Quantum, Climate & Cybersecurity: AI’s UK Frontier with Darshna Shah

AI expert Darshna Shah reveals how UK industries can harness AI for innovation, not just efficiency. Trends, risks, and quantum computing’s future impact explored

The UK's traditional industries face an innovation paradox: AI's transformative potential is immense, yet bureaucratic inertia and data illiteracy threaten to widen the gap between AI pioneers and obsolete enterprises.

Darshna Shah, Chief AI Officer at a leading tech consultancy, dissects this urgent dichotomy. With the UK trailing global AI leaders despite its foundational role in the field, she argues that sectors like finance, energy, and legal must pivot from efficiency-focused automation to AI-native business models, or risk irrelevance.

Drawing on real-world Azure deployments and emerging trends in quantum computing, Shah maps a blueprint for scalable, ethical innovation while spotlighting climate tech, healthcare, and cybersecurity as critical growth frontiers.

From your perspective as an AI expert, how is AI currently being used to drive innovation within traditional UK industries such as finance, legal, or energy — and what trends or technologies are making the biggest impact?

1. Move fast or become obsolete

AI is no longer a peripheral technology — it’s at the heart of transformation across traditional industries. What I’m seeing on the ground is a clear divergence between organisations that are moving decisively and those at risk of being left behind. In just a few years, we’ve gone from ChatGPT to a growing ecosystem of foundation models, retrieval-augmented generation (RAG), multi-agent systems, and purpose-built AI developer tools.

The organisations making headway aren’t necessarily the biggest — they’re the ones that can prototype fast, experiment boldly, and deploy AI solutions with minimal friction. What slows many organisations down is bureaucracy and decision-making by those without data fluency. The winners will be those who can combine agile delivery with clear business objectives and responsible AI principles.

2. Applied data intelligence — not just algorithms

Microsoft has highlighted the high failure rate of AI projects, and from what I’ve seen as the Chief AI officer of tech consultancy is that this often comes down to a fundamental gap: organisations don’t fully understand how to connect the potential of AI with the shape and quality of their own data.

Success requires a deep contextual understanding — not just of AI technology, but of the use case, the business model, and the domain-specific data. The most effective teams can speak both languages in this sense: they can translate between technical possibility and commercial reality.

3. Moving beyond efficiency to new business models

The early wave of AI adoption has focused on improving operational efficiency — automating tasks, reducing manual effort, and accelerating processes. While valuable, this is just the beginning. Forward-thinking organisations are now asking: How can AI help us reinvent how we do business?

Whether it’s automating legal reasoning to create entirely new legal tech services, using GenAI to reshape ESG reporting, or building AI-native products that open up new revenue streams, we’re seeing a shift from optimisation to innovation.

Looking ahead, what do you see as the key growth opportunities for AI adoption in the UK over the next 2–3 years — and how can organisations best position themselves to take advantage of these trends?

Given that the birthplace of AI was Bletchley Park, it’s disappointing to see the UK trailing behind global innovation leaders. But that also creates a clear opportunity — to focus less on playing catch-up and more on accelerating practical adoption in sectors where we still hold deep expertise. While public sector momentum is slow, the private sector has real potential to lead.

The most exciting areas of growth are no longer just about efficiency gains — but about reshaping how we approach problems in climate, health, security, and education.

Climate: AI beyond ESG dashboards

The climate crisis remains one of the most urgent challenges of our time, and AI can move us beyond just monitoring ESG metrics. We’re starting to see genuinely transformative use cases such as concrete that turns heat into electricity, solar panel windows that generate electricity, paint more effective than air-conditioning, sustainable cosmetics, etc. These are the types of applications where the UK with strong science, manufacturing, and energy sectors — can create impact if R&D is well-supported.

Security: Keeping pace with a faster, more complex threat landscape

AI isn’t just being used for good. We’re seeing automated phishing, AI-generated social engineering, and more sophisticated cyberattacks. Traditional SOC triage systems aren’t built for this scale or complexity. Over the next few years, there’s a clear need for AI-native security systems — ones that can learn, adapt, and respond to new forms of attack in real time. This is a national resilience issue, not just a corporate one.

Healthcare: From backlog reduction to new treatments

The NHS continues to face mounting operational and clinical pressures — and AI can help on both fronts. Tools like OpenEvidence in the US are streamlining research for clinicians, and AlphaFold has opened the door to radically faster drug discovery. The recent launch of the UK’s Isambard-AI supercomputer, powered by over 5,000 Nvidia Grace Hopper chips, could help unlock new breakthroughs in medical research and personalised treatment, but only if well-integrated with frontline systems and workflows. A

dditionally mental health issues and lonliness are at an all time high in the UK, particularly amongst younger and older generations, therefore exploring the effects of AI on psychological health remain critical. Open AI has recently announced the hiring of a forensic psychiatrist for this very purpose

Education: Modernising our strongest asset

The UK still has a global reputation for educational excellence, but this needs to evolve fast. With AI reshaping nearly every sector, we need to build AI literacy into mainstream education, not just in higher ed or specialist courses, but across the whole system. Otherwise, we risk falling behind not just on innovation, but on the talent pipeline to support it.

So how can organisations position themselves to benefit from this?

1. Focus on applied, use-case-specific innovation — not generic AI platforms, but tools built to solve real problems with sector-specific data.

2. Don’t wait for perfect policy frameworks — align with emerging best practices on AI safety and governance, but avoid analysis paralysis.

3. Bridge the gap between domain experts and AI teams — the most impactful projects come from close collaboration, not hand-offs.

4. Invest in foundational skills, not just tech — from prompt engineering to responsible deployment, capability-building is key.

5. Stay curious, but be critical — not every new model or tool is the answer. Start small, test fast, and measure impact carefully.

With evolving UK regulations around AI — particularly around transparency and accountability — how do you approach aligning with or influencing these frameworks in your day-to-day work?

There are several responsible AI frameworks worth being familiar with, but in my day-to-day work building AI solutions on Azure, we anchor our approach in Microsoft’s Responsible AI principles — ensuring fairness, reliability and safety, privacy and security, inclusiveness, transparency, and accountability are considered at every stage of development and deployment.

We embed this by using a range of Azure-native tools: for example, Azure AI Foundry supports robust PII detection and redaction, and Azure AI Content Safety enables intelligent filtering and moderation of harmful or inappropriate content. In addition, we’ve developed a custom AI red teaming agent that extends Microsoft’s capabilities — enabling us to simulate a wide range of adversarial threats in alignment with established taxonomies like MITRE ATLAS and AVID.

Ultimately, integrating these responsible AI tools and principles directly into the development lifecycle — rather than treating them as an afterthought — is crucial for building safe, secure, and compliant AI systems.

Have you noticed any shifts in demand from UK clients regarding AI capabilities—such as a move from predictive analytics to generative AI or real-time decision systems?

There has absolutely been a clear shift in demand, especially since the generative AI hype cycle took off. Initially, organisations rushed to adopt generative AI across the board, largely driven by fear of missing out. The “powered by AI” tagline quickly became ubiquitous, but in many cases, it failed to deliver tangible value, leading to growing scepticism and, for some consumers, even fatigue.

What we’re seeing now is a more measured, thoughtful approach. Many UK organisations are layering generative AI on top of structured data and established predictive models, not to replace them, but to improve accessibility and user experience. For example, natural language interfaces are being used to open up complex analytics tools to non-technical users — helping democratise data-driven decision making without compromising rigour.

This hybrid approach, combining the strengths of traditional AI with the usability of generative interfaces, is where I see the most meaningful traction right now, especially in sectors like finance, legal, and public services where explainability and trust still matter deeply.

Looking ahead, what emerging AI technologies or trends do you believe will most significantly impact the UK business landscape in the next five years?

The pace of AI development has been severely underestimated as we have seen quick advancements of ChatGPT alone go from advancement to advancement with chain of thought reasoning, deep research skills and more recently agent mode. These smaller advancements will continue coming at us as a pace we are probably not prepared to adapt to.

Looking further ahead, one of the most disruptive shifts will likely come from the convergence of AI and quantum computing. While still emerging, quantum’s potential to radically speed up model training and optimisation could redefine the limits of what’s computationally feasible in areas like drug discovery, materials science, logistics, and finance. But it also poses risks: the arrival of “Q-Day”, the point at which quantum computers can break today’s encryption, could fundamentally reshape cybersecurity and trust in digital infrastructure.

UK businesses must prepare for this next wave by investing not only in talent and technology, but also in adaptive governance structures that can respond rapidly to breakthroughs.