AI joined the team. It's still on probation.

AI confidence has outpaced AI capability

AI is rapidly changing how workforce management teams think, plan, and operate. But the question isn't whether AI is being used, it's whether teams have the systems, confidence, and capability to achieve real value every day.

In May 2026, Rotageek surveyed 400 senior workforce management decision-makers across UK organisations with 500+ employees.

98% are either using AI in workforce management or planning to, and 86% already use it today. 83% feel fairly or very confident in AI-supported decisions and 87% said AI is already saving managers and administrators time.

Most organisations use AI, but few have embedded it deeply enough to shape how they plan labour, respond to demand, or make judgement calls under pressure. AI maturity matters because it enables operational efficiencies, not just through cost savings, but by empowering teams to focus on higher-value work and deliver better service.

Closing the maturity gap doesn't mean a complete transformation. Organisations can start small and prove value, project by project. Crucially, they must put their people first from the outset and appreciate the importance of change management. Technology is only as good as the people using it.

Achieving maturity also requires workforce teams to overcome common barriers such as trust and transparency, lack of internal skills, compliance, data quality and integration, and legacy systems.

Drawing from fresh insight, this report explores where workforce management teams are now, why AI confidence may be running ahead of capability, and how organisations can start to close the AI maturity gap.

AI is moving faster than many businesses can comfortably absorb. In just a few years, the conversation has shifted from big data and machine learning to generative AI and agentic systems. This isn't just another technology wave, it's a reset in how organisations think, plan, and operate.

In workforce management, manual processes have long persisted because they balance complex factors such as labour costs, compliance, and employee preferences. Many organisations have since moved from spreadsheets to dedicated platforms. Now, AI-assisted processes are helping them move beyond reactive scheduling to data-informed, strategic decisions.

Confident AI adopters

The survey suggests confidence in AI-supported workforce management is at least keeping pace with adoption. 83% feel fairly or very confident in AI-supported decisions, and positivity was visible across all demographic groups. Only 5% said they weren't confident.

Confidence often lags adoption when complex tools arrive faster than training. Workforce management seems to be bucking that trend. The profession isn't only adopting AI, but in many cases, trusting it too.

Confidence is an important foundation for maturity, but it's not the same as consistent embedding, measurable outcomes, or operating-model change.

The AI maturity gap in workforce management

Only 17% say AI is central to workforce decisions. Confidence, expectations and capability are pulling apart.

Adoption isn't the same as maturity. Having the technology is one thing; using it well enough to improve decisions, change behaviours, and deliver measurable outcomes is another.

By AI maturity, we mean AI embedded into manager and employee workflows and connected to reliable data, used consistently to improve specific operational decisions, governed well enough to earn trust, with meaningful operational outcomes to show for it.

Our survey suggests organisations can be at various stages of AI maturity. While confident about AI, they're not always equipped to enable better workforce decision-making around forecasting, staffing, scheduling, labour modelling, reporting, and planning.

More than half of respondents (56%) believe their organisation is ahead of competitors. The largest organisations (2,500+ employees) are least likely to say so (49%), while businesses with 1,000 to 2,499 employees are most likely to think so (68%).

Age shapes perception too: 17% of 18–34 year-olds said they're “far ahead” of competitors, compared with only 3% of those aged 55 and over.

Why the maturity gap matters

Across the wider business landscape, AI maturity is becoming a marker of operational effectiveness. Yet EY's 2025 research suggests UK organisations are missing up to 40% of potential AI-driven productivity gains because of talent shortages, delayed readiness, and integration gaps.

Workforce management sits right in the middle of this challenge. The opportunity isn't simply to use AI more often, but to use it in ways that improve how people plan, allocate labour, respond to demand, and make decisions under pressure.

Eight barriers. Six that really bite.

Trust and transparency

Workforce management depends on people acting on AI insight, not just having access to it. Without trust, managers quietly override or ignore recommendations, and the technology sits underused, eroding the return on whatever's been invested in it.

Lack of internal AI skills

Staff can't realise the benefits of AI without the confidence and skills to use it well. Low capability means missed productivity gains, so the performance improvements AI promises simply don't materialise.

Compliance concerns

Many organisations worry: is the data secure, could confidentiality be breached, and is it clear how an AI decision was made? AI often touches sensitive employee data and decisions with real employment implications, if a team can't explain why an AI-driven schedule was made, that's a fairness and compliance risk, not just a technical one.

Data quality and integration

Data accuracy issues and difficulty integrating AI with legacy systems are a real barrier. Without solving it, teams rely on data they don't trust, spread across disconnected tools.

Competing priorities

When organisations face several pressures at once, AI maturity can slip down the list, and company attitudes towards technology investment vary. Yet busy workforce teams need robust systems and clear insight to perform well, and AI is a key route to better outcomes.

Legacy constraints

Some legacy systems make it hard to embed AI, so many organisations see the benefit but face real hurdles before they can upgrade. That gap is costly: teams end up manually patching problems AI could have solved, and the gains promised elsewhere stay out of reach.

Chris says organisations should treat AI agents like employees: set objectives and review performance regularly. Most, he says, are yet to achieve such a robust review process.

Trust shouldn't rest on a vendor's word alone; it's why Rotageek holds the ISO/IEC 42001 certification for artificial intelligence management, achieved in 2026 as part of the ELMO Software group.

Technology alone can't deliver AI maturity, empowering people matters too. When an organisation commits to bringing everyone on the journey, the impact of technology change can be significantly greater.

Our own survey found 66% of leaders agree the biggest challenge is people, not technology. Forrester's 2025 State of AI Survey flags a related risk: 48% of firms have already used AI to cut headcount, while change management and employee experience remain low investment priorities for 2026.

AI isn't about cutting teams. It should enable staff to perform better and make more informed decisions, eliminating routine tasks so people can focus on higher-value work.

The Forrester survey also showed that change management and employee experience are a low priority for 2026. The result may be predictable: staff feel unsupported and fear slows AI adoption. Forrester argues that unless organisations put people at the centre of their AI strategy, the gains will remain marginal.

Rotageek's experience with multi-site retailers and large enterprises supports this view. Successful workforce management transformation takes more than the right technology. It requires clear intent, disciplined execution, and a serious commitment to bringing people with you.

Closing the gap: education, then technology

Many organisations think AI maturity means teaching people how to use the tools better, and our survey backs that up. When we asked what extra AI capabilities respondents wanted, most described things their existing tools can already do. If organisations want to mature AI faster, they must prioritise education and help people build sustainable workflows.

Maturity is also about having the right technology. Half of organisations plan to upgrade or replace workforce management systems to better support AI, while 42% plan to redesign roles or operating models around better use of data and AI. As leaders shift from experimenting with AI to embedding it into workforce operating models, they plan to focus investment on three areas over the next 12 months.

Mature AI depends on strong core systems, and some organisations still need to catch up. Their next step is to strengthen the workforce management systems AI relies on, without solid foundations, the tools built on top won't deliver. A priority for organisations still building the infrastructure later maturity depends on.

AI can surface sharper analytics, forecasting, and reporting, and deeper insight leads directly to better workforce decisions, which is what most leaders want from AI. The top investment focus overall, with 2,500+ employee organisations placing the strongest emphasis on it.

Organisations need in-house skills, access, and trust for AI to deliver results, tools only create value if people can reach them and know how to use them. AI isn't just for senior teams: respondents said training would be a major focus as leaders widen access.

Mature AI embeds directly into existing workflows rather than running as its own process.

Leaders measure impact on overtime, payroll spend, service levels and absence, not activity.

Recommendations are explainable, governed, and open to human oversight.

Performance is reviewed over time as organisations learn what actually works.

The Entertainer

Store managers used to spend hours each week building rotas by hand, then cross-checking timesheets manually. With 170+ stores and concessions in more than 1,000 Tesco locations, that approach struggled to keep pace, especially when shifts changed at short notice. Given its rapid growth, the retailer also needed any new system to be scalable and easy to use.

Managers now build schedules with automated tools rather than starting from scratch each week, a pay rules engine applies rates automatically, and they have real-time visibility of scheduled hours and labour spend by location. Staff use a mobile app to swap shifts, request leave, and share availability.

Lush

As stores evolve, Lush needs the flexibility to offer expert category advice and specialist services while balancing walk-in customers with pre-booked appointments. Getting that balance right depends on having the right people scheduled at the right times, something manual, spreadsheet-based rota planning struggled to keep up with.

Store teams now see schedules, read updates, swap shifts, pick up extra hours and request leave in seconds, instead of manual processes that took hours. Store and warehouse managers report spending less time on scheduling admin and more with their teams and customers.

Three steps workforce leaders can take to drive AI maturity

Many organisations are held back by poor data quality and integration challenges with legacy systems. It's not AI in isolation that improves decision-making; it's how the wider system connects, governs, and applies data. Guardrails matter: clear rules on what AI can act on automatically, and what still needs a manager's sign-off.

Without reliable foundations, AI risks becoming another disconnected tool rather than a trusted part of workforce management. By continually taking small steps and piloting new functionality, organisations can build from a position of strength.

Organisations must move beyond isolated AI use and embed tools into the workflows that shape workforce decisions. Mature adoption is less about how AI fits existing processes and more about designing processes that make the best use of AI. The strongest gains appear when AI supports day-to-day decisions such as forecasting, scheduling, and reporting, and with only 17% saying AI is central, there's work to be done yet.

Unlike a standalone pilot, a purpose-built platform already understands labour rules, contracted hours, and demand patterns, so recommendations plug straight into existing workflows, with guardrails and an audit trail from day one. Given 42% believe wider AI use could save managers 5 to 9 hours a week, and a further 26% think 10 to 15 hours is possible, testing AI-assisted workflows is a proactive move.

Training staff to engage with and trust AI tools determines whether capability translates into productivity, so people must be at the centre of the journey to maturity. While confidence is high, that has yet to enable widespread maturity. People need to understand how AI works, where it helps, how decisions are governed, and what it means for their role, and they need the access to actually use it, not just senior teams at HQ. Getting this right is as much about culture as capability: teams need to feel like participants in the change, not recipients of it.

Access matters as much as training. Rolling tools out to frontline staff, not just leaders, is what determines whether the investment reaches the people making day-to-day decisions, reducing risk and building a plan everyone buys into.

AI adoption in workforce management is already widespread. What's still developing is maturity: embedding AI into trusted, everyday decision-making rather than treating it as a separate initiative running alongside normal operations.

Getting there depends on more than technology. It calls for reliable systems and accurate data, workforce decisions and operating models built around AI rather than bolted onto it, and a people-centred approach that brings staff along at every level.

Leaders can start small: focused, measurable, low-risk opportunities that prove value without disrupting daily operations, built in-house or with an experienced workforce management partner.

Standing still is riskier than taking small steps forward, but responsible progress still means planning carefully, not rushing. Organisations that make those choices well, and start making them now, will set the pace for everyone else.

All findings are based on respondents' self-reported perceptions and experiences, rather than independently verified data or outcomes. External research referenced is attributed to its original source and included to indicate broader industry context only.