PhD, IIM Ahmedabad / ex-faculty, XLRI / Head of Product, Rehearsal AI

For CXOs asking where GenAI adoption should start.

I am Dr. Shiva Kakkar: PhD, IIM Ahmedabad; ex-faculty, XLRI; and Head of Product at Rehearsal AI. If you are here, you are probably asking the question I hear from leaders most often: where do we start with GenAI adoption, and how do we identify the use cases inside our organisation that are actually worth pursuing? That is the work I help leadership teams structure.

Here's how Let's talk

Illustrated portrait of Dr. Shiva Kakkar

I teach GenAI transformation to executives, managers, and faculty from organisations across India.

Multiplier
Multiplier

How not to do AI adoption

Start with a framework, not a tool list.

The useful starting point is the work itself: where it repeats, who can change it, what evidence shows the problem, and what risk has to stay visible while the team experiments.

Read the framework A fuller discussion of how to choose where GenAI belongs. Check readiness Use cases, workflow ownership, review rules, and adoption risk.

Start with the KPIs

Name the business problem before naming the tool.

In CXO conversations, the first confusion is rarely about whether AI matters. It is about where to begin: customer work, HR work, finance work, compliance work, sales work, or internal productivity.

RCA old transformation failures

Diagnose why the last AI effort did not stick.

Many organisations already have a dead proof of concept, unused ChatGPT subscriptions, or a workshop that created interest but no operating change. That history is not a failure to hide. It is the adoption data.

Choose the right use cases

Choose use cases by readiness, not by trend.

A good GenAI use case is not just exciting. It has a clear owner, accessible data, reviewable output, manageable risk, and a team that can absorb the change. This is the framework for deciding where to start.

Tools are temporary. Workflows are permanent.

Training becomes useful when it is tied to a live workflow.

Once the first use cases are chosen, re-training can become specific: what employees will do differently, what managers will review, what evidence is required, and what the next 30 days should prove.

About me

Some Backstory About Me...

Executive classrooms

From academic faculty to AI product builder.

GenAI moves fast; education moves slowly. I left the safer academic track to stay close to how AI is operationalised in business workflows. At Jaipuria Group, I created the internal AI CoE that evolved into Gradeless AI, where we redesigned book-planning and publishing workflows, built internal MCP servers to connect business data, and built Rehearsal, an AI-native ambient learning system now used by more than 5,000 learners.

Public artifacts

OpenAI Academy featured my work.

One early project was practical: reduce dependence on expensive Harvard Business School (HBS) simulations by building our own. At Jaipuria, we created in-house cases and simulations, and built tools that help faculty move from consuming external teaching content to building their own learning artifacts. OpenAI Academy later featured my GPT-4.1 micro-simulation for MBA critical reading and writing, a public signal for the same democratization agenda.

Product work

The learning product sits behind the teaching.

Across programmes, I have taught more than 2,000 corporate executives and managers. As visiting faculty at XLRI, I was also the primary instructor for the MCTP programme for CBDT, delivered in collaboration with XLRI. My GenAI programmes for managers and CXOs are socio-technical: tools matter, but so do workflows, adoption anxiety, review norms, and domain-specific applications in HR, marketing, operations, finance, and leadership work.

Writing

Recent essays

All writing

Start with the work in front of the team.

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