Most AI programmes end with a demo and a list of prompts. Ours end with two or three workflows your people own, a standard for judging the output, and a measurement of what actually moved.
Every organisation in India now has the same subscriptions. What separates the ones getting value is judgement about where the tool belongs, and a standard for what comes out of it.
A tip that worked on last year's model is a habit that produces confident nonsense on this year's. Workflow design outlasts model versions.
Two people, same tool, same task, different quality. Without a review standard the variance lands on your customer, not on the model.
Confidential data pasted into a public tool, a policy nobody read, a vendor nobody reviewed. Governance is a training problem before it is a legal one.
Run them as a sequence or pick the rung your organisation is stuck on. Each track is written against your workflows, your documents and your policy context.
What the models actually do, where they fail, what never goes into a prompt, and how to check an answer before it reaches a customer.
A shared literacy floor and a one-page use policy the team can follow.
We take the work the team already does — proposals, JD drafting, call summaries, MIS commentary — and rebuild two or three of those workflows around the tool.
Working workflows, not prompt lists. Each one tested on your own material.
Judging output quality, deciding what may be delegated to a model, setting review standards, and managing the change when half the team is ahead and half is wary.
A review standard and a team-level adoption plan.
Data protection under the DPDP Act, confidentiality and IP, bias and disclosure, vendor and tool review, and what to write into policy so the rules survive a change of leadership.
A draft AI-use policy mapped to your existing HR and IT processes.
Four steps, usually across six to eight weeks. The lab is where most of the time goes, because that is where behaviour changes.
A short readiness diagnostic across roles: who is using what, where the risk sits, which workflows are worth rebuilding first.
Two or three workflows chosen with the business, not for it. The programme is written around those workflows.
Hands-on sessions on your own documents and data. People leave with something that already works.
Manager check-ins, a review standard, and a re-measure at 60 days so adoption is a number and not a feeling.
We baseline AI readiness by role before the programme and re-measure at 60 days on BolderBrain, our people-intelligence platform. You see which workflows actually changed, which teams stalled, and where the next investment belongs.
Talk about a baselineCapability. Tool training dates within a quarter. We teach the judgement that survives a model change: what the tool is good at, how to check it, where it must not be used, and how the work should be designed around it.
Only to the material you choose to bring into the lab, and usually on your own systems. Where data cannot leave your environment, the lab runs on anonymised samples and the workflows are rebuilt on-site.
A vendor teaches their product. We have no product to sell, so the programme is built around your workflows and your risk. Where a tool is the wrong answer, we say so.
Yes. On-site, online or blended, through the same pan-India trainer network that delivers our behavioural programmes. One design, one standard, many cities.
