# People | Process | Things

> People | Process | Things is the organizational advisory practice of Indra Klavins. It helps leaders understand why things should work but don't.

Organizations often bring in Indra after reasonable or standard solutions have been tried, but expected results are not following or the same problems keep resurfacing.

Indra works with founders, CEOs, executive teams, and investors during periods of growth, restructuring, integration, leadership transition, and organizational change.

Her work is diagnostic before it is prescriptive. She begins with observation to understand what is producing the visible problem before recommending action. Recommendations are grounded in the organization's specific context, including its people, systems, incentives, structures, decisions, hidden work, and organizational dynamics.

Engagements include Diagnostic Sprints, Immersive Diagnostics, and ongoing Strategic Partnerships. Her work can extend beyond assessment and recommendations into executive advisory, facilitation, leadership development, and embedded operational support.

Indra's perspective is grounded in nearly twenty years of working inside organizations. Her approach grew from observing recurring organizational patterns across industries and periods of change, not from applying a predetermined framework or standardized solution.

## Indra Klavins

Indra Klavins is an organizational strategist, executive advisor, public speaker, and writer. Her work focuses on organizational dynamics, organizational change, hidden and invisible work, decision-making, and the patterns that shape how work actually gets done.

She is the creator of the Glue Work Framework and host of The Messy Middle Matters podcast. Her talks include Nobody Asked Us: Recognizing the Patterns That Shape Organizational Change, which helps leaders recognize the human signals that emerge during periods of significant change, and Making AI Legible: Bridging the Gaps Between AI and Practical Application, which helps organizations build the shared understanding needed to move from AI possibility to practical application.