AI transformation succeeds only when leaders redesign how work gets done. In a recent Reejig webinar, Siobhan Savage, CEO of Reejig, spoke with Ben Schreiner, Head of AI and Modern Data Strategy at AWS. They discussed what it takes to build an AI-enabled workforce at enterprise scale.
This was not a conversation about experimentation. It focused on leadership, operating models, and cultural shifts. These are what turn AI into a durable business capability.
Key takeaway: AI changes how work operates. Leadership determines whether it delivers value. Every enterprise is deploying AI. Almost none can see the work they're deploying it into.
AI only delivers value when tied directly to how the enterprise creates value.
Too many organizations treat AI as a technical deployment owned by IT. According to Schreiner, this is the most common reason AI initiatives underperform.
"AI transformation is not an IT project. It's a work transformation agenda."
When AI is disconnected from business outcomes, it drifts into experimentation. No accountability follows. Leaders must anchor AI to explicit questions:
Without this clarity, AI investments stay fragmented. They fail to scale.
AI belongs on the core business agenda. It needs clear ownership and outcome metrics.
Early AI success comes from improving high-volume, low-ambiguity work.
Schreiner advised against broad AI programs across the enterprise. Instead, leaders should begin with work that is:
Examples include:
These areas demonstrate tangible value quickly. They reduce organizational risk.
"The best place to start is high-volume, repetitive tasks with clearly defined inputs and outputs."
Visible wins build trust and momentum. They do not trigger cultural resistance.
Enterprises stall when they optimize for experimentation instead of outcomes.
Both speakers highlighted a common failure mode: pilot purgatory. Organizations run small tests. Those tests never translate into scaled impact.
The alternative: deploy AI into real, contained workflows. Measure results immediately. At Reejig, Savage shared that teams implement working agentic AI systems in weeks. Not quarters. Each ties directly to operational metrics.
"We need to stop talking about proof of concept and start talking about proof of value."
What to measure early:
If AI cannot be measured like any other business improvement, it is not ready to scale.
AI fails when employees are unclear how it fits into daily work.
Even technically sound AI systems see lagging adoption. This happens when leaders assume employees will "figure it out." Savage emphasized that adoption breaks down when guidance is abstract or self-directed.
"Prompt training is not the answer. We need to show people exactly how the work changes."
Schreiner reinforced that trust is built through:
"Culture is the hard part."
Adoption accelerates when leaders remove ambiguity. They must make clear how work is expected to change. This is the principle behind Stealth Change Management. The new way of working becomes the default before anyone has to "adopt" it.
AI transformation is a shared leadership responsibility across HR, IT, and operations.
No single function owns AI. Organizations that succeed treat it as a coordinated effort across:
Savage argued that HR must evolve from stewardship to active workforce innovation. HR must partner closely with IT to operationalize change.
"This is a big leadership moment. You have to set a direction and a vision for your company."
The future of work is designed, not deployed.
|
CHRO Focus |
CIO Focus |
Shared Outcome |
|
Workforce operating model |
Enterprise systems and data |
Scalable AI-powered work |
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Role and workflow clarity |
Reliability and governance |
Trust and adoption |
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Change readiness |
Architecture and integration |
Measurable value |
AI transformation is not primarily a technology initiative. It is a work transformation agenda. It requires leadership ownership and operating model change.
Enterprises should start AI with high-volume, clearly defined work. Choose areas where outcomes are measurable quickly.
Most AI pilots fail to scale for a specific reason. They optimize for experimentation instead of operational value and ownership.
HR plays a leading role in AI transformation. HR should lead workforce and workflow redesign in partnership with IT and operations.
Leaders accelerate adoption by making changes to work explicit. Changes must be visible and supported by leadership.
AI does not transform organizations on its own. Leaders do.
Enterprises that succeed with AI redesign how work is structured. They redesign how it is measured. They redesign how it is supported by both human and digital systems. That responsibility sits with executive leadership.
The opportunity is not automation. The opportunity is building an AI-powered workforce that delivers sustained business value.
Book a demo to redesign how work operates in an AI-powered enterprise.