How UK Organisations Can Rapidly and Responsibly Adopt AI

The UK risks falling behind in the race to harness AI’s benefits, across both the public and private sectors. The good news? There’s a solution

In January, the UK Government unveiled plans to deploy AI throughout government, from the civil service to the NHS, supporting homegrown AI companies in the process. Prime Minister Keir Starmer claimed, “artificial intelligence will drive incredible change in our country… it has the potential to transform the lives of working people.”

But with high expectations comes high responsibility. Public sector AI integrations must be approached with care. Ensuring thoughtful, well-managed adoption will be key to avoiding unnecessary costs, maintaining public trust, and unlocking the full potential of AI to improve services and deliver value.

In the private sector too, many organisations have been keen to adopt GenAI – often very quickly. To do so, they’ve spun up pilots and proofs of concept (POC), pumped in their data and, in many cases, seen good results. But it’s what comes next where we regularly see organisations, both in the public sector and private, bump into issues.

Bridging pilots and processes

The biggest challenge revolves around how to take successful POCs and scale up the results to generate real value – either by creating income or making savings. To do so, some might advocate for dynamic and revolutionary changes to organisational processes, to best capitalise on the power of AI (which, they’d argue, is a dynamic and revolutionary technology). I’d recommend the opposite approach. Instead, find ways to ‘plug in’ GenAI and ML into existing processes.

As a trusted AI implementation partner for scaling AI, Netcompany brings more than 25 years of experience helping organisations to turn pilot projects into impactful, enterprise-ready solutions. We begin where it matters most: by strengthening the digital backbone through clean, accessible data and modernising legacy systems into cloud-native, API-friendly environments.

Nine steps to responsible AI deployment

We’ve developed a nine-step process for deploying AI assistants – one we use ourselves and recommend to clients – but the points are also relevant for other AI/ML projects:

Define the AI assistant’s use case
Identify the specific business problem or opportunity the AI assistant will address, ensuring the use case is clearly described and tied to measurable value.
Establish the AI assistant on a flexible technical platform
Select or develop a technical platform that allows secure, scalable, and adaptable deployment of AI assistants, considering integration and future upgrades.
Assess necessary data and data processing
Determine what data the AI assistant needs, how it will be processed, and ensure data quality and relevance for the intended tasks.
Address the legal framework
Review and comply with applicable legal requirements, such as the EU AI Act (AIA) and GDPR, to ensure lawful and responsible use.
Set boundaries for the abilities and responsibilities of the AI assistant
Clearly define what the AI assistant can and cannot do, including its decision-making authority and areas where human oversight is required.
Build structured quality assurance
Implement processes to systematically test, validate, and monitor the AI assistant’s performance and outputs for accuracy and safety.
Measure and store relevant data on the use of the AI assistant
Collect and retain usage data to enable monitoring, evaluation, and continuous improvement while ensuring compliance with privacy regulations.
Plan organisational implementation and training
Prepare the organisation for adoption by planning rollout, change management, and comprehensive training for all users.
Establish follow-up and support structures
Set up ongoing support, maintenance, and feedback mechanisms to ensure the AI assistant remains effective and up-to-date.

Netcompany works closely with clients to define the outcomes they want to achieve – whether it’s improving performance, reducing complexity or creating smarter, more responsive services.

Tools for real-world impact

To deliver on these ambitions, we use two core offerings. EASLEY AI helps organisations integrate and scale GenAI solutions, giving them full control over their data and model choices – no vendor lock-in. With robust oversight and governance, clients can scale AI responsibly, in line with European regulations and their own needs.

PULSE acts as a control tower for real-time data, breaking down silos and providing a single, unified view of operations. This enables smarter planning, forecasting, and decision-making, turning data into actionable insights. By pairing sector expertise with proven delivery, we ensure AI isn’t just a buzzword – it’s a driver of lasting value.

Success stories: AIin action

This approach is already delivering results. In 2024, Topdanmark, Denmark’s leading insurance provider, partnered with Netcompany to launch TopGPT – the country’s first client-facing GenAI chatbot in the financial sector. Replacing a clunky, rule-based bot, TopGPT delivers tailored, accurate responses without accessing personal data-improving compliance and customer satisfaction. Since launch, it’s handled over 100,000 conversations and seen a tenfold increase in positive reviews.

We’re also launching one of Denmark’s largest AI projects with DSB, Scandinavia’s biggest train operator. After testing Netcompany’s predictive AI and machine learning – 98% of predictions are now more precise than previously – DSB is rolling it out nationwide. Our AI-powered Pulse platform analyses historical

data to deliver real-time forecasts of train departures and arrivals, improving the accuracy of station screens, journey planners and the DSB app.

People make the technology work

AI is a powerful tool, but it’s not a silver bullet. Success depends on managing people, projects, and processes – not just technology. The UK’s AI ambitions are bold – both across the public and private sector – but real progress will come from combining strong leadership, clear governance, and a spirit of collaboration. Get those right, and AI can deliver real, responsible value -at scale.

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