Data compiled from international research and field observations
Rate of corporate AI projects failing to generate expected value within the first 18 months
In corporate AI failures, the root cause is not technology but coordination and leadership gap
Process efficiency improvement reported by organizations working with a structured AI committee
Key finding: Employees are using AI individually; but this use isn't sufficiently reflected in business processes and corporate decisions. The committee is the most systematic way to close this gap.
Most organizations get stuck in the same cycle. The committee breaks this cycle.
Employees are using AI individually. But this use isn't reflected in business processes, team decisions, or corporate strategy. The gap is growing.
International research shows that the vast majority of corporate AI initiatives fail to generate expected value in the first 18 months. The root cause isn't technology — it's lack of coordination and leadership.
Every organization has a "Pioneer" group that has embraced AI. This group has the potential to transform their surroundings. But without structure, this energy dissipates. The committee systematizes this potential.
Organizations that take early steps in AI transformation report significant improvements in process efficiency. Each passing month, this advantage compounds.
"Thinking with AI" book methodology · yzolgunluktesti.com
Committee design is customized according to the current distribution in your organization.
Not technical knowledge, but openness to change and organizational impact capacity are decisive.
The natural change ambassador within the organization. Pioneer profile. Builds both technical and strategic bridges.
Determines which processes will be redesigned with AI. The architect of operational transformation.
Drives change management. Turns resistance into opportunity. Accelerates team adaptation.
Aligns AI initiatives with organizational goals. Bridges C-Level. Prioritizes.
A three-phase transition from individual maturity to organizational transformation.
The figures above are representative targets and sample outputs based on typical development ranges observed in similar processes. Actual results may vary depending on the organization's starting level, team size, and intensity of engagement in the process.
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