AI workflow redesign fails at scale when organizations stop at the task level and never reach the subtask. The gap between knowing which roles are AI-ready and actually rebuilding how work runs is where most enterprise AI programs stall.
This organizational readiness gap sits at 71%, reinforcing that enterprise readiness requires more than deploying AI: organizations must unpack workflows, identify opportunities for compounding value, and define the workforce of the future at scale.
Sarah Bernstein, VP of Organization Transformation and Capability Development at Lumen Technologies, is working through that challenge in real time, transforming a legacy telco into a digital network service provider across every function and workflow simultaneously..
This conversation, from Reejig's Work Blueprint series, covers why subtask depth is non-negotiable for true AI reimagination, why just-in-time learning is the missing piece after workflow redesign, what it means to make people pilots rather than passengers, and why AI fear-mongering by AI companies made the adoption challenge harder for everyone.
Task-level data tells you where AI potential exists. Subtask-level data tells you how to actually rebuild the work. These are different problems, and confusing them is why so many AI programs produce insight but no redesign.
The practical implication is significant. When Lumen mapped invoice processing, the agent does not take the full payment invoice. The subtasks, each step required to complete the payment, are where the agent operates. Without that subtask map, the agent build is incomplete. Without the work context at each handoff point, which system the work moves to and under what conditions, the agent Blueprint is only 80 percent right. As Siobhan Savage, Founder and CEO of Reejig, observed: "80% is not good enough if we want to be able to build the capability across all of our teams to do this at scale."
Every enterprise is deploying AI. Almost none can see the work they're deploying it into. The Work Context Graph is the structured record that makes that visibility possible: Work Architecture as the foundation, Work Context as the reality of how work actually flows, and Work Record as the governance layer. That is the infrastructure that makes agent building reliable at enterprise grade.
Some enterprises “solved” their workflow redesign problem and are now discovering the adoption problem.
Agents are built.
Workflows are redesigned.
Employees receive prompt training.
Then nothing changes, because no one showed people their actual new way of working.
Bernstein’s response at Lumen is to build learning journeys directly from the agent design session. Out of the workflow redesign workshop, two parallel tracks run: one builds the learning path for the new agent-enabled workflows, while the other builds the always-on judgment and adaptability capabilities every function needs as the redesign continues. Learning is built into the redesign, not added afterward.
Reejig’s Work Operating System surfaces standard operating procedures automatically when a workflow is published, giving employees a concrete, step-by-step picture of the new way of working. This is Stealth Change Management in practice: continuous, embedded change delivered inside the systems people already use. No kickoff meetings. No separate change program.
Pilots not passengers: the change mindset that determines adoption
People do not naturally embrace change, and the AI era has added a new layer of uncertainty that can make adoption even harder. Bernstein's framing for Lumen's second-day session was clear: the goal is to move people from passengers to pilots. Borrowing from Uplift's language, passengers experience change as something happening to them, while pilots take ownership, build capability, and actively shape how AI transforms their work. The shift requires people to apply judgment, curiosity, experimentation, and product thinking to what their function is becoming, rather than simply adapting to new tools.
That clarity is the practical obligation. Employees who do not understand which tasks are changing, which remain uniquely human, and how they are expected to reinvest newly available capacity will struggle to translate technology adoption into business outcomes. Communication must reach the individual role level, not just the function or business unit. This is the activation gap between a redesigned workflow and an organization that genuinely works differently.
Closing that gap also requires reinforcing the Lumen Behaviors for Learning. Becoming a pilot means being curious enough to explore what AI makes possible, courageous enough to experiment and learn in public, accountable enough to apply new capabilities to real work, and generous enough to share lessons so others can accelerate. AI transformation is not simply a technology deployment; it is a true transformation. The organizations that move fastest will be the ones that help employees build confidence through clarity, practice, and continuous learning, turning passengers into pilots who can navigate the future of work.
The AI companies that built the narrative of job elimination created a direct obstacle for every enterprise trying to drive genuine AI adoption. When employees are primed to see AI as a threat, they resist the new workflows that would actually make them more capable. The harder the fear-mongering, the harder the change management.
Savage was direct: "I'm kind of cranky at the AI companies for actually making the fear worse. They have demonized their own product in market to the everyday consumer by kind of fear-factoring it. Which means that people are also quite resistant to actually adopting a new way, which means it's gonna be so much harder to actually cut through what was already gonna be a pretty difficult adoption journey."
The entry-level job crisis narrative misses the more important question: how do you design AI into an early career person from day one? Bernstein's observation from working with early career professionals at Lumen is that the trajectory question matters more than the role question.
If agents handle the repetitive execution that used to take three to four years to get through, and the early career person is building judgment, adaptability, and domain understanding from the start, the question becomes whether a two-year journey to genuine capability is achievable where a four-year journey was the norm. Bernstein put it precisely: "Can I, with AI, move them through what would have been a year-over-year journey? Can we accelerate some of that, both in our onboarding and how we develop them?"
The second dimension is capturing the expertise of twenty-year domain veterans and pairing it with the AI-first instincts of people just entering the workforce. Neither group alone has what the other has. The organizations that figure out how to combine institutional expertise with AI-native thinking, and wrap agents around both, are the ones that compound fastest.
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CHRO Focus |
CIO Focus |
Shared Outcome |
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Subtask-level work mapping, new workflow design, and organizational redesign that combines people and AI capabilities to unlock greater value. |
Agent inventory, approved AI stack, and Work Context Graph infrastructure |
Agent Blueprints that are complete, context-rich, and ready to deploy at enterprise grade |
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Just-in-time learning journey design, built from the redesigned workflow |
Standard operating procedure generation and workflow publication tooling |
Employees who know their new way of working from day one, not after a separate training program |
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Pilots-not-passengers change leadership: equipping people leaders to guide their teams through continuous redesign |
Change logging and audit trail for every workflow and agent modification |
An organization that absorbs continuous AI-driven change as standard operating rhythm |
The real work of AI transformation happens after the workflow is redesigned. Showing every person affected by that redesign exactly how to work in the new way, building learning into the deployment rather than bolting it on afterward, is what converts agent investment into actual change in how the organization operates. The enterprises that get this right are the ones treating AI redesign and people enablement as the same program, not sequential ones.
Book a demo to see how Reejig's Work Operating System maps your work at the subtask level, generates Agent Blueprints with full work context, and surfaces just-in-time learning journeys for every redesigned workflow.