GenAI programmes for decisions, practice, and reviewable work.

I work with CXOs, CHROs, L&D teams, managers, employees, and faculty groups when GenAI has to move from interest to changed work.

The format is chosen after the starting question is clear: workflow, audience, review rule, and evidence after the session.

Discuss a programme

Start with the decision, then choose the format.

A good programme starts from the question in the room. Which workflow should change, who will review it, and what artifact should exist after the session?

Half-day or one-day working session CXOs, CHROs, Deans, L&D heads, and AI councils

AI starting-point diagnostic

For teams that know GenAI matters but are still choosing the first serious move from tool excitement, vendor pitches, or scattered internal experiments.

What happens inside

Inside the work

Map the decisions, workflows, policies, and knowledge sources the AI effort would touch.
Separate convenience tasks from work where speed, defensibility, quality, or coordination actually matters.
Score use cases for value, feasibility, risk, ownership, and the evidence needed after the first test.

Output A ranked starting-point map: one first-priority workflow, boundary conditions, evidence to collect, and a 30-day next action.

Why this format exists The diagnostic comes before training because the wrong first use case makes even a good workshop feel like theatre.

Short intensive programme CXO cohorts, HR leaders, business heads, faculty groups

1-3 day AI leadership or faculty lab

For senior cohorts that need enough shared judgment to sponsor AI work without pretending that every leader must become a technologist.

What happens inside

Inside the work

Work through live examples from strategy, HR, finance, teaching, assessment, communication, and customer work.
Ask participants to commit to a judgment before showing AI output, so the gap becomes visible instead of being hidden by a polished answer.
Translate each promising use case into owner, context, review rule, risk boundary, and follow-up decision.

Output A leadership use-case portfolio, review rubric, and first version of an AI-assisted workflow or learning artifact.

Why this format exists The lab is not an AI tour. It is a way to make senior judgment sharper around workflow, context, evidence, and accountability.

Modular cohort for a function or role family Managers, employees, functional teams, high-potential cohorts

Employee and manager practice cohort

For organisations that want everyday work to change: writing, research, analysis, review, communication, reporting, and decision preparation.

What happens inside

Inside the work

Start from real work samples, not artificial prompt drills.
Build practice loops where employees draft, inspect, verify, and revise with AI.
Train managers to review the evidence trail: source use, assumptions, risk, alternatives, and final human judgment.

Output A role-specific task library, verification habits, manager review rules, and examples of acceptable AI-assisted work.

Why this format exists The goal is not to create prompt collectors. The goal is to help people produce work that a manager can trust and improve.

Cohort build with office hours and capstones AI champions, leadership cohorts, L&D academies, schools and universities

3-6 month AI capability build

For institutions that do not want a one-off workshop to carry the whole adoption burden.

What happens inside

Inside the work

Run cohorts through assignments, office hours, use-case clinics, and capstone reviews.
Move from individual confidence to shared operating habits: what to automate, what to inspect, and what to refuse.
Use leadership reviews to decide which prototypes deserve budget, policy support, or workflow redesign.

Output AI champions, capstone prototypes, adoption rhythm, leadership review notes, and a prototype-to-practice roadmap.

Why this format exists Capability compounds only when the organisation sees practice evidence between sessions, not just attendance at sessions.

Focused engagement after a diagnostic or lab Teams with a chosen workflow and a real implementation appetite

Consulting plus build track

For organisations ready to move from training into context engineering, workflow automation, pilot planning, or selected implementation support.

What happens inside

Inside the work

Inventory what the AI system should see, trust, remember, ignore, and refuse.
Design the workflow, human review points, data boundaries, and proof trail before rushing into a tool.
Support prototypes, vendor conversations, internal pilot planning, and governance rhythms where useful.

Output Context inventory, pilot design, review architecture, workflow prototype, and a practical production roadmap.

Why this format exists This is where training connects to implementation. The hard part is usually not the model; it is the context and review system around it.

Faculty lab, institution build, or assessment redesign sprint Universities, B-schools, faculty teams, programme offices

Higher-ed AI adoption track

For institutions where AI has already entered through students, faculty, and administrators, but policy has outrun practical capability.

What happens inside

Inside the work

Redesign assignments around judgment traces, oral defense, source discipline, and AI-use disclosure.
Help faculty build learning artifacts instead of only listening to lectures about tools.
Map AI use across research, teaching, assessment, administration, and student support.

Output AI-native assessment designs, faculty-built artifacts, academic-integrity choices, and an institution adoption map.

Why this format exists The faculty-builder lens matters here. The proof is not a talk about AI; it is a teaching object faculty can actually use.

When the brief has already named the doorway.

A CXO, HR team, dean, or L&D buyer may arrive through a familiar label: employee re-training, ChatGPT for work, AI in higher education, or a leadership programme. I use that language for orientation, then move the room to the real decision: which workflow is ready, who owns it, what must be checked, and what will be reviewed after 30 days.

Corporate AI training in India

When the mandate is enterprise-wide and the buyer needs a credible, senior-facing starting point.

AI training for employees

When the problem is everyday work: writing, research, analysis, reporting, review, and communication.

ChatGPT training for work

When people already use ChatGPT informally and the organisation needs safer, more useful habits.

GenAI course for working professionals

When managers need fluency without being sent into a technical bootcamp.

AI adoption framework

The deeper method behind starting maps, pilot autopsies, use-case filters, and manager review.

GenAI for business leaders

When leaders need to sponsor AI work without reducing it to a tool demo.

AI in higher education

When faculty, assessment, student practice, and institutional policy have to move together.

How to choose the right format.

Which programme format should we start with?

If the starting point is unclear, begin with the diagnostic. If leaders already agree on the first few workflows, use a 1-3 day lab. If adoption must change everyday work, run an employee and manager practice cohort. If the organisation needs champions, capstones, and review over time, use the 3-6 month capability build.

Is this prompt engineering training?

Prompting appears inside the work, but it is not the centre. The centre is workflow choice, context quality, review habits, source discipline, risk boundaries, and the question of who owns the final judgment.

What makes the programmes practical?

Participants work on familiar tasks and commit to a judgment before AI reveals a better or different path. The output is not only awareness. It is a map, rubric, prototype, task library, assessment design, or follow-up plan someone can inspect later.

Can the same method work for HR, finance, banking, or higher education?

The examples must change by domain, but the discipline is stable: choose work that matters, assemble legitimate context, define the review rule, make evidence visible, and decide what changes after the session.

When the programme has to fit your operating context.

Share the audience, function, cohort size, and the business problem behind the GenAI effort. The right format may be a CXO conversation, a manager workshop, an employee cohort, a readiness diagnostic, or a short adoption sprint built around a specific workflow.

Email Dr. Shiva Kakkar