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.
† First use: see Glossary for TRiSM, Shadow AI, and Notice / Commit / Change.
Full score grid is on the next page. Numbers above are highlights only.
How you get a dramatic improvement from where you are now: specific to your scores and situation.
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.
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, Change: how work gets done: is the weak spot. Fix that before big career bets you cannot easily undo. If noticing is weak, look around more before a pivot. If commit is weak, run small experiments people can see. If change is weak, fix workflows and help 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.
Automate and standardize where AI tends to replace or 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.
| Task bucket | % week | How AI pairs | 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 %.
| 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.
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. (See the score page for how AI pairs with you, and exposure.)
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 |
Treating AI as an IT install underweights who owns workflows, who is allowed to fail safely, and what managers reward. Your "helping the team adopt AI" pillar at 56/100 should be read against policy clarity 2/5: helping others without clear policy breeds Shadow AI†.
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 small experiment people can see and one real change to how work gets done: 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.
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.
First use: TRiSM† (trust, risk, and security management). See Glossary.
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). Verification checklist:
Efficiency 45 vs new offers 51 (balanced). Delegation failures often show up as efficiency overload: more AI drafts, same human stuck point.
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: |
Patterns that may have gotten you here include: jumping in with answers before others finish; needing to win / be right in ai debates; defensiveness when challenged on judgment or ai use. Strengths that built your seat can become friction when AI changes how teams decide. Feedforward, not autopsy: pick one habit to practice differently for 30 days, tell one trusted colleague what you are trying, and ask only for future-facing observations.
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 | : | : | : |
| Helping the team adopt AI | 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. |
| Unofficial tools / 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.
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.
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.
Short definitions for terms that appear in this report.
| HITL | Human-in-the-loop: you keep evaluation, escalation, and the final say; AI drafts or accelerates. |
| TRiSM | Trust, Risk, and Security Management: bias, IP, privacy, and compliance checks that should scale with tool use. |
| Shadow AI | Personal or team AI tools used ahead of (or outside) formal employer policy. |
| Protect / Drop / Learn | Keep & deepen; automate or offload with your QA; next capability bet: the three plan cards. |
| Notice / Commit / Change | Plain labels for Sense / Seize / Transform: spot shifts, put resources behind a bet, change routines so it sticks. |
| Sense / Seize / Transform | Same three muscles (theory names): notice opportunities/threats; commit resources; change structure and routines. |