The Work Blueprint

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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Explore the AI Work Design Blueprint
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In this episode, Siobhan Savage sits down with Sarah Bernstein, VP of Organizational Transformation and Capability Development at Lumen Technologies, fresh off a live workshop where Reejig, Microsoft, and Lumen's team spent a full day redesigning real workflows together.
Lumen is going through multiple transformations at once: shifting from a traditional telco to a digital network service provider, while simultaneously reimagining how its people and AI agents work side by side. Sarah leads that effort, spanning workforce strategy, learning and development, and the operating rhythms that turn AI ambition into results people can actually feel.
This conversation goes beyond the keynote version of AI transformation. Sarah and Siobhan get into what it actually looked like in the room: mapping tasks down to the subtask level, deciding what stays human and what gets handed to an agent, and the moment a shipped bug revealed that nobody on the team could read the code anymore because AI had written it. They also unpack why entry-level roles need to be redesigned rather than eliminated, and what a real AI work design team looks like once you move past borrowed expertise and start building the capability in-house.
In this episode, you'll learn:
What it looks like inside a live workshop redesigning work with AI
Why understanding work at the subtask level matters more than a high level task list
The four steps Lumen uses to redesign an operating model for AI
Why just-in-time learning has to replace one-time prompt training
What happens when AI-written code creates bugs nobody can find
How AI can be used to fast-track early career development instead of cutting it
What competencies and roles make up a real AI work design team
Sarah Bernstein is the Vice President of Organizational Transformation and Capability Development at Lumen Technologies, where she leads enterprise-wide efforts spanning AI workforce transformation, leadership development, and learning strategy as the company evolves into an AI-enabled digital network service provider.
More from Reejig
Explore the AI Work Design Blueprint
Explore Certified AI Workflows for HR
Take The Work Architect Course
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In this episode, Siobhan Savage sits down with Robyn Tombacher, who leads the Integration Management Office and global business redesign team at Warner Bros. Discovery, to unpack why most companies are stuck testing AI instead of scaling it, and what it actually takes to fix that.
Robyn's career has taken her from digital producer to operations leader to workforce strategy at WPP, and now to sitting at the center of one of the biggest media mergers in the industry. That mix, someone who understands the technology, the business, and the people side all at once, is exactly why this conversation goes further than most.
This conversation moves past the pilots and platforms and into what's really slowing AI adoption down: companies spending heavily on enterprise tools without redesigning the work, roles, and responsibilities underneath them. Robyn and Siobhan get into why "dabbling" in AI doesn't drive material impact, why CIOs, CHROs, and CFOs now have to own this together, and why every organization is going to need someone who can bridge all three.
In this episode, you'll learn:
Why dabbling in AI without a plan costs companies money and delivers little return
How CIO, CHRO, and CFO ownership of AI adoption is converging into one shared conversation
Why redesigning the work itself matters more than automating tasks in isolation
How boards are shifting from "cut headcount" thinking to "where's the AI opportunity" thinking
Why every organization will need a capability that bridges technology, business, and people
What it takes to build AI capability inside a company instead of relying on outside consultants
Robyn Tombacher leads the Integration Management Office and global business redesign team at Warner Bros. Discovery. Over a 25-plus year career, she has led major change efforts at the intersection of people, operations, and technology, including workforce strategy at WPP, and now sits at the center of the Warner Bros. Discovery and Paramount Skydance merger.
More from Reejig
Explore the AI Work Design Blueprint
Explore Certified AI Workflows for HR
Take The Work Architect Course
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In this episode, Siobhan Savage sits down with Michael Fraccaro, former Chief Human Resources Officer at Mastercard and now a Reejig advisor, to unpack what it actually takes to lead an organization through the AI era.
Every CEO and board is pushing AI adoption, yet most companies remain stuck in early pilots, unsure of their workflows, their risk exposure, or how to bring their people along. Michael has spent his career at the center of large scale organizational change, and he now advises CHROs and executives across industries navigating this exact shift.
This conversation moves past the hype and into the practical questions leaders are actually facing. Michael and Siobhan get into why AI adoption is not a technology problem but a leadership one, how HR's role in this shift is evolving, why the capability to redesign work has to live inside the company rather than with outside consultants, and what this all means for how organizations develop talent, structure jobs, and think about reward.
In this episode, you'll learn:
Why AI adoption is one of the biggest leadership tests organizations have faced
How HR's role in AI adoption is shifting from ownership debates to shared responsibility
Why the capability to redesign work needs to be built internally, not outsourced
How organizations should rethink developing junior talent in an agent-enabled workplace
Why job architecture needs to evolve into a more dynamic work architecture
What the shift to agentic work means for compensation and reward design
Michael Fraccaro is the former Chief Human Resources Officer of Mastercard. He now advises organizations and executives on leadership and workforce transformation, teaches at several universities, and works closely with startups shaping the future of work.
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Explore the AI Work Design Blueprint
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In this episode, Siobhan Savage sits down with Jessica Neal, former CHRO of Netflix and current advisor to Reejig, to open the first conversation in the Work Blueprint series.
AI capability is compounding. Work visibility is not. Jessica led people through Netflix's shift from DVD to streaming, one of the most dramatic platform changes in business history, and now advises CHROs across the world's top technology companies as they face an equally dramatic shift with AI.
This conversation is raw and unscripted. Jessica and Siobhan get into what actually happens inside large enterprises redesigning work for AI, why judgment is becoming more valuable than execution, why change management as we know it is obsolete, and why this moment is HR's biggest opportunity to become a true strategic partner to the business.
In this episode, you'll learn:
What Netflix's shift from DVD to streaming teaches us about leading through platform change
Why judgment and decision making matter more than functional expertise in the AI era
How to use an AI led versus human led framework to evaluate work
Why enterprises may be more intentional about AI than fast moving tech companies
Why some tasks should never be automated, even when they can be
How to talk to employees about the hours AI unlocks in their day
Why change management needs to become continuous, not a one time program
How HR business partners, work designers, and work architects are becoming new critical roles
Jessica Neal is the former Chief Human Resources Officer of Netflix, where she led people strategy through the company's transformation from a DVD business into the world's largest entertainment company. She now advises leading technology companies and CHROs on how AI is reshaping work and leadership.
🔗 More from Reejig
Explore the AI Work Design Blueprint
Explore Certified AI Workflows for HR
Take The Work Architect Course
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In this episode, Siobhan Savage sits down with Ben Schreiner, Head of AI and Modern Data Strategy Business Development at Amazon Web Services (AWS), to explore how enterprises are scaling AI beyond experimentation.
AI creates value only when it is applied to real work. Organizations that succeed map work at the task level, redesign workflows, and deploy AI into execution with clear measurement of impact.
This conversation breaks down how leading enterprises move from proof of concept to operational scale. Ben shares how companies are structuring work, integrating AI into workflows, and building systems that deliver measurable outcomes while maintaining human oversight. The focus is practical. What works. What fails. And how leaders make AI part of how the business runs.
In this episode, you’ll learn:
How do enterprises move from AI pilots to real business impact?
Why mapping work at the task level is critical before deploying AI
What Ben Schreiner sees as the biggest blockers to AI adoption
How AWS customers scale AI across workflows and operations
What responsible AI execution looks like inside global enterprises
Ben Schreiner leads AI and modern data strategy business development at AWS. He works with global organizations to design and scale AI strategies that deliver measurable outcomes across complex environments.
 
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In this episode, Siobhan Savage sits down with Itai Asseo, Head of Incubation and Brand Strategy, AI Research at Salesforce, to explore how enterprises redesign work to make AI deliver real outcomes.
AI is not plug-and-play. It requires a clear understanding of how work runs today and how it should change. Organizations that succeed focus on workflows, tasks, and execution, not isolated tools or experiments.
This conversation unpacks how leading companies rethink operating models, redesign workflows, and integrate AI into real business processes. Itai shares how enterprises translate AI into measurable outcomes while maintaining creativity and human judgment at the center of execution.
In this episode, you’ll learn:
Why AI fails when work is not clearly defined
How Salesforce approaches AI incubation and experimentation
What it takes to redesign workflows for AI execution
How companies balance automation with human creativity
What separates AI pilots from real transformation
Itai Asseo leads incubation and brand strategy within AI research at Salesforce. His work focuses on how emerging technologies translate into real enterprise use cases and measurable impact.
 
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Access our Workforce Reinvention Blueprint
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In this episode, Siobhan Savage sits down with Dr. Kathi Enderes, SVP Research and Global Industry Analyst at The Josh Bersin Company, to explore how AI is changing how work is designed.
Work is shifting from static roles to dynamic, task-based execution. Organizations that adapt gain visibility into how work runs and can redesign workflows to improve outcomes.
This discussion breaks down new research on dynamic work design and how enterprises move from skills-based models to task-level execution. Kathi shares how leading organizations are building adaptive systems that connect AI to real business outcomes and enable continuous work redesign.
In this episode, you’ll learn:
What dynamic work design means in practice
Why job-based models are breaking down
How task-level visibility improves execution
What leading enterprises are doing differently
How AI changes workforce structure and capability
Dr. Kathi Enderes is SVP of Research and Global Industry Analyst at The Josh Bersin Company. She leads research on workforce transformation, AI, and how organizations redesign work for the modern enterprise.
 
🔗 More from Reejig
Access our Workforce Reinvention Blueprint
Explore the Work Architect Academy
Run an AI Impact Analysis
Book a demo
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Follow Reejig: LinkedIn
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