About
About Dr. Shiva Kakkar
My work sits between management education, AI-native product-building, and the practical question Indian leaders are asking now: where should GenAI enter the work, and what should change after the first session?
Open source contributions
Alongside Rehearsal AI, we have also made open-source contributions to the GenAI community in two practical areas: AI memory management and database security.
Context Hub is an open-source MCP server that keeps goals, projects, preferences, decisions, and working rules available across AI clients such as ChatGPT, Claude, Cursor, Perplexity, and Codex. It was engineered by Mayank Bohra with me and the Rehearsal team.
Database Sentinel is an open-source database security audit skill for teams building fast with AI. It checks RLS policies, storage rules, exposed keys, auth roles, and database access paths before user data is put at risk. It was created by Parth Jha with me and the Rehearsal team.
Frequently asked questions
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What kind of programmes do you teach?
I teach management development programmes (MDPs), in-company customised programmes (ICPs), workshops, and consulting-led adoption sprints on GenAI strategy, AI transformation, future of work, and function-specific use cases.
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What kind of content do you cover?
The work usually covers AI strategy, use-case identification, workflow redesign, prompt and tool fluency, governance, review loops, and role-specific applications across HR, marketing, finance, operations, education, and public services. You can see the broader structure on the programmes page.
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How long should a programme be?
It depends on the audience and the decision the organisation wants to make. Workshops usually span 1-3 days. A basic adoption sequence should include one functional workshop, one technical workshop, one C-suite workshop, and a final capstone evaluation with mixed teams.
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What is the difference between an MDP and an ICP?
An MDP is industry-agnostic and usually works across a mixed cohort. It focuses on horizontal GenAI use cases that cut across domains. An ICP is company-specific: it goes deeper into the organisation’s workflows, data realities, use-case pipeline, adoption risks, and implementation priorities.
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Do participants need to be technical?
No. Most business cohorts need enough technical fluency to ask better questions, judge outputs, and work with technical teams. Technical cohorts can go deeper into workflow automation, internal tools, AI agents, model choice, MCPs, and data-access design.
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What should a team leave with?
At minimum, a use-case map, readiness screen, practice task, review rubric, pilot charter, or AI council agenda that can be used after the session. I prefer programmes that leave behind decision artifacts, not only attendance records.
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Do you provide certificates?
Programmes run in association with institutions such as XLRI can carry the certificate structure of that institution. Programmes I run independently are designed for capability and adoption outcomes, not certificate signalling.
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Is this a prompt-engineering programme?
No. Tool fluency matters, but the core work is adoption design: where GenAI should enter the business, which use cases are ready, who owns the change, what employees should practise, and what managers should review when output is AI-assisted. The detailed logic is on the AI adoption framework.
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Who is this not for?
This is not a good fit when the goal is only to complete a training calendar, spend a year-end L&D budget, or maximise attendance numbers. It works best for teams treating GenAI adoption as a serious strategic lever.
What should remain after the session
My bias is toward artifacts: use-case maps, readiness screens, review rubrics, practice tasks, AI council agendas, and follow-up rhythms.
These artifacts matter because GenAI adoption fails when nobody can inspect the work after the session. A leader should know who owns the next move. A manager should know what needs review. Employees should practise on work that resembles their actual day.
Programme inquiries
If you are trying to decide where GenAI adoption should begin in your organisation, write with the audience, function, cohort size, and the business problem behind the effort.