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, about what it really takes to build an AI-enabled workforce at enterprise scale.
This was not a conversation about tools or experimentation. It focused on leadership, operating models, and the cultural shifts required to turn AI into a durable business capability.
Key takeaway: AI changes how work operates. Leadership determines whether it delivers value.
AI only delivers value when it is directly tied 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 without accountability. Leaders must instead anchor AI to explicit questions:
Without this clarity, AI investments remain fragmented and fail to scale.
AI belongs on the core business agenda, with clear ownership and outcome metrics.
Early AI success comes from improving high-volume, low-ambiguity work.
Schreiner advised against launching broad AI programs across the enterprise. Instead, leaders should begin with work that is:
Examples include:
These areas allow leaders to demonstrate tangible value quickly while reducing organizational risk.
“The best place to start is high-volume, repetitive tasks with clearly defined inputs and outputs.”
Why this matters: Visible wins build trust and momentum without triggering 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 that never translate into scaled impact.
The alternative is to deploy AI into real, contained workflows and measure results immediately. At Reejig, Savage shared that teams regularly implement working agentic AI systems in weeks, not quarters, tied directly to operational metrics.
“We need to stop talking about proof of concept and start talking about proof of value.”
What to measure early
Leadership standard: 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 when AI systems are technically sound, adoption lags if leaders assume employees will “figure it out.” Savage emphasized that adoption breaks down when enablement 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.”
Executive insight: Adoption accelerates when leaders remove ambiguity from how work is expected to change.
AI transformation is a shared leadership responsibility across HR, IT, and operations.
AI is not owned by a single function. Organizations that succeed treat it as a coordinated leadership effort across:
Savage argued that HR must evolve from stewardship to active workforce innovation, partnering closely with IT to operationalize change.
“This is a big leadership moment. You have to set a direction and a vision for your company.”
Core principle: The future of work is designed, not deployed.
|
CHRO Focus |
CIO Focus |
Shared Outcome |
|
Workforce operating model |
Enterprise systems and data |
Scalable AI-enabled work |
|
Role and workflow clarity |
Platform reliability and governance |
Trust and adoption |
|
Change enablement |
Architecture and integration |
Measurable value |
Is AI transformation primarily a technology initiative?
No. It is a work transformation agenda that requires leadership ownership and operating model change.
Where should enterprises start with AI?
With high-volume, clearly defined work where outcomes can be measured quickly.
Why do most AI pilots fail to scale?
Because they optimize for experimentation instead of operational value and ownership.
What role should HR play in AI transformation?
HR should lead workforce and workflow redesign in partnership with IT and operations.
How do leaders accelerate adoption?
By making changes to work explicit, visible, and supported by leadership.
AI does not transform organizations on its own. Leaders do.
Enterprises that succeed with AI are those that intentionally redesign how work is structured, measured, and supported by both human and digital systems. That responsibility sits squarely with executive leadership.
The opportunity is not automation. The opportunity is building an AI-enabled workforce that delivers sustained business value.
Book a workforce transformation strategy session with Reejig to redesign how work operates in an AI-enabled enterprise —> Book a demo