WorkJam adds autonomous AI tools for frontline retail

WorkJam has added autonomous artificial intelligence tools to its frontline operations platform. The new features are available now.

The update adds an AI layer to WorkJam's software for task management, communications, learning and shift management, used by retailers including TJX, Ulta Beauty, Marks & Spencer and Shell. The system can assign work, shift priorities as conditions change and confirm when tasks have been completed.

Unlike workforce software that limits AI to recommendations for managers, WorkJam is positioning the release around automated execution on the shop floor. The software can adjust tasks in real time based on local conditions at individual stores rather than applying the same instructions across an entire chain.

That means a store's task list can change as staffing levels, demand, traffic or customer feedback change. Each decision is also shaped by what the software learns from outcomes at the store, district, regional and broader estate levels.

How It Works

The system is designed to create a closed loop between central business systems and frontline staff. In practice, enterprise data feeds into store-level decisions, which are then carried out through WorkJam's existing modules for communications, task assignment, training reinforcement and staffing.

Managers can still intervene. Every AI decision is governed by operational limits such as shift schedules, paid working time, certifications, labour rules and an organisation's own policies and standard operating procedures.

Managers can override autonomous decisions, and all actions are logged for audit and compliance purposes. WorkJam also said no data is duplicated between systems, with actions based on live operating conditions.

Steven Kramer, Chief Executive Officer of WorkJam, described the product as moving beyond manager dashboards and recommendation engines.

"Most frontline AI tools deliver better visibility or recommendations for managers to act on," said Steven Kramer, Chief Executive Officer of WorkJam. "The real breakthrough comes when you combine a strong execution platform with autonomous intelligence that actually closes the loop. The right work gets done, the system learns from what happened at each store, and performance compounds across the chain. That's what we built."

Retail Focus

The pitch is aimed squarely at large retailers and other employers with dispersed frontline teams. These businesses often struggle to coordinate central priorities across hundreds or thousands of locations while accounting for local differences in staffing, customer demand and compliance requirements.

WorkJam said the new AI tools allow each store to receive a different set of priorities based on its own circumstances. At the same time, the broader system continues to absorb patterns from across the organisation, which WorkJam argues should improve decision-making over time.

The software is also built to work with a customer's existing technology stack. According to WorkJam, internal IT teams can connect existing decision systems, surface enterprise data through custom widgets and build their own AI workflows on the platform. For customers without their own AI models, WorkJam can provide the full software layer itself.

Next Phase

A further component, called the AI Task Priority Engine, is due in the second half of 2026. WorkJam said it will extend the autonomous loop by continuously calculating a priority score for every task using inputs such as labour, traffic, demand, customer feedback and outputs from a retailer's own AI systems.

Each recommendation from that engine will include a plain-language explanation, giving managers a record of why a task was prioritised and allowing them to review or override the decision if needed.

According to WorkJam, its platform serves more than 1.5 million employees across its customer base. It combines employee communications, task management and audits, learning, shift management and self-service tools in a single application for frontline teams.

The launch comes as software suppliers push artificial intelligence deeper into day-to-day operational systems rather than keeping it at the level of reporting and forecasting. In retail, that shift centres on whether AI can influence what workers do in stores minute by minute while staying within staffing rules, training requirements and manager control.

WorkJam said its system was built around those constraints, with every autonomous action tied to the practical realities of frontline work, including schedules, certifications and company policies.