Senior Director, Data Analytics & AI Enablement · Supply Chain
Tue May 26 Time TBC · Home Office (Phila.)
Persona
The Bold PulseSteady Beat
Location
Outside Philadelphia, PA · Filming at home office
Segment
Supply Chain Analytics & AI Enablement
Priority
High
Campaign Use
Supporting feature / AI-forward vignette / recruiting proof point
Title Note
Source material lists both "Senior Director of Data Analytics and AI Enablement" and "Director, Supply Chain Analytics & AI." Confirm with Purav which is current for on-camera text overlay.
Story in One SentencePurav became an unexpected AI leader who helps colleagues move from fear to fluency while improving the systems that help healthcare technology reach customers and patients.
Emotional TargetConfidence · Momentum · Curiosity · Empowerment · The future is something capable people are actively shaping
Story SpineLong-tenured supply chain analyst → post-MBA career lull → AI sparks new energy → teaches himself, experiments → Craig Shaffer asks him to help lead the supply chain AI effort → builds super-user group, documents 25 use cases, trains colleagues → people move from intimidated to capable → ultrasound/family story bridges to mission
Story Hierarchy — Rich Must Know ThisSpine: The unexpected AI leader arc — from career lull to self-teaching to becoming the person others look to.
Hero moment: The training-room transformation — a specific moment when someone went from skeptical or intimidated to confident. This is the most filmable emotional proof.
Emotional bridge: The ultrasound/family connection — nephew on a GE Vivid system, wife's health scare. Use at the close, not the center.
"Work less, do more" is a strong supporting soundbite but not a story center. It needs the leadership arc around it to land.
What Rich Must Get
The specific moment Purav became the AI leader — who asked, what was said, what he felt — plus one concrete before/after example (the on-time delivery model revision is the strongest candidate: four weeks vs. months, clear outcome, direct customer impact) — plus one training-room moment where someone went from intimidated to capable — plus the ultrasound/family bridge handled carefully at the close.
Purav is articulate and energized but will drift into AI philosophy, platform names, and abstract transformation language. Keep redirecting into moments: what happened, who was in the room, what changed.
What Paul Must Capture
Warm home-office interview, sanitized AI/dashboard visuals, hands typing prompts, mock use-case documentation, Purav sketching a process map by hand (order → delivery → install → customer/patient impact), mock training call setup, and at least one or two non-screen moments: Purav stepping away from the desk to think, reviewing physical notes, or a brief outdoor walk-and-talk if space allows. Not a static laptop-only shoot. The story needs texture beyond screens.
Sensitivity — ConfidentialityLive dashboards, internal data, supply chain data, AI platform access, customer data, vendor references, proprietary use cases, AI prompt history, and internal workflow details must not be filmed unless fully sanitized. All screen content must be pre-approved or use fake/sample data. Vendor logos (Copilot, ChatGPT, AWS Bedrock, Databricks, Power BI) must not appear.
Sensitivity — AI Disruption LanguagePurav may speak enthusiastically about AI replacing manual work or making roles obsolete. If this happens, Rich should redirect immediately: "What does that free people up to do?" or "What becomes possible when the repetitive work goes away?" The campaign message is empowerment, not displacement. AI helps people become more capable — it does not make them unnecessary.
Key Coaching NoteThe strongest opportunity is not "Purav uses AI." It is that Purav became an unexpected AI leader inside supply chain by teaching himself, stepping into a gap, and helping others move from fear to fluency. Story, not concept. Moments, not philosophy.
Soundbite Targets
◦ I was not supposed to be the supply chain AI leader
◦ I help connect the dots
◦ It demystifies a lot of it
◦ The AI part is easy; the hard part is the data foundation
◦ Work less, do more
◦ The dashboard isn't the outcome — the decision is
◦ The work behind the scenes still reaches the patient
◦ Once people try it, the fear starts to drop
Recommended Interview Arc
Help Purav settle in and describe his world in plain language before getting into AI, data, and mission. He'll be energized and ready to go — the goal is to ground him in specifics early.
1
"For someone who has no idea what your job is, how would you describe what you do at GE HealthCare?"
2
"What does a good day at work look like for you?"
3
"You've described yourself as a dot connector. What does that mean in real life?"
4
"What kind of problems usually land on your team's plate?"
5
"What part of your work gives you the most energy right now?"
Listen for: "Turning data into insights," "connecting dots," "helping people make decisions," "making work easier," "visibility," "AI acceleration."
If vague, probe: "Can you give me one example from this month?" · "Who would feel the impact of that?" · "What would be harder if your team wasn't doing that work?"
Make the audience understand why supply chain analytics and AI enablement matter inside a healthcare technology company.
1
"What are the systems or teams your work touches?"
2
"When you talk about order execution, install, planning, inventory, and logistics — what does that mean in practical terms?"
3
"How does better data visibility help a team make a better decision?"
4
"What happens when teams don't have the right visibility?"
5
"How does on-time delivery connect to hospitals and customers?"
If vague, probe: "What's one decision that becomes easier because of your work?" · "Who is depending on that information?" · "What's the downstream consequence of getting that right?"
Guide Purav into the strongest story thread: from career lull to AI spark to unexpected AI leadership. This is the spine.
The Leadership HandoffCraig Shaffer (Purav's supply chain leadership) recognized Purav's energy and asked him to help lead the supply chain AI effort. Get the scene: Who asked? What was said? What was happening? What did Purav feel?
1
"You said you were not supposed to be the supply chain AI leader. Take me back to that moment. What was happening?"
2
"You had finished your MBA and described being in a bit of a lull. What did that feel like?"
3
"What changed when AI came into the picture?"
4
"What did you start doing before anyone formally asked you to lead anything?"
5
"When Craig Shaffer asked you to help lead the supply chain AI effort, what did that mean to you?"
6
"What did you feel responsible for once people started looking to you?"
Listen for: Personal reinvention, curiosity, self-teaching, "locked in," recognition by leadership, stepping into a gap, not waiting for permission.
If vague, probe: "Was there a specific meeting or conversation where you realized you had become the AI person?" · "What did Craig see in you?" · "Did it feel like pressure, opportunity, or both?"
Capture the usable proof. The on-time delivery model revision is the strongest concrete example — four weeks vs. months, clear outcome, direct customer impact. Get the before/after.
Proof Point PriorityThe "25 use cases" number is context, not climax. Rich should not lean on it as a talking point. Instead, get Purav to walk through one use case in detail — ideally the on-time delivery model revision. That's the proof that should carry the weight.
1
"What was the super-user group, and why did it matter?"
2
"How did that group move from general curiosity to practical use cases?"
3
"Tell me about the on-time delivery work. What were you trying to solve, and what changed?"
4
"What did that process look like before, and what did it look like after?"
5
"Tell me about a dashboard or data example where people saw something come together quickly."
6
"What changed in the room when people realized AI could help them do something they thought was difficult?"
Listen for: People moving from intimidated to capable, practical examples, "That was it?", demystifying AI, speed with purpose, human judgment still required.
If vague, probe: "What did people's faces look like?" · "What did someone say afterward?" · "How long would it have taken without these tools?" · "What still required human judgment?"
Move from AI and analytics into healthcare impact without forcing a patient story. The ultrasound connection is the emotional bridge — don't rush it.
1
"How does this work connect to hospitals receiving equipment when they need it?"
2
"When order execution, delivery, or install data is clearer, what becomes possible downstream?"
3
"You talked about seeing your nephew on a GE ultrasound system. Take me into that moment."
4
"What did you notice when you saw the image?"
5
"You've also mentioned your wife's ultrasound after a health scare. How did that affect how you think about GE HealthCare's work?"
6
"What does it mean to be behind the scenes and still feel connected to patient care?"
Listen for: Personal pride, responsibility, family connection, equipment in the room when it matters, "our work is there," behind-the-scenes impact.
If vague, probe: "What did you feel in that moment?" · "Did it change how you think about your everyday work?" · "How would you connect that image back to your work in supply chain analytics?"
1
"What does your story say about the kind of person who can thrive at GE HealthCare?"
2
"What gave you room to step into this AI leadership role?"
3
"Who supported or trusted you along the way?"
4
"How do you try to help other colleagues become more confident with new tools?"
5
"What do you hope your team learns from the way you approach change?"
6
"When you tell your team to 'work less, do more,' what do you really mean?"If he delivers it flat, follow up: "What does 'more' look like? What becomes possible when the busy work goes away?"
Listen for: Trust, growth, empowerment, self-direction, leadership support, coaching, curiosity, building capability in others.
If vague, probe: "Who helped make that possible?" · "What did the culture allow you to do?" · "How did you know it was okay to experiment?"
Capture short, human, campaign-usable reflections. Let him answer imperfectly, then bring him back to specifics.
1
"Why does this work matter to you personally?"
2
"What would you want someone outside GE HealthCare to understand about your role?"
3
"What does #EveryRoleIsVital mean when your work is behind the scenes?"
4
"What makes you proud to work here?"
5
"For someone considering a career in AI, analytics, or operations here, what would you want them to know?"
If vague, probe: "Say that again, but as if you were talking to a friend." · "What's the simplest version of that?" · "Can you finish this sentence: 'My role is vital because…'"
Must-Ask Questions
1
"You said you were not supposed to be the supply chain AI leader. How did you become that person?"
2
"What was happening in your career when AI first caught your attention?"
3
"What did AI unlock for you personally?"
4
"When Craig Shaffer asked you to help lead this effort, what did you feel?"
5
"What were people afraid of or unsure about before they saw what these tools could do?"
6
"Tell me about a moment when someone realized, 'This isn't as hard as I thought.'"
7
"Walk me through the on-time delivery work. What did the process look like before, and what changed?"
8
"You've said the AI part is easy and the hard part is the data foundation. Why does that matter?"
9
"How does your behind-the-scenes work connect to hospitals, customers, patients, or families?"
10
"When you saw your nephew on a GE ultrasound system, what did that do for you?"
11
"What does #EveryRoleIsVital mean when your work is behind the scenes?"
Should-Ask Questions
1
"Which use case best shows AI becoming practical instead of theoretical?"
2
"What did the super-user group make possible?"
3
"When you tell your team to 'work less, do more,' what do you really mean?"
4
"How do you balance speed with responsibility?"
5
"What does good AI use look like in a healthcare company?"
6
"What still requires human judgment, even when AI helps?"
7
"How has this changed the way you lead your team?"
8
"What would you tell someone who is nervous about AI?"
Rescue Questions
For when Purav becomes too abstract, technical, philosophical, or platform-forward.
R
"Can you give me one specific example?"
R
"What did that look like in the moment?"
R
"Who was in the room?"
R
"What did someone say that made you realize it was working?"
R
"Say that without the platform names."
R
"What's the human version of that?"
R
"Can you connect that back to the hospital or customer?"
R
"What did that feel like personally?"
R
"Why did that matter beyond the dashboard?"
R
"What would have happened if the team didn't have that visibility?"
Interviewer Coaching — For Rich
Best StrategyPurav gives you energy and material. Your job is to redirect that energy into moments — not to slow him down, but to keep him in story instead of concept. When he drifts into AI philosophy, bring him back to what happened, who was there, and what changed.
Purav's Profile
Articulate, energized, smart, and future-facing. He will give you a lot of material — the challenge isn't drawing him out, it's keeping him specific. He's strongest when talking about what changed for him personally, what he saw before others did, and how he helps people become less intimidated.
He becomes less useful when talking generally about AI transformation, referencing platform names, or explaining technical architecture. Ask him to tell the story, not explain the concept.
AI Disruption — Active Steering RequiredIf Purav starts talking about AI replacing jobs or making roles obsolete, do not let it sit. Redirect immediately:
"What does that free people up to do?" "What becomes possible when the repetitive work goes away?" "So what do people get to focus on now?"
The campaign message is empowerment. AI helps people become more capable — it does not make them unnecessary. This needs active steering, not just awareness.
Four Key Moments to Slow Down1. The career lull / AI spark — This gives the story a human beginning. Ask what the lull felt like and why AI gave him new momentum. Don't let him skip past the vulnerability.
2. The leadership handoff — "I was not supposed to be the supply chain AI leader" is the story hook. Get the scene: Craig Shaffer (supply chain leadership) asked him to help lead the effort. What was said? What was happening? What did Purav feel?
3. The training-room transformation — This is the hero moment. Look for the specific moment where someone went from skeptical or intimidated to confident. What did their face look like? What did they say? This is the most filmable emotional proof in the interview.
4. The ultrasound connection — This is the heart bridge. Don't rush it. Ask what he felt when he saw the GE HealthCare connection on the image. Ask how that changed the way he thinks about his work. Save it for the close.
Slow Down When He Mentions
◦ Being in a career lull after his MBA
◦ Teaching himself AI before anyone asked him to
◦ Craig Shaffer recognizing his energy and asking him to lead
◦ The super-user group forming and learning together
◦ Someone going from intimidated to capable
◦ The on-time delivery model revision (four weeks vs. months)
◦ Seeing his nephew on a GE ultrasound
◦ His wife's ultrasound after a health scare
◦ "Work less, do more" — but only if he explains what "more" means
Don't Spend Too Long On
◦ General AI philosophy or transformation theory
◦ Platform names: Copilot, ChatGPT, AWS Bedrock, Databricks, Power BI
◦ Technical architecture of dashboards or data dictionaries
◦ The number "25 use cases" as a standalone proof point — it's context, not climax
◦ Machine learning, predictive analytics, or backend access explanations
◦ AI job-disruption language — redirect immediately
◦ Abstract definitions of success measured only by speed
"Work Less, Do More" — Guardrails
This phrase is strong but lands badly without context. If Purav says it flat — as if the point is simply doing less work — follow up immediately:
"What does 'more' look like? What becomes possible when the busy work goes away?"
The audience should hear this as empowerment: better tools create room for better thinking and more meaningful work. Not as reduced effort or headcount justification.
Language That Unlocks Stronger Answers
take me into that momentwhat did you see on their faceswhat did you feelwho was depending on thatwhat was different afterwardsay that without the technical languagewhat does that mean to you personallywhat's the human version of thatconnect that back to the hospitalwhy did that matter beyond the dashboard
Main Steering MoveWhenever Purav drifts into AI philosophy, platform references, or abstract transformation language, bring him back to one person, one moment, one change: "Can you give me that as a specific example?" or "What did that look like in the room?"
Story Beats to Hit
1 · The Setup
Long-tenured GE HealthCare colleague in supply chain analytics and AI enablement. Small team, dot-connector identity, data-into-insights work.
Strong — get a clean, plain-English description of the role
2 · The Tension
Post-MBA career lull, complex data, siloed teams, manual work, uneven AI confidence, need to move faster without losing accuracy or trust.
Needs Sharpening — capture a specific moment when the tension became visible
3 · The Action
Taught himself, experimented, helped lead the supply chain AI effort, shaped the super-user group, supported training, documented use cases, improved data foundations.
Strong — get the on-time delivery before/after as the anchor
4 · The Collaboration
Craig Shaffer (supply chain leadership) recognized Purav's energy and asked him to help lead. Work involved super users, business partners, analytics teams, AVS/ultrasound partners.
Needs Sharpening — get a human collaboration moment, not just a stakeholder list
5 · The Outcome
Use cases documented; training demystified AI; dashboard/data work accelerated; teams gained clearer visibility; on-time delivery model revised in weeks rather than months.
Moderate-Strong — one measurable "what changed" moment
6 · The Meaning
Vital work happens behind the scenes. AI, analytics, and supply chain systems help equipment reach hospitals and create moments like a family seeing an ultrasound image.
Very Strong — let him connect the ultrasound moment to his daily work in his own words
Personal Meaning
AI re-energized me at a moment when I was thinking about my next chapter.
Prompt: "You said you were in a bit of a lull after your MBA. What changed when AI came into the picture?"
I didn't wait for permission — I leaned in because I saw the future coming.
Prompt: "What did you see in AI that made you think, 'I need to understand this now'?"
The bigger and harder the problem, the more meaningful the work feels.
Prompt: "What kind of problem gets you excited?"
"Work less, do more" means using tools to create room for better thinking and a better life.
Prompt: "When you tell your team to work less and do more, what do you really mean? What does 'more' look like?"
Patient / Customer / Clinician Impact
Behind every delivery metric is a hospital waiting for equipment.
Prompt: "How does on-time delivery connect to the hospital or customer?"
Data visibility helps equipment get where it needs to go.
Prompt: "What becomes possible when teams can see the right information sooner?"
Seeing my nephew on a GE HealthCare ultrasound made the mission personal.
Prompt: "When you saw your nephew on that ultrasound image, what went through your mind?"
I may not build the product, but my work helps support the systems behind those moments.
Prompt: "How do you connect that family moment back to the work you do every day?"
Team and Culture
GE HealthCare gave me room to step into a new kind of leadership.
Prompt: "What made it possible for you to become someone others looked to for AI guidance?"
The super-user group helped people learn together and build confidence.
Prompt: "Why was it important to have a group of people learning and experimenting together?"
Craig trusting me to help lead this changed my trajectory.
Prompt: "What did it mean to you that Craig Shaffer trusted you to help lead this?"
Helping someone go from intimidated to capable is the most rewarding part.
Prompt: "What is satisfying about seeing someone realize they can use these tools?"
#EveryRoleIsVital Connective Lines
Vital roles are not always visible.
Prompt: "What does #EveryRoleIsVital mean for someone working behind the scenes in analytics and AI?"
The dashboard isn't the outcome — the decision is.
Prompt: "Why isn't the dashboard itself the finish line?"
Every role connects to a larger system of care.
Prompt: "How does your role fit into the larger system that supports customers and patients?"
Innovation is strongest when it helps people do meaningful work better.
Prompt: "What is the difference between using AI as hype and using it in a way that actually helps people?"
Montage-Friendly Lines
"I help connect the dots."
Prompt: "If you had to describe your role in five words, what would you say?"
"I was not supposed to be the supply chain AI leader."
Prompt: "How did you end up leading the AI effort?"
"Once people try it, the fear starts to drop."
Prompt: "What changes when someone sees AI work for the first time?"
"The work behind the scenes still reaches the patient."
Prompt: "How does behind-the-scenes work show up in the real world?"
"The AI part is easy — the hard part is the data foundation."
Prompt: "What do people get wrong about AI?"
Visual Guidance — For Paul and Crew
If Time Is Limited — Top 5 Shots
1. Purav in a clean, warm home-office interview setup
2. Purav sketching a simple order → delivery → install → customer/patient impact flow by hand on paper
3. Hands typing into a sanitized AI prompt using dummy data
4. Mock dashboard / Power BI–style screen with fake data, filmed safely
5. Purav on a mock training call or reviewing a generic AI use-case template
Purav's home office — Desk, monitor setup, laptop, workstation. This is where the work actually happens. Prioritize warm, authentic light and clean framing.
Secondary location in the home — A warmer, reflective angle for closing reflection. Kitchen counter, living area, or any space that feels less "at the desk."
Brief outdoor moment — If outdoor space exists (porch, yard, sidewalk), a short walk-and-talk or stepping-outside thinking moment helps break the visual monotony of a home-office shoot. Do not over-produce.
◦ Purav opening laptop and beginning the workday
◦ Hands typing into a sanitized AI prompt interface
◦ Mock dashboard or Power BI–style visual using fake/sample data
◦ Sanitized "AI use case template" document
◦ Purav sketching a flow by hand: order → delivery → install → customer readiness → patient impact
◦ Purav reviewing notes, highlighting a document, or organizing information on paper
◦ Purav on a Teams-style mock call or training setup
◦ Close-ups of hands, keyboard, notebook, monitor edges, process notes
◦ Over-the-shoulder shots with screen content blurred or pre-sanitized
◦ Purav stepping back from the computer to think — reinforcing "work less, do more"
◦ Purav walking through a doorway, stepping outside, or moving between spaces
◦ Cat in the home office only if it appears naturally and does not trivialize the story
Messy notes → clean diagram: Complexity becoming clarity through human effort.
A process map connecting disconnected points: The dot-connector identity made visual.
Hands simplifying a complex workflow: Human judgment turning data into decisions.
A notebook labeled generically: "Use cases," "Workflow," "Data foundation," or "Training notes." Physical artifacts of invisible work.
Purav closing the laptop and stepping away: Better tools create space for better thinking.
A quiet home-office detail: This global healthcare work happens from a human, everyday environment.
The core visual challenge is making data analytics and AI enablement visually compelling in a home office. Screen-based shots are necessary but not sufficient. Paul needs texture beyond the laptop:
Hand-drawn process map: Give Purav a marker and paper. Ask him to sketch the order-to-patient flow. Hands drawing is more filmable than hands typing.
Physical notes or printed materials: If Purav has notebooks, printed use-case lists, or training outlines, film him reviewing and annotating them.
Walk-and-talk: If outdoor space exists, even a brief moment of Purav walking and talking to Rich breaks the four-walls problem.
Whiteboard or sticky notes: If a whiteboard is available, Purav mapping a workflow on it is far more visually dynamic than a screen recording. Clear all sensitive content first.
Transition shots: Coffee, stepping away from the desk, looking out a window, opening a door. Small human moments that say "this person lives here and works here."
Real dashboards, supply chain data, customer or hospital information
Internal AI prompts or chat histories
Vendor logos or platform-forward shots (Copilot, ChatGPT, AWS Bedrock, Databricks, Power BI)
Proprietary workflows, internal system names or screenshots
Whiteboards containing real process details
Personal medical information or family photos/documents unless approved
Anything on screen that reveals names, dates, orders, locations, metrics, or internal business details