AI work transformation fails when organizations chase AI for its own sake rather than targeting the tasks where it creates the most material impact. The enterprises getting real ROI from AI are the ones who have moved beyond proof of concept into deliberate, task-level work redesign, governed by a coalition of CHRO, CIO, and CFO. Robyn Tombacher, EVP, Global Business Transformation and IMO Operations at Warner Bros. Discovery, is leading exactly that challenge through one of the most complex media mergers in recent history.
This conversation, from Reejig's Work Blueprint series, covers how the C-suite coalition for AI transformation is shifting, why dabbling costs more than it delivers, how to bring people along through continuous change, and why HR business partners are the most underleveraged activation layer in the enterprise.
The buying coalition for AI transformation has shifted. HR and IT co-ownership is now established. The CFO is the next critical voice, and the organizations that bring all three together are the ones moving from proof of concept to proof of value.
The pressure driving this is straightforward. Boards are asking not just how much AI is being deployed, but what it is returning.
Tombacher described the CFO alignment as a pull-through requirement: "You have to see the pull through to the P&L. With any big investment of this scale, you have to model that out and see when this is going to start landing in performance." The organizations where CHRO, CIO, and CFO are aligned on a shared framework are the ones that can answer that question. The ones where AI transformation sits in a single function cannot.
The most common and expensive mistake in AI transformation is deploying AI broadly without task-level discipline. Every enterprise is deploying AI. Almost none can see the work they're deploying it into.
The discipline required is selecting tasks based on material impact: where will this create real improvement in how work runs? As Tombacher put it: "Where are we going to get the most bang for the buck? Where are we really going to make a material impact?" A task done once a week by two people is not a redesign priority. A workflow that runs across hundreds of people every day is.
This is the core argument for Work Architecture. You cannot make those decisions without a task-level map of how work actually runs. From Job Architecture to Work Architecture is not a cosmetic upgrade. It is the prerequisite for every AI deployment decision that follows.
Every wave of transformation in Tombacher's career, from the dot-com era through mergers, restructures, and now AI, has confirmed the same pattern. Top-down mandates without engagement do not deliver. Organizations that bring people into the design of change get adoption. Those that do not get resistance and cost overrun.
The practical mechanism Tombacher described is co-design: bring the people who do the work into the room, workshop the new way of working together, get to an 80/20 that reflects their input. When people shape the change, they become its advocates. When change is done to them, they fill their freed-up time with whatever they were already doing.
This is precisely why Stealth Change Management matters. Continuous, embedded change delivered inside the systems your people already use. No kickoff meetings, no posters, no separate program. Just the work, updated. The goal is an organization that absorbs continuous change as standard operating rhythm, not as a series of disruptions.
The most underleveraged asset in AI transformation is the HR business partner network. They have the direct relationships with leaders and managers. They understand how the work gets done. They know who the rising people are, and who needs development. That is exactly the capability set needed to activate work redesign at the business unit level.
Tombacher described the evolution she wants to see: "Those individuals who are now advising leaders and dysfunction groups on what kind of change they need to see in their people and the output of those organizations. It's in those individuals that I think HR can really get to a place of being almost like the lead for this AI transformation."
The model emerging across large enterprises pairs a small central workforce innovation team, the work architects and work designers who map and redesign workflows, with HR business partners who activate the change inside each department. The central team does the wiring. The HR business partner network ensures the people actually work the new way.
Consultants are a bridge, not a destination. The organizations treating each wave of AI redesign as a consulting engagement will always be behind, always paying for the same capability, and never building the institutional knowledge of how their own work runs.
Tombacher's framing was precise: "The bridge is not the destination. The destination is driving this out inside of your own organization." The internal team model she described is a cross-functional AI COE, with technology, business, and people function representatives, working in agile sprints alongside business units, sitting with the teams doing the actual change in real time.
Savage made the same argument from the Reejig experience: "We can't rely on externals. We have to build the capability inside our businesses. This is not a one-time change. This is a forever shift." The seven-stage loop, Map, Analyze, Build, Run, Measure, Log, and Update, is designed for exactly this. Work keeps changing as agents get stronger. The internal capability to keep redesigning is what compounds.
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CHRO Focus |
CIO Focus |
Shared Outcome |
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Task-level work redesign and AI-led versus human-led classification |
Agent inventory and approved AI stack governance |
A unified map of which workflows to redesign, in what order, with what guardrails |
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HR business partner activation: bringing business units through the change |
Engineering support for AI workflow deployment and integration at scale |
Work redesign that lands in how people actually operate, not just what is on paper |
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Employee communication at the role level: the what, why, and what next of workflow changes |
Change logging and audit trail for every workflow and agent modification |
AI adoption that employees understand, trust, and can operate within |
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Internal AI COE capability building: work architects, work designers, HR business partner upskill |
Technology architecture and tooling for the COE to run at enterprise grade |
A permanent, internal work redesign function that compounds rather than restarts each cycle |
Why do CHRO, CIO, and CFO need to co-own AI transformation? AI work transformation produces cost, organizational change, and financial return simultaneously. No single function owns all three. When the CHRO owns change without CIO support, AI does not get deployed. When the CIO deploys AI without CHRO support, people do not change how they work. When neither answers to the CFO, boards cannot see the return. All three must align on a shared framework and shared metrics.
How should organizations decide which tasks to redesign for AI? Task selection for AI redesign should be based on material impact: frequency, scale, and cost of the current workflow versus the cost and effort of redesigning it. A task done once a week by a small team is not a priority. A workflow that runs across hundreds of people daily, consuming significant time and producing clear outputs, is the right target. AI for its own sake, applied to low-frequency or low-value tasks, produces cost with no return.
What is Work Architecture and why does it matter for AI transformation? Work Architecture is the entity model that replaces static job architectures: every department, job, level, role, workflow, task, and subtask, structured for both humans and agents. It is the foundational map that makes AI deployment decisions possible. Without task-level visibility into how work actually runs, organizations cannot identify which tasks to automate, which to augment, and which must remain human-led.
Why does bringing people along through change affect AI ROI? When employees do not understand what is changing and why, they fill freed-up time with whatever they were already doing. The AI investment lands in hours saved on paper, not in capacity redirected to higher-value work. Co-designing the change with the people affected, communicating expectations at the role level, and giving people a clear path forward are not soft considerations. They are the mechanism through which AI ROI becomes real.
Why should enterprises build internal AI capability rather than relying on consultants? AI work transformation is not a bounded project. As agents get stronger, workflows change again. An organization that relies on external consultants for each cycle will always be behind, always paying for the same capability, and never building institutional knowledge of how its own work runs. Internal capability compounds. The right model uses consultants to bridge while internal capability is built, then stops relying on them.
What role should HR business partners play in AI transformation? HR business partners are the activation layer for AI work transformation. They have direct relationships with leaders and managers, they understand how work gets done inside their business units, and they have the context to advise on how role changes affect people. Investing in HR business partners as informed advisors, rather than administrators, is how central work redesign translates into actual change in how people operate day to day.
AI work transformation is not a technology deployment. It is a deliberate redesign of how work runs, governed by a CHRO-CIO-CFO coalition, activated through HR business partners, and sustained by internal capability built to keep redesigning as agents get stronger. The organizations that treat it as a one-time project, or delegate it entirely to consultants, will keep paying for the same problem. The ones that build the internal architecture to manage continuous change will compound their advantage every cycle.
Book a demo to see how Reejig's Work Operating System gives your team the task-level visibility and workflow redesign infrastructure to move from proof of concept to proof of value.