Executive Team AI Exposure Dashboard — 14 Leadership Roles × 142 Tasks
WiseTech is cutting 2,000 jobs because AI can now write code — yet its C-suite's own exposure is only 9.9% Automate Now, barely above GenusPlus's 4.0% (a physical infrastructure company). The paradox: the people deciding to replace workers with AI are themselves largely un-automatable. 58.5% of executive tasks sit in the Augment zone — AI can assist, but human judgment, relationship capital, and restructuring leadership remain essential. The CEO (Zubin Appoo) shows 91.7% Augment and 0% Automate Now — because leading a 2,000-person workforce reduction is the most un-automatable task in the building.
Caroline Pham's CFO function is the outlier: 50% Automate Now, 0.66x headcount multiplier, and a 0.4 deskilling index. Financial reporting, variance analysis, forecasting, and compliance are overwhelmingly Bits-native tasks with high verifiability (F=4-5) and high decomposability (S=5). This makes the CFO function the most AI-exposed executive role at WiseTech — and across both companies in this portfolio. The strategic question is not whether AI will do CFO work, but how fast it shifts from "AI drafts, human signs" to "AI executes, human audits."
GenusPlus: AN 4.0% | AUG 60.0% | CONV 2.9% | HF 33.1%. WiseTech: AN 9.9% | AUG 58.5% | CONV 9.2% | HF 22.5%. The Augment zones are almost identical (~60%) — both companies' executives face the same "AI can help but can't replace" dynamic despite being in completely different sectors. The contrast is in the tails: WiseTech has 2.5× more Automate Now tasks (Bits advantage), while GenusPlus has 50% more Human Fortress tasks (Atoms protection). WiseTech's Convenience pool (9.2% vs 2.9%) reflects the SaaS company's abundance of low-stakes, AI-feasible administrative tasks.
Click any card to view detailed task analysis in the Task Map tab.
| Task | Category | IMPACT | FAVES | Quadrant | Freq% |
|---|
WiseTech's scatter pattern shows a wider FAVES spread than GenusPlus — tasks stretch from 1.0 to 5.0 on the feasibility axis, reflecting the mix of pure-Bits work (financial reporting, code review) and pure-Cognitive work (restructuring leadership, founder relationships). GenusPlus was tightly clustered in the low-FAVES zone. The CFO's tasks are the green cluster in the top-right — the automation frontier. The CEO's tasks are the orange cluster in the top-left — high business case, low feasibility. The structural message: even in a software company, executive work is overwhelmingly judgment, not execution.
Across WiseTech's leadership team, the E dimension (inverse systems entanglement) scores consistently low. Even in a pure-software company, executive tasks require reading from and writing to multiple enterprise systems — CRM, ERP, HRIS, ASX platforms, CargoWise itself. The integration tax means many theoretically-automatable tasks remain in the Augment zone because the "last mile" of connecting AI output to systems-of-record hasn't been built. This is the Bits constraint analogue of GenusPlus's Atoms constraint — different substrate, same blocking pattern.
Caroline Pham's 0.66x headcount multiplier is the standout — meaning AI could theoretically compress 34% of the CFO function's workload. This is nearly double the next-most-compressed role (Brett Shearer CTO at 0.84x) and far above GenusPlus's most-compressed role (Mike Green at 0.87x). The driver: financial reporting and analysis tasks score F=4-5, A=5, S=5 — they are highly verifiable, fully digitised, and decomposable. The Institutional constraint (ASX disclosure, audit sign-off) is the only structural limiter preventing full automation.
The deskilling index measures what proportion of each role's highest-value expert tasks fall in the Automate Now quadrant.
| Role | Deskilling Index | Interpretation |
|---|---|---|
| Richard White | 0.0 | Complex work remains human |
| Zubin Appoo | 0.0 | Complex work remains human |
| Caroline Pham | 0.4 | AI absorbing complex work — active deskilling |
| Vlad Bilanovsky | 0.0 | Complex work remains human |
| Brett Shearer | 0.2 | Some complex tasks automatable |
| Gene Gander | 0.1 | Some complex tasks automatable |
| Mark Hall | 0.1 | Some complex tasks automatable |
| Ian Larsen | 0.2 | Some complex tasks automatable |
| Katrina Johnson | 0.0 | Complex work remains human |
| Gareth Russell | 0.0 | Complex work remains human |
| Matt Fielder | 0.0 | Complex work remains human |
| Maree Isaacs | 0.12 | Some complex tasks automatable |
| Angelina McMenamin | 0.0 | Complex work remains human |
| Marijana Okanovic | 0.12 | Some complex tasks automatable |
The CFO (0.4) and CTO (0.2) show meaningful deskilling — AI is absorbing their high-value complex tasks (financial modelling, architecture review), not just routine work. The CEO shows zero deskilling — restructuring leadership, investor management, and enterprise negotiation are entirely in the Augment/Fortress zones. The CPO (Gareth Russell) also shows zero deskilling despite managing the restructuring — the human judgment required to lead a 2,000-person reduction cannot be decomposed into verifiable steps.
With ~10% Automate Now, the recommended allocation is: 40% Operator / 20% Fortress / 40% Escalate. Unlike GenusPlus (which should fortress), WiseTech should lean into Operator — master the AI tools that are reshaping their own industry. The 2,000-job restructuring is itself an Operator move. The Escalation path is equally critical: wrap AI-generated logistics intelligence in branded advisory services that their top 25 forwarder clients cannot replicate.
WiseTech is already executing this: AI-assisted code generation, automated customer service, AI agents in CargoWise. The CFO function should operationalise AI for automated reporting, variance flagging, and compliance monitoring. The CTO should embed AI into the development pipeline as the new default. The Comms team can deploy AI-drafted content at scale. This is where WiseTech has natural advantage over infrastructure peers — their entire value chain is digital.
The fortress is smaller but critical: (1) Gene Gander's enterprise relationships with the top 25 global forwarders — these are trust-based, built over 20+ years; (2) Richard White's founder network and industry credibility; (3) Gareth Russell's restructuring leadership — managing a 2,000-person reduction requires judgment AI cannot provide; (4) Brett Shearer's 30-year customs domain expertise — regulatory compliance knowledge that cannot be learned from public data. Invest in deepening these irreplaceable assets.
The pricing model shift (per-seat → transaction-based) IS a value chain escalation: commodity outputs (reports, compliance filings) become embedded in platform transaction value. Further escalation paths: (1) CargoWise AI agents that help clients cut their own labour costs by 50% — WiseTech captures value from the productivity gain, not the software seat; (2) Regulatory intelligence services where AI-processed customs data becomes branded advisory; (3) Supply chain predictive analytics where AI commodity output is wrapped in WiseTech's network advantage across 75% of global customs transactions.
| Role | AI Commodity Layer | Human Escalation Layer | Value Chain Position |
|---|---|---|---|
| Richard White — Exec Chair | Competitive landscape reports, AI integration assessments | Innovation vision, investor trust, founder network | Transformation |
| Zubin Appoo — CEO | Board pack drafts, strategy papers, market analysis | Restructuring leadership, culture during change, enterprise negotiations | Transformation |
| Caroline Pham — CFO | Automated reporting, variance analysis, forecasting models, compliance checklists | Capital structure decisions, $3B debt management, M&A valuation judgment | Service → Experience |
| Vlad Bilanovsky — CXO | Partner program analytics, pipeline reporting | M&A execution, partner relationship management, regional strategy | Experience |
| Brett Shearer — CTO | Code reviews, architecture validation, technology assessments | Platform architecture vision, customs domain expertise (30yr), AI integration design | Transformation |
| Gene Gander — Sales | Pipeline analytics, playbook generation, proposal drafting | Enterprise negotiations with top 25 forwarders, relationship capital | Experience → Transformation |
| Mark Hall — M&A/e2open | Due diligence checklists, integration KPI dashboards | Cultural integration, shareholder negotiation, operational turnaround leadership | Experience |
| Ian Larsen — Operations | Productivity dashboards, incident analytics, capacity forecasts | Cross-team orchestration, AI workflow design, SLA management | Service → Experience |
| Katrina Johnson — CoSec | Regulatory monitoring, Board resolution drafting, compliance checklists | Multi-jurisdiction governance judgment, ASX disclosure timing | Experience |
| Gareth Russell — CPO | Workforce analytics, policy drafting, comms templates | Restructuring leadership, talent retention during crisis, legal compliance | Experience |
| Matt Fielder — IS | Capacity forecasts, vendor analysis, cost optimisation models | Data centre strategy, security governance, DR decisions | Service |
| Maree Isaacs — Licensing | License administration, billing automation, compliance reports | Co-founder stakeholder trust, customer relationship history | Branded Product |
| Angelina McMenamin — Culture | Engagement survey analytics, capability framework templates | 1:1 leadership coaching, culture shaping during transformation | Transformation |
| Marijana Okanovic — Comms | Press release drafting, content generation, media monitoring | Crisis communications judgment, executive messaging during restructuring | Experience |
Developed by Walter Adamson (OutcomesNow). This dual-axis framework separates the business case for AI automation (IMPACT) from the technical feasibility of generative AI execution (FAVES), producing a 2×2 decision matrix that is substantially more actionable than single-composite scoring approaches.
Inspired by the Trust Insights TRIPS framework (Christopher S. Penn & Katie Robbert), which pioneered task-level AI prioritisation scoring. IMPACT × FAVES extends this foundation by identifying that most TRIPS dimensions measure economic opportunity rather than technical feasibility, and introducing a dedicated feasibility axis (FAVES) with factors absent from prior frameworks: output verifiability, systems-of-record entanglement, consequence asymmetry, and step decomposability.
Role resolution: WiseTech Global leadership team page (wisetechglobal.com), ASX announcements, media coverage of February 2026 restructuring announcement. O*NET-SOC taxonomy (923 data-level occupations). AI benchmark calibration: Anthropic Economic Index (January 2026), METR evaluations, frontier model benchmarks (including Claude Opus 4.6 and GPT 5.3 Codex referenced in WiseTech's own restructuring rationale). WiseTech-specific context: 2,000 job cuts announced 24 Feb 2026, product & development and customer service teams to be reduced by up to 50%, E2open workforce potentially halved, pricing model transition from per-seat to transaction-based.
This analysis covers executive/senior leadership roles only. The 2,000 jobs being cut are predominantly in product development and customer service — roles below executive level whose AI exposure profile would show dramatically higher Automate Now percentages. Task lists are inferred from public information and may not reflect actual day-to-day priorities. WiseTech's leadership team has undergone significant turnover (CEO, CFO, and CPO are all new or interim in 2025-2026), meaning role responsibilities may still be evolving. FAVES scores are point-in-time; WiseTech's own CEO stated that AI capabilities are improving faster than expected.