Artificial intelligence has evolved from an academic curiosity to a core feature in many UK products. By late 2025 the UK government estimated the domestic AI market at more than 72 billion pounds, with projections of a 1 trillion pound market by 2035. Over 432,000 organisations were reported to have adopted at least one AI technology, yet adoption remains uneven: 68% of large companies, 33% of medium-sized firms and 15% of small businesses had implemented AI. The result is a growing divide between firms building AI responsibly and those taking shortcuts. In 2026 the pressure to commercialise AI comes with new expectations around ethics, transparency and security.
ISO 42001 and the UK AI Code of Practice
The world's first certifiable AI management system standard, ISO 42001, was published in December 2023 and is rapidly becoming a benchmark for trustworthy AI. The standard provides a repeatable, auditable framework covering risk management, data governance, documentation and monitoring. It encourages organisations to define clear roles and responsibilities, assess the purpose and risks of each AI system, and embed fairness and transparency from the outset. While certification is voluntary, early adopters can demonstrate maturity to regulators, customers and investors.
Parallel to this, the UK has introduced a voluntary AI cyber security code of practice which emphasises risk assessment, secure development, supply-chain assurance and ongoing monitoring. It complements ISO 42001 by requiring companies to build security into models and guard against poisoning or model extraction attacks. There is no UK AI Act yet: the government deliberately delayed legislation until mid-2026. That means UK founders must navigate a patchwork of guidance while anticipating more prescriptive rules coming from the EU's AI Act, whose obligations on high-risk systems will start to bite in 2026 and 2027.
Designing for accountability and human oversight
Building AI responsibly is not just about complying with standards; it involves making deliberate product choices. Models should incorporate human-in-the-loop mechanisms for critical decisions, with clear escalation paths when confidence is low. Data provenance and lineage must be tracked to enable audits and to respect data minimisation principles. The UK's AI voluntary code encourages companies to assess the potential for bias and to document testing procedures. This aligns with ISO 42001's requirement to monitor models after deployment and to involve diverse stakeholders in governance.
For example, a health-tech startup using AI to triage patients must balance speed against safety. A false negative in diagnosing sepsis could be fatal, whereas too many false positives overwhelm clinicians. Under ISO 42001 the company would need to quantify those trade-offs and maintain a risk register. In practice this means allocating engineering resources to build monitoring dashboards, creating simulation datasets and organising periodic fairness reviews. These tasks do not directly generate revenue but are crucial for regulatory trust.
Closing the skills gap
Adhering to best practices requires talent. The Institution of Engineering and Technology's 2025 skills survey found that 76% of engineering employers struggled to recruit for key roles, and only 61% felt their workforce was fit for the future. 42% of firms ranked innovative thinking as the most vital skill, ahead of digital and technical expertise. In AI specifically, 58% of organisations reported some use of AI, but only 18% used it regularly. Key skills deficits included automation (30% lacked capability), data engineering (17% struggled to recruit) and software engineering (17% struggled). Without addressing these gaps, it is impossible to implement ISO 42001 effectively.
Training and recruitment efforts should be inclusive. The Skills England report on AI skills emphasises transferable competencies such as critical thinking, responsible design and interpreting AI outputs. Barriers include training programmes that are too technical, poor support for women and older workers, and limited provision outside major hubs. Startups should not rely solely on hiring from London; partnerships with regional universities and remote working policies can tap into a broader talent pool. Apprenticeships and flexible internships can help upskill non-traditional candidates while building loyalty.
Balancing innovation and risk
One of the tensions for 2026 is between rapid experimentation and controlled deployment. Generative models have captivated investors, but they are expensive to run and prone to hallucination. Some startups have opted to fine-tune open models on private data and deploy them behind API gateways, while others are building smaller domain-specific models. There is no one-size-fits-all; the choice depends on budgets, latency requirements and data privacy obligations. The UK government's pledge to invest 900 million pounds in AI supercomputing infrastructure could lower the cost of large models, but early access will likely be restricted to consortiums or researchers.
A further complication is the reputational risk associated with AI misuse. Surveys show 59% of Britons have concerns about dependence on AI. Without transparent communications and user controls, companies risk a backlash. Product teams should include ethicists and legal counsel from the start, and marketing should be honest about limitations. The impending EU AI Act will require transparency measures such as labelling synthetic content and forbidding high-risk systems from making autonomous decisions without human oversight. Even before the law applies in the UK, conforming voluntarily can signal responsibility.
Conclusion
In 2026 building an AI-driven product is as much about governance and talent as it is about models and code. ISO 42001 and the UK's voluntary AI code of practice provide a pathway to embed ethics, transparency and security into development. The challenge for founders is to see compliance not as a cost but as a competitive differentiator. Organisations that invest in responsible AI today will be better placed to navigate the incoming wave of regulation and to earn the trust of users sceptical about the technology.