Chief Executives
11-1011.00Median wage $213,990/yr204,350 employed (US)Rank #707 of 923 scored · top 77% by substitution
Determine and formulate policies and provide overall direction of companies or private and public sector organizations within guidelines set up by a board of directors or similar governing body. Plan, direct, or coordinate operational activities at the highest level of management with the help of subordinate executives and staff managers.
Sub-scores
0–100 · band = confidence interval from rater disagreement
Substitution19
Exposure18
Augmentation61
Substitution — the headline: capability discounted by cost, barriers and adoption.
Exposure — technical capability alone, regardless of whether anyone deploys it.
Augmentation — how much AI assists without replacing. High here + moderate substitution = a changing job, not a disappearing one.
Tasks on the substitution scale
31 rated tasks, binned by substitution score.
Position among all scored occupations
Distribution of 923 occupation scores; the marker is this occupation.
Tasks with substitution ≥ 70
0%
Run 1.0.0-draft.1 · computed 2026-08-05 · rater panel: claude-sonnet-5, claude-haiku-4-5-20251001 · intervals span rater disagreement.
Why this score
The five weighted dimensions of the composite, averaged across this occupation's tasks (importance-weighted, panel mean). Exact weights and formulas: /api/v1/methodology.
Task automatabilityw 35%19
panel mean rating 1.8/5 → substitution pressure 19/100
Technical feasibility todayw 20%17
panel mean rating 1.7/5 → substitution pressure 17/100
Cost vs. human wagew 15%17
panel mean rating 1.7/5 → substitution pressure 17/100
Adoption barriersw 20%inverted — strong barriers lower the score18
panel mean rating 4.3/5 (barrier strength) → substitution pressure 18/100
Sector adoption velocityw 10%26
panel mean rating 2.1/5 → substitution pressure 26/100
Task breakdown (31 tasks)
Substitution pressure per task, weighted by O*NET importance in the composite. Expand a task for the full rater audit trail — every rating, every model, every rationale.
Prepare or present reports concerning activities, expenses, budgets, government statutes or rulings, or other items affecting businesses or program services.
42CI 28–57 · exposure 38 · augmentation 88 · importance 4.2/5 · click for rater detail
Prepare or present reports concerning activities, expenses, budgets, government statutes or rulings, or other items affecting businesses or program services.
42| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Enterprises are adopting AI for report *components* (dashboards, data synthesis, draft sections) at moderate pace, particularly in large tech and financial firms; but full replacement of executive reporting remains rare and adoption is primarily assistive rather than substitutive. |
| Sector adoption velocity | claude-sonnet-5 | 4/5 | Executive and professional services functions are among the fastest adopters of generative AI tools for report drafting, summarization, and presentation support. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI significantly enhances CEO productivity via rapid data retrieval, draft generation, formatting, and compliance cross-checks, enabling faster turnaround and better-structured reports while the executive retains strategy, framing, and sign-off authority. |
| Augmentation potential | claude-sonnet-5 | 5/5 | AI tools are widely used today to draft, summarize, and format reports, dramatically speeding up preparation while the executive retains responsibility for accuracy, judgment, and delivery. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can draft report sections and compile data from structured sources, the task requires synthesis of complex business context, strategic judgment, and accountability that demands human authorship. Current AI systems produce usable first drafts but not autonomous, production-ready executive reports at the 50% time-saving bar. |
| Task automatability | claude-sonnet-5 | 3/5 | AI can draft reports, summarize data, and generate narrative content from inputs, but synthesizing organizational context, judgment on emphasis, and stakeholder-specific framing still requires human oversight for a complete deliverable. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Executives face strong accountability and reputational barriers—reports bearing a CEO's name and signature must reflect personal judgment and trustworthiness; regulatory/fiduciary duty expectations, investor scrutiny, and board governance norms all require human authorship and accountability. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensing requirement mandates a human executive personally produce these reports, though ultimate accountability and signature authority for compliance-related reporting remains with the executive. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI report-drafting and BI tools cost hundreds to thousands per month in subscriptions and integration, while the human (a CEO) represents far higher opportunity cost; the economic case for full automation is weak given the quality and liability requirements. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | AI drafting tools reduce time spent on report preparation substantially, but the executive's oversight, data verification, and presentation delivery still carry significant labor cost, keeping overall savings moderate rather than order-of-magnitude. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | AI tools can assist with data aggregation, formatting, and template-based sections, but no deployed product reliably produces executive-quality reports without significant human review and rewriting. Mature systems exist for report drafting and analytics, but not for the full end-to-end task as a CEO would execute it. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Products like Copilot, ChatGPT, and BI tools with generative summarization are used in production to draft reports and summaries, but accuracy on nuanced business/regulatory content and final presentation quality still requires human review. |
Prepare budgets for approval, including those for funding or implementation of programs.
37CI 25–50 · exposure 38 · augmentation 63 · importance 4.3/5 · click for rater detail
Prepare budgets for approval, including those for funding or implementation of programs.
37| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Adoption of AI in budget preparation is slow and confined to narrow data-prep tasks in larger firms. Most organizations still rely on spreadsheets and ERP systems with human-driven logic. Executive-level budget synthesis has not seen meaningful AI automation in production deployments, even in information-sector leaders. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Corporate finance and executive functions are adopting AI-assisted analytics and forecasting tools at a moderate pace, with pilots and partial integration common but full automation of budget preparation still rare. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can meaningfully assist with budget preparation by automating variance reporting, scenario modeling, historical data synthesis, and draft program justifications. These augmentation capabilities improve an executive's analytical capacity and reduce manual compilation work, though the strategic decision-making and approval step remains human-centered. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI significantly augments budget preparation by automating data aggregation, forecasting, scenario analysis, and drafting narrative justifications, letting executives focus on judgment and approval. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Budget preparation requires substantial domain knowledge, strategic judgment about organizational priorities, and integration of multiple data sources. While AI can assist with data aggregation and variance analysis, the core task of justifying allocations and strategic trade-offs requires human executive decision-making. Current AI cannot autonomously prepare budgets meeting the 50% time-saving threshold end-to-end. |
| Task automatability | claude-sonnet-5 | 3/5 | AI can draft budget frameworks, project figures, and organize supporting data quickly, but final judgment on strategic tradeoffs, political considerations, and approval still requires human executive input, so only partial time savings are realized at equal quality. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Budget approval for organizations carries fiduciary and governance responsibilities. The Chief Executive must legally attest to budget reasonableness and strategic alignment; boards, shareholders, and regulators expect human accountability and executive judgment. These accountability requirements and the non-delegable fiduciary duty create substantial barriers to full automation. |
| Adoption barriers | claude-sonnet-5 | 3/5 | There's no strict licensing requirement for budget preparation, but fiduciary responsibility, board accountability, and organizational governance norms create meaningful friction against fully automating this task. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI assistance for budget preparation is inexpensive per hour, but the task requires high-touch executive involvement. The loaded cost of a Chief Executive's time far exceeds any AI tool cost, making end-to-end automation economically attractive in theory but not yet feasible in practice. Current cost advantage is minimal because meaningful automation doesn't exist. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | AI tools can reduce analyst hours needed for budget drafting, but executive-level oversight, negotiation, and validation still require significant costly human time, keeping overall cost roughly comparable rather than dramatically cheaper. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No production AI systems autonomously prepare executive budgets for organizational approval. Budget software aids formatting and calculation, but executive-level budget synthesis—weighing strategic priorities, organizational constraints, and program justification—remains firmly a human task. AI tools exist only for narrow components like data extraction. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Financial planning software and AI copilots (e.g., in ERP/FP&A tools) already assist with budget drafting and scenario modeling in production, but fully autonomous budget preparation for executive approval is not yet standard practice. |
Represent organizations or promote their objectives at official functions, or delegate representatives to do so.
32CI 7–57 · exposure 33 · augmentation 63 · importance 3.4/5 · click for rater detail
Represent organizations or promote their objectives at official functions, or delegate representatives to do so.
32| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Large organizations are deploying AI for executive support (scheduling, briefing prep, stakeholder communication) at growing rates, but actual delegation and representation tasks remain executive-controlled; adoption is visible in digital-native firms but slower in traditional sectors. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Executive functions and public representation show minimal AI adoption; AI is used for scheduling or briefing prep but not for the representational act itself. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI significantly augments executives by automating preparation (materials, briefings, schedule optimization, attendee research) and enabling delegation decisions through better information synthesis, while the executive retains control over actual representation and commitment. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help draft speeches, talking points, briefing materials, and background research to prepare the executive or delegate for the function. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | Most coordination aspects—scheduling, drafting talking points, managing attendee logistics, and preparing briefing materials—can be substantially automated. However, the presence requirement and the core need for executive authority to make real-time decisions at functions themselves prevent full end-to-end automation, though the preparatory and delegatory work yields significant time savings. |
| Task automatability | claude-sonnet-5 | 1/5 | Representing an organization at official functions requires physical presence, relationship-building, and real-time judgment that AI cannot perform end-to-end; only minor prep support is possible. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Legal and organizational requirements mandate that a human executive (or explicitly delegated representative) perform the representation function and be present at official functions; liability, brand authority, and fiduciary duty create hard barriers to full automation. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Organizational, reputational, and stakeholder-trust norms strongly require a human (often the CEO or designated delegate) to represent the organization; symbolic authority cannot be delegated to software. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | AI-assisted preparation, communication drafting, and logistics coordination are substantially cheaper than executive time; the marginal cost of these tools is an order of magnitude below the fully-loaded rate of a C-suite executive's labor. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI equivalent that could replace the human's presence and social capital, so cost comparison favors the human by default. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | AI tools can draft communications, manage scheduling, and prepare materials reliably in production; however, no deployed system can independently represent an organization at official functions or make binding executive decisions, limiting feasibility to support tasks rather than the core responsibility. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product substitutes for a human executive's public representation role; this remains entirely human-performed in practice. |
Nominate citizens to boards or commissions.
32CI 0–64 · exposure 41 · augmentation 50 · importance 4.1/5 · click for rater detail
Nominate citizens to boards or commissions.
32| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Government and nonprofit boards move slowly on governance automation due to tradition, legal conservatism, and oversight requirements. While some larger organizations pilot AI for candidate sourcing, deep production adoption of end-to-end nomination systems remains limited. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | This is a governance/political task embedded in public administration and executive leadership, sectors with minimal AI adoption for this specific function. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI can dramatically assist executives by surfacing qualified candidates, flagging gaps in diversity or expertise, drafting nomination justifications, and managing documentation—substantially reducing time spent on research and administrative work while the executive retains final authority. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could help research candidate backgrounds, qualifications, or draft nomination communications, but it offers limited assistance for the core relational and judgment-based nomination decision. |
| Task automatability | claude-haiku-4-5-20251001 | 5/5 | AI systems can identify candidates matching specified criteria (qualifications, experience, diversity requirements), generate nomination documents, and manage the nomination workflow end-to-end with significant time savings. This is a structured task amenable to data matching and document generation. |
| Task automatability | claude-sonnet-5 | 1/5 | Nominating citizens to boards or commissions requires personal judgment, political relationships, trust-building, and accountability that AI cannot substitute for; it is fundamentally a human decision-making and relational act. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Nomination decisions often require human judgment on governance, institutional politics, and legal compliance; boards and commissions typically expect accountability to rest with human executives, and many jurisdictions have requirements that the nominating official personally certify selections. These organizational and legal barriers limit full automation. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Nominations to boards/commissions are typically vested by law, charter, or governance structure in a specific accountable official (e.g., mayor, governor, CEO), making this a hard legal/authority barrier. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | AI systems can perform candidate research, filtering, and document generation at a fraction of the human labor cost (reducing hours of administrative work and research), making the cost ratio strongly favorable compared to staff time. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing this task, so cost comparison favors the human entirely; any AI attempt would add cost without replacing the judgment required. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | Candidate screening and ranking tools exist and are deployed in some organizations, but full end-to-end nomination with legal and governance sign-off still typically requires human review and decision-making. Mature automation exists for parts of the workflow but not fully autonomous execution. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product performs civic/political nominations; this remains entirely a human executive function with no automation precedent in production. |
Interpret and explain policies, rules, regulations, or laws to organizations, government or corporate officials, or individuals.
31CI 25–36 · exposure 25 · augmentation 75 · importance 4.1/5 · click for rater detail
Interpret and explain policies, rules, regulations, or laws to organizations, government or corporate officials, or individuals.
31| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Adoption remains cautious in most sectors due to liability concerns and the preference for human judgment on policy matters. While large firms experiment with AI-drafted policy guidance, replacement of executives' policy-explanation role remains minimal and mostly assistive. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Executives and their teams increasingly use AI for research and drafting regulatory summaries, but adoption for actual interpretive authority is slow and mostly assistive rather than transformative. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI can substantially assist executives by drafting clear explanations, flagging ambiguities, and generating multiple interpretations for review. This augmentation raises executive productivity without removing the executive from the loop, making it a strong use case for human-AI collaboration. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI is highly useful for quickly parsing, summarizing, and explaining complex regulations or legal text, giving executives faster grounding before they apply judgment and communicate interpretations. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can summarize and draft plain-language explanations of policies, this task requires contextual judgment, nuance, and organizational awareness. Explaining policies to officials often involves adapting language to specific stakeholder concerns and navigating ambiguities—capabilities current AI struggles with reliably enough to meet the 50% time-saving threshold without significant human oversight. |
| Task automatability | claude-sonnet-5 | 2/5 | AI can draft explanations of policies or regulations from text, but interpreting them for specific organizational context, stakeholder relationships, and authoritative judgment requires human expertise and accountability that current systems cannot replicate end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | While no explicit licensing requirement mandates human explanation, organizational liability and reputational risk create practical friction. Incorrect policy interpretation can expose organizations to legal or compliance risk, making autonomous AI substitution organizationally risky even where not legally prohibited. |
| Adoption barriers | claude-sonnet-5 | 4/5 | This task often carries legal and fiduciary weight—executives are accountable for interpretations affecting compliance, contracts, or governance—creating strong liability and authority-based barriers to full AI substitution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 3/5 | AI inference costs are modest, but the task requires integration with organizational knowledge systems and human oversight to ensure accuracy and liability management. The all-in cost approaches parity with mid-level staff time after accounting for verification and risk. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | While AI drafting of regulatory summaries is cheap, the liability and credibility requirements mean a CEO's time and judgment remain necessary, keeping effective all-in cost comparable to or only modestly cheaper than the human alternative. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | AI can produce policy summaries and explanations at scale, but deployed products lack the judgment needed for consequential policy interpretation in organizational or legal contexts. Error rates remain material when stakes are high, and most production use remains assistive rather than autonomous. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Legal/regulatory summarization tools and chatbots exist and are used for research assistance, but no deployed product reliably performs authoritative interpretation and explanation of policy to officials or individuals as a substitute for executive judgment. |
Deliver speeches, write articles, or present information at meetings or conventions to promote services, exchange ideas, or accomplish objectives.
30CI 28–32 · exposure 30 · augmentation 75 · importance 3.9/5 · click for rater detail
Deliver speeches, write articles, or present information at meetings or conventions to promote services, exchange ideas, or accomplish objectives.
30| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Many organizations use AI to draft or polish executive speeches and articles, and pilots are common in large firms, but few rely on it for delivery or public-facing content without executive refinement. Adoption remains in the assistive phase rather than autonomous replacement. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Executives increasingly use AI to draft communications and talking points, but the actual delivery and representation function remains firmly human-driven across sectors. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI is already widely used to assist executives by drafting talking points, generating article outlines, and suggesting messaging frames. These tools demonstrably improve productivity and speed of communication preparation while keeping the executive in final control and delivery role. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI substantially helps executives draft speeches, articles, and talking points, improving speed and quality while the executive still delivers and personalizes the message. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can draft speeches and articles efficiently, delivery requires authentic presence, real-time audience responsiveness, and executive credibility that current systems cannot fully replicate. The task involves strategic judgment about framing and stakeholder messaging that demands human oversight, limiting end-to-end automation to well below 50% time savings at equal quality. |
| Task automatability | claude-sonnet-5 | 2/5 | AI can draft speeches or articles, but delivering speeches and presenting persuasively at meetings requires human presence, credibility, and live judgment that current AI cannot replace end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Executive communications carry high reputational risk and stakeholder trust requirements; audiences expect authentic, accountable voice from the CEO. Regulatory filings and investor relations have explicit disclosure rules, and organizational norms strongly favor human-signed communications, creating meaningful friction against full substitution. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Stakeholder and public trust strongly favor the actual executive being physically present and personally accountable; delegating this to AI risks reputational and credibility damage. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | While AI dramatically reduces drafting labor, the final output still requires executive time for review, refinement, and delivery—costs that remain substantial. The per-task savings do not yet justify full displacement of human executive communication work. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI can cheaply generate draft text, but the executive presence, credibility, and live delivery component still requires the human, so overall cost savings are limited. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | AI tools (GPT-based drafting, presentation generators) exist and perform parts of this task reliably in production, but they require substantial human editing for tone, strategic alignment, and factual accuracy. No deployed system currently generates speeches or articles ready for C-suite delivery without material revision. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | AI writing tools reliably help draft content, but no deployed product autonomously delivers CEO-level public speeches or handles live Q&A representing an organization. |
Review and analyze legislation, laws, or public policy and recommend changes to promote or support interests of the general population or special groups.
29CI 25–34 · exposure 25 · augmentation 63 · importance 3.8/5 · click for rater detail
Review and analyze legislation, laws, or public policy and recommend changes to promote or support interests of the general population or special groups.
29| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | While larger organizations use AI for legislative monitoring and analytics, actual adoption of AI-generated policy recommendations at the C-suite remains rare and cautious. Most sectors treat policy leadership as a core human executive function, not a candidates for automation. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | While executives increasingly use AI for research and summarization, formal adoption of AI-driven policy recommendation processes in leadership functions remains nascent and pilot-stage. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist meaningfully by synthesizing legislation, identifying relevant precedents, and flagging stakeholder impacts, raising efficiency in research and drafting phases. However, the final judgment and advocacy remain human-led, making this a moderate augmentation play rather than transformative. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI tools are quite effective at summarizing legislation, tracking policy changes, and drafting analysis to support executives, meaningfully speeding up this research-heavy task. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can summarize legislation and identify policy implications, recommending changes to promote interests requires political judgment, stakeholder analysis, and normative reasoning about competing values that AI cannot reliably perform end-to-end. An AI might draft talking points or flag relevant clauses, but a Chief Executive must synthesize constituent interests and organizational strategy, which remains heavily human-dependent. |
| Task automatability | claude-sonnet-5 | 2/5 | AI can help summarize and analyze legislation but generating credible, strategic policy recommendations that require judgment, stakeholder awareness, and organizational values is not yet fully automatable end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Chief executives bear personal and fiduciary responsibility for policy recommendations; regulators, boards, and stakeholders expect human expertise, judgment, and accountability. Organizational and governance norms strongly favor human leadership in public-facing advocacy roles, creating high friction for full AI substitution. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No strict licensing requirement for executives to do this personally, but organizational trust, liability for public policy positions, and reputational risk create meaningful friction against full delegation to AI. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI tools for legislative analysis (LLMs, document search) cost far less than executive time, but the task itself is high-value intellectual work where the savings are modest relative to the human cost and outcome sensitivity. Integration and executive review still dominate total cost. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | AI can cheaply produce first-pass legislative summaries and drafts, but the human executive review, judgment, and validation still dominate cost, making overall savings moderate rather than dramatic. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed AI system reliably recommends public policy changes at executive level; products exist for legislative tracking and document summarization, but none credibly substitute for expert policy analysis and stakeholder negotiation that chief executives perform. Current systems lack the contextual judgment and accountability required for actionable policy recommendations. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Legal/policy analysis tools exist (e.g., legislative tracking, summarization products) but reliable, decision-grade recommendation generation for executives is not a mature deployed capability. |
Coordinate the development or implementation of budgetary control systems, recordkeeping systems, or other administrative control processes.
29CI 25–32 · exposure 25 · augmentation 75 · importance 3.8/5 · click for rater detail
Coordinate the development or implementation of budgetary control systems, recordkeeping systems, or other administrative control processes.
29| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | While large organizations are piloting AI for financial analysis, the actual *coordination* of control system implementation remains human-driven; adoption of full automation is slow due to governance and oversight requirements. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Finance and administrative functions are adopting AI tools steadily (e.g., automated reporting, dashboards), but executive-level coordination of control systems remains a human-led, slower-adopting activity. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI can meaningfully assist by drafting control frameworks, analyzing compliance gaps, and generating documentation—all of which augment executive decision-making while the chief executive retains responsibility for final implementation strategy. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI tools can significantly assist executives by analyzing data, drafting policies, flagging anomalies, and generating reports, improving efficiency in designing and monitoring control systems even though humans retain oversight. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can assist with data analysis and documentation of control processes, coordinating development and implementation across organizational stakeholders requires judgment about feasibility, change management, and business context that current systems cannot reliably handle end-to-end. |
| Task automatability | claude-sonnet-5 | 2/5 | AI can help design and configure budgetary control or recordkeeping systems, but the coordination role—aligning stakeholders, setting policy, and overseeing implementation across an organization—requires judgment and authority that current AI cannot exercise end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Chief executives bear fiduciary and legal responsibility for control systems; boards, auditors, and regulators expect human judgment and accountability, creating strong organizational and liability barriers to full automation. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing requirement bars AI use, but organizational governance, accountability for financial controls, and fiduciary responsibility create meaningful friction against full delegation to AI. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI can reduce documentation and analysis overhead, but the coordination, stakeholder management, and oversight required still demand significant human input; total cost savings are modest relative to executive compensation. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | While AI-powered analytics tools can lower some administrative costs, the executive coordination function still requires substantial human oversight, keeping overall costs comparable to or only modestly below human-only execution. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Some AI tools can draft budget frameworks or analyze control requirements, but no mature product reliably coordinates the full organizational implementation of administrative control systems—this involves stakeholder alignment and organizational change that resists full automation. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Deployed finance/ERP software with AI features can assist in building control systems, but no product autonomously coordinates the organizational implementation of such systems at the executive level. |
Organize or approve promotional campaigns.
29CI 25–32 · exposure 25 · augmentation 63 · importance 3.5/5 · click for rater detail
Organize or approve promotional campaigns.
29| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | While large enterprises use AI-assisted tools for campaign analytics and content generation, adoption of AI for campaign organization and approval decisions remains limited to pilots and support functions. True autonomous approval is not yet adopted in production systems. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Marketing and executive functions in information-heavy sectors are adopting AI tools for campaign support at a moderate pace, though full delegation of approval remains rare. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI offers useful assistance through market analysis, competitive benchmarking, audience segmentation, and content ideation that can improve campaign quality and speed up planning. However, the executive's role in judgment and approval remains central, making augmentation meaningful but not transformative. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI significantly aids campaign ideation, content drafting, market analysis, and performance prediction, substantially boosting executive productivity while the human retains final decision authority. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can assist with campaign idea generation, market analysis, and content drafting, the core task of organizing and approving campaigns requires strategic judgment, brand alignment, and executive decision-making that remains fundamentally human. Current systems cannot autonomously handle the full end-to-end process with 50% time savings at equal quality. |
| Task automatability | claude-sonnet-5 | 2/5 | AI can draft campaign ideas and content, but final organizing and approval requires executive judgment, brand strategy alignment, and accountability that current AI cannot autonomously provide end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | This task has substantial barriers to automation: it involves fiduciary responsibility, brand and legal liability for campaigns, regulatory compliance (advertising standards, FTC requirements), and organizational authority that typically require executive sign-off. The executive's accountability cannot be delegated. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No formal licensing requirement, but organizational governance, brand risk, liability for public-facing campaigns, and stakeholder trust create meaningful friction against full AI approval authority. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | While AI tools for campaign support are relatively inexpensive, the executive time required for meaningful oversight and final approval remains high, making the all-in cost comparable to or potentially exceeding a human campaign manager's time investment. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | While AI can cut costs on content drafting and analysis, the approval and organizational oversight component still requires expensive human executive time, keeping overall cost comparable to human-led process. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | AI tools exist for campaign planning support and content generation, but no mature product reliably performs the complete task of organizing and approving promotional campaigns at the executive level in production. Systems lack the contextual understanding and strategic nuance required for genuine approval authority. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Marketing AI tools exist for content generation and analytics, but no deployed product independently organizes or approves full promotional campaigns at executive-decision level in production. |
Analyze operations to evaluate performance of a company or its staff in meeting objectives or to determine areas of potential cost reduction, program improvement, or policy change.
28CI 28–28 · exposure 25 · augmentation 75 · importance 4.1/5 · click for rater detail
Analyze operations to evaluate performance of a company or its staff in meeting objectives or to determine areas of potential cost reduction, program improvement, or policy change.
28| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Large corporations and financial services firms routinely deploy analytics and monitoring dashboards, but these augment rather than replace executive analysis. Adoption is broad in digitized sectors but adoption of *automation* of the evaluative decision-making itself remains limited, with most organizations treating AI as an assistant to executives rather than a substitute. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Executives increasingly use AI-powered dashboards and analytics tools, but adoption of AI for strategic evaluation and policy-setting itself is still nascent and pilot-stage. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI-powered analytics, dashboards, anomaly detection, and scenario modeling strongly assist executives in analyzing operations by synthesizing vast datasets, flagging outliers, and modeling cost scenarios in real time. These tools measurably raise productivity and decision quality while keeping the executive in the evaluative and policy loop. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can significantly speed up data aggregation, benchmarking, and scenario analysis, giving executives richer input for their evaluations even though final judgment remains human. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can gather and aggregate operational data, dashboards, and financial metrics at scale, the core task requires strategic judgment about what constitutes 'performance,' contextual understanding of business objectives, and synthesis across human and organizational factors. AI can automate data collection and flagging of anomalies, but cannot independently determine whether objectives have been met or recommend policy changes without senior human oversight, falling short of the 50% time-saving bar for end-to-end performance. |
| Task automatability | claude-sonnet-5 | 2/5 | AI can assist with data analysis and generate reports, but synthesizing organizational performance into strategic evaluation requiring contextual judgment, politics, and accountability remains largely human-driven. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Chief executives and senior strategists are typically required by governance and organizational structure to personally evaluate performance and make policy decisions. Board oversight, fiduciary duty, and accountability requirements create legal and organizational barriers to full automation; stakeholders expect human accountability at the executive level. |
| Adoption barriers | claude-sonnet-5 | 4/5 | This is a core fiduciary and governance responsibility often tied to board accountability and legal liability, making full delegation to AI organizationally and legally constrained. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Analytics platforms and AI tooling require setup, maintenance, and domain expertise to interpret outputs. The all-in cost of building and maintaining such systems for organizational evaluation is comparable to or exceeds the cost of executive and analyst time, particularly because output validation and strategy setting remain manual. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI can cheaply crunch data, but the executive judgment, cross-functional context-gathering, and accountability portions still require expensive human oversight, making all-in cost comparable rather than dramatically cheaper. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Business intelligence and analytics platforms exist and can surface operational metrics, but no deployed system reliably performs the full task—especially the evaluative and strategic components—without significant human direction and validation. These tools support rather than replace the analysis; production use requires humans to interpret findings and make judgment calls. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Business intelligence and analytics tools are deployed widely, but no product autonomously performs holistic executive-level operational evaluation and policy recommendation in production. |
Review reports submitted by staff members to recommend approval or to suggest changes.
28CI 28–28 · exposure 25 · augmentation 75 · importance 4.1/5 · click for rater detail
Review reports submitted by staff members to recommend approval or to suggest changes.
28| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Information and professional services sectors are piloting AI-assisted report review and summarization, but autonomous approval remains rare. Most adopters use AI to augment executive review rather than replace it, placing adoption in the middling range. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Executives and professional services broadly are adopting AI tools for drafting and summarization at a moderate pace, though approval authority itself remains largely untouched. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI can substantially assist by rapidly summarizing key findings, flagging inconsistencies, extracting metrics, and highlighting risk areas, allowing executives to focus review on strategic and exception items. This meaningfully raises executive productivity while keeping the human accountable for approval decisions. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can efficiently summarize lengthy reports, highlight inconsistencies, and suggest edits, significantly speeding up the executive's review process while they retain final judgment. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can summarize and flag issues in reports, the final approval decision requires executive judgment about strategic implications, organizational context, and stakeholder concerns that extend beyond the report content itself. Current AI cannot reliably perform the full task of reviewing, contextualizing, and making approval decisions at executive level. |
| Task automatability | claude-sonnet-5 | 2/5 | AI can summarize and flag issues in reports, but final approval decisions integrate organizational context, politics, and accountability that current systems cannot reliably replicate end-to-end.' |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Executive sign-off on major organizational decisions carries fiduciary and legal responsibility; delegating approval authority to AI without human accountability creates liability and governance issues. Boards and stakeholders expect human executive judgment on material decisions, creating organizational and legal barriers to full automation. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Executive approval carries legal and fiduciary accountability that must rest with a human officer, creating strong organizational and liability barriers to full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Chief executives command high hourly rates ($150–500+/hour all-in). While AI summarization costs are low per document, the savings are modest because the human executive still reviews the AI output, requiring significant time investment. The cost comparison does not strongly favor automation. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | An AI review tool is cheap per document, but the executive's judgment and accountability still require human oversight, keeping overall cost savings modest rather than transformative. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | AI-powered document analysis and summarization tools exist and are deployed, but they typically serve as assistance rather than autonomous decision-makers. No mature product reliably performs independent executive-level approval decisions; human review remains standard practice in production settings. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Products like AI summarizers and document analysis tools exist and are used to review drafts, but no deployed system autonomously approves executive-level reports at scale in production. |
Direct or conduct studies or research on issues affecting areas of responsibility.
28CI 23–32 · exposure 25 · augmentation 75 · importance 3.7/5 · click for rater detail
Direct or conduct studies or research on issues affecting areas of responsibility.
28| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | While C-suite leaders increasingly use AI for data analysis and report preparation, genuine delegation of study direction and research strategy to AI remains exploratory; adoption is cautious and limited to supportive tools rather than autonomous research governance. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Executives in many sectors are adopting AI-assisted research and analytics tools, but this is a general middling trend with pilots more common than deep production integration in the C-suite context. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI significantly augments executive research capacity through rapid literature synthesis, data analysis, scenario modeling, and drafting; these capabilities enhance productivity and inform strategic decision-making while the executive retains full authority and judgment. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI tools can significantly speed up literature reviews, data aggregation, trend analysis and drafting of research reports, meaningfully augmenting an executive's or their staff's research capacity. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can assist with literature reviews, data synthesis, and report drafting, the core task of directing or conducting studies requires strategic oversight, judgment about research priorities, and human accountability that current AI cannot independently handle end-to-end. |
| Task automatability | claude-sonnet-5 | 2/5 | AI can help gather information and draft research summaries, but 'directing or conducting studies' involves setting strategic research agendas, judgment about organizational priorities, and synthesizing findings into executive decisions that current AI cannot fully replace. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Directing organizational research and strategic initiatives is a core executive prerogative with fiduciary and governance responsibility; boards, stakeholders, and regulators expect human leadership judgment that cannot be lawfully delegated to AI systems. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No licensing barrier per se, but organizational trust, accountability for strategic decisions, and the need for contextual judgment create meaningful friction against full delegation to AI. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Chief executives are highly paid; even with AI-assisted research infrastructure, the overhead and oversight costs are substantial relative to what AI systems provide, and most value still accrues to human judgment and leadership. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI can cheaply produce data summaries and literature reviews, but the executive judgment, stakeholder scoping, and oversight required still demand significant human time, keeping overall cost savings modest. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | AI tools exist for research support and analysis, but no deployed system can autonomously direct or conduct a study in a C-suite context; production systems lack the strategic judgment, stakeholder management, and decision-making authority this role demands. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Research and analytics tools exist (market research platforms, AI-assisted data analysis) but no deployed product autonomously directs strategic research programs for executives at scale. |
Prepare bylaws approved by elected officials, and ensure that bylaws are enforced.
25CI 25–25 · exposure 25 · augmentation 63 · importance 3.9/5 · click for rater detail
Prepare bylaws approved by elected officials, and ensure that bylaws are enforced.
25| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Municipal and organizational governance processes are slow to digitize; bylaw preparation and enforcement remain firmly rooted in deliberative legal and political processes. Adoption of AI for this task in production is minimal outside of supporting drafts. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Executive/governance functions in government and nonprofit contexts adopt AI slowly, with most current use limited to drafting assistance rather than institutionalized workflow automation. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist a chief executive by drafting initial bylaw language, identifying gaps, and cross-referencing existing codes, reducing manual drafting effort. However, the strategic, approval, and enforcement responsibilities require sustained human leadership, so augmentation is moderate rather than transformative. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can meaningfully speed up drafting, formatting, and cross-referencing of bylaws, and can help track enforcement compliance, substantially aiding the executive while they retain final responsibility. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | AI can draft bylaw language and flag consistency issues with existing codes, but cannot independently obtain approval from elected officials or ensure real-world enforcement without human judgment and discretionary authority. The approval and enforcement components require human decision-making and accountability. |
| Task automatability | claude-sonnet-5 | 2/5 | AI can draft bylaw language and check consistency, but the core task requires negotiation with elected officials, judgment about enforcement priorities, and accountability that current systems cannot autonomously execute end-to-end.} |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Bylaws must be formally approved by elected officials or governance bodies, and enforcement often requires legal authority vested in humans. Liability for incorrect or unenforced bylaws rests with leadership, creating a significant human sign-off and accountability requirement. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Bylaws typically require formal approval by elected officials and often legal certification, creating strong procedural and authority-based barriers to full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI can reduce drafting time, but the total cost of a chief executive's bylaw oversight—including securing approvals, legal review, and enforcement—is dominated by human judgment and liability. AI assistance on drafting alone does not drive cost per completed task below human wage. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI drafting tools are cheap for text generation, but the human oversight, legal review, and enforcement judgment needed keep overall cost comparable to or only modestly cheaper than an executive/legal team handling it. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | While language models can generate bylaw templates and assist with drafting, no deployed product reliably handles the full cycle of drafting, securing elected approval, and enforcing compliance. The approval and enforcement steps remain heavily dependent on human discretion and legal authority. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Legal drafting assistants exist and are used to produce first drafts, but no deployed product manages the full cycle of bylaw approval and enforcement oversight in production for executives. |
Negotiate or approve contracts or agreements with suppliers, distributors, federal or state agencies, or other organizational entities.
24CI 11–37 · exposure 25 · augmentation 75 · importance 4.1/5 · click for rater detail
Negotiate or approve contracts or agreements with suppliers, distributors, federal or state agencies, or other organizational entities.
24| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | While contract-tech adoption is growing in legal departments and procurement, C-suite substitution remains limited. Most organizations use AI as a preliminary tool rather than a replacement for executive negotiation and approval, reflecting risk aversion in high-stakes settings. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | While legal and contract-adjacent AI tools are being piloted in many industries, actual negotiation and approval authority remains firmly human-held with slow structural change. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI tools that summarize contract terms, flag deviations from templates, surface regulatory gaps, and generate alternative language substantially improve a CEO's ability to review and negotiate faster and with fewer blind spots. This augmentation is already in production in many forward-leaning organizations. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can meaningfully assist by summarizing contract terms, flagging risks, drafting language, and modeling negotiation scenarios, significantly boosting executive efficiency while they retain final control. |
| Task automatability | claude-haiku-4-5-20251001 | 3/5 | AI can assist with contract analysis, clause generation, and risk flagging at scale, but negotiation requires judgment on trade-offs, relationship dynamics, and strategic priorities that typically demand human oversight. A CEO could save 30–50% of time on routine contract review and initial drafting, but final approval and material negotiation remain human-dependent. |
| Task automatability | claude-sonnet-5 | 1/5 | Contract negotiation and final approval require judgment, relationship management, and accountability that current AI cannot autonomously execute end-to-end; AI can draft or analyze but not negotiate or approve with authority. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Legal liability for contract terms, fiduciary duty, and enforceability concerns create strong organizational and legal incentives to retain human decision-making at senior levels. Boards and legal departments typically require executive sign-off, and error costs are asymmetric and high. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Contract approval typically requires legal authority, fiduciary responsibility, and signatory power vested in a specific executive, creating strong legal and organizational barriers to full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 3/5 | AI contract-review systems cost thousands to tens of thousands annually, while a CEO's time on complex negotiations is valued at high hourly rates. For routine or template contracts, AI cost is favorable; for unique or high-stakes agreements, all-in cost remains comparable to the human overhead it displaces. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI tools can cheaply support contract review, but the human negotiation, judgment, and signing authority remain necessary, so overall cost savings versus the executive's time are limited. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Contract-drafting and review tools exist and perform well on standardized clauses, but no production system reliably handles the full lifecycle of high-stakes negotiation and approval across diverse counterparties and regulatory contexts. AI typically surfaces risks and generates language; humans finalize terms. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Products exist for contract analysis, redlining, and clause suggestion, but no deployed system autonomously negotiates or approves agreements on behalf of an executive in production. |
Direct non-merchandising departments, such as advertising, purchasing, credit, or accounting.
24CI 20–28 · exposure 20 · augmentation 75 · importance 3.8/5 · click for rater detail
Direct non-merchandising departments, such as advertising, purchasing, credit, or accounting.
24| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | While information-sector companies have adopted AI dashboards and analytics, actual delegation of department direction to AI systems remains rare. Adoption is limited to augmentation (reporting, forecasting) rather than replacement of executive decision-making in production environments. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Executive-level functions in finance, advertising, and accounting are adopting AI tools for analysis and drafting at a moderate pace, but full managerial direction remains firmly human-led. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI significantly assists executive decision-making by synthesizing large volumes of departmental data, generating budget scenarios, trend analysis, and performance summaries. Tools for financial forecasting, demand planning, and departmental analytics substantially improve executive productivity while maintaining human strategic control. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI significantly aids executives via dashboards, forecasting, report synthesis, and drafting communications across advertising, purchasing, credit, and accounting functions, improving decision speed and quality while the executive retains control. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can assist with specific analytical and reporting components (budget analysis, purchasing data synthesis, accounting summaries), the core task—directing departments—requires strategic decision-making, stakeholder management, and executive judgment that current systems cannot perform end-to-end with 50% time savings at equal quality. |
| Task automatability | claude-sonnet-5 | 2/5 | Directing departments involves setting strategy, exercising judgment over people and priorities, and holding accountability that current AI cannot autonomously perform end-to-end; only sub-components like reporting or scheduling are automatable. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Legal and fiduciary duties typically require a human executive to be accountable for department direction, budget allocation, and personnel decisions. Organizational liability, governance structures, and shareholder/stakeholder expectations create strong legal and institutional barriers to full automation or delegation to AI. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Corporate governance, fiduciary duty, and accountability structures require a human executive to be legally and organizationally responsible for departmental direction, creating strong structural barriers to full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | The loaded wage of a chief executive or department director is very high (often $150k+/year fully burdened), while AI systems provide only narrow analytical support. The total cost of AI infrastructure plus human oversight does not approach an order of magnitude advantage for the overall directing task. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | An executive's role integrates accountability, negotiation, and relationship management that AI cannot replicate, so replacing the function costs more in oversight, risk, and residual human management than it saves. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed product reliably performs full department direction. AI tools exist for isolated subtasks (e.g., invoice processing, ad campaign analytics), but orchestrating cross-functional oversight, setting strategic direction, and managing personnel remains firmly in human hands in production systems. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product manages a department's direction and personnel oversight; AI tools support analytics or drafting but do not act as departmental directors in production. |
Administer programs for selection of sites, construction of buildings, or provision of equipment or supplies.
23CI 20–25 · exposure 20 · augmentation 50 · importance 3.5/5 · click for rater detail
Administer programs for selection of sites, construction of buildings, or provision of equipment or supplies.
23| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | While large construction and corporate real estate firms are exploring AI for project analytics and risk modeling, actual displacement of executive decision authority in site selection and capital administration remains minimal; pilots are far more common than production substitution. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Executive-level administrative program management in construction/facilities sectors shows slow AI adoption; these are physical, capital-intensive processes with limited digitization of the core administrative judgment tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can meaningfully assist executives by analyzing site data, generating cost and risk models, and optimizing supply chains, but the executive must remain in the loop to weigh strategic, stakeholder, and regulatory factors. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI tools can meaningfully assist with site analysis, cost estimation, vendor comparison, and scheduling, improving efficiency, but the human executive retains core decision-making and coordination responsibility. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can assist with data analysis, cost modeling, and supply chain optimization, the task requires end-to-end judgment on site selection, construction oversight, and equipment/supply decisions that involve complex stakeholder management, regulatory compliance, and strategic trade-offs that current AI cannot fully automate at equal quality. |
| Task automatability | claude-sonnet-5 | 2/5 | This involves coordinating physical site selection, construction oversight, and procurement decisions requiring on-the-ground judgment, negotiation, and cross-functional accountability that current AI cannot execute end-to-end. Only sub-components like data analysis or document drafting are automatable. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Chief executives hold legal and fiduciary accountability for construction, site, and equipment decisions; liability, regulatory sign-off requirements, and stakeholder approval create substantial organizational and legal barriers to full automation, though AI-assisted decision support is permitted. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Executive accountability, contractual/legal liability, regulatory compliance (zoning, construction codes, procurement law) and fiduciary duty create strong barriers requiring human sign-off and responsibility. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI tools for planning and analysis are relatively affordable, but full administration of these programs requires significant human oversight, validation, and decision-making, keeping total cost per outcome close to or exceeding the loaded wage of the executive performing the task. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI can cheaply assist with research, scheduling, or vendor comparisons, but the overall program administration still requires expensive human oversight, negotiation, and liability-bearing decisions, keeping costs comparable to or higher than pure AI substitution. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed product reliably performs the full scope of site selection, building construction administration, and equipment provisioning end-to-end; AI tools exist for isolated components (e.g., cost estimation, risk assessment) but lack the integrated decision authority and accountability required in production. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product administers full facility/construction/procurement programs autonomously; this remains a human executive management function with AI only used for supporting analytics. |
Direct or coordinate an organization's financial or budget activities to fund operations, maximize investments, or increase efficiency.
22CI 16–28 · exposure 25 · augmentation 75 · importance 4.5/5 · click for rater detail
Direct or coordinate an organization's financial or budget activities to fund operations, maximize investments, or increase efficiency.
22| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | While finance and tech-forward organizations use AI analytics and FP&A tools, adoption of AI for actual budget direction and financial strategy coordination remains very limited; most organizations still treat this as an irreducibly human executive function. |
| Sector adoption velocity | claude-sonnet-5 | 3/5 | Finance functions are adopting AI tools for forecasting and analysis at a moderate pace, but executive-level financial direction remains largely human-led with AI as a pilot-stage support tool. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI is already transforming executive financial work through real-time dashboards, scenario modeling, anomaly detection in spending, and forecast generation; a CFO or CEO using these tools can make faster, better-informed decisions while remaining firmly in control of strategic direction. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI significantly enhances financial modeling, scenario planning, and data synthesis, giving executives better and faster inputs for decision-making. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can assist with financial analysis, forecasting, and scenario modeling, the task fundamentally requires executive judgment on strategic priorities, risk tolerance, stakeholder alignment, and organizational context that current AI systems cannot autonomously handle end-to-end with reliable quality at 50%+ time savings. |
| Task automatability | claude-sonnet-5 | 2/5 | AI can analyze budgets and generate recommendations, but directing/coordinating financial strategy requires accountability, negotiation, and organizational authority that current AI cannot exercise end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Hard legal and fiduciary barriers exist: a C-suite executive or board must legally sign off on financial strategy, budget allocation, and investment decisions; regulatory compliance, audit trails, and liability require human accountability that cannot be delegated to an AI system. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Fiduciary duty, board accountability, and legal liability for financial decisions mean a human executive must formally direct and sign off on these activities. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The loaded cost of a Chief Executive overseeing financial strategy is very high; AI tools that address fragments of this (analytics software, FP&A platforms) remain far more expensive than the human wage for the full integrated task, especially when oversight and governance are factored in. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI tools reduce analytical costs but the executive oversight, judgment, and fiduciary responsibility still require a highly paid human, keeping overall cost comparable rather than dramatically cheaper. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Some AI tools perform well on isolated components (budget forecasting, cost analysis, optimization), but no deployed product reliably coordinates full financial direction and budget strategy for an organization; successful deployment remains research-stage or limited to narrow, well-structured financial sub-tasks. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Financial analytics and forecasting tools are deployed widely, but no product autonomously directs or coordinates an organization's financial activities; human executives retain decision authority. |
Confer with board members, organization officials, or staff members to discuss issues, coordinate activities, or resolve problems.
16CI 7–25 · exposure 13 · augmentation 50 · importance 4.3/5 · click for rater detail
Confer with board members, organization officials, or staff members to discuss issues, coordinate activities, or resolve problems.
16| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Adoption of AI for core executive conferencing is minimal; most organizations use AI only for ancillary support (note-taking, scheduling). The task remains almost entirely human-performed because the interpersonal and decision-making elements are central to executive authority and accountability. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | While executives use AI tools for prep and analysis, the core interpersonal conferring function shows little to no displacement trend in adoption data. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can meaningfully augment executive conferencing through real-time transcription, document retrieval, summarization, and pre-meeting briefing generation, helping an executive prepare and manage information flow during meetings. However, the human executive remains fully in the loop and essential. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help prepare briefing materials, summarize prior discussions, draft agendas, or analyze options ahead of these conversations, offering moderate productivity support. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can draft meeting agendas, summarize documents, and suggest talking points, the core task—conferring to discuss complex issues, coordinate activities, and resolve problems—requires human judgment, interpersonal negotiation, and contextual authority that current AI cannot replicate end-to-end. AI lacks the ability to genuinely negotiate, build consensus, or make binding decisions. |
| Task automatability | claude-sonnet-5 | 1/5 | This is a live, interpersonal negotiation and decision-making task requiring judgment, relationship management, and authority that AI cannot substitute for end-to-end today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Board and executive conferences carry fiduciary and legal obligations that typically require a human executive to be present and accountable. Regulatory expectations, liability asymmetry, and the implicit requirement for authorized human presence create substantial adoption friction. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Governance structures, fiduciary duty, and legal accountability require a human executive to personally engage with boards and staff; this is deeply embedded in corporate law and organizational trust norms. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI tools for meeting support (transcription, note-taking) are relatively cheap, but the cost of oversight, human review, and the retained need for an executive to ultimately conduct the conference makes AI only marginally cheaper than the executive's time, if at all. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing this task independently, so cost comparison favors the human by default since AI cannot deliver the output at all. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed product can reliably conduct board or executive-level conferences autonomously. AI can support preparation (transcription, summarization, memo drafting) but cannot substitute for the executive presence, real-time decision-making, and accountability that characterizes this task in production. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product conducts executive-level conferring, conflict resolution, or organizational coordination autonomously; AI is at best a note-taker or scheduler adjunct. |
Make presentations to legislative or other government committees regarding policies, programs, or budgets.
14CI 9–20 · exposure 20 · augmentation 63 · importance 3.8/5 · click for rater detail
Make presentations to legislative or other government committees regarding policies, programs, or budgets.
14| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Adoption of AI for live government testimony is negligible; the inherent legal requirement for personal testimony by the named executive, combined with political and accountability norms, prevents any meaningful substitution even in digitized sectors. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | While executives increasingly use AI for prep and drafting, the sector of executive government relations and testimony is slow to change due to institutional, ceremonial, and accountability norms. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can meaningfully assist in preparing presentations, drafting testimony, anticipating questions, and refining arguments, improving executive productivity in preparation. However, the live delivery component remains human-centric, limiting overall augmentation scope. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI tools are very useful for drafting presentation content, anticipating questions, summarizing budget data, and rehearsing responses, meaningfully boosting preparation efficiency even though delivery remains human. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can generate presentation content, slides, and talking points, the live delivery of nuanced policy arguments to government committees requires real-time judgment, political sensitivity, and credibility that demands human presence. AI cannot reliably perform the full task end-to-end at equal quality. |
| Task automatability | claude-sonnet-5 | 2/5 | AI can help draft talking points and slides, but the actual act of presenting to and engaging with a government committee, including live Q&A and judgment calls, requires human presence and authority that current AI cannot substitute end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Strong legal and procedural barriers exist: government committees require testimony from the actual responsible official (the CEO), not a substitute or agent. Political accountability, oath-taking, and cross-examination demands necessitate human presence, making automation legally and institutionally infeasible. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Legislative and governmental protocol requires an authorized human representative (often legally accountable) to present and answer questions; this is a role tied to executive authority and legal accountability that cannot be delegated to AI. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | The high stakes and human-required nature of testimony mean AI cost savings are marginal; oversight and integration of AI-generated materials still requires senior executive time and legal review, keeping total cost near human wage levels. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Since AI cannot substitute for the executive's actual appearance and accountability before the committee, there is no viable AI-alone cost comparison; the human must still perform the core task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed product reliably handles the end-to-end task of delivering government testimony. AI excels at drafting materials, but no system can substitute for the live presentation, answer hostile questions, negotiate in real-time, or carry the legal/political weight of a chief executive's testimony. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | There are no deployed products where AI autonomously delivers or represents an organization's testimony before legislative bodies; this remains firmly human-performed with only drafting-adjacent AI tools available. |
Direct or coordinate activities of businesses involved with buying or selling investment products or financial services.
14CI 3–25 · exposure 13 · augmentation 50 · importance 3.9/5 · click for rater detail
Direct or coordinate activities of businesses involved with buying or selling investment products or financial services.
14| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | While financial services sectors digitize rapidly, executive leadership functions remain predominantly human-centric; AI adoption here is limited to decision support and analytics rather than autonomous direction, reflecting organizational conservatism and regulatory resistance. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | While financial services broadly adopts AI tools for analytics and operations, executive direction and coordination roles themselves show minimal AI-driven displacement or restructuring. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI tools (dashboards, predictive analytics, portfolio analysis, market intelligence) can meaningfully assist CEOs in financial services by accelerating insight generation and risk assessment, though the human executive remains essential for strategic direction and accountability. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can assist executives with market analysis, reporting dashboards, and decision-support data, improving efficiency in oversight without replacing the coordinating and directing function itself. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can assist with data analysis, market monitoring, and reporting, the core task of directing and coordinating business activities requires strategic judgment, stakeholder relationship management, and accountability that cannot be fully automated. AI lacks the agency and liability capacity to replace executive decision-making. |
| Task automatability | claude-sonnet-5 | 1/5 | This is an executive leadership task involving strategic direction, accountability, and cross-functional coordination that requires human judgment, relationship management, and legal responsibility beyond what AI can perform end-to-end today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Regulatory frameworks (SEC, FINRA, fiduciary standards) explicitly require licensed, accountable human executives to direct financial services operations. Board governance, shareholder accountability, and legal liability create hard barriers to full automation of executive direction. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Corporate governance law, fiduciary duty, securities regulation, and board/shareholder accountability require a legally responsible human executive to direct such activities. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | The cost of AI systems with sufficient maturity to assist executive functions (data platforms, analytics suites, advisory systems) is typically high and requires significant integration, while the loaded wage of executives is also very high, making direct cost replacement unlikely in the near term. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI substitute performing this executive coordination function, so no comparable cost basis exists; human executive judgment remains irreplaceable at this scope. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed product performs end-to-end executive direction or coordination at scale. AI tools exist for analysis and planning support, but no production system reliably replaces or replicates a CEO's coordination of complex business operations, risk management, and strategic direction. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product autonomously directs or coordinates a financial services business; AI serves only as analytical or informational support to human executives who retain decision authority. |
Establish departmental responsibilities and coordinate functions among departments and sites.
12CI 3–21 · exposure 13 · augmentation 50 · importance 4.0/5 · click for rater detail
Establish departmental responsibilities and coordinate functions among departments and sites.
12| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Organizations are slow to automate core governance and structural decisions even when technology is available. Most deployments remain assistive (helping analyze options) rather than replacing executive judgment on departmental responsibility allocation, limiting real-world adoption velocity. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | While executives increasingly use AI for analytics and planning support, actual delegation of organizational design decisions to AI is essentially nonexistent in practice. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can meaningfully assist by analyzing organizational dependencies, suggesting process improvements, and simulating coordination scenarios, helping executives make better-informed decisions. However, the core responsibility for alignment and accountability remains with human leadership. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help analyze org charts, benchmark structures, model scenarios, and draft communication plans, providing meaningful support to executives making these decisions. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | Establishing responsibilities and coordinating functions requires organizational knowledge, stakeholder alignment, and judgment about priorities that vary significantly by context. AI systems can draft org structures or identify coordination points, but cannot autonomously make the strategic choices and negotiation decisions required to genuinely establish and coordinate departmental roles. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires deep organizational judgment, political navigation, and authority to reorganize people and reporting lines—something AI cannot execute end-to-end today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Chief executives have fiduciary and legal responsibilities for organizational structure and inter-departmental accountability. Regulatory oversight, shareholder expectations, and organizational governance frameworks typically require human executives to sign off on and own these decisions, creating substantial legal and accountability barriers. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Organizational authority, accountability, and legal responsibility for structuring a company are inherently vested in human executives and boards, creating a hard barrier to automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The cost of integrating, configuring, and overseeing AI for enterprise-wide organizational coordination, combined with the need for human validation and adjustment, likely exceeds the cost of executive time spent on direct coordination with competent staff. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI substitute performing this task, so cost comparison favors the human entirely; AI cannot produce the output at all. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | While AI tools can assist with organizational analysis and process mapping, no deployed product reliably performs autonomous departmental coordination and responsibility-setting end-to-end. Existing tools require heavy human interpretation and decision-making; the task remains primarily human-driven in organizations. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product autonomously establishes departmental structures or coordinates cross-site functions; this remains a human executive function with AI only as an input tool. |
Implement corrective action plans to solve organizational or departmental problems.
8CI 0–16 · exposure 13 · augmentation 63 · importance 4.1/5 · click for rater detail
Implement corrective action plans to solve organizational or departmental problems.
8| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Adoption of AI for autonomous corrective action planning is minimal even in digitally advanced sectors. Executives rely on advisory tools and dashboards, but actual plan formulation and implementation remain human-driven, reflecting deep governance and accountability requirements. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | While executives increasingly use AI analytics for diagnosis, actual implementation of corrective measures remains a slow-adopting, judgment-heavy leadership function. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI provides moderate assistive value by analyzing data, simulating outcomes, and drafting plan components, reducing the analytical burden on executives. However, the augmentation is partial—AI does not fundamentally transform an executive's ability to make the final strategic decision or navigate organizational dynamics. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can strongly assist by analyzing root causes, modeling scenarios, and drafting action plans, meaningfully boosting executive decision-making speed and quality. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can help generate and analyze potential corrective action plans (e.g., via data analysis and scenario modeling), the task fundamentally requires executive judgment about organizational politics, culture, stakeholder buy-in, and strategic priorities. Current AI systems cannot autonomously implement these plans or make the contextual leadership decisions needed, though they can accelerate components of the analysis phase. |
| Task automatability | claude-sonnet-5 | 1/5 | Implementing corrective action requires organizational authority, stakeholder negotiation, and accountability that AI cannot execute end-to-end; AI can inform but not enact these decisions or drive their execution. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | This task is legally and structurally protected: a licensed or authorized executive must personally own and sign off on corrective action plans. Organizational accountability, fiduciary duty, and liability frameworks require human leadership approval and responsibility, creating hard barriers to automation. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Executive accountability, fiduciary duty, and legal responsibility for organizational decisions mean a human must own and authorize corrective actions. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The cost of AI systems capable of advising on corrective action planning (data infrastructure, modeling, oversight) combined with the irreducible human executive time still required exceeds the marginal value over a seasoned executive's unaided judgment for most organizations. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing this task independently, so cost comparison favors the human executive who retains legal and operational responsibility. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed AI product reliably performs this task end-to-end in production. AI tools can support plan generation and impact forecasting, but actual implementation—which involves directing teams, managing resistance, adjusting in real time based on human feedback—remains a human executive function with no mature AI replacement. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product autonomously implements corrective organizational action plans; this remains a human executive function involving judgment, authority, and interpersonal leadership. |
Serve as liaisons between organizations, shareholders, and outside organizations.
8CI 0–16 · exposure 8 · augmentation 50 · importance 3.9/5 · click for rater detail
Serve as liaisons between organizations, shareholders, and outside organizations.
8| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | This task involves legally and fiducially sensitive external representation where human authorization and accountability are non-negotiable; adoption of AI replacement is not occurring and faces insurmountable legal and organizational barriers. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | While executives use AI tools for communications and reporting support, actual liaison and relationship-representation functions show minimal displacement or agent-driven adoption in real organizations. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI can assist by preparing briefing materials, drafting communications, or analyzing stakeholder information, but the core liaison function—presence, judgment, and accountability—remains with the human executive. Augmentation is limited to information preparation tasks. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can meaningfully assist with drafting shareholder communications, summarizing stakeholder sentiment, preparing briefings, and tracking correspondence, enhancing the executive's effectiveness in this role. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Serving as a liaison requires relationship management, negotiation, political judgment, and trust-building with external parties—dimensions where current AI cannot substitute end-to-end. While AI can draft communications or summarize information, it cannot authentically represent an organization or make commitments in high-stakes external negotiations. |
| Task automatability | claude-sonnet-5 | 2/5 | This liaison role requires relationship management, trust-building, negotiation, and representing organizational interests in nuanced political and social contexts that current AI cannot perform end-to-end.rapid summarization and drafting can help but the core relational function is not automatable. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Hard barriers exist: shareholders, regulatory bodies, and external organizations expect to communicate with authorized human representatives who can be held legally and fiducially accountable. Liability, contract law, and governance requirements mandate human sign-off and presence. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Fiduciary duty, legal accountability to shareholders, and governance/regulatory requirements (e.g., certifying officer roles, SEC disclosures) create strong barriers requiring an accountable human executive. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The task fundamentally requires human presence and accountability; there is no AI system that can serve as a legal or fiduciary liaison at any cost, so cost comparison is not applicable. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI substitute performing this function, so cost comparison favors the human significantly; any AI cost would be additive to human oversight rather than replacing it. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI system can perform the role of organizational liaison independently; this requires human judgment, accountability, legal authority, and the ability to make binding commitments on behalf of the organization—all beyond current AI capabilities. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product acts as an autonomous liaison between an organization's executives, shareholders, and external parties; this remains firmly a human relationship function. |
Direct or coordinate activities of businesses or departments concerned with production, pricing, sales, or distribution of products.
5CI 3–7 · exposure 0 · augmentation 63 · importance 4.1/5 · click for rater detail
Direct or coordinate activities of businesses or departments concerned with production, pricing, sales, or distribution of products.
5| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Adoption of AI in executive support roles is slow and limited to analytics and reporting functions. Actual delegation of direction and coordination authority to AI remains negligible across sectors; most organizations retain human CEOs and senior leadership making core strategic decisions. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | While executives increasingly use AI tools for analytics and reporting, actual coordination and directive authority remain unautomated across sectors. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI tools meaningfully assist executives by providing real-time dashboards, predictive analytics on sales and production, and scenario modeling, raising their decision-making quality and speed on specific domains. However, the human executive remains the central decision-maker and strategist. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can meaningfully assist executives with data synthesis, forecasting, and communication drafting, enhancing decision quality and speed while humans retain control. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Directing and coordinating business activities requires real-time judgment, stakeholder negotiation, strategic decision-making, and accountability that cannot be meaningfully automated end-to-end. While AI can assist with analytics and forecasting, the executive judgment and human authority required to steer organizations remain fundamentally human responsibilities. |
| Task automatability | claude-sonnet-5 | 1/5 | This task requires cross-functional judgment, organizational authority, and real-time decision-making across teams that AI cannot exercise or execute end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Executives bear legal fiduciary responsibility for business outcomes and must make binding decisions on behalf of shareholders and stakeholders. Liability, regulatory accountability, and the requirement for a human agent to sign off on major business decisions create hard legal barriers to full automation. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Legal accountability, fiduciary duty, and organizational governance require a human executive to hold authority and liability for these decisions. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The cost of deploying AI oversight, validation, and integration for executive-level decisions—combined with required human accountability—exceeds the labor cost of competent human executives who perform this work directly. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI substitute performing this role, so cost comparison favors the human by default; any AI role is purely supportive, not substitutive. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI system autonomously directs business operations or makes binding strategic decisions about production, pricing, and sales. AI tools exist for analysis and recommendation, but no product reliably performs the full coordination and direction task independently. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product directs or coordinates business operations autonomously; this remains firmly a human executive function today. |
Appoint department heads or managers and assign or delegate responsibilities to them.
4CI 0–9 · exposure 8 · augmentation 50 · importance 4.1/5 · click for rater detail
Appoint department heads or managers and assign or delegate responsibilities to them.
4| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Adoption is near-zero because this task is structurally reserved for human executives. Appointment decisions carry legal, fiduciary, and reputational risk that organizations will not delegate to AI systems, regardless of sector. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Executive appointment and delegation decisions show no meaningful AI adoption trend in any sector; this remains a purely human governance activity. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist by analyzing candidate profiles, summarizing qualifications, or flagging organizational needs, helping the CEO make more informed decisions. However, the augmentation is limited because the task is primarily about judgment and organizational authority rather than information synthesis. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can assist by analyzing candidate performance data, organizational structure options, or succession planning scenarios, but the final appointment and delegation decision remains human-driven. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | AI cannot autonomously appoint personnel—this requires human authority, judgment about individual capabilities, organizational fit, and final decision-making by the executive. AI might assist with candidate screening or recommendation, but cannot end-to-end execute the appointment and delegation with the required accountability. |
| Task automatability | claude-sonnet-5 | 1/5 | Appointing department heads requires organizational judgment, trust-building, and strategic fit assessments that are fundamentally human relational decisions; no AI system can perform this end-to-end today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Legal and organizational barriers are substantial: only authorized executives can appoint managers; liability for hiring decisions falls on the organization and its leadership; HR and employment law restrict automated personnel decisions. Human discretion is legally and organizationally mandated. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Corporate governance, fiduciary duty, and legal authority structures require this decision to be made and formally executed by an authorized human executive or board, making it a hard institutional barrier. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | AI cannot replace this task at any cost advantage because the task fundamentally requires human executive authority and accountability. The loaded cost of a CEO making this decision is far lower than attempting to replace that decision-making with AI infrastructure and oversight. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing this task, so cost comparison favors the human entirely; any AI cost would be additive rather than substitutive. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product performs this task reliably in production. While AI can generate recommendations or summaries of potential candidates, actual personnel appointment requires human executive judgment and organizational authority that current systems do not possess. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product autonomously appoints managers or delegates executive authority; this remains entirely a human executive function with no production AI analog. |
Conduct or direct investigations or hearings to resolve complaints or violations of laws, or testify at such hearings.
4CI 0–7 · exposure 5 · augmentation 50 · importance 4.0/5 · click for rater detail
Conduct or direct investigations or hearings to resolve complaints or violations of laws, or testify at such hearings.
4| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | There is no meaningful automation or agent-based substitution of this task in practice. Organizations rely on human investigators and executives to maintain legal standing and accountability in dispute resolution. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | While executives use AI tools for research and drafting, actual investigation-directing and hearing testimony show negligible AI adoption due to legal and accountability constraints. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can usefully assist by organizing evidence, flagging document anomalies, or summarizing complaints, helping the human investigator and hearing conductor work more efficiently, though the core judgment and authority remain strictly human. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help executives prepare by summarizing evidence, drafting questions, or organizing case materials, but it doesn't touch the core testimony or investigative authority itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task fundamentally requires human judgment, legal authority, and decision-making power that cannot be automated. AI cannot conduct hearings, render decisions with legal standing, or testify in official capacity—all core elements of the task. |
| Task automatability | claude-sonnet-5 | 1/5 | Directing investigations and testifying at hearings requires judgment, authority, credibility, and legal accountability that current AI cannot exercise or substitute for end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Hard regulatory and legal barriers require a licensed executive or official to conduct investigations and hearings; statutory authority to make determinations and testify cannot be delegated to or replaced by AI systems. Liability and accountability are personal and non-transferable. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Testimony and formal investigative authority typically require a specific, accountable human (often under oath or legal duty), making this a hard-barrier task with legal and liability requirements. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | AI assistance in investigation support (document analysis, summarization) remains marginal relative to the core task. The irreplaceable human investigator and hearing-conductor cost far exceeds any current AI tool cost savings. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | AI cannot perform the core act of directing/testifying, so there is no viable cost comparison—human execution is mandatory. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | While AI can assist with document review and evidence analysis, no deployed system can independently conduct investigations, manage hearings, or make binding determinations. The legal and human-authority requirements mean production systems do not and cannot perform this task end-to-end. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product conducts or directs formal investigations or testifies at hearings; this remains firmly a human executive/legal function. |
Direct, plan, or implement policies, objectives, or activities of organizations or businesses to ensure continuing operations, to maximize returns on investments, or to increase productivity.
1CI 0–3 · exposure 0 · augmentation 63 · importance 4.2/5 · click for rater detail
Direct, plan, or implement policies, objectives, or activities of organizations or businesses to ensure continuing operations, to maximize returns on investments, or to increase productivity.
1| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | CEO replacement is not happening. While executives use AI tools for analysis, no sector is automating or delegating CEO-level direction to AI agents; the role remains entirely human-occupied across all industries. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | While executives increasingly use AI tools for analysis and forecasting, adoption of AI into actual strategic decision-direction and accountability structures remains slow and largely advisory. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist CEOs with data analysis, scenario modeling, market intelligence, and report drafting, improving decision speed. However, augmentation is limited to information synthesis; strategic judgment and organizational accountability remain human functions. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI tools significantly enhance executive decision-making through data analysis, scenario modeling, and drafting strategic documents, improving productivity while the executive retains ultimate judgment and accountability. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires strategic vision, organizational judgment, stakeholder management, and accountability for outcomes that current AI cannot autonomously execute. While AI can assist with analysis and planning, the core act of directing and implementing organization-wide policy remains inherently human-dependent. |
| Task automatability | claude-sonnet-5 | 1/5 | This task requires holistic organizational judgment, accountability, stakeholder negotiation, and strategic vision that current AI cannot execute end-to-end; AI can inform but not direct or implement executive policy autonomously. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Legal, fiduciary, and regulatory frameworks explicitly require a human CEO with personal liability and accountability. Boards, shareholders, regulators, and stakeholders mandate human executive oversight—this is a hard structural barrier. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Legal accountability (fiduciary duty, board governance, regulatory reporting, liability for corporate decisions) requires a human officer to hold this role; no AI can legally serve as CEO. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | A CEO's role involves fiduciary responsibility, liability, and strategic judgment that requires human executive compensation. AI assistive tools are negligible in cost relative to executive wages and cannot replace the economic value extracted from the role. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI substitute performing this task independently, so no meaningful cost comparison favors AI; the human executive role remains irreplaceable at current capability levels. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product performs end-to-end CEO-level strategic direction and implementation. AI tools exist for analytics and decision support, but no system can reliably own organizational strategy and accountability at production scale. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product autonomously sets or implements organizational policy and strategy; this remains firmly a human executive function with AI only as an analytical aid. |
Direct human resources activities, including the approval of human resource plans or activities, the selection of directors or other high-level staff, or establishment or organization of major departments.
1CI 0–3 · exposure 0 · augmentation 38 · importance 4.1/5 · click for rater detail
Direct human resources activities, including the approval of human resource plans or activities, the selection of directors or other high-level staff, or establishment or organization of major departments.
1| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | CEO-level HR and organizational strategy decisions are not being displaced by AI in any sector; human executives remain the sole decision-makers. Adoption of AI in HR remains limited to low-stakes screening and administrative tasks, not executive direction. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | While executives increasingly use AI-assisted analytics for planning, adoption of AI in actual HR governance decisions and executive appointments remains extremely limited and cautious. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI can provide data summaries, candidate analytics, or comparative analyses to assist an executive's deliberation, but it offers narrow augmentation because the core task—judgment about people, strategy, and accountability—remains fundamentally human. The executive workflow is only marginally enhanced by current tools. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can meaningfully assist by analyzing HR data, benchmarking compensation, or drafting organizational proposals, but the final directive and approval role remains human-driven. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Directing HR activities and selecting high-level staff require nuanced judgment about organizational strategy, cultural fit, and personnel capability—decisions that involve human stakeholder input, accountability, and subjective evaluation. Current AI cannot autonomously make binding decisions on senior hiring or organizational structure. |
| Task automatability | claude-sonnet-5 | 1/5 | This task requires high-stakes judgment, relationship management, organizational politics, and accountability that current AI cannot execute end-to-end; it involves final authority decisions inherently reserved for a human executive. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Legal, fiduciary, and corporate governance structures require a human executive to hold final authority and personal liability for HR direction and senior staffing decisions. Boards, shareholders, and employment law mandate that these decisions be made by accountable human officers. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Legal, fiduciary, and governance structures require a human executive (often board-approved) to make final decisions on senior hires and organizational structure, creating hard institutional and legal barriers. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | AI could assist with data analysis or candidate screening, but the core task—making and defending high-stakes staffing and organizational decisions—must remain human-driven. The overhead of AI oversight and human decision-making combined exceeds the cost of human executives performing the task directly. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI substitute performing this executive function, so no meaningful cost comparison applies; human executive judgment remains necessary and cannot be replaced by cheaper inference costs. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product reliably performs CEO-level HR direction, senior staff selection, or departmental reorganization. These tasks require executive authority, legal accountability, and stakeholder consensus that only humans can provide today. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product autonomously directs HR strategy or selects high-level executives; existing HR AI tools only assist with screening or analytics, not executive-level direction and approval. |
Preside over, or serve on, boards of directors, management committees, or other governing boards.
0CI 0–0 · exposure 0 · augmentation 38 · importance 3.8/5 · click for rater detail
Preside over, or serve on, boards of directors, management committees, or other governing boards.
0| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Zero adoption because the task is legally and structurally impossible for AI to perform; boards remain exclusively human-composed in all sectors. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | There is no observable trend of AI systems taking board seats or chairing governance committees; adoption in this specific function is essentially nonexistent. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI can assist with information synthesis and agenda preparation for board members, but cannot augment the core act of governance, deliberation, voting, or accountability that defines board service. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can assist by summarizing board materials, drafting agendas, analyzing financials, or preparing briefing documents, meaningfully aiding preparation even though it cannot perform the governance role itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Presiding over boards and serving on governing bodies requires fiduciary judgment, accountability, legal responsibility, and interpersonal leadership that cannot be automated. AI cannot legally serve as a board member or assume directorial liability. |
| Task automatability | claude-sonnet-5 | 1/5 | Presiding over or serving on a board involves fiduciary duty, relationship-based judgment, negotiation, and legal accountability that cannot be delegated to AI systems today.atab |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Regulatory and legal barriers are absolute: board membership, directorial duties, and fiduciary responsibilities are reserved by law to human individuals who can be held personally and collectively accountable. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Corporate law, fiduciary duty statutes, and governance regulations require named human directors/officers to bear legal liability and formally preside over boards. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | AI cannot perform this task at all, making cost comparison meaningless. Board service is about governance accountability, not task automation. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI substitute performing this function, so no meaningful cost comparison exists; the human cost is irreducible for legal governance roles. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No AI system can serve as a board member or preside over a board in any deployed capacity; these roles require legal personhood and personal accountability that AI cannot assume. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs board governance or fiduciary oversight; this remains entirely a human role in practice. |
Refer major policy matters to elected representatives for final decisions.
0CI 0–0 · exposure 0 · augmentation 38 · importance 3.7/5 · click for rater detail
Refer major policy matters to elected representatives for final decisions.
0| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | No sectors are adopting AI to automate referrals to elected bodies because the task is fundamentally about human political accountability and discretion, which cannot be automated without violating the governance structure itself. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Executive governance and policy referral functions show essentially no AI adoption; this is a high-trust, low-digitization interpersonal and political process. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could provide limited assistance by analyzing policy issues and flagging considerations for a CEO to review before referral, but the core task—deciding what matters are major and when to escalate—remains a human judgment with modest opportunity for assistive augmentation. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help draft briefing materials, summarize policy options, and prepare talking points to support the executive's referral, but the core decision and interaction remain human-driven. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task inherently requires human political judgment and democratic accountability; an AI cannot perform the political decision-making authority that is legally and constitutionally vested in elected representatives, and referral itself is a judgment call involving stakeholder assessment that lacks objective criteria. |
| Task automatability | claude-sonnet-5 | 1/5 | This task is fundamentally a governance/political judgment and relational act—deciding what rises to elected officials and how to frame it—requiring accountability and authority AI cannot hold, so no end-to-end automation is feasible today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Strong legal and institutional barriers exist: elected representatives themselves must make final decisions by definition, and a CEO cannot delegate away accountability for major policy matters through automation; organizational governance structures require human sign-off at this level. |
| Adoption barriers | claude-sonnet-5 | 5/5 | This task is inherently tied to legal authority, fiduciary responsibility, and governance structures requiring a specific accountable executive to interact with elected representatives—an unavoidable human/legal requirement. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | The task is not one that would be cost-reduced by automation because the actual work—understanding policy significance and making a judgment call about escalation—involves human oversight that would be required anyway; AI cannot replace this judgment without introducing legal and political risk. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing this function, so cost comparison favors the human by default since the AI alternative doesn't exist as a product. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed product can autonomously decide which matters are 'major policy' and execute formal referrals to elected bodies with legal effect; this requires interpretation of organizational context and political authority that current AI systems cannot reliably perform. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs this referral function; it involves organizational judgment, political sensitivity, and legal accountability that current AI products do not address. |
Attend and participate in meetings of municipal councils or council committees.
0CI 0–0 · exposure 0 · augmentation 38 · importance 3.6/5 · click for rater detail
Attend and participate in meetings of municipal councils or council committees.
0| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | No adoption of AI participation in municipal governance meetings has occurred or is legally permissible in practice, as this task is intrinsically tied to elected human representation. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Government and municipal governance functions are slow-moving, low-digitization environments with minimal AI agent deployment for actual participatory governance roles. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI might assist a chief executive by drafting talking points, summarizing prior meeting minutes, or preparing policy briefs, but the participation itself remains fundamentally a human responsibility. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can assist with meeting prep, summarizing agendas, drafting talking points, and generating minutes, improving efficiency even though it cannot replace the executive's participation. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | This task requires real-time participation in governance proceedings, including speaking, voting, and representing constituents—activities demanding human judgment, legal authority, and democratic legitimacy that current AI cannot perform end-to-end. |
| Task automatability | claude-sonnet-5 | 1/5 | This requires physical or live presence, real-time judgment, negotiation, and public representation that current AI cannot perform end-to-end in place of a human executive. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Hard legal and constitutional barriers: municipal council members must be elected or appointed humans with voting authority, and statutes typically require personal presence and sworn duty for official action. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Municipal governance typically requires the designated, accountable official to attend and participate personally; legal/organizational authority and representation cannot be delegated to an AI system. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | There is no cost comparison because the task cannot be automated—a human official must attend and vote. Attempting AI substitution would violate governance rules and laws. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing this task, so any cost comparison favors the human by default since the AI alternative doesn't exist as a full replacement. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI system can legally or meaningfully participate as a voting member in municipal council meetings; this requires human presence, identity verification, and legal accountability. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product attends and substantively participates in governmental meetings on behalf of an executive; at most AI provides transcription or note-taking support. |
Related occupations — Management
How to read this
A high substitution score does not mean this job disappears — it means a large share of its current tasks face replacement pressure, so the mix of tasks is likely to change. High augmentation alongside substitution typically means the occupation reorganizes around the protected tasks. Wide confidence intervals mean the rater panel disagreed: treat those scores as open questions, not verdicts.
What would change this score
New model capabilities (automatability, feasibility), falling inference costs (cost ratio), regulation and licensing shifts (barriers), and measured sector adoption (velocity) all re-enter at every index release. Each release is recomputed, versioned and kept queryable — scores are claims with a date on them, not permanent labels.