The Challenge
A large enterprise had invested significant resources in an IT-led AI initiative that was running behind schedule and over budget. Meanwhile, approximately 70% of employees were already using their own AI tools — unsanctioned, ungoverned, and outside any security perimeter. The organization faced a widening gap between its internal AI build and actual employee adoption, creating data security vulnerabilities and wasted investment. Without intervention, the initiative would deliver a solution to a problem that no longer existed.
What They Built
Alex Goryachev intervened in a stalled enterprise AI initiative by conducting employee discovery sessions to surface shadow AI adoption patterns, then restructured workflows using existing AI tools already trusted by employees — reducing manual effort by 40% and relaunching the initiative within six months.
Alex Goryachev began by diagnosing why the initiative had stalled. The IT-led approach had been building a solution while approximately 70% of employees were already using their own AI tools — creating a parallel, ungoverned reality. Alex redirected attention from the technical build to the people. He ran employee discovery sessions to map which tools were in use, for what purposes, and why — surfacing real patterns of shadow AI adoption. This informed a fundamental build-vs-buy reassessment: rather than continuing to build internally, the organization could leverage tools employees already trusted. Alex helped establish a clear AI policy and created psychological safety for employees to disclose AI usage without fear of reprisal. IT and HR were repositioned as co-owners of the initiative rather than adversaries. Workflows were restructured using ChatGPT and AutoML tools already embedded in employee practice. The result: a 40% reduction in manual effort across restructured workflows and an initiative back on track within six months.
AI Role
The relaunched AI initiative delivered measurable business results within six months of intervention — from stalled and over budget to producing value.
Infrastructure
• ChatGPT (workflow integration, employee-adopted) • AutoML (workflow automation) • AI governance framework and policy documentation
Integration Points
• Employee discovery sessions → shadow AI usage mapping → build-vs-buy analysis • Existing employee AI tools (ChatGPT) → formal workflow integration with governance layer • HR and IT co-ownership model → AI policy and psychological safety framework • Restructured workflows → AutoML automation → 40% manual effort reduction
Best for large enterprises or mid-market organizations that have launched an AI initiative under IT leadership, are over budget or behind schedule, and suspect their employees have already moved ahead with their own AI tools — particularly organizations without a formal AI governance policy or cross-functional ownership structure.
