Josh Bersin recently named Reejig among the companies building the job and task intelligence enterprises need to redesign work for AI. But the most important point in his article isn’t simply that organizations need better task data. It’s that knowing the tasks is only the beginning.
As Bersin argues, tasks don’t exist in isolation. They sit inside roles, workflows and business outcomes. Some are suited to AI, others are better handled through traditional automation, outsourcing or human judgment. That distinction matters because the goal isn’t to build the biggest possible inventory of tasks.
The goal is to understand which work should change, how it should change and what impact that change can have.
Job-task analysis isn’t new. What has changed is how quickly organizations can now understand work, and what they can do with that intelligence once they have it. AI can help enterprises identify and catalog the work happening beneath jobs at a scale that previously required months of interviews, surveys and analysis.
Once that work is visible, teams can start asking better questions: Which work should be automated? Where should AI augment people? Which activities still depend heavily on human judgment?
That is where Work Intelligence becomes valuable: not as another taxonomy, but as a way to make better decisions about how work should run.
One of Bersin’s strongest points is his warning against treating task analysis as a project in itself. A static inventory of tasks can become outdated quickly as work, technology and processes change.
The same task can also mean something different depending on the role, workflow or business outcome around it. So task visibility alone isn’t enough. You need to understand how work connects, where judgment matters and whether AI should automate, augment or redesign it.
That context is what turns task data into useful Work Intelligence.
Reejig gives enterprises a live view of the work beneath every job, then connects that intelligence to the decisions and workflows required to redesign work for AI.
Rather than treating task analysis as a one-off project, Reejig creates a continuous loop between understanding work and changing it. Work Architecture maps roles, tasks, subtasks and workflows. From there, organizations can assess AI potential, identify what should be automated, augmented or kept with people, and redesign how people and agents work together.
They can then turn that redesigned work into AI workflows and agent blueprints, measure what changed and feed those learnings back into the Work Architecture as the work evolves.
That is the difference between maintaining a task database and building a system for continuously redesigning work.
Read Josh Bersin’s full article, What Is Task or Work Intelligence And Can We Decompose Every Job For AI?, then watch our 3-minute product tour to see how Fortune 50 organizations use Reejig to understand work at the task level, identify AI opportunities and redesign how work gets done.