Jasmine spent most of her career in management consulting, leading large-scale global transformations anchored in business strategy and value delivery. Along the way she launched startups and built an experience innovation practice.
At Boston Scientific she leads AI organization transformation, covering work and role redesign, and is building the HR capabilities needed to lead that change across the business.
Customer preparation involved information the proposed agent hadn’t accounted for. Some of the administrative work included in the savings calculation wasn’t happening consistently in the first place. For Jasmine, the experience showed why understanding work matters before making promises about what AI will improve.
One early lesson was about scope. Trying to address the entire enterprise at once would stretch limited expertise too thin. A meaningful area of work offered somewhere to learn, refine the approach and demonstrate what redesign could accomplish.
Sales provided a revealing example. The team examined preparing for customer calls, conducting them and documenting interactions in the CRM. But job descriptions and high-level conversations could only take them so far. They described broad responsibilities without revealing the information, systems and individual practices involved.
Working with Reejig, Boston Scientific looked beyond job descriptions to how work actually happened. Breaking the work into tasks and subtasks, alongside the people doing it every day, changed the picture.
Sales reps prepared for calls using information scattered across different places, including their own phones, so the initial agent couldn’t give them everything they needed. Many reps also weren’t consistently recording meetings in the CRM. The estimate had counted savings against work that, in some cases, wasn’t being done, and requiring that documentation could actually add time.
Employees’ knowledge exposed gaps in both the workflow and its business case. The progress was a clearer understanding of what needed to change and which assumptions could withstand scrutiny.
Jasmine is also helping teams account for the work AI creates: reviewing outputs, applying judgment, correcting problems and remaining accountable for the result. Under pressure to show productivity gains, those steps are easy to compress or skip.
People also need experience to recognize when an output is wrong, so decisions about what to automate need to consider how employees build that expertise. Research and presentations can generate interest, but practical examples make work design tangible.