Saugat Dawadi · Solutions Engineer

CONFIDENTIALClient and partner names are withheld under confidentiality agreements. Each story is described only by industry and size.

• AI transformation is a workforce design problem.

  Buying AI tools changes tasks, not organizations. The value only shows up when roles, spans, skills and career paths are redesigned around those new tasks. Most programs stop at the pilot because nobody owns that redesign step.

  In practicePair every AI pilot with a role-by-role plan: what changes, who's affected, and what each person does next.

• Start with activities, not job titles.

  Two people with the same title can spend their weeks very differently. Breaking roles into activities, and scoring how routine and judgment-heavy each one is, shows what AI can absorb, what it can augment, and what it should leave alone.

  In practiceScore roles on a handful of dimensions, then sort them into redesign, upskill or monitor, as in my demo.

• Context beats prompts.

  Clever prompts help a little. Giving AI your real data, your domain knowledge and your tools helps a lot. That's true for one person getting work done and for an enterprise rolling AI out across thousands of people.

  In practiceBefore choosing tools, decide what data and knowledge the AI needs to reach, and how it will reach it safely.

• Inside data needs outside data.

  An org chart tells you what you have. Labor market intelligence tells you what you can hire, what it will cost and how fast skills are changing. Workforce plans built on internal data alone tend to assume talent that isn't there.

  In practiceCheck every build, buy or borrow decision against real market supply before committing to it.