
Developing future-ready AI talent
Just like how infrastructure plays a key role in the development and deployment of AI, so does human capital.
The UK is grappling with a digital skills gap. This was real before the AI boom, but it is particularly felt now as this technology could promise incredible benefits to the UK as a whole – but won’t be able to deliver if we don’t have skilled people in place.
Our members are in agreement that the talent challenge spans the entire educational spectrum, from primary education through lifelong learning.
The campaign highlighted the importance of collaboration between industry and academia in developing relevant curricula and training programmes, with contributions from both major professional services firms and innovative skills development organisations. The EY Foundation shared their extensive AI education initiatives and youth development programmes, while specialist companies like Resolutiion and TechSkills (the skills body for tech in the UK and the accreditation body for Tech Industry Gold) demonstrated innovative approaches to AI skills development tailored for specific industry needs.
Particular attention was paid to creating inclusive AI career pathways that encourage diverse participation in the field. This focus on inclusivity reflects recognition that AI’s societal benefits can only be fully realised when its development and deployment involve perspectives from across the population.
Looking forward: from campaign to action
Through our AI campaign week we wanted to do more than just present a series of discussions – we wanted to create a catalyst for ongoing action. The themes explored during the week continue to inform techUK’s year-round activities, including policy development, industry events, and member collaboration initiatives.
As the UK continues to navigate the opportunities and challenges presented by AI technology, initiatives like this campaign week prove essential for maintaining momentum and ensuring that the promise of AI translates into tangible benefits for businesses, public services, and society as a whole.
The £550 billion GDP boost that AI could deliver by 2035 is not merely a projection – it represents a collective commitment to excellence in AI development, adoption, and governance. Through continued collaboration and focused action on the themes explored during our AI Campaign Week, the UK is well-positioned to seize this AI opportunity and continue leading in the global AI revolution.
