Package: Comprehensive Plan + Coach Call ($595) · Archetype: Hybrid: Evangelist / Orchestrator · Posture: Protect and compound. Challenge openness: med.
Lead with their success definition and non-negotiables; posture is “Protect and compound”; AI leadership archetype: Hybrid: Evangelist / Orchestrator.
Client report
What it means for your job next quarter: how exposed your day-to-day work looks, where AI helps or replaces, where your company helps or hurts, and a clear Protect / Drop / Learn plan.
Energy and how you pair with AI are assets. Protect scarce judgment and relationship time; grow them with a few selective “create new work with AI” bets.
As Director of Operations Strategy at Harborline Health Systems (SF Bay Area), this verdict is about your week: not your title. What you do, and how AI pairs with that work, set the plan below.
Where Harborline Health Systems helps or hurts shows up in four leadership pillars. The weak spot right now is Change (notice, commit, or change how work gets done). Scores live on the next page; this page is the story.
Signals support a careful experiment: not a panic pivot. Primary decision: augment current role. Horizon: 12 months.
Your focus for the coach: “Harborline is piloting copilots in finance/ops; I am asked to “champion AI” but policy is murky and my week is still 28% pack assembly. Should I lean in as internal orchestrator or protect my seat and wait?”
Success you defined: “Be the person who redesigns ops workflows around AI without burning out my team or breaking compliance.”
Working label for how you show up with AI: Hybrid: Evangelist / Orchestrator. Short take: Risk of tool enthusiasm without clear trust, risk, and ROI checks.
This could flip the advice: AI pilot discussed alongside hiring freeze / headcount reduction / reorg: “Ops copilots discussed in same quarter as soft hiring freeze and a quiet reorg rumor in finance/ops.”
Treat “champion AI” asks as political risk until headcount climate is clear. Prefer small experiments where you keep the final say.
Financial runway: 3-6 months. Risk tolerance: 3/5. Balance Protect / Drop / Learn. No panic pivot. No heroic sprint. Constraints: “Dual income; one dependent in school; mortgage: can absorb a short gap but not a year of underemployment.”.
Risk of tool enthusiasm without clear trust, risk, and ROI checks. Blind spot: unofficial tools and gimmick projects that erode trust with peers who still need room to learn safely. Secondary lean (Orchestrator): Risk of over-owning "help everyone adopt AI" while under-protecting your own deep work time. Blind spot: becoming the unpaid AI project office and starving your own judgment and creative work.
Full index and pillar grid with bars: Dual Dashboard (next). KPI cards here are highlights only.
Bands: High ≥72 · Moderate 48-71 · Low ≤47. For automation exposure, High is needs attention; for other indices and pillars, High is favorable. Every score shows its band label next to the number.
| Index | Score (0-100) | Reading |
|---|---|---|
| Automation exposure | Pressure from your task mix: not title folklore | |
| Complementarity | Whether AI tends to replace, assist, or create | |
| AI readiness | You and where the company helps or hurts | |
| Engagement | Energy, meaning, recovery | |
| Mobility / org buffer | Portable strengths vs lock-in |
| Pillar | Score (0-100) | Plain reading |
|---|---|---|
| Cognitive & psychological readiness | Ambiguity comfort · willingness to unlearn | |
| Strategic value creation | Spotting real bets vs gimmicks · committing | |
| Helping the team adopt AI | Safety · upskilling · redesigning work | |
| Governance, ethics & risk | Bias, IP, privacy, compliance attention |
Top scores answer “what is happening to my week?” Bottom pillars answer “can I notice the shift, commit, and change how work gets done?” High exposure with weak capacity to change routines is a different problem than high exposure with Orchestrator strengths. Numbers are not restated in prose elsewhere on purpose.
For the curious: role-capital layer + executive leadership layer (Sense / Seize / Transform; ambidexterity).
Three practical muscles for the next two quarters (Notice / Commit / Change): notice opportunities and threats, commit resources to a real bet, and change routines and structures so the bet sticks. Mapped to your intake:
For Maya Chen as Director of Operations Strategy, the bottleneck on Transform should gate irreversible career bets. Weak notice leads to more scanning before a pivot. Weak commit leads to smaller experiments with visible artifacts. Weak change-the-system leads to redesign work and enable the team before stacking more tools.
Efficiency / automation score 45/100; new offers / novel work 51/100; balance 94/100 (balanced). Creative share is 15% and routine 28%:use that tilt so Protect / Drop / Learn does not over-weight efficiency theater.
For the curious: Teece Sense-Seize-Transform; O’Reilly & Tushman ambidexterity.
Automate and standardize where complementarity is replace/assist:especially routine information work you already named for offload: Status packs, meeting notes, first-pass variance analysis.
Invent tasks AI makes newly valuable. Learn bet: Small experiments people can see (AI as junior analyst), with trust/risk/compliance checks Sequence Learn behind clarified headcount climate.. You keep ownership of evaluation: AI drafts; you decide.
Job titles hide what you actually do. This pilot uses your weekly shares and whether AI tends to replace, assist, or create in each bucket:not a generic occupation map. Partial redesign beats “job death” narratives.
For the curious: Autor task approach; OECD complementarity / partial redesign.
| Task bucket | % week | Complementarity | What it means for you |
|---|---|---|---|
| Routine information work Rules-heavy assembly and statusing: easiest for tools to take a first pass. |
28% | Replace | At 28% of your week as Director of Operations Strategy, AI stance “Replace” means urgent offload with your quality standards (you keep the final say). |
| Complex judgment / problem-solving Ambiguous decisions: keep accountability human; AI can assist analysis. |
30% | Create | Protect accountable decisions for Harborline Health Systems; AI may assist analysis but cannot own your liability narrative. |
| Interpersonal / leadership Trust, coaching, negotiation: hard for tools to own alone. |
22% | Assist | Relationship density in SF Bay Area and at Harborline Health Systems is a buffer:do not let AI prep replace presence. |
| Physical / on-site work Hands / place / presence: onsite work is a buffer when used for trust, not email. |
5% | Unclear | Physical/on-site share (5%) interacts with your presence mode (light). |
| Creative / novel work Inventing new offers and frames: where AI can create work, not just polish. |
15% | Create | Creative share (15%) is where new-offer work should grow if you have room to commit. |
You asked to protect: Accountable ops redesign decisions and board-facing narrative. Willing to automate/offload: Status packs, meeting notes, first-pass variance analysis. Tools in use: ChatGPT Enterprise, Copilot, Claude, Notion AI, light Power Automate.
Rising routine share (somewhat higher than 12-24 months ago; ~18% then → 28% now). A flat exposure score understates the trend: pack/assembly work is taking more of the calendar, not just sitting at a static %.
Coach note: rising routine share ≠ flat same exposure score. Compare calendar trajectory to the snapshot table above.
| Undergraduate | UC Berkeley: BA Economics |
| Graduate | Stanford GSB: MBA |
| Highest credential / field | MBA: Operations / strategy |
| License / regulated practice | no |
| Tenure (role / occupation / industry) | 3 / 8 / 11 yrs |
| Portable strengths | Cross-functional orchestration; ops redesign; stakeholder translation |
| Recent formal learning (24 mo) | Internal AI product clinic (12 hrs); prompt engineering workshop; Teece on dynamic capabilities |
| Prior roles | Senior Manager, Ops Excellence: Harborline (2019-2023); Analyst: Deloitte (2015-2019) |
| Current seat | Director of Operations Strategy @ Harborline Health Systems · SF Bay Area |
Transition options and constraints: mobility index 92/100 (High band). No regulated license on file in this intake:pivot options are wider on paper, but signaling still depends on credentials, proof people can see, and a clear career story rather than AI skill alone.
Schools and field (UC Berkeley: BA Economics / Stanford GSB: MBA · Operations / strategy) shape network access and employer screens. OECD AI/skills guidance for most workers emphasizes AI literacy plus complementary human skills, not specialist ML credentials. Your stated portable strengths:Cross-functional orchestration; ops redesign; stakeholder translation:should headline external or internal repositioning stories more than tool lists.
Recent learning (Internal AI product clinic (12 hrs); prompt engineering workshop; Teece on dynamic capabilities) is a commit signal: convert it into a visible workflow artifact inside Harborline Health Systems within 90 days so learning changes how work gets done: not just résumé decoration.
Ask whether tenure in occupation (8 yrs) feels like deep specific capital or lock-in. Specific capital raises the bar for external pivots; general strengths and hybrid presence in SF Bay Area can offset.
Retrieval, templating, and first-draft speed are getting cheaper. Accountable judgment, trust-bearing coordination, and human verification are gaining pricing power. Bars below are directional from your title, week, tools (ChatGPT Enterprise, Copilot, Claude, Notion AI, light Power Automate), and AI attitudes:not a psychometric test.
Implication: stop competing on speed of depreciating work. Route it through assist/replace with your QA standard.
Implication: calendar-protect appreciating work. These are the assets behind posture “Protect and compound.”
Appreciating capabilities should map to Protect and Create buckets; depreciating ones to Drop with you keeping the final say. If you continue pricing yourself on depreciating speed inside Harborline Health Systems, exposure pressure will feel like wage pressure even when your judgment moat is intact. (Complementarity and exposure: Dual Dashboard.)
Where Harborline Health Systems helps or hurts on AI: maturity pilot; manager support 3/5; training 3/5; peer safety 3/5. Adoption is tools plus structure, culture, and behavior: not an IT install. Unofficial-tool risk is elevated: personal tools (“ChatGPT Enterprise, Copilot, Claude, Notion AI, light Power Automate”) may outrun formal policy at Harborline Health Systems.
| Maturity | pilot |
| Manager support | 3/5 |
| Policy clarity | 2/5 |
| Training access | 3/5 |
| Peer safety | 3/5 |
| Personal AI use / usefulness / efficacy / anxiety | 4 / 4 / 3 / 3 (1-5) |
| Preferred learning mode | project |
| Tools known / used | ChatGPT Enterprise, Copilot, Claude, Notion AI, light Power Automate |
Socio-technical framing: treating AI as an IT project underweights structure (who owns workflows), culture (who is allowed to fail safely), and behavior (what managers reward). Your org/talent pillar at 56/100 should be read against policy clarity 2/5: enablement without clarity breeds Shadow AI.
What work already runs through unapproved tools? Who would be surprised? What IP or client/patient data is in prompts? This is a trust, risk, and compliance conversation: not a gotcha. Especially acute in Harborline Health Systems given maturity “pilot.”
Energy 3/5. Meaning 4/5. Cynicism 2/5. Recovery 3/5. Leave intent 2/5. (Self-report: not a clinical screen.) Mixed engagement. Keep experiments time-boxed with clear success criteria.
| Signal | Self (1-5) | Reading |
|---|---|---|
| Energy | 3 | Fuel for experiments |
| Meaning | 4 | Why the seat still matters |
| Cynicism / detachment | 2 | Risk signal (not a diagnosis) |
| Recovery quality | 3 | Whether rest actually restores |
| Leave intent (12 mo) | 2 | Decision readiness, not diagnosis |
Plan implication against exposure 47/100: channel engagement into one Seize artifact and one Transform routine change:avoid scattering across every pilot at Harborline Health Systems.
Unlearning under threat (anxiety 3/5) is harder when recovery is thin. If fear of becoming obsolete is high, pair every Drop experiment with a Protect story that preserves who you are at work:so learning does not feel like erasure.
For the curious: UWES / Maslach-informed engagement signals; Kegan adult development (unlearning).
High exposure + low engagement leads to redesign/recovery. High exposure + high engagement leads to a focused sprint on how AI pairs with your work. Low exposure + low engagement leads to role craft or exit clarity: not more AI theater. Your pair: exposure 47 (Low) with engagement 72 (High).
You located yourself in SF Bay Area with physical presence “light” and organization type “corp.” Wage/spatial notes are directional only (no fabricated OEWS percentiles).
Physical mode “light” in SF Bay Area. Hybrid/onsite presence can buffer pure digital substitution when you deliberately use it for trust-bearing interpersonal work (22% of week):not for more email.
Organization type “corp” shapes adoption friction. Use the buffer to become the safe implementer who changes routines, not to wait passively while unofficial tools spread.
| Cross-border digital competition | Moderate: presence mode adds friction |
| Local relationship density option | SF Bay Area · use for stakeholder work AI cannot own |
| Org adoption tempo | pilot maturity at Harborline Health Systems |
Pillar score 44/100 (Low). Guardrails should scale with tool use (4/5) and org maturity (pilot). Self-ratings: bias awareness 3/5 · IP/privacy 3/5 · compliance attention 3/5.
Tools currently touching regulated / client / patient data: ChatGPT Enterprise; Microsoft Copilot; Notion AI; CRM / client records; Internal ops dashboard with PHI-adjacent fields.
IT / compliance formal review status: Partial review. Governance findings here are tool-specific: not ratings alone.
For the curious: AI TRiSM / trust-risk-security management.
Mixed task load: verify whether AI is creating new tasks (Acemoglu-Restrepo reinstatement) or merely accelerating existing ones. Trend note: Rising routine share (somewhat higher than 12-24 months ago; ~18% then → 28% now). A flat exposure score understates the trend: pack/assembly work is taking more of the calendar, not just sitting at a static %.
Inferred from task mix + role + AI use (not calendar import). Coach verification checklist:
Exploit 45 vs Explore 51 (balanced). Delegation failures often show up as exploit overload: more AI drafts, same human bottleneck.
Success you defined: “Be the person who redesigns ops workflows around AI without burning out my team or breaking compliance.”. Non-negotiables: “No patient-data exposure in consumer tools; keep team trust; preserve board credibility”. AI drafts; you own outcomes.
Accountable ops redesign decisions and board-facing narrative. Headcount/freeze/reorg beside AI pilot: bias toward Protect and small experiments where you keep the final say: not uncritical championing.
Status packs, meeting notes, first-pass variance analysis
Small experiments people can see (AI as junior analyst), with trust/risk/compliance checks Sequence Learn behind clarified headcount climate.
Drop does not mean abdicate. You keep evaluation, escalation, and accountable judgment. AI accelerates drafts; humans own outcomes:especially under license constraints (no).
Goal: Harborline is piloting copilots in finance/ops; I am asked to “champion AI” but policy is murky and my week is still 28% pack assembly. Should I lean in as internal orchestrator...
Turn the plan into clear Will: one dated Protect, one dated Drop (you keep the final say), one dated Learn sized to runway 3-6 months.
| Bet | Commitment | By when | Who knows |
|---|---|---|---|
| Protect | Accountable ops redesign decisions and board-facing narrative | date: | name: |
| Drop | Status packs, meeting notes, first-pass variance analysis | date: | name: |
| Learn | Small experiments people can see (AI as junior analyst), with trust/risk/compliance checks Sequence Learn behind clarified headcount climate | date: | name: |
Self-report plus optional prior feedback and 360 proxy. 360 invitations pending; send in progress. Manager: alex.manager.demo@harborline.example; peer: jordan.peer.demo@harborline.example.
| Prior feedback history | Answer |
|---|---|
| Formal 360 | Yes |
| Useful supervisor feedback (last 12 months) | Yes |
| Sources selected | 360, supervisor |
| How clear / actionable / fair | 3/5 · 2/5 · 4/5 |
| Impact on AI / career decisions | 2/5 |
| Main message | Be more visible as an AI champion and tighten how ops experiments get approved. |
| What changed (or not) | Started an internal AI clinic and documented one pilot SOP; still avoided consumer tools with PHI. |
| Still unresolved | Still unclear whether “champion AI” is career-safe while the soft freeze and murky policy continue. |
| 360 proxy self-item | Self (1-5) |
|---|---|
| My manager would say I create space for safe AI experiments | 3 |
| Peers see me as careful on risk | 4 |
| I am seen as pushing AI faster than the org can absorb | 4 |
| Lens | Self (tonight) | Peer | Manager | Direct reports |
|---|---|---|---|---|
| Archetype lean | Hybrid: Evangelist / Orchestrator | : attach when available: | : | : |
| Cognitive readiness | 50 Moderate | : | : | : |
| Strategic value creation | 67 Moderate | : | : | : |
| Org / talent enablement | 56 Moderate | : | : | : |
| Governance / ethics | 44 Low | : | : | : |
| Operational focus | Current | Target | Strategic objective |
|---|---|---|---|
| Routine information work | 28% | 13% | Offload to assist/replace: you keep quality checks |
| Complex judgment / problem-solving | 30% | 35% | Deepen accountable decisions AI cannot own |
| Interpersonal / leadership | 22% | 25% | Protect trust work; use AI for prep not presence |
| Physical / on-site work | 5% | 5% | Keep on-site / presence work as a buffer |
| Creative / novel work | 15% | 22% | Expand small create-new-task experiments |
Stated primary decision: augment current role. Horizon: 12 months. Stage: establishment.
Success: “Be the person who redesigns ops workflows around AI without burning out my team or breaking compliance.”. Focus: “Harborline is piloting copilots in finance/ops; I am asked to “champion AI” but policy is murky and my week is still 28% pack assembly. Should I lean in as internal orchestrator or protect my seat and wait?”.
Runway 3-6 months · risk tolerance 3/5. Plan implication: balanced Protect / Drop / Learn: calibrated experiment, not panic pivot. Constraints: “Dual income; one dependent in school; mortgage: can absorb a short gap but not a year of underemployment.”.
This could flip the advice: AI pilot discussed alongside hiring freeze / headcount reduction / reorg: “Ops copilots discussed in same quarter as soft hiring freeze and a quiet reorg rumor in finance/ops.” Treat “champion AI” asks as political risk, not only opportunity. Prefer Protect plus small experiments where you keep the final say: until headcount climate clarifies.
| Option | Rationale | Risk |
|---|---|---|
| Augment in role | Lowest switching cost; depends on org climate and your capacity to commit resources. | Becoming unpaid AI PMO (Orchestrator overload). |
| Internal lateral toward judgment-heavy work | Uses mobility 92 and org relationships. | Waiting on slow org redesign. |
Attach when available: do not invent benchmarks
| Industry automation / AI adoption benchmark | Source TBD: attach when available (e.g., sector report). No fabricated McKinsey/Gartner percentiles. |
| Shadow AI / tech spend snapshot | Attach when available from CIO / IT: tool inventory vs policy. |
| Workforce sentiment / training uptake | Attach when available from HR / L&D. |
| Regulatory / compliance heatmap | Attach when available for sector + geo (SF Bay Area). |
This diagnostic combines a picture of your week (exposure, how AI pairs with you, readiness, engagement, mobility) with a picture of how you lead through the shift (four pillars; notice / commit / change; efficiency vs new offers; AI leadership archetype). Inputs are self-report ratings and task shares.
For the curious: role-capital layer + executive leadership layer (Teece Sense/Seize/Transform; ambidexterity; AI TRiSM; Autor task approach).
Self-report bias; no clinical diagnosis; job-automation headlines are feasibility folklore, not individual destiny; this pilot is not a full occupation-task forensic unless tasks are later mapped; 360 and secondary benchmarks are placeholders until collected; scores are coaching signals for Bharat Rao / Core Loop Press, not employment decisions.
Methods spine: Autor task approach; Acemoglu & Restrepo; OECD partial redesign & complementarity; TAM / AI anxiety & self-efficacy; UWES / Maslach-informed engagement; Teece dynamic capabilities; O’Reilly & Tushman ambidexterity; Kegan unlearning; socio-technical systems; human-in-the-loop; AI TRiSM; Shadow AI.
Autor, D. (2013). The “Task Approach” to Labor Markets: An Overview (IZA DP 7178).
Frey, C. B., & Osborne, M. A. (2013/2017). The Future of Employment: How Susceptible Are Jobs to Computerisation? (feasibility folklore; caveated).
Acemoglu, D., & Restrepo, P. (2017/2019). Robots and jobs; Automation and new tasks. NBER / JEP.
Nedelkoska, L., & Quintini, G. (2018). Automation, skills use and training (OECD).
OECD (2023). OECD Employment Outlook 2023: skills in the age of AI.
Teece, D. J. (2007). Explicating dynamic capabilities. Strategic Management Journal.
O’Reilly, C., & Tushman, M.: ambidextrous leadership / organization.
Kegan, R.: adult development (self-authoring -> self-transforming) as interpretive lens for unlearning.
Venkatesh, V., & Bala, H. (2008). TAM3; Venkatesh (2000) on self-efficacy, anxiety, facilitating conditions.
Schaufeli: UWES engagement; Maslach burnout dimensions as related signals only.
Gartner AI TRiSM (Trust, Risk, Security Management): governance vocabulary; not a proprietary score here.
Complete confidentiality guaranteed. Core Loop Press · confidential coaching document. This diagnostic is for career coaching with Core Loop Press / Bharat Rao. It is not clinical, legal, or employment advice. Automation and leadership scores are task- and self-report-based signals, not predictions of termination or promotion. Contact your administrator for access or deletion requests.
Page 1 already lists the 10 standouts: do not bury them; open the call from that list.
Challenge openness: med · Package: Comprehensive Plan + Coach Call · Archetype: Hybrid: Evangelist / Orchestrator
Lead with their success definition and non-negotiables; posture is “Protect and compound”; AI leadership archetype: Hybrid: Evangelist / Orchestrator.
Call focus: Single 45-60 min call: diagnose posture + archetype, lock Protect/Drop/Learn bets, schedule one 30-day check if needed.
Avoid: You can challenge title-based safety narratives; keep Frey/Osborne as folklore, not destiny. Stress-test Evangelist claims with TRiSM.
Focus: Harborline is piloting copilots in finance/ops; I am asked to “champion AI” but policy is murky and my week is still 28% pack assembly. Should I lean in as internal orchestrator or protect my seat and wait?
Success: Be the person who redesigns ops workflows around AI without burning out my team or breaking compliance.
Non-negotiables: No patient-data exposure in consumer tools; keep team trust; preserve board credibility
Runway: 3-6 months · Risk tol: 3/5 · Constraints: Dual income; one dependent in school; mortgage: can absorb a short gap but not a year of underemployment.
Headcount/freeze/reorg signal: yes: Ops copilots discussed in same quarter as soft hiring freeze and a quiet reorg rumor in finance/ops.
360 invites pending: alex.manager.demo@harborline.example · jordan.peer.demo@harborline.example
Compliance tools: ChatGPT Enterprise; Microsoft Copilot; Notion AI; CRM / client records; Internal ops dashboard with PHI-adjacent fields · review Partial review
Maya Chen · maya.chen.demo@example.com ·
Cognitive 50 · Strategic 67 · Org/talent 56 · Governance 44
Teece: Sense 72 / Seize 60 / Transform 50 (weakest: Transform)
Rate 1-5; collect peer / manager / report. Attach to Perception vs Reality table when available. 360 invites pending for captured emails: no send infra tonight.
| Item | Construct | Self | Other |
|---|---|---|---|
| Stays effective amid ambiguous AI outcomes | Cognitive readiness | ||
| Distinguishes ROI bets from gimmicks | Strategic value | ||
| Creates safety for others to experiment | Org / talent | ||
| Flags bias, IP, privacy, compliance risks early | Governance / TRiSM | ||
| Orchestrates humans + AI without bottlenecks | Archetype check |
Legal has not approved vendors; team already uses consumer ChatGPT with client text. Client is Maya Chen at Harborline Health Systems. Ask: Sense / Seize / Transform moves in 14 days without becoming compliance theater.
Board wants “AI everywhere” in 90 days. Archetype risk: Evangelist. Ask client to draft a TRiSM-gated Seize plan with two explicit Kill criteria.
Call spine. Open from the 10 standouts, then walk Goal to Reality to Options to Will.
| Goal | Harborline is piloting copilots in finance/ops; I am asked to “champion AI” but policy is murky and my week is still 28% pack assembly. Should I lean in as internal orchestrator or protect my seat and wait? Real challenge: Whether championing AI is career-safe for me while policy is murky and a soft freeze sits next to the pilot. |
| Reality | Exposure 47/100 · Engagement 72/100 · Routine share 28% · Runway 3-6 months · Risk 3/5. Success on file: Be the person who redesigns ops workflows around AI without burning out my team or breaking compliance. |
| Options | Protect: Accountable ops redesign decisions and board-facing narrative · Drop: Status packs, meeting notes, first-pass variance analysis · Learn: Small experiments people can see (AI as junior analyst), with trust/risk/compliance checks Sequence Learn behind clarified headcount climate |
| Will | Dated Protect / Drop / Learn commitments + who knows. Fill the Will table on the plan page; leave blanks only if the client refuses a date. |
Product voice. Use on the call; do not lecture the model.
Protect: Accountable ops redesign decisions and board-facing narrative
Automate: Status packs, meeting notes, first-pass variance analysis
Strengths: Cross-functional orchestration; ops redesign; stakeholder translation
Learning: Internal AI product clinic (12 hrs); prompt engineering workshop; Teece on dynamic capabilities
Tools: ChatGPT Enterprise, Copilot, Claude, Notion AI, light Power Automate