In 2025, that phrase feels woefully irresponsible – particularly as we see digital technologies advance at unprecedented speed, often causing real-world harm for citizens and the planet.

It is often claimed that Artificial Intelligence (AI) will be the biggest disruptor the world has seen since the Industrial Revolution. A multi-faceted technological colossus set to reshape every corner of society and business, yet still largely a mystery to most.

Experimental sketch of the ecosystem – showing complexity.

Investment into the AI industrial complex is staggering. There is a global race for AI superpower status, with countries such as Saudi Arabia, the UK and France squaring up to the US and China, to be a major force. And guess what, people are moving fast and breaking things; massive data centres are being built which consume enormous amounts of energy, algorithms are being trained on biased data, and the threat of an employment crisis is very real – with low-wage workers and women set to be disadvantaged the most.

Globally, governments are actively investing in AI to streamline public services, improve efficiency and to better serve citizens. However, AI readiness and the ability, or inability, of countries to adopt and regulate these technologies will also shape international competitiveness, equity between citizens and countries, ethical standards and future governance.

While its past, current and potential impact is alarming, the future of AI is not predetermined. It takes many human decisions to set the course and decide how things evolve and who sets to benefit; but ethical governance is needed to develop ethical policies and procedures.

In the UK, the Government published its AI Opportunities Action Plan in January, outlining plans to maximise AI’s potential to drive growth and benefit people across the UK – including advocating for its use in the public sector. The plan notes the potential for AI to radically change public services by automating routine tasks, making services quicker and more efficient, and making better use of Government data to target support at those that need it.

Both National and Local Government bodies are being encouraged to adopt AI within their services and processes. Photo: Bridgend District Council.

“But it also brings with it risks that must be managed to effectively support adoption and maintain public trust,” the plan acknowledges, citing fairness, safety and privacy. Indeed, it was a major point of discussion at the Digital Leaders Public Sector Insights AI Week in March, where I gave a presentation on digital innovation in uncertain, complex and emergency environments.

With so much at stake, it has arguably never been more important not to move fast and break things. But we can still move quickly – carefully. With that in mind, this article will reflect on the debate, while drawing on guidance, toolkits and principles to highlight the importance of care-ful innovation and good governance.

Arm yourself with knowledge

The need to arm ourselves with knowledge was a key takeaway from the Digital Leaders event, and one that underpinned my presentation.

The ever-changing narrative around AI and its sociotechnical impacts can lead to feelings of overwhelm; many of us feel daunted by the intensification of automation across all sectors, organisations and individual practices – whether you are working in it or observing from afar.

AI chatbots and automation tools are increasingly being pushed on citizens within digital workspaces and personal communication – Microsoft, Google Gemini, WhatsApp – often without explicit consent. It has become our individual responsibility to improve our knowledge and understanding of these tools and technologies; to think critically, challenge hype and, fundamentally, not accept a tech-determinist roadmap from tech companies at the helm of AI – many of whom, arguably, will be thinking about the financial bottom line and power that comes with it.

AI adoption in government

Thankfully, many responsible leaders are advocating for tools to better support the public procurement of AI. One such person is Professor Alan Brown of Exeter University, who is also AI director at Digital Leaders and held a fellowship at the Alan Turing Institute.

Discussing how to find the balance in digital power dynamics, Brown says we must first acknowledge the political dimensions of AI-based technologies and not pretend they are neutral tools: “When evaluating new AI technologies, my personal approach is to focus not just on defining ROI and efficiency goals, but also describing their governance implications: Who gains authority and who loses it? What values are encoded in this system? What dependencies are we creating? Whose interests are prioritized by default?”

These are all critical questions that need answering truthfully and thoughtfully.

Albert Sanchez-Graells, Professor of Economic Law and Co-Director of the Centre for Global Law and Innovation (University of Bristol Law School), meanwhile, recognises another problem: there is a gap in public sector digital skills preventing the public buyer from adequately understanding the technologies it seeks to buy. Therefore, “the public buyer risks procuring AI it does not understand, which is already a widespread phenomenon in the private sector.”

It doesn’t help that there is so much noise, marketing hype and complexity surrounding it all. On my own quest for clarity, I have come across some useful resources, which I have rounded up below to help others feel less overwhelmed and more informed.

Navigating AI with confidence

Representing a significant stepchange in responsible AI adoption, the UK Government published a playbook for AI in February. The playbook – designed specifically to offer public sector organisations to use AI safely, effectively and securely – includes 10 principles that should be upheld when using AI. These range from understanding the capabilities and limitations of AI, to working with commercial colleagues from the outset, to simply having the skills and expertise needed to implement AI solutions.

Again, Professor Alan Brown has published an in-depth analysis of the playbook, which I would highly recommend reading. According to Brown, while the principles reveal a thoughtful approach to balancing innovation with responsibility, there are additional considerations in the context of digital transformation at large, complex organisations – for instance, integration with legacy systems and cross-department coordination.

Signalling a national commitment to responsible AI use, similar guidance has been produced in Wales and Scotland. The Welsh version of the guidance includes a range of examples of successful uses of AI in the public sector – from helping to diagnose cancer to creating a ‘lost woodland’ dataset – while the Scottish AI Playbook includes a case study about how AI-automated image cropping helped estimate heat loss in homes.

These examples demonstrate the wide-ranging impact and potential of AI, and the importance of ensuring the public sector is equipped with the skills, knowledge and confidence to use it in a way that mitigates risk and maximises positive impact for people and planet.

Cyber Security and specialised toolkits

A major part of the AI education piece is around data and privacy, with cyber security risks intensifying as businesses embed AI into various operations.

According to the National Cyber Security Centre, some of the most dangerous flaws in AI systems are ‘AI hallucination’ (producing incorrect statements), being biased or gullible, creating toxic content and ‘data poisoning’ (susceptibility to being corrupted by manipulating data it is trained on).

These are serious issues that have serious consequences, so it is paramount that tools are developed ethically and security integrated into AI projects and workflows from the outset.

Another significant milestone this year came with the launch of the UK’s Code of Practice for Cyber Security of AI’, which addresses cyber security risks to AI. The Information Commissioner’s Office has also designed an AI toolkit for those seeking to better understand best practice in data protection-compliant AI – to “reduce risk to individuals’ rights and freedoms” caused by their own AI systems.

There are plenty of other specialised toolkits elsewhere, detailing how to harness opportunities in areas such as digital accessibility and civic AI. This toolkit, for example, gives advice on how civil society organisations and local authorities can empower communities to address the climate crisis. These are matters that have a critical real-world impact and which require innovation with care.

Final thoughts

There is no fixed position on AI; it is shifting sands so it is crucial to ask questions and get informed – particularly when employing it in the public sector, where the risk of causing harm is significant.

It is a challenge, but with the right collaboration, partnerships and care, it is possible to deliver digital innovation with demonstrable positive impact. In practice, it requires policymakers to implement robust data privacy and transparency regulations, ensure that labour laws protect fair treatment of workers, and embed diversity and inclusion into government funding frameworks.

It’s a challenge that Calvium is well-prepared to address, as proud members of the Digital Leaders AI Expert platform. As proponents of tech as a force for good, we combine data ethics with ISO and Cyber Essentials Plus accreditation, and clearance for government contracts. This means we help clients adopt AI responsibly, considering impacts at every stage and ensuring innovation is both safe and valuable.

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Modern technologies can streamline regulatory processes, enhance decision-making, and scale oversight capabilities to address the growing complexity of globally connected economies. Regulators can harness Supervisory Technology (SupTech) to combine innovation with resilience, efficiency with integrity, and modernization with public trust. By utilizing cloud services, supervisors can rapidly deploy new supervisory tools, adapt to emerging financial risks, and efficiently process vast amounts of regulatory data while maintaining the highest standards of security and operational resilience. Cloud technology provides the agility and cost-effectiveness needed to keep pace with the rapidly evolving financial sector.

To achieve enhanced resilience and operational excellence in cloud-based supervisory systems, supervisory authorities can implement the shared responsibility model with their cloud service provider (CSP). This delineates security and operational accountabilities between both parties.

A white paper produced by Alliance for Innovative Regulation (AIR), and supported by Amazon Web Services (AWS), offers guidance that supervisory authorities can use to implement SupTech. Beyond Pilots and Sandboxes: Regulatory Innovation through SupTech Case Studies and Leading Practices, recommends five steps for success:

1. Start with a clear vision
2. Develop robust data governance frameworks and analytics capabilities supported by investments in modern cloud-based data platforms and talent
3. Collaborate internally and externally on innovation
4. Work iteratively and leverage agile methodologies
5. Balance innovation with security and resilience

The paper sets out ten core capabilities required for regulators to effectively supervise, monitor, and manage risks in the financial services sector and identifies those that are further ahead and those that could most benefit from knowledge-sharing and collaboration. The core capabilities range from licensing and case management to investigation and enforcement of regulatory violations.

For example, for licensing and case management, regulators can leverage optical character recognition (OCR), natural language processing (NLP), and generative AI capabilities such as large language models (LLMs) to transform how they review and analyze documents such as business plans, financial statements, and compliance records. The AIR report cites the example of The Australian Securities and Investments Commission (ASIC), Australian Prudential Regulation Authority (APRA), and the Reserve Bank of Australia (RBA) which recently worked alongside AWS to build a generative AI proof of concept (PoC) solution to compare, query, and summarize documents. Prioritizing responsible AI principles, the PoC achieved promising results, including as much as 93 percent confidence in some model outputs using publicly available documents. The experiment has been shared with dozens of other regulators and provides a glimpse into the future of regulatory practices and financial oversight.

This is an example of regulatory reporting and data collection, which are fundamental processes for financial regulators, enabling them to make informed decisions to safeguard financial stability. The growing complexity of financial instruments, coupled with the globalization of markets, has resulted in an exponential increase in data volume and diversity.

SupTech solutions can streamline data collection and reporting through automation, advanced analytics, and seamless integration, allowing regulators to process vast datasets efficiently, and maintain data quality and compliance.

For example, the Financial Industry Regulatory Authority (FINRA) operates one of the most sophisticated cloud-based systems for regulatory reporting and data management, overseeing the activities of US brokerage firms and exchange markets. Processing more than 100 billion daily market events, FINRA uses real-time surveillance and analytics to identify unusual trading patterns, detect fraudulent activities, and flag market anomalies. By combining real-time monitoring with historical data analysis, FINRA proactively mitigates risks and maintains compliance. The system’s cloud-based architecture provides scalability, allowing it to adapt seamlessly during periods of increased market activity or volatility, enabling uninterrupted data processing and robust surveillance. Encryption, strong protocols, and disaster recovery mechanisms safeguard data integrity. The adoption of open, standardized data formats enhances interoperability and facilitates efficient communication with external systems, streamlining reporting to other bodies.

The report concludes: “The road to a fully modernized regulatory framework is challenging but achievable. Through strategic investments, thoughtful integration of technology, and unwavering commitment to public trust, regulators can position themselves as stewards of a future-ready financial system, capable of navigating the complexities of the digital age.”

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Artificial intelligence (AI) holds immense potential to support disabled jobseekers in overcoming barriers to employment, yet its implementation remains disproportionately focused on macro-level policymaking rather than individual-level interventions. This paradoxical approach risks deepening the employment gap for disabled individuals, as systemic improvements fail to translate into tangible support for applicants during recruitment processes.

AI in shaping disability employment policy

At the macro level, AI demonstrates extraordinary capabilities in refining policies. For instance, tools like “Humphrey” assist in consultations where vast amounts of feedback pour in when governments consider disability employment strategies. Humphrey can swiftly sort through millions of comments, distinguishing critical insights such as “reasonable adjustments” recommendations from suggestions on tax incentives, and identifying emerging trends to help policymakers act promptly. Similarly, AI contributes to high-level meetings where ministers and civil servants discuss strategies to reduce the persistent 30-point employment rate gap between disabled and non-disabled people. In these settings, AI delivers real-time digests, summarising ideas with cross-party support, highlighting recommended metrics, and pinpointing potential bottlenecks flagged by analysts. These applications illustrate AI’s ability to drive policy change effectively.

Missed opportunities in individual-level AI support

Unfortunately, this powerful technology often stalls when addressing the needs of disabled jobseekers directly. For candidates who self-identify as disabled, interactions with the recruitment system remain largely impersonal and underwhelming. For example, after submitting an application, disabled applicants often receive generic feedback—if any at all. There is currently no system in place to generate even a basic summary of why an applicant fell short during the selection stage or specific guidance on improving their interview performance. This lack of personalised feedback leaves individuals to navigate employment processes without meaningful support, undermining their ability to succeed and grow.

The disability confident scheme: Ideals vs. reality

The UK’s Disability Confident scheme aims to go beyond mere non-discrimination by actively promoting the inclusion and success of disabled candidates. Among its promises are guarantees of interviews for any disabled applicant meeting minimum criteria, offering reasonable adjustments throughout recruitment, and providing constructive feedback to help candidates focus on areas for improvement. Yet, the lived reality often diverges from these commitments, with many disabled jobseekers left lacking the guidance and resources needed to thrive in competitive employment environments.

Bridging the gap with AI

To truly level the playing field, AI must be integrated not only into policymaking but also into recruitment systems at the individual level. Imagine an AI-powered assistant that generates personalised feedback for each disabled applicant, outlining specific reasons for rejection and actionable steps to enhance future applications. Furthermore, AI could enable recruiters to deliver tailored recommendations for reasonable adjustments, ensuring all candidates can perform at their best. With such tools, AI would no longer be limited to shaping policies from a distance but could become a transformative force in empowering disabled jobseekers directly.

Conclusion

Leveraging AI to support disabled individuals during recruitment is essential to close the employment gap and fulfil promises like those of the Disability Confident scheme. By addressing gaps in individual-level interventions, AI has the potential to reshape the lived experiences of disabled jobseekers, ensuring that technology serves as a bridge to inclusion rather than a barrier.
We already have that capability in 2025, so why, on earth, can’t we use AI to help individual disabled applicants by summarising feedback, coaching on application weaknesses, or even suggesting reasonable adjustments in interview processes?
Because right now, all too ironically, we’re are heading to a situation where we are using AI to refine policies about disability employment, while leaving disabled applicants, say neurodivergent, non traditional, or “you’re not typical” candidates, to fend for themselves. That has the perverse effect of widening the very employment gap we claim to want to close.

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Many companies have rushed to invest in an AI solution for fear of missing out, unsure why they need it and how, when and where they plan to use it. Often, they haven’t trained their employees or set up any processes for using AI, leaving the gates open to security threats and employees producing off-brand, generic, and poor-quality products that will be detrimental to their reputation and brand.

Back to basics

AI’s powers are undoubtedly mind-blowing, but without a strategy and understanding of your objectives, the technology won’t be utilised fully, and the benefits will go undiscovered. This is the same when investing in any technology. If you don’t know what problems you want to solve and what your objectives are, it is unlikely to be a success. 

The Why

Any business that wants to implement AI or another transformative technology needs to investigate why it wants it. AI is the latest disruptive technology everyone is talking about, but too many businesses are deploying it because they don’t want to miss out. This is not a race; if you don’t know why you want it or how you will use it, then it is a waste of investment. Without a strategy, it could also have the reverse effect and be detrimental to your brand.

The technology market has exploded, and it can be challenging for businesses to understand what technology they need and why, and which vendor to buy it from. By working with a technology consultant, they will help you identify and solve problems with AI and other technologies, joining up the dots between business needs and technology. 

Have a Plan

For AI to be a success and deliver, it is critical to invest the time in understanding why you need it and to explore the following:

1. What do you want to improve? 
2. Why do you want to improve it?
3. How are you planning to improve?
4. Where do you want to be?

By exploring these areas, you can devise an AI strategy, setting measurable objectives and a success criterion so you can evaluate as you go on, ensuring you are succeeding. Incorporating AI or any transformative technology into your business is an evolutionary journey. It takes time to learn what works and what doesn’t, and time for employees to get used to the technology.   

Levelling up the Why

Asking why provides the detail you need to transform your customer experience or streamline processes, or produce content using AI.

Getting the best out of AI is about taking a creative approach, applying critical thinking and getting beyond the surface. The good news is that humans excel in these skills and machines don’t! This level of thinking is like an investigator drumming up theories before searching for clues. You can’t seek out clues if you don’t know why and what you are looking for.

Take the time to go to the next level of thinking, understanding the what’s and whys, and drilling down, asking detailed questions to get the answers. An AI solution can analyse your customer database in seconds, unearthing trends and patterns that reveal problems you didn’t know existed or providing detailed information on the ones you did. By diving into the details, you understand issues more deeply and can construct relevant solutions to fix them.

Think of AI as a treasure chest. It’s all there, but you must find it. How you find the treasure and what you will do with it is up to you. A technology consultant is advisable to help you with this process. 

The same applies with AI prompts. The more detail and better you craft it, the more you will get out of it to help you. What it returns will ignite an idea for you to go back and ask another prompt, and so forth, until you are happy with what you have got. 

Deconstructing AI

Digital transformation can be overwhelming, but it is essential to understand that it can be broken down into manageable areas, so you don’t need to do everything all at once. By breaking it down into steps, you can delve into the details and test the solution with a proof of concept to explore what you want to disregard and develop, helping you move forward.

A technology consultant can guide you on this journey and ensure that your systems and existing technology are integrated seamlessly.

You will need to test and train your solutions and your employees. Fear stems from the unknown and the new. Employees should be involved from the start of introducing AI into the business. Encourage them to share the challenges they face daily and make suggestions on improvements that could be made. This way, everyone understands why the AI solution will benefit them, lessening the fear that AI will replace them. 

Lessons in AI

A complete training programme should be implemented so everyone understands how AI fits into the process, enabling them to work smarter and know when and where to use it. AI literacy is essential for its success, your safety and security, and your reputation and should be combined with training programmes, resources and support.  TechUK reported that 97% of HR leaders said their organisations offered AI training, but a mere 39% of employees received it. 

 AI has and will change how we work, and new skills are required for the modern workplace, such as AI prompting, particularly for content generation. Prompt engineering should be precise with plenty of detail, defining tone, audience, context and examples of what is good. Like anything, you get what you put into it. Ensure you invest in the time and expertise to skill up your employees. 

Gartner predicts that 80% of all creative roles will need to integrate generative AI into their work processes over the next few years. So, skilling up and setting guidelines and guardrails is essential to protect your brand. Asana reported that only 13% of organisations have developed and shared AI guidelines with their employees. 

Guidelines and guardrails also protect companies’ data, ensuring they meet compliance and regulations, and the data is secure. By putting these in place, you enforce what they use in AI and what they can’t, therefore not exposing sensitive and confidential data to the likes of ChatGPT and other large language models.

Generative AI enables people to produce content in seconds. However, caution must be applied so employees don’t have free rein to churn out generic content that compromises your tone of voice and quality, which could potentially damage your brand.  Devise a process so AI-generated content is proofed to check your tone of voice, key messages, fact checking, screening for bias and transparency, and to add the human touch. This will stop employees from having free rein using AI and the danger of sounding generic and losing your voice, which can harm your brand.

The Power of Humans

Soft skills, such as critical thinking, creativity, problem-solving, and empathy, are becoming increasingly important in the workplace today. A recent report from Microsoft revealed that 48% of employees said AI has increased the need for skills such as problem-solving (39%), critical thinking (385%), and analytical thinking (37%) as the most crucial.

 AI can streamline processes and analyse data in seconds, but it does not have human experience, nuance, emotional intelligence, or the ability to connect and form relationships.  This is why AI cannot replace humans, but when AI and humans work together, that is a powerful force.

Employees who understand the why, when and where of AI will become AI fluent, using their abilities to work with AI to speed up and streamline processes. By following the guidelines and guardrails, they can use their judgment and editing skills to ensure content is on brand and original. 

Shadow IT & Cyber Security

Employees who download AI apps for work that the company does not authorise are known as ‘shadow IT’ putting the company at risk from cyber-attacks which are increasing by the day. It use to about “bring your own device’ and now it is ‘bring your own AI assistant’ Instructing what AI apps can and can’t be used will reduce your risk of cybercrime and protect your network.  

AI has escalated cyber-crime, but it can also combat it by detecting anomalies in real-time on your network. Often resolving issues before they even arise.

Reducing the Fear

AI is not new; it has been around for a long time, but over the past two years, it has accelerated into the different ways we can use it in our personal and work lives. The word AI makes employees nervous because they think it going to replace their jobs. CEOs and senior management are anxious because they know they need it but are not sure why. IT Directors, CIOs etc are nervous because they are scared it may fail and put their jobs on the line.

But by embracing the ‘why’ and devising a strategy, the people mentioned should feel more confident as risks are mitigated. No one likes change, even if it’s for the better. Learning how to use technology and new ways of working takes time, but the benefits and ROI are worth it.

Why Not?

The time to invest in AI is now, but remember it is not a race and it is advisable to invest in your time to devise a strategy, set objectives and a success criteria. Discover why you need it and what problems you want to solve and by working through this methodology you will set yourself up for success. When you reap the benefits of an improved customer experience, streamlining process, saving time and increasing productivity. The only question you will be asking is – why didn’t we do this sooner? 

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The offensive power of AI

Cybercriminals have embraced AI as a force multiplier. With the help of generative AI models, attackers can now craft tailored phishing emails that mimic human language with alarming accuracy, bypassing traditional spam filters. AI-powered bots can scan for vulnerabilities across thousands of systems simultaneously, drastically reducing the time needed for reconnaissance. Worse yet, malware is becoming adaptive—using reinforcement learning to modify its behaviour in real time and avoid detection.

This new generation of smart malware is capable of identifying its environment, detecting when it’s being analysed in a sandbox, and changing tactics accordingly. It’s no longer a simple script but a dynamic adversary.

AI in defence: Fighting fire with fire

To counter these evolving threats, cybersecurity vendors and organisations are turning to AI-driven solutions. Machine learning models can now detect subtle anomalies in user behaviour, network traffic, and file access patterns that would otherwise go unnoticed. User and Entity Behaviour Analytics (UEBA) powered by AI are helping analysts detect insider threats and compromised accounts.

AI is also powering Security Orchestration, Automation, and Response (SOAR) platforms, which can automatically triage alerts and execute predefined responses. In Security Operations Centres (SOCs), large language models are being deployed as AI co-pilots to help interpret logs, write scripts, and even suggest remediation steps.

Who has the edge?

The core question in this AI arms race is: who has the advantage? Attackers benefit from fewer constraints and can test new AI tools in the wild. Defenders, on the other hand, face greater regulatory, ethical, and operational limitations. Defensive AI must be explainable, trustworthy, and accurate—qualities that can be difficult to achieve without introducing delays or false positives.

Moreover, there’s a growing concern about the use of open-source AI tools by attackers. The same models released for research or transparency can be repurposed to generate malicious content at scale.

The ethical dilemma

This battle also raises ethical concerns. As AI models grow more autonomous, who is accountable for their actions? If an AI system flags a benign user as a threat and locks them out of critical infrastructure, where does the liability lie? Conversely, how do we regulate the use of AI by attackers who operate beyond jurisdictional boundaries?

Governments and institutions are beginning to recognise the dual-use nature of AI and are proposing frameworks for its responsible use in cybersecurity, but enforcement remains challenging.

Looking ahead

The AI vs. AI dynamic will only intensify. We can expect to see smarter bots, more evasive malware, and increasingly autonomous cyber defences. Defensive systems will need to adopt continuous learning models, mimic attacker behaviour, and even run simulations to predict future tactics.

Yet no matter how advanced these systems become, human oversight will remain essential. In the end, it’s not just AI vs. AI—it’s human intelligence guiding artificial intelligence in a perpetual race to stay one step ahead.

As AI becomes both our strongest shield and our greatest threat in cybersecurity, the question isn’t just who wins—but who stays ahead. 

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Artificial Intelligence (AI) has dominated conversations in recent years and continues to do so. The World Economic Forum reports that 86% of employees believe AI will be a leading factor in driving business transformation. Alongside this, we have seen significant legislation and investment in AI from major Western governments, including the EU, UK, and US. It feels hard to escape the AI bubble that has become all-encompassing, impacting tech, politics, and various sectors. However, amidst this AI hype, the most crucial piece of the puzzle seems to have fallen by the wayside: education. While the EU AI Act has made it compulsory to provide appropriate upskilling to employees involved in AI systems, this initiative has not gained much traction elsewhere. I often encounter stakeholders who can engage in conversations about AI but lack a deeper understanding of how to build and harness this technology safely. This reality should concern us all. Organisations are eager to capitalise on the economic benefits of AI, yet many do not fully grasp how to develop these systems responsibly or how they impact society and the environment. Are we, as a society, convinced that organisations building AI systems — which significantly affect our lives — are suitably knowledgeable in creating safe and responsible AI?

Lack of AI literacy contributes to Catastrophe

We have already witnessed the catastrophic consequences of seemingly innocent AI products. For example, Character.AI, an AI chatbot that can be customised as various characters, lacked safeguards, which tragically led to the suicide of a teenage boy. This incident, among others, highlights the urgent need for AI literacy, particularly ethical AI literacy, for the organisations creating these technologies. AI is relatively nascent in the commercial field, having transitioned from labs and universities to Silicon Valley and beyond. Consequently, I am not convinced that CEOs, product leaders, engineers, or designers are fully aware of the catastrophic risks posed by unaligned AI systems and how to mitigate them. Education is the key to AI safety and a crucial foundation to differentiate AI from short-term technology hype to long-term success.

Many organisations trying to capitalise on the AI bandwagon are repurposing existing governance structures, policies, and best practices that may not be applicable to AI development. Artificially intelligent systems differ significantly from traditional software like websites and apps. AI relies on ingesting large amounts of often poor-quality data, producing responses that can exacerbate bias, amplify inequalities, and hallucinate. A 2019 study by the US government showed that facial recognition systems were between 10 and 100 times more likely to misidentify Black individuals than white individuals. This discrepancy was evident in the case of Robert Williams, a man from Detroit wrongfully held in custody in 2023. Williams was accused of stealing $30,000 worth of luxury watches, despite being innocent. His conviction stemmed from an AI facial recognition system that matched his image with CCTV footage, leading to a wrongful arrest. This case exemplifies what occurs when AI systems are built without necessary safeguards and used without adequate education on their limitations.

What can the future look like?

The current discourse on AI safety alignment focuses heavily on technical alignment, research, and policy, often neglecting education. This narrow approach fundamentally weakens the cause of AI safety, creating a chasm of AI literacy between frontier labs and the product teams responsible for building these systems. I urge businesses developing or using AI systems — especially those outside EU regulations — to prioritise AI upskilling for their employees. AI literacy will vary by organisation, but some commonalities exist. It is essential for businesses to assess what approach fits their context and values. Organisations can draw inspiration from a plethora of freely available resources online, including Udemy courses, YouTube tutorials, and LinkedIn Learning. Additionally, more bespoke training can be developed in collaboration with subject matter experts to create tailored AI literacy pathways. These can be delivered through a mix of online, in-person, or hackathon-style training sessions. Understanding and implementing AI literacy not only enables teams to build safer AI systems but also prepares them for the future of work. To quote Giovanni and Tiribelli, we must empower our teams to “turn AI systems into weapons of moral construction rather than weapons of mass destruction.”

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How data visualisation empowers you and your organisation

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Interoperability has been a buzzword in the blue-light sector for some time and is a key focus for our emergency services and their industry partners, both determined to drive tangible improvements in public safety outcomes. Despite this focus, interoperability often means different things to different people. Is interoperability police forces who share a border working together? Is interoperability police, fire and ambulance services in the same geographical area working together? Or, is it appropriate and relevant data being shared across the government, blue-light and third sectors?

The answer? All of the above. 
The challenge around interoperability is not necessarily technical complexity. In today’s world, our data is shared and linked across organisations and platforms seamlessly, often to the point where we don’t realise it’s happening. PayPal checkouts or logging in to third parties with Google account details are prime examples of this. However, tighter restrictions around sensitive data make it much harder to exploit its potential value in the public sector. Add to this the fast-paced, risk saturated nature of cross-agency collaboration for public safety agencies, and the challenge is even more complex.

We no longer need to ‘prove’ interoperability. Widely reported cases in the media, such as the findings of the public inquiry into the Manchester Arena attack, are stark reminders that holistic views would enable better emergency service care and response. In a truly interoperable world, data sharing would allow the blue-light sector to view a response call in the context of an individual, rather than just a singular incident. This shift could lead to significant public safety benefits, where resources could be deployed more effectively, reducing the risk of duplicate or repeated demand. Action would be assigned to the correct teams, facilitating the sector move to Right Care, Right Person. And, most importantly, those in distress or vulnerable situations who have reached out to the emergency services would get a complete, human-centred service.

So, if that’s the utopia, how do we get there?
It won’t be easy, and the goal should always be making iterative moves forward, rather than expecting an immediate, complete solution. At times, it will be tiny steps, and other times it will be giant leaps, but always dependant on drivers for success.  The requirements for this success can be broadly organised into four themes:

1. Data standardisation 

Blue-light services are nuanced, and what is best practice and process for an urban area may not suit a rural location. Similarly, local cultures come into play; what works in Wales, may not be applicable in Scotland for example. However, there are commonalities, and these should be used to create standards, perhaps considered as an extension of POLE (Person, Object, Location, Event). This will require elements of compromise and process change, however moving towards a standardised data approach will build the foundations for enhanced interoperability.  Sopra Steria can support with data transformation programmes, allowing agencies to maximise the value hidden within their existing data sets. Data quality is always the uncompromised foundation, and the vital ingredient to successful outcomes.

2. Create expertise 

It’s no secret that the challenges facing technological advancements in the blue-light sector are often centred around capability or capacity. Highly technical, highly specialised skillsets can be rare in the blue-light sector. Recruiting in these skills is often outside of budget, and in a resource constrained industry, can be almost impossible to facilitate. However, as the Police Foundation’s ‘The Power of Information’ report highlighted, there are options that can be explored to make progress.  A symbiotic relationship where suppliers provide advice, guidance, and training can help customers to grow their people’s skillsets, whilst benefitting the private sector through increased access to the sector’s nuances, including customer data and systems.

Prioritising the conscious investment across the blue-light sector to invest in its people, and building technical expertise balanced with indepth sector knowledge, will open up swathes of opportunity to maximise data and technology. This, in turn, will enhance interoperability.

3. Incentivise collaboration 

Interoperability must be a shared responsibility across the sector, for suppliers and customers alike. As an active member of the Interoperability Working Group and one of the earliest signatories of the Policing Charter, Sopra Steria takes this obligation seriously. Collaboration may not always be smooth sailing at times, but the overarching goal of better public services must prevail. Progress must be made in line with carefully considered frameworks, that consider legal and ethical considerations of proportionate and appropriate data sharing, driven by sector-wide commitment. Clear goals for collaboration and data sharing are essential for retaining momentum, and all parties must understand the ultimate goals and benefits. These benefits will be multi-faceted; motivating individuals, teams, organisations and sectors to overcome collaboration hurdles. By doing this effectively and exhaustively, the business case for prioritising interoperability is compelling, with industry and consumers alike incentivised by tangible benefits to cost savings, efficiency, and public safety.

4. Time, Investment and Responsibility 

Whilst achieving data sharing interoperability is not technically impossible, it is complex. Moving towards a holistic overview centred around people is not achievable overnight. The goal of interoperability is a responsibility for all in the sector, from national policy makers all the way through to individual blue-light agencies, and it’s one that must be shouldered collectively. Political differences and business processes will require engagement and compromise. The sector and suppliers need to work together, coalescing around a common aim focused on realising significant public safety benefits; whether that’s top-down government pressure to achieve targets, financial constraints impacting appropriate resourcing, or the innate desire of the sector to provide the best possible public service.  Interoperability is not a UK-specific or even blue-light-specific challenge. Publications from across the EU and globally, as well as from a spectrum of sectors, show that this challenge is nuanced and far-reaching. Sopra Steria actively promotes Interoperability, working closely with customers, industry and government organisations, and our work in the defence sector on secure collaboration is a compelling example of this.  By engaging across sectors and geographical lines we can share ideas, learning and best practice.

Interoperability won’t happen by accident, and it also won’t happen quickly, or without investment and compromise.

Yet, by understanding and clearly articulating the benefits of cross-force, cross-sector and cross-agency collaboration, steps can be taken to improve the outcomes our blue-light sector can achieve for the public.

Originally posted here

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A Recent Success Story from the Department for Work and Pensions  (DWP) 

In a recent post, I highlighted how Helen Wylie, CTO of DWP Digtal, and her  team were able to utilise AI alongside a pre-existing robotic process automation  (RPA) system: An RPA system introduced seven years ago has been categorising  22,000 daily letters into a high-level priority order. However, some urgent cries  for help occasionally slipped through the net and were not addressed as quickly  as desired. Recently, an AI tool was implemented to add contextual analysis to  each letter, ensuring that these cries for help are now pushed to the top of the  priority list. This simple example illustrates how AI can seamlessly integrate  into everyday operations, reducing manual workload and improving customer  experience. 

Shifting the Meta: The Role of Platform Discovery 

A common theme across many talks at Digitech24 was the critical need to  assess the organisational landscape to identify kinks before embarking on AI  projects. A comprehensive platform piscovery provides a full view of platform  performance, helping identify where AI can drive improvements—or where  alternative solutions like better content design and an effective information  architecture might suffice. 

Rather than seeing AI as just a tool, consider it an enabler that unlocks untapped  potential. We emphasise platform discovery to uncover areas where AI can  streamline processes, cut costs, and enhance service delivery. However, it might  also show that AI isn’t necessary, providing evidence to support a business case  for or against its implementation.

I hope you find these insights useful and that they inspire you to explore AI’s  potential in your own organisations – please do reach out if you’d like to know  more about how we use platform discovery in blueprinting a streamlined future  for our customers’ services.

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