In this episode, Siobhan Savage sits down with Jasmine Jaco, VP of Organization Transformation and HR Strategy at Boston Scientific. Jasmine leads AI org transformation, spanning work, role and organizational redesign, while also building the HR capabilities needed to lead that transformation across the company.

Jasmine came to this work through management consulting, startups and experience design, and joined Boston Scientific about a year ago. She explains why so many companies keep investing in AI without seeing a material return, and why the answer is to redesign how work gets done rather than hand people new tools for today's work.

The standout story is a sales example. Boston Scientific's initial assumptions suggested agents could free up around four hours a week per sales rep. Once the team broke the work down to the task and subtask level with the reps themselves, they found call prep drew on more sources than expected, including information reps kept on their phones, and that CRM documentation often wasn't happening at all. The new workflows would have added time instead of freeing it, undermining the value case.

Jasmine also shares how she decides which work is worth redesigning, why workflows need built-in time for people to review AI output and stay accountable for it, and how a shared work architecture stops business units and regions from redesigning the same work twice. She closes with the lessons that shaped her practice: proof points create belief, and progress matters more than perfection.

In this episode, you'll learn:

• Why job titles and descriptions don't show how work really gets done
• How subtask-level data exposed flawed assumptions in a sales AI value case
• The data points Boston Scientific uses to decide which work is worth redesigning
• How a tiered service delivery model stretches scarce AI work design talent
• Why workflows need explicit time for people to review AI output and apply judgment
• How a shared work architecture reduces duplicate investment and informs workforce planning
• Why Boston Scientific's AI transformation pods bring every discipline into one team

Jasmine Jaco is the Vice President of Organization Transformation and HR Strategy at Boston Scientific, where she leads AI org transformation spanning work, role and organizational redesign. She spent most of her career in management consulting leading large-scale global transformations, launched startups, and built an experience innovation consulting practice before joining Boston Scientific.

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In this episode, Siobhan Savage sits down with Abby Rich, who leads the enterprise and workforce design practice at Medtronic, the largest medical device company. Abby's team sits within enterprise transformation and HR, and most of its time now goes into building a methodology and blueprint for AI modernization.

Like many companies, Medtronic's first move into AI for the workforce was to push tools at people and hope for adoption. People were left asking what the training was for and how it fit into their roles. That led Abby's team to start with the work itself: deconstructing roles, deciding where work should sit before any AI is involved, and only then working out the right mix of people and machines for each task.

This conversation is candid about what goes wrong when companies move fast. Abby explains why cutting jobs to fund new technology tends to backfire, because the work never went away and has to be hired back. She shares how bottom-up experimentation in a global organization of more than 100,000 people led to widespread duplication of agents, and why Medtronic is now pairing top-down enterprise plays with continued personal productivity. She also talks about her personal experience of becoming more productive, only to have more meetings fill the time she freed up.

Abby and Siobhan also get into the people side of AI work design: what to do with freed capacity, why the most surprising data has been about the work that must stay uniquely human, how task adjacency can open nontraditional career paths, and whether employees should ever see AI impact data without the context to interpret it.

In this episode, you'll learn:

• Why pushing AI tools and prompt training at people rarely changes how they work
• How Medtronic decides where work should sit before deciding where AI fits
• Why cutting jobs to fund AI technology often means hiring that work back later
• The difference between outcome-level task data and the subtask detail needed to redesign a workflow
• How data on uniquely human work can guide hiring, upskilling and career mobility
• How Medtronic is balancing enterprise-wide AI plays against bottom-up experimentation
• Why AI "impact" needs careful context before it is shared with employees

Abby Rich has spent about 11 years at Medtronic across a range of HR roles, from leadership development and executive learning to HR strategy, where she served as chief of staff to the SVP of Talent and owned strategic workforce planning. She now leads Medtronic's enterprise and workforce design practice, which sits within enterprise transformation and HR.

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In this episode, Siobhan Savage sits down with Santosh Singh, Senior Vice President at DENSO, where he oversees human resources, legal and corporate communications. DENSO is one of the world's largest Japanese automotive technology companies, with more than 160,000 employees globally.

Santosh is an engineer by trade. He began as a design engineer on mining construction equipment, spending months on the production floor building machines from nuts and bolts, before moving through Six Sigma and process excellence and eventually being asked to lead HR. That background shapes how he approaches AI: start with the purpose and the problem, not the tool, and keep people at the center.

Santosh draws on manufacturing to explain why human judgment matters more in the age of AI, not less. He describes gemba, the practice of going to the place of work to see, sense and act, and makes the case that discernment is a skill organizations have to deliberately build. He also proposes a thought experiment for leaders: run a business continuity drill imagining your workforce five years from now, and ask how many people could still discern and problem solve if AI shut down.

On the practical side, Santosh walks through how the Six Sigma define, measure, analyze, improve and control method applies to work redesign, how DENSO validated task and subtask data with its leaders until it aligned with what people actually did around 90% of the time, and why reimagining work across functions is really value stream mapping at scale. He closes on how that same work data feeds career pathways and development, and why treating AI as a tool purchase rather than a cultural change is the biggest risk he sees.

In this episode, you'll learn:

• Why DENSO starts AI change with purpose rather than tools
• What the manufacturing principle of gemba teaches about human judgment and AI
• Why leaders should pressure test their AI plans with a business continuity drill
• How the Six Sigma DMAIC method translates to AI work redesign
• How validating task and subtask data with leaders builds business trust in the data
• How work architecture can feed career pathways, development plans and workforce planning
• Why treating AI as a budget line rather than a cultural change is the biggest risk

Santosh Singh is Senior Vice President at DENSO, overseeing human resources, legal and corporate communications in North America. An engineer by trade, he began his career as a design engineer for mining construction equipment, moved through Six Sigma and process excellence, and launched a global engineering design center in India. He has spent nearly 25 years helping large industrial companies transform, and joined DENSO in 2021.

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