Collaboration over competition: How people and AI can win together

According to the UK government’s AI Opportunities Action Plan, AI adoption could boost the UK economy by up to £400 billion by 2030. The potential is huge, but many CIOs face a familiar challenge. They are being asked to deliver transformative results without the budget or strategic clarity to match.

This reality raises an important question: not whether to adopt AI, but how to do so responsibly, effectively and within financial constraints. The organisations that succeed will focus on three critical areas including smarter prioritisation, building human infrastructure and ensuring quality in AI outputs, all while positioning AI as a collaborator, not a competitor and connecting it to measurable business outcomes.

Smarter prioritisation over scale

AI adoption is no longer about chasing every shiny object. It’s about asking where AI can truly create impact and deliver measurable business value.

Organisations need to connect AI initiatives back to their core priorities, making smarter, risk-aware bets and staying the course on selected initiatives versus reacting to weekly new developments. That shift requires discipline but also agility. As the market and solutions evolve rapidly, leaders must distinguish when to stay the course versus when to pivot, always with an eye on value creation.

When budgets are limited, organisations should prioritise initiatives that solve specific business pain points and demonstrate measurable ROI—such as reducing cycle times, improving compliance or accelerating product delivery. These quick wins build confidence, deliver results and potential savings that can allow for investment in future AI initiatives, and create a foundation for more scaled solutions later. Considering the relative immaturity of many AI solutions, or gaps in data, starting with more limited use cases allows for learning in a more contained environment. This approach matters because AI ROI is often slower than expected. A 2025 Deloitte survey found that only 6% of organisations achieve payback within a year, while most take two to three years to see returns. Prioritising impact over scale ensures every pound spent works harder.

Building human infrastructure

Prioritisation alone isn’t enough. AI success depends on people working with technology, not as competitors, but as collaborators. Viewing AI as an enabler and teammate changes how we think about roles and skills.

Skills are the ultimate differentiator. Organisations that understand the skills within their organisations, starting with a skills mapping exercise, —using AI-driven taxonomies to identify

gaps and align talent to business priorities—will move faster and with greater confidence. It's no longer about augmenting tasks and aligning static roles to granular activities. Instead, it’s about building a Skillforce by understanding, developing and deploying both technical and power skills such as curiosity, adaptability and critical thinking dynamically so they can adapt quickly and use AI as a true partner in transformation.

For example, developers are spending less time writing code, and more time reviewing the quality of AI generated code, a fundamental change in the skills needed to succeed in this role. The more detailed organisations can get in understanding tasks and mapping skills, both human and AI, the more flexibility they have to align talent and technology to business priorities.

Viewing your workforce as a Skillforce helps break down silos because work becomes about matching skills to priorities and outcomes, not rigid reporting lines. Investing in continuous learning ensures employees can identify the opportunities to leverage AI responsibly, turning AI technology from an experiment into a trusted teammate. When humans and AI collaborate effectively, organisations move beyond automation to innovation—and skills become the common language between them.

Avoiding ‘workslop’: Why quality matters

Even with strong prioritisation and skilled teams, quality remains a critical challenge. Harvard Business Review defines ‘workslop’ as ‘AI-generated work content that masquerades as good work but lacks the substance to meaningfully advance a given task’. This issue is growing rapidly. A recent survey revealed that 40% of US desk workers believe they have received workslop in the last month, and 53% admit that some of their own output may fall into this category.

Poor-quality AI outputs waste time, erode trust and slow progress. To prevent this, organisations must build quality checks and transparency into AI solutions. This includes setting clear guardrails through robust AI policies, empowering employees to spot low-quality outputs, and embedding critical thinking into workflows.

Employees can identify poor AI outputs by looking for generic, repetitive language that lacks specificity, factual inaccuracies or fabricated sources. Overconfidence without evidence is another red flag, and every claim should be verified. Interestingly, AI can also help validate itself. Asking AI systems for opposing viewpoints or requiring it to qualify its sources can strengthen reliability and reduce risk.

Why clear AI policies are non-negotiable

Quality control starts with governance; AI policies are like brakes on a car. They don’t slow you down, but they help you move faster with confidence. Strong policies prevent misuse of sensitive data, support regulatory compliance and protect organisational reputations. However, policies only work if they are understood and applied. Building a culture of

continuous learning ensures that guardrails evolve alongside technology and regulation, turning AI from novelty into a competitive advantage.

AI as a collaborator

Ultimately, AI adoption is not a race to implement every new tool but a strategic journey that demands focus and collaboration. In an environment where budgets are tight, success depends on prioritising initiatives that deliver real impact, building the human infrastructure to support responsible use and embedding quality checks to avoid the pitfalls of workslop.

The future-ready workforce will not be defined by humans versus machines, but by humans and intelligent systems working together.

Organisations that recognise skills as the common language between AI and humans, and invest in those skills, will transform AI from a novelty into a trusted business enabler. Leaders who combine clear policies with continuous learning and empower teams to validate and optimise AI outputs will not only navigate today’s constraints but also secure a lasting competitive advantage in the years ahead.