About
Midhu Balan
AI Enablement · Organizational Effectiveness · Program Design
I teach at Katz Business School — strategic management, strategic leadership, computational thinking for business leaders, data programming with Python, and the technical foundations of generative AI. I also build: translating organizational workflows into agent systems with verifiable end states, on Gemini Enterprise and GCP Agent Development Platform. Most people occupy one end of that range. I work across it deliberately, because neither vision nor execution produces results without the other.
Fifteen years in the classroom with executives and managers — the people who will make the actual decisions about AI deployment — has given me an unusual vantage. I know where the organizational assumptions break down, where the frameworks disconnect from the work, and why most AI adoption programs are designed for the organization that leadership wishes it had rather than the one it has. I measure success through behavioral change, not completion metrics.
I'm currently developing a corporate training curriculum on organizational AI readiness, field-testing it with executive and professional master's cohorts. The thesis: the technology is never the bottleneck. Organizational metabolism is. Every major technology transition of the last 40 years has followed the same arc, and AI is not different.
My LinkedIn profile reflects a career break from 2020 to 2024 — COVID-era homeschooling followed by a parenting commitment. I returned to teaching at Katz in January 2024 and have been actively building since. My undergraduate degree is in Textile Technology, which grounds my thinking in how work actually happens on the floor — before it gets abstracted into a framework.
What this background means in practice
Strategic management, strategic leadership, computational thinking for business leaders, data programming with Python, and the technical foundations of generative AI — in the same classroom, to the same cohorts. The range is intentional. Closing the AI readiness gap requires understanding both ends — the strategy and the system.
Translating everyday organizational workflows into ReAct agent loops with verifiable end states — in practice, on Gemini Enterprise and GCP Agent Development Platform. The hard part is not the infrastructure. It is knowing which workflows are worth automating, what a good outcome looks like, and how to confirm the system actually got there.
Getting an organization to agree on what information its AI systems should see — and in what form — is as much a coalition-building effort as a technical one. It requires building consensus around what matters, who owns it, and what the canonical version is. Most implementations fail here, not in the model.
EMBA-H healthcare executives, MBA students, academic administrators across healthcare, finance, and education. Not as a researcher — as the person running the room. I know where the organizational assumptions break down before the frameworks do.
B.Tech in Textile Technology. An instinct for how work actually happens at the task level — before it gets abstracted into a competency model or a framework someone downloaded.
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