Team leads get one view of how work happens across the team, which workflows to change first, and whether those changes reduce completion time and rework or help the team deliver more.

of employees who use AI at work have made mistakes in their work because of AI.

Course completion shows that someone finished the program. It doesn’t verify whether they can apply AI to a role-specific task.

of US organizations have developed generative AI training plans. Yet their biggest challenge is limited understanding of employees’ AI skilling needs.

Course completion shows that someone finished the program. It doesn’t verify whether they can apply AI to a role-specific task.

of employees who use AI at work have made mistakes in their work because of AI.

It happens across trackers, documents, repositories, threads, and handoffs, and often depends on standards that still live in people’s heads.

Workflow visibility

A unified map of how the team works

Evolve Work brings individual workflows into one team view, showing recurring work, handoffs, areas of high load, existing AI use, and the processes that still depend on undocumented knowledge.
Opportunity prioritization

A roadmap tied to delivery metrics

The product identifies where agents can create value and prioritizes each opportunity against the metrics the team already tracks, such as cycle time, rework, or throughput.
Human oversight

A record of where human judgment stays

Each workflow documents what an agent can handle, where a standard must be defined first, and where a person still needs to review, decide, or remain accountable.
Implementation

Agent building with expert support

Owners build the proposed agents through chat, without a separate engineering project. They review every output before it reaches a real tool, with support from Nebius Academy experts until the change is live.

With the team’s permission, a mapping agent reviews how work happens across the tools they already use. A short AI interview fills in the gaps, and each person reviews their workflows before they are added to the map.

Individual workflows are connected into one team map, showing recurring work, handoffs, areas of high load, existing AI use, and steps that depend on undocumented knowledge.

The team lead selects the delivery metrics that should improve. Evolve Work then identifies what can move to agents, where a standard is needed first, where human judgment remains, and who owns each proposed change.

Owners build the proposed agents through chat and approve their outputs before they reach real tools. Nebius Academy experts support the team until each change is live and can be measured against the agreed delivery metrics.

Leaders can see which changes were implemented, who owns them, and whether they improved cycle time, rework, throughput, or another agreed metric. The same process can be repeated across the organization.

The pilot includes one mapping session per person and one conversation with the team lead. It produces a team workflow map, a staged roadmap, named owners, and the delivery metrics that will be used to evaluate each change.

The map uses information from the tools where the team already works, supplemented by a short AI interview.

Each person reviews and corrects their own workflows before they are included in the team map.

Each change is evaluated using cycle time, rework, throughput, or another metric the team lead already reports.

Measure how people apply AI, identify gaps by role and skill, and generate personalized development paths.

Map how teams get work done and turn real workflows into owned plans for AI-enabled change.

Improve how people work with AI through brief, evidence-based coaching inside the tools they already use.

An AI workflow is a sequence of tasks in which AI supports, performs, or coordinates part of the work. AI may classify information, produce an output, recommend a next step, or complete defined actions. Evolve Work maps these workflows and defines what AI can handle, what needs a standard, and where human judgment remains.

AI workflow mapping documents how work moves across tasks, people, systems, decisions, and handoffs. Evolve Work combines information from the tools a team already uses with a short AI interview and participant review to produce individual workflow maps and a unified view of the team’s work.

Process mining uses structured event logs to reconstruct and analyze processes. Workflow mapping also captures cross-tool handoffs, informal steps, exceptions, and human decisions that may not appear in system logs. Evolve Work uses this broader view to build a roadmap for agents, standards, and human involvement.

Strong candidates usually involve recurring work, accessible information, measurable friction, and clear review points. Organizations should also consider business value, implementation effort, data availability, and risk. Evolve Work evaluates opportunities in the context of the team’s real workflows and organizes them into a staged roadmap tied to delivery metrics.

Evolve Work delivers individual workflow maps, a unified team workflow map, and a staged roadmap tied to delivery metrics. The roadmap identifies what can move to agents, what needs a standard first, where human judgment remains, and who owns each proposed change.

No. Evolve Work maps how work is structured, not how well an individual performs. Each person reviews and corrects their workflows before they enter the team map. Evolve Work does not score employees or use workflow information to evaluate individual performance.

A pilot covers one team. Each person completes a mapping session, and the team lead selects the delivery metrics that should improve. The team workflow map and staged roadmap are typically produced within two weeks. Implementation support continues as owners put the proposed changes into practice.

In two weeks, receive a team workflow map and a staged roadmap tied to your delivery metrics, followed by support to implement the proposed changes.