The RCN opposes the use of artificial intelligence (AI) in NHS job evaluation (JE). JE must remain a human-driven, partnership-based process that recognizes the complexity and nuances of the nursing role. The use of AI in JE risks undermining the NHS’s core values of fairness, transparency and fairness and deepening inequalities in the workforce.
Also read the advice from the NHS Staff Council Job Evaluation Group (JEG) on the use of artificial intelligence (AI) in the job evaluation process.
1. Background – What is JE?
The NHS Job Evaluation (JE) system, based on the Agenda for Change (AfC), underpins fair, consistent and legally robust pay decisions for NHS staff. Developed through a partnership between employers and trade unions, the system ensures that pay is based on the demands of the job, not the person doing the job, and that the process is transparent, accountable and rooted in equality.
2. What is the “meaning of AI in JE”?
Suggestions for using AI in JE include:
- We use machine learning algorithms to assign bandings based on job description.
- Automate role and country profile matching.
- Replace panel judgment with an AI scoring engine.
Although digital tools, including artificial intelligence (AI), are increasingly being introduced into the workplace, their role in job evaluation must continue to be severely limited. The RCN does not accept the use of AI in scoring, banding or decision-making in job evaluations.
We recognize that AI may play a small, supporting role in administrative tasks such as spell checking, grammar, formatting, and document management. However, this should not interfere with the integrity of the process.
3. Reasons to say no: risks and concerns
3.1 Undermining fairness and equality
AI systems are trained on historical data and risk replicating and widening existing inequalities, including historical under-banding in job performance outcomes. They are unable to capture the nuances necessary to assess complex nursing roles, including clinical judgment, professional accountability, emotional labor, and leadership. AI also lacks the lived-in workplace insights that staff-side panelists bring to job evaluation discussions.
3.2 Reduced transparency
AI decision-making is often opaque, with little clarity on how results are produced. This undermines the fundamental principle that staff should be able to understand, question and challenge job evaluation decisions. Machine-generated output makes meaningful scrutiny and accountability difficult.
3.3 Employee voice and partnership will be weakened
Job evaluation is built on a social partnership between employers and trade unions. Introducing AI into the process risks removing democratic participation, alienating trained panelists, and reducing employee voice in pay decisions.
3.4 Oversimplifying complex roles
NHS roles are diverse and continually evolving, with significant changes even within the same scope. Evaluating work relies on professional judgment and contextual discussion and cannot be replaced by automated pattern matching or standardized algorithms.
3.5 Legal risks
The use of AI in job evaluations can undermine compliance with equal pay laws, especially if decisions are not transparent or adequately explained. Employers may face legal challenges if AI affects outcomes without audit responsibility, accountability, or collective agreements.
The integrity of NHS JES depends on the functioning of partnerships and the voice of our people. These are not options. These are the foundations of fair pay and recognition in the NHS.
Checklist for personnel and executives:
- Ask your organization to disclose its current or planned use of AI or digital tools in job evaluations.
- Ask your organization to disclose any current or planned use of AI or digital tools that may indirectly impact JE, such as performance management systems.
- Ask if there are any plans to implement or procure new software that allows AI or digital applications to be used for job evaluations.
- Request a written statement from your employer confirming that AI is not being used to assign scores or bands and that such plans cannot proceed without consultation and union agreement.
