Signal & Steer AI-Enabled Transformation Leadership

Every organisation now has AI pilots. Very few have leaders who can turn them into results. This programme is for the programme managers, directors and VPs who will. In 18 Saturdays you take one real initiative from your own organisation from problem to working prototype to a roadmap you defend in front of a board panel, and you leave with your own AI Playbook. You build with AI in every session, so you leave knowing what it can and cannot do from your own hands, not from a slide.

18 Saturdays

from 16 January 2027

About 58 hours

live and hands on

1 real initiative

yours, worked on every week

Live online

about 20 leaders per cohort

Hands on

build with AI, no code needed

What you walk away with

Your AI Playbook

Eight chapters, from strategy to roadmap, written for your own organisation

A working prototype

An AI assistant or agent for your own use case, built without code and tried out with real users

A pilot plan ready for your board

A 90 day roadmap pressure tested by a board panel of peers and guests

The leader's toolkit

Templates for every decision in your Playbook, ready for your teams the Monday after

Who it is for

Programme and portfolio managers, delivery and engineering leaders, directors, VPs and functional heads in technology companies, global capability centres and digital teams. You own outcomes across teams you do not fully control, and you decide where AI goes in your area. You do not need to code. You will use AI in every session, but the live time goes on judgement, decisions and leadership, not on learning to type prompts. The intake call makes sure you sit with peers at your level.

It is not for you if you want to learn to code or train models, or if you want a certificate without doing the work in between sessions.

By week 18 you will be able to

See through the hype. Know what a large language model or an AI agent actually does, why it can be confidently wrong, and the checks that catch it before your customer does.
Set strategy as bets. Turn market signals and scenarios into three testable bets, and decide what you will stop.
Frame the right problem and AI-fy the workflow. Map the work step by step and decide which steps to automate, assist with AI, hand to an agent, or keep human. 3
Make your data decision-ready. Prepare, clean and segment it, analyse it, and spot the dashboard that is quietly misleading you.

Design and prototype an agent. Define its job, its limits and its human checkpoints, and build a working prototype without writing code.
Prove it. Run real experiments, check for bias, and build a business case that stands up to your finance team.
Govern it. Set risk tiers and approvals, meet India's DPDP Rules, and stay steady in the first hour when an AI system goes wrong.
Lead the people side. Coach your leaders, plan across teams you do not control, shape the culture, and make the case for your roadmap to a board.

How it runs

Fully virtual, fully live

Every session runs live online, so you can join from any city. Cameras on, real conversation, small breakout pods.

Your time

Three hours every Saturday (four hours in four of the weeks), plus about two hours in between: a 30 minute call with your learning pod and work on your own initiative.

Safe to bring real work

A short intake call before you join. Competitors never share a pod, everyone signs a cohort confidentiality agreement, and your company data never goes on a shared board.

If you miss a Saturday

Sessions are recorded, and the recording is shared only with anyone who missed it. Your pod brings you up to speed before the next one.

What you need

A laptop, one real initiative you own, and access to a general AI assistant. Every template is provided.

Certificate

A certificate of completion showing your hours, awarded when you present your AI Playbook in week 18.

Eighteen weeks, five phases

Phase 1. Lead and understand (weeks 1 to 3)

1. The AI-enabled transformation leader. What changes in your role when agents join the team, and where your time must go
2. Inside the machine. How large language models and agents
work, why they make things up, and the checks that catch them
before your customer does
3. Strategy as bets. Market signals, scenarios and new business
models turned into three testable bets

Phase 2. Find the value (weeks 4 to 7)

4.Frame the right problem. Customer discovery with AI: interview
synthesis, problem statements and opportunity sizing templates
5. Map it, then AI-fy it. Map the value stream end to end, find
where work waits, and mark each step: automate, assist, agent or
keep human
6. AI across the functions. Engineering and product, go to market
and customer, operations, finance and HR, in function clinics
7. Data that decides. Data preparation and quality, segmentation,
analytics, and making decisions the data can actually support

Phase 3. Build, prove and grow your leaders (weeks 8 to 12)

8. Agents at work. Research tools, automation, assistants and
agents: what each does, where it sits in the workflow, and how to
design an agent’s job, limits and checkpoints
9. Prototype lab. Build and test a working assistant or agent for your
own use case, no code needed
10. Prove it. Experiments, success measures, evaluation and bias
checks
11. The business case. Full costs, benefits that finance accepts, and
build, buy or partner
12. Coaching leaders and building the bench. Grow the
managers who will run the change with you

Phase 4. Lead and scale (weeks 13 to 17)

13. Risk, regulation and responsible AI. Risk tiers, approvals and
India’s DPDP Rules
14. Leading across teams. A live planning simulation on risks and
dependencies
15. Leading under pressure. A live AI crisis simulation, with
feedback on how you show up
16. Culture and change that sticks. Fear, trust, champions and
adoption you can measure
17. Scaling. The operating model, benefits ownership and the
governance rhythm that keep AI working

Phase 5. Launch (week 18)

18. Your AI Playbook, pitched to a board. Go, go with conditions,
or back to the drawing board

Four pillars, one programme

TRANSFORMATION

Strategy, business models, business case, roadmap

PEOPLE

Your role, coaching, leading under pressure, the bench

CULTURE

Responsible AI, trust, adoption, change that sticks

PROCESS

Workflows, data, agents, prototypes, cross-team planning

Leading transformation that lasts

Your facilitator

Sriharsha Sriram

ICF PCC certified executive coach with more than 28 years leading business transformation at Visa, PayPal, Standard Chartered, Clarivate and SANRIA Engineering, and more than 1,000 professionals trained. Over the past year he has led AI transformation inside delivery teams:

Product owners now use Atlassian Rovo to write complete epics and user stories with proper acceptance criteria.
With product owners, tech leads and architects, he set up Claude to take a module from RFP to pre production: given the RFP and access to the existing code, it built, tested and deployed the whole module, with people stepping in only where
judgement was needed.
Roles were redesigned around this work, so people moved to the decisions that matter and productivity rose across the team.

These are the kinds of agents you will design and lead in this programme.