Material judgment
86Prioritizing impacts needs context and stakeholder input.
AI can accelerate document review, classification and first-draft disclosure work.
Human responsibility remains central where evidence is incomplete, trade-offs are contested and public claims create legal or ethical consequences.
Moderate
Share of the work a machine could do
Routine research and reporting can automate, but materiality, verification and accountable decisions remain human work.
Scored from the tasks, not the job title. Lower is safer.
Jobs AI cannot take →What machines cannot take
Accountability
90Organizations need people responsible for public claims.
Ethical trade-offs
84Systems cannot settle contested values alone.
Stakeholder trust
74Change requires credible human relationships.
Independent verification
70Evidence needs challenge beyond an automated draft.
What they already take
Document classification
78Systems can sort policies and supplier responses.
First-draft reporting
74Tools can assemble repetitive narrative and tables.
Routine research
70AI can summarize defined information sources.
Data cleaning
68Standardized data checks increasingly automate.
How the work is changing
More assurance
Evidence and traceability become central.
Continuous data
Monitoring moves beyond annual reports.
Supply-chain visibility
Tools improve but do not solve supplier data gaps.
AI governance
Sustainability teams assess technology’s own impacts.
New jobs branching off
Climate-risk analyst
Connects climate scenarios with finance and strategy.
Sustainability assurance specialist
Tests disclosures and evidence.
Circular-economy manager
Redesigns materials and product systems.
Responsible AI analyst
Assesses social and environmental technology impacts.
Outlook
The role will spend less time assembling routine documents and more time verifying evidence and directing change.
Its value rests on connecting credible measurement to accountable operational decisions.
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