Project Management Specialists

13-1082.00

Median wage $102,320/yr1,066,670 employed (US)Rank #86 of 923 scored · top 9% by substitution

Analyze and coordinate the schedule, timeline, procurement, staffing, and budget of a product or service on a per project basis. Lead and guide the work of technical staff. May serve as a point of contact for the client or customer.

Sub-scores

0–100 · band = confidence interval from rater disagreement

Substitution49

Exposure46

Augmentation79

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

20 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

20%

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%47

panel mean rating 2.9/5 → substitution pressure 47/100

Technical feasibility todayw 20%44

panel mean rating 2.8/5 → substitution pressure 44/100

Cost vs. human wagew 15%47

panel mean rating 2.9/5 → substitution pressure 47/100

Adoption barriersw 20%inverted — strong barriers lower the score57

panel mean rating 2.7/5 (barrier strength) → substitution pressure 57/100

Sector adoption velocityw 10%50

panel mean rating 3.0/5 → substitution pressure 50/100

Task breakdown (20 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.

Produce and distribute project documents.

CI 75–87 · exposure 83 · augmentation 100 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	4/5	Professional services, finance, and tech-forward enterprises have rapidly adopted AI-assisted and automated document generation (via Copilot, ChatGPT, native platform AI). Adoption is measurably faster in digitized, information-intensive sectors where project management specialists work.
Sector adoption velocity	claude-sonnet-5	4/5	Project management and professional services sectors show fast adoption of AI-assisted documentation tools embedded in widely used platforms like Microsoft 365 and Asana.
Augmentation potential	claude-haiku-4-5-20251001	5/5	AI dramatically augments human productivity in document work: drafting, formatting, version control, and distribution suggestions allow specialists to focus on content accuracy, stakeholder communication, and strategic coordination rather than clerical assembly.
Augmentation potential	claude-sonnet-5	5/5	AI substantially speeds up drafting, formatting, and distributing project documents while the specialist retains control over content accuracy and stakeholder judgment.
Task automatability	claude-haiku-4-5-20251001	5/5	Producing and distributing project documents is highly automatable. Current AI can generate status reports, meeting minutes, schedules, and technical documentation from templates and data; file systems and email clients enable end-to-end distribution with minimal oversight, easily meeting the ≥50% time-saving threshold.
Task automatability	claude-sonnet-5	4/5	Generating status reports, meeting minutes, charters, and distributing them via email/collaboration tools is largely templated text generation and file handling, which current LLM-based tools handle well with modest setup for project-specific data integration.4/5 reflects that some judgment on content prioritization remains.
Adoption barriers	claude-haiku-4-5-20251001	2/5	Few legal or regulatory barriers exist; documents must sometimes be reviewed for accuracy and compliance, but no licensing requirement mandates human authorship. Light organizational friction remains around stakeholder preferences for human review, but nothing legally prevents substitution.
Adoption barriers	claude-sonnet-5	2/5	No licensing or legal sign-off is typically required for internal project documents; main friction is organizational preference for human-reviewed accuracy and version control practices.
Cost vs. human wage	claude-haiku-4-5-20251001	5/5	AI document generation and distribution costs (template setup, inference, integration fees) are orders of magnitude cheaper than the fully-loaded wage of a project coordinator tasked with this work, with near-zero marginal cost per document after infrastructure investment.
Cost vs. human wage	claude-sonnet-5	4/5	AI drafting and templated distribution costs a small fraction of a project coordinator's hourly rate for equivalent document production, though human review/editing time still adds some cost.
Technical feasibility today	claude-haiku-4-5-20251001	4/5	Multiple deployed products (Notion, Microsoft 365 with AI plugins, monday.com, Slack integrations) reliably automate document generation and distribution at scale. Some inconsistency remains in template adherence and stakeholder-specific formatting, but production systems handle routine document lifecycle tasks effectively.
Technical feasibility today	claude-sonnet-5	4/5	Products like Microsoft Copilot, Asana AI, and Notion AI already draft and distribute project status updates, meeting notes, and documentation in production use across many organizations, though accuracy on nuanced project details still requires review.

Monitor costs incurred by project staff to identify budget issues.

CI 59–92 · exposure 75 · augmentation 75 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	4/5	Finance and professional services sectors (where project management specialists concentrate) are rapidly adopting AI-driven cost monitoring and variance alerting as part of broader digital transformation and cloud ERP migration.
Sector adoption velocity	claude-sonnet-5	3/5	Project management and finance functions are moderately digitized with growing BI/AI tool adoption, but many organizations still rely on manual spreadsheet-based tracking.
Augmentation potential	claude-haiku-4-5-20251001	4/5	AI cost monitoring tools augment project managers by automating routine flagging and aggregation, freeing them to focus on root-cause analysis, corrective action, and strategic budget planning rather than data collection and manual calculations.
Augmentation potential	claude-sonnet-5	4/5	AI-powered dashboards and anomaly alerts significantly speed up identification of budget issues, letting project managers focus on resolution rather than manual data compilation.
Task automatability	claude-haiku-4-5-20251001	5/5	Current AI systems can fully automate cost monitoring by ingesting timesheets, expense reports, and accounting data, then flagging budget variances, anomalies, and forecast deviations in real time—meeting the ≥50% time-saving threshold with no loss of quality compared to manual spreadsheet review.
Task automatability	claude-sonnet-5	3/5	AI can pull data, flag anomalies, and generate cost variance reports, but interpreting cause and deciding corrective action still requires human judgment, so only part of the workflow meets the 50% time-saving bar.
Adoption barriers	claude-haiku-4-5-20251001	2/5	Few regulatory or licensing barriers exist for automated cost tracking, though some organizations maintain internal policies requiring human sign-off on budget decisions; financial governance and integration friction are the main adoption friction points rather than legal restrictions.
Adoption barriers	claude-sonnet-5	2/5	No licensing requirement blocks automation, though organizational trust in financial reporting accuracy and internal controls create some friction before fully delegating budget monitoring to AI.
Cost vs. human wage	claude-haiku-4-5-20251001	5/5	Once integrated into existing ERP/accounting infrastructure, algorithmic cost monitoring costs pennies per project per month, while a specialist performing equivalent manual review would cost thousands in loaded labor annually.
Cost vs. human wage	claude-sonnet-5	4/5	Automated cost tracking and alerting via existing software is far cheaper than manual reconciliation by a project manager, though some human oversight cost remains.
Technical feasibility today	claude-haiku-4-5-20251001	5/5	Mature products (Quickbooks, SAP Analytics, Workday, and custom BI tools with AI alerting) are deployed at scale in organizations today to automatically monitor project costs, flag exceptions, and generate variance reports with high reliability.
Technical feasibility today	claude-sonnet-5	3/5	Financial dashboards, ERP analytics, and BI tools with anomaly detection are deployed in production, but full autonomous budget monitoring with contextual judgment remains narrow and error-prone.

Create project status presentations for delivery to customers or project personnel.

CI 75–75 · exposure 75 · augmentation 100 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	4/5	Professional services, finance, and technology sectors (where most project managers work) are adopting AI presentation tools rapidly in production. Many organizations now use AI-assisted or AI-generated status decks, reflecting fast and measurable displacement of manual drafting.
Sector adoption velocity	claude-sonnet-5	4/5	Professional services and project-heavy sectors (IT, consulting, construction management) are rapidly adopting AI reporting tools embedded in PM software, though full replacement of the presenter role lags.
Augmentation potential	claude-haiku-4-5-20251001	5/5	AI powerfully augments project managers by auto-generating drafts, extracting metrics from data sources, and formatting multiple presentation variants, while the manager retains full control over message, strategy, and sign-off. This is a canonical high-augmentation scenario.
Augmentation potential	claude-sonnet-5	5/5	AI substantially speeds up drafting, formatting, and summarizing status updates into presentations, letting the specialist focus on stakeholder communication and judgment calls.
Task automatability	claude-haiku-4-5-20251001	4/5	Current AI systems can automate significant portions of status presentation creation—gathering data, structuring narrative, generating charts, drafting slides—achieving well over 50% time savings. However, final customization for audience, tone calibration, and high-stakes messaging typically requires human oversight, preventing a full 5 rating.
Task automatability	claude-sonnet-5	4/5	AI can generate slide decks and status summaries from structured project data (timelines, budgets, risks) with minimal human editing, meeting much of the time-saving bar for the drafting portion.4
Adoption barriers	claude-haiku-4-5-20251001	2/5	Few hard regulatory or licensing barriers exist. Organizational friction includes customer preference for human expertise, need for domain knowledge oversight, and reputational risk if AI output is poor. These are real but surmountable, not prohibitive.
Adoption barriers	claude-sonnet-5	2/5	No licensing or legal requirement mandates a human create status reports; the main friction is organizational preference for a named accountable presenter and stakeholder trust in the human relationship.
Cost vs. human wage	claude-haiku-4-5-20251001	4/5	AI inference cost for generating a complete presentation is typically $1–5, plus modest integration overhead, versus a human specialist's loaded cost of $50–150+ per presentation-hour. AI is clearly cheaper by an order of magnitude for commodity status presentations.
Cost vs. human wage	claude-sonnet-5	4/5	Generating a status presentation via AI costs a few cents to dollars in compute versus an hour or more of a project specialist's loaded wage, though some human review keeps the ratio from reaching the extreme end.
Technical feasibility today	claude-haiku-4-5-20251001	4/5	Deployed products (Claude, GPT-4, specialized presentation tools with AI) demonstrably perform slide generation, chart creation, and content synthesis in production environments. Some material variability in output quality and occasional misalignment with brand/context standards keeps this below 5.
Technical feasibility today	claude-sonnet-5	4/5	Tools like Copilot in PowerPoint, Gamma, and AI-integrated PM platforms (Asana, Monday, Jira plugins) already generate status decks from live project data in production use, though customization for audience nuance still needs review.

Monitor project milestones and deliverables.

CI 55–87 · exposure 70 · augmentation 75 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	4/5	Project management software adoption and AI-powered analytics are deep in information-sector, consulting, and tech firms. Automated milestone tracking is already common in DevOps and agile contexts; adoption is accelerating in broader enterprise PM.
Sector adoption velocity	claude-sonnet-5	3/5	Project management is a white-collar, information-heavy function with growing AI tool adoption, but many organizations still rely on manual or semi-automated tracking rather than full AI-driven monitoring.
Augmentation potential	claude-haiku-4-5-20251001	4/5	AI can continuously monitor dashboards, generate proactive alerts, surface trends, and draft status narratives, substantially augmenting a project manager's ability to stay on top of milestones without the human needing to manually check systems throughout the day.
Augmentation potential	claude-sonnet-5	4/5	AI dashboards and automated alerts significantly help project managers track progress, surface risks, and generate reports faster, meaningfully boosting productivity while the human remains responsible for decisions.
Task automatability	claude-haiku-4-5-20251001	5/5	Monitoring project milestones and deliverables is highly routine and data-driven, involving tracking against predetermined schedules, comparing actual progress to planned timelines, and flagging deviations. Current AI systems can parse project management tools (Jira, Asana, Monday.com), compare metrics, generate status reports, and alert stakeholders—delivering >50% time savings at equal quality with off-the-shelf integrations.
Task automatability	claude-sonnet-5	3/5	AI can track deliverables against schedules and flag deviations when integrated with project management tools, but requires human judgment for contextual interpretation and stakeholder communication, so only partial time savings are realized end-to-end.
Adoption barriers	claude-haiku-4-5-20251001	2/5	Few legal or licensing barriers exist; monitoring is a data-aggregation task with no human-contact requirement. Main friction is organizational (preference for human judgment on escalations) and integration overhead, but these are surmountable.
Adoption barriers	claude-sonnet-5	2/5	No licensing requirement exists for this task, though organizational trust in human judgment for critical milestone decisions and accountability for reporting inaccuracies creates moderate friction.
Cost vs. human wage	claude-haiku-4-5-20251001	5/5	Automated monitoring via API integrations and LLM-based summary generation costs pennies per project per month, orders of magnitude cheaper than the loaded cost of a specialist manually reviewing timelines, compiling reports, and sending notifications.
Cost vs. human wage	claude-sonnet-5	3/5	AI monitoring tools reduce time spent on manual status tracking but still require licensing costs, integration effort, and human review, making costs roughly comparable to a human doing lighter-touch oversight.
Technical feasibility today	claude-haiku-4-5-20251001	4/5	Deployed products from Atlassian, Microsoft Project, Wrike, and AI-native agents already monitor milestones and trigger alerts in production environments. While some customization is often required for complex project rules, baseline monitoring is mature and reliable at scale in many organizations.
Technical feasibility today	claude-sonnet-5	3/5	Products like Asana, Monday.com, and Jira have AI features that track milestones and generate status reports, but these still require human oversight for accuracy and nuanced status assessment, so scope remains narrow.

Prepare and submit budget estimates, progress reports, or cost tracking reports.

CI 55–75 · exposure 62 · augmentation 88 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	4/5	Professional services and information-sector organizations are actively deploying AI-powered reporting and cost-tracking automation. Many enterprise PMO tools now include generative capabilities; adoption is visible and accelerating in digitally mature sectors.
Sector adoption velocity	claude-sonnet-5	3/5	Project management functions span many industries with mixed digitization; larger firms in tech/finance are piloting AI reporting tools but broad production-scale adoption is still emerging.
Augmentation potential	claude-haiku-4-5-20251001	5/5	AI augmentation is already transformative here: specialists use AI to draft sections, validate numbers, surface anomalies, and iterate on narratives while retaining full control and accountability. This is one of the highest-impact use cases for generative AI in office roles.
Augmentation potential	claude-sonnet-5	4/5	AI substantially speeds up drafting of progress and cost reports, summarizing data and generating narrative sections, while the specialist verifies accuracy and finalizes submissions.
Task automatability	claude-haiku-4-5-20251001	4/5	Current AI systems can reliably extract project data, populate budget templates, generate progress summaries from logs, and produce formatted reports with >50% time savings. However, some judgment around narrative framing and stakeholder-specific emphasis typically requires human review, preventing a full 5.
Task automatability	claude-sonnet-5	3/5	AI can draft budget estimates and progress/cost reports from structured data with significant time savings, but compiling accurate underlying data and validating figures still requires human input and judgment, limiting full end-to-end automation.
Adoption barriers	claude-haiku-4-5-20251001	2/5	Few regulatory or legal barriers exist; reports are typically internal or client-facing but do not require licensed sign-off. Organizational friction around audit trails and change control can slow adoption, but nothing prevents automation from a compliance standpoint.
Adoption barriers	claude-sonnet-5	2/5	No licensing requirement mandates a human for budget/report preparation, though internal governance and accountability for financial accuracy create some organizational friction.
Cost vs. human wage	claude-haiku-4-5-20251001	4/5	Inference costs for document generation and data extraction are low relative to the 1–2 hours a specialist typically spends manually compiling, formatting, and cross-checking reports. AI integration is often bundled into existing project management subscriptions, making the marginal cost negligible.
Cost vs. human wage	claude-sonnet-5	3/5	AI tools reduce time spent drafting reports, but integration with financial systems, oversight, and correction of errors keeps costs roughly comparable to a specialist's efficient use of software rather than an order-of-magnitude cheaper.
Technical feasibility today	claude-haiku-4-5-20251001	4/5	Mature products (e.g., enterprise BI platforms, AI-powered project management tools, document generation systems) routinely automate report generation and cost tracking in production environments. Error rates are low for data-driven sections, though custom narratives occasionally need refinement.
Technical feasibility today	claude-sonnet-5	3/5	Products like AI-enhanced project management tools (e.g., Asana, Monday.com, Microsoft Project with Copilot) generate reports and summaries today, but reliability on complex cost tracking across systems is still narrow and requires human review.

Communicate with key stakeholders to determine project requirements and objectives.

CI 32–87 · exposure 58 · augmentation 88 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	4/5	Professional services, finance, and tech firms are rapidly adopting AI-assisted requirement gathering and stakeholder communication tools; deployment is moving from pilots into production workflows at scale in digitized sectors.
Sector adoption velocity	claude-sonnet-5	3/5	Project management occurs across many sectors with mixed digitization; AI note-taking and requirements-gathering copilots are being piloted but not universally deployed.
Augmentation potential	claude-haiku-4-5-20251001	5/5	AI markedly augments project managers by automating stakeholder follow-up, multi-party communication synthesis, and requirements consolidation, freeing PMs to focus on strategic interpretation and relationship-building while the system handles routine intake and documentation.
Augmentation potential	claude-sonnet-5	4/5	AI can transcribe meetings, summarize stakeholder input, draft requirement documents, and flag inconsistencies, meaningfully boosting the human's efficiency while they retain the interpersonal lead.
Task automatability	claude-haiku-4-5-20251001	5/5	Modern AI agents with email, meeting, and document analysis capabilities can systematically gather requirements from stakeholders, summarize objectives, and consolidate findings into structured formats, achieving >50% time savings by automating the intake, synthesis, and documentation phases that historically required manual effort.
Task automatability	claude-sonnet-5	2/5	This requires building trust, reading stakeholder priorities, negotiating conflicting interests, and interpreting ambiguous or political dynamics that AI cannot yet independently navigate end-to-end.
Adoption barriers	claude-haiku-4-5-20251001	2/5	While organizational culture may favor human-led stakeholder engagement and there is modest oversight friction, no licensing requirement or legal mandate requires a human to conduct initial requirement gathering, enabling relatively swift AI substitution.
Adoption barriers	claude-sonnet-5	3/5	No licensing requirement, but strong organizational and interpersonal expectations mean stakeholders expect to interact with accountable human project managers, creating moderate friction against full automation.
Cost vs. human wage	claude-haiku-4-5-20251001	5/5	Inference and integration costs for AI-driven stakeholder communication analysis and consolidation are typically orders of magnitude lower than the fully-loaded labor cost of project managers spending days in meetings and manual synthesis.
Cost vs. human wage	claude-sonnet-5	2/5	Human relationship-building and judgment remain necessary, so AI mainly supplements rather than replaces the labor, limiting cost savings to marginal efficiency gains in documentation.
Technical feasibility today	claude-haiku-4-5-20251001	4/5	Deployed AI systems (Claude, GPT-4, Teams integration tools) demonstrably handle stakeholder communication mining and requirements synthesis in production settings, though final sign-off validation by humans remains common due to liability and nuance in complex organizational contexts.
Technical feasibility today	claude-sonnet-5	2/5	AI tools can assist with meeting notes, transcription, and requirement drafting, but no deployed product autonomously conducts stakeholder discovery conversations reliably in production.

Report project status, such as budget, resources, technical issues, or customer satisfaction, to managers.

CI 50–55 · exposure 50 · augmentation 75 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	3/5	Pilot adoption is visible in information-sector organizations (software, consulting), but full replacement remains rare; most teams use dashboards and AI drafting aids to speed reporting while humans remain the voice and decision-maker for escalations.
Sector adoption velocity	claude-sonnet-5	3/5	Project management and professional services sectors are adopting AI reporting features at a moderate pace, with pilots and add-on tools common but full reliance on AI-generated status reports still rare.
Augmentation potential	claude-haiku-4-5-20251001	4/5	AI excels at augmenting this task: automated dashboard generation, anomaly detection flagging budget overruns or risk signals, and LLM-based draft summarization significantly accelerate a PM's ability to produce comprehensive, timely status reports while the human interprets context and decides messaging.
Augmentation potential	claude-sonnet-5	4/5	AI substantially speeds up drafting of status updates, summarizing budget/resource data, and flagging technical issues, letting the PM focus on interpretation and stakeholder communication.
Task automatability	claude-haiku-4-5-20251001	3/5	Status reporting involves collecting, organizing, and summarizing structured data (budget, resources, technical issues, satisfaction metrics) which AI can now do efficiently, but typically requires human judgment on interpretation, risk prioritization, and strategic recommendations—automating the full task to 50% time-saving requires significant setup and human oversight.
Task automatability	claude-sonnet-5	3/5	AI can aggregate data from project tools and draft status reports, but synthesizing nuanced context, judging what matters to stakeholders, and framing sensitive issues still requires human input for most of the task's value.
Adoption barriers	claude-haiku-4-5-20251001	3/5	Modest friction exists: organizations value a human PM's judgment and stakeholder communication around status, there are internal norms that reports come from the project owner, and many senior managers prefer direct human accountability and dialogue rather than fully automated feeds.
Adoption barriers	claude-sonnet-5	2/5	No licensing or legal requirement mandates human-only reporting, though organizational trust and accountability for reporting accuracy to managers creates some friction against full automation.
Cost vs. human wage	claude-haiku-4-5-20251001	3/5	AI-driven status aggregation and report generation is now cost-competitive with junior analysts, but remains comparable to the loaded hourly rate of a mid-level project coordinator when accounting for integration, validation, and management oversight costs.
Cost vs. human wage	claude-sonnet-5	3/5	AI reporting tools are cheap to run relative to a PM's time spent compiling reports, but the need for human verification and judgment narrows the cost advantage to roughly comparable when factoring oversight.
Technical feasibility today	claude-haiku-4-5-20251001	3/5	Tools exist (BI platforms, project dashboards, summarization APIs) that can extract and format status data, but real-world deployment still struggles with incomplete data, integration across fragmented systems, and the judgment call on what constitutes a meaningful risk or issue worth escalating.
Technical feasibility today	claude-sonnet-5	3/5	Products like AI-powered dashboards (e.g., in Asana, Monday.com, Jira with AI add-ons) generate status summaries today, but they still require human review and often miss nuanced context or customer sentiment accuracy.

Propose, review, or approve modifications to project plans.

CI 32–70 · exposure 45 · augmentation 88 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	4/5	Information and professional services sectors (consulting, tech, enterprise software) are rapidly adopting AI-powered project planning and modification assistants; adoption is accelerating in both pilots and production use, with high digital infrastructure maturity.
Sector adoption velocity	claude-sonnet-5	3/5	Project management is a professional-services-adjacent function with growing AI tool adoption (e.g., AI scheduling assistants), but full agentic adoption for approving changes remains in pilot stages.
Augmentation potential	claude-haiku-4-5-20251001	5/5	AI excels at augmenting project managers by generating alternative plans, flagging risks, optimizing schedules, and surfacing trade-offs, significantly boosting productivity while the PM retains decision authority and judgment over final approvals.
Augmentation potential	claude-sonnet-5	4/5	AI can meaningfully assist by simulating scenarios, summarizing impacts of proposed changes, and drafting revised plans, significantly speeding up the human's review and decision process.
Task automatability	claude-haiku-4-5-20251001	4/5	AI systems can generate, critique, and suggest modifications to project plans (Gantt charts, timelines, resource allocation, risk assessments) with substantial time savings. However, approval typically requires human accountability and judgment about organizational priorities, making full end-to-end automation without human review unlikely in practice, though the time-saving threshold is likely met for drafting and review phases.
Task automatability	claude-sonnet-5	2/5	Proposing and reviewing plan modifications requires judgment about tradeoffs, stakeholder priorities, and risk that current AI cannot reliably exercise end-to-end, though it can draft options and flag impacts.assistance is partial, not full substitution.
Adoption barriers	claude-haiku-4-5-20251001	2/5	Formal approval authority typically remains with a licensed PM or executive, creating some friction; however, AI-assisted review and proposal generation faces minimal legal or regulatory barriers, and organizational practice is shifting toward AI drafting tools without blocking adoption.
Adoption barriers	claude-sonnet-5	3/5	Approval of plan changes often requires accountable human sign-off (budget, contractual, or organizational authority), creating moderate procedural and liability barriers even though no formal licensing is required.
Cost vs. human wage	claude-haiku-4-5-20251001	4/5	AI inference and integration costs for plan analysis and modification are orders of magnitude lower than the loaded cost of a project manager reviewing and modifying plans manually, particularly for large or repetitive projects.
Cost vs. human wage	claude-sonnet-5	2/5	AI tools can cheaply generate draft change suggestions, but human review, approval authority, and context-checking remain necessary, keeping overall cost comparable to or only modestly cheaper than a human doing this.
Technical feasibility today	claude-haiku-4-5-20251001	3/5	Deployed products (project management suites with AI planning assistants, generative agents for plan generation) exist and perform plan modification suggestions reliably in narrow scope, but error rates in complex multi-constraint scenarios and integration with legacy systems remain material, limiting production-scale reliability.
Technical feasibility today	claude-sonnet-5	2/5	Some PM software includes AI features that suggest schedule or resource adjustments, but no deployed product autonomously proposes/approves plan changes with reliable accuracy in production at scale.

Request and review project updates to ensure deadlines are met.

CI 46–55 · exposure 50 · augmentation 75 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	3/5	Mid-sized and larger organizations are piloting AI-assisted project tracking and automated status reporting, but production-scale displacement remains limited; many firms still rely on manual meetings and email for deadline oversight, reflecting uneven digital maturity.
Sector adoption velocity	claude-sonnet-5	3/5	Project management and professional services are moderately fast adopters of AI-enabled PM tools, with pilots and some production use common but full deep automation still limited.
Augmentation potential	claude-haiku-4-5-20251001	4/5	AI augmentation is strong here: automated status aggregation, deadline risk alerts, and summary generation meaningfully boost a project manager's ability to monitor and respond to schedule slippage across many projects while the manager focuses on judgment and remediation decisions.
Augmentation potential	claude-sonnet-5	4/5	AI substantially aids by auto-aggregating status data, generating reminders, and highlighting at-risk deadlines, meaningfully boosting a PM specialist's productivity while they retain oversight.
Task automatability	claude-haiku-4-5-20251001	3/5	AI can automate parts of this task by extracting and summarizing project status from logs, dashboards, and emails, and flagging deadline risks. However, nuanced review requiring judgment about mitigation actions, priority trade-offs, and stakeholder communication still requires human oversight, limiting end-to-end automation to roughly half the work.
Task automatability	claude-sonnet-5	3/5	AI can automatically pull status updates from integrated tools and flag deadline risks, but reviewing update quality and making judgment calls on remediation still requires human input for most projects.
Adoption barriers	claude-haiku-4-5-20251001	3/5	Organizational friction exists: teams often prefer human accountability for deadline assurance, stakeholders expect human sign-off on schedule changes, and liability concerns around missed deadlines create reluctance to rely solely on automation. However, no strict regulatory or licensing requirement mandates a human must perform this task.
Adoption barriers	claude-sonnet-5	2/5	No licensing or legal requirement mandates a human perform this task; the main friction is organizational trust and the need for accountable decision-making on schedule risk.
Cost vs. human wage	claude-haiku-4-5-20251001	2/5	Current AI solutions (LLM API calls, dashboard integrations, oversight) remain costly relative to a project specialist's incremental time cost for routine update review, especially when accounting for integration and monitoring overhead.
Cost vs. human wage	claude-sonnet-5	3/5	Automated update requests and dashboards are cheap, but the interpretive review and follow-up still require a paid specialist's time, keeping overall cost roughly comparable.
Technical feasibility today	claude-haiku-4-5-20251001	3/5	Products exist (project management platforms with automated status dashboards, Slack bots that summarize updates, AI-assisted anomaly detection) but they operate with narrow scope and still require substantial manual review and interpretation to validate accuracy and completeness.
Technical feasibility today	claude-sonnet-5	3/5	Project management software (Asana, Monday, Jira with AI features) offers automated status pings and deadline dashboards, but reliable end-to-end 'review and ensure deadlines met' judgment is still narrow and error-prone in production.

Schedule or facilitate project meetings.

CI 41–55 · exposure 42 · augmentation 75 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	3/5	Scheduling tools are widely adopted in professional services and tech, but autonomous meeting facilitation adoption remains in pilot/proof-of-concept phase. Most deployment is still human-led with AI scheduling assistants, not AI-facilitated meetings.
Sector adoption velocity	claude-sonnet-5	3/5	Scheduling automation is broadly adopted in professional services and project-driven organizations, though full meeting facilitation by AI remains at pilot stage.
Augmentation potential	claude-haiku-4-5-20251001	4/5	AI strongly augments this task: automated scheduling, real-time transcription, agenda drafting, note-taking, action-item tracking, and follow-up summarization all boost human facilitator productivity without requiring full autonomy. These capabilities are deployable today and materially improve workflow efficiency.
Augmentation potential	claude-sonnet-5	4/5	AI substantially aids this task via automated scheduling, agenda generation, transcription, and action-item summarization, meaningfully boosting the human's productivity while they retain facilitation control.
Task automatability	claude-haiku-4-5-20251001	2/5	Scheduling meetings can be partially automated (finding times, sending invites, creating agendas), but facilitation—actively guiding discussion, managing conflict, making real-time decisions—requires human judgment and presence. Current AI lacks the contextual awareness and adaptability to handle dynamic meeting facilitation at quality parity with human specialists.
Task automatability	claude-sonnet-5	3/5	AI scheduling assistants can find times and send invites automatically, but 'facilitating' meetings—managing discussion, resolving conflicts, driving decisions—still requires human presence and judgment, capping full end-to-end automation.
Adoption barriers	claude-haiku-4-5-20251001	3/5	Organizational norms and stakeholder preference for human-led meetings create friction. No legal barrier exists, but many organizations require human project managers to own meeting outcomes, and clients often expect human facilitation for complex stakeholder meetings, creating adoption friction.
Adoption barriers	claude-sonnet-5	2/5	No licensing requirement, but organizational and interpersonal norms favor a human facilitator for accountability, relationship management, and real-time decision-making in project meetings.
Cost vs. human wage	claude-haiku-4-5-20251001	3/5	Scheduling automation is cheap compared to labor, but end-to-end meeting management (including live facilitation oversight) requires human intervention, bringing costs closer to parity. For routine scheduling alone, AI is cheaper; for the full task, costs converge.
Cost vs. human wage	claude-sonnet-5	3/5	Scheduling tools are cheap and widely integrated, lowering cost for that sub-task, but since a human must still facilitate most meetings, overall cost savings versus a human PM are moderate rather than order-of-magnitude.
Technical feasibility today	claude-haiku-4-5-20251001	3/5	Scheduling automation tools (Calendly, AI assistants in Outlook/Google Calendar) work reliably for routine meeting setup, but no deployed product handles full meeting facilitation (participant engagement, conflict resolution, documentation with context) at production maturity. Narrow, task-specific products exist; holistic facilitation remains limited.
Technical feasibility today	claude-sonnet-5	3/5	Calendar/scheduling assistants (e.g., AI meeting schedulers, Copilot, Clockwise) are deployed and reliable for logistics, but AI facilitation of live meetings (running the meeting, steering conversation) is not a mature production capability.

Negotiate with project stakeholders or suppliers to obtain resources or materials.

CI 25–62 · exposure 41 · augmentation 88 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	3/5	Mid-market and enterprise project management is increasingly digitized and tech-forward, but actual AI-driven negotiation agents in production remain limited; most adoption is in narrower domains like vendor RFQ automation rather than full stakeholder negotiation cycles.
Sector adoption velocity	claude-sonnet-5	2/5	Project management spans many sectors including construction and manufacturing with slower AI adoption for interpersonal negotiation tasks specifically, even as adoption for adjacent planning tasks grows faster.
Augmentation potential	claude-haiku-4-5-20251001	5/5	AI significantly augments project managers by generating negotiation options, drafting terms, analyzing supplier proposals, and tracking obligations in real time, allowing humans to focus on relationship management and high-level strategic decisions while AI handles research and drafting.
Augmentation potential	claude-sonnet-5	4/5	AI can meaningfully assist by analyzing supplier data, modeling scenarios, drafting negotiation strategies, and summarizing stakeholder positions, substantially boosting preparation productivity even though humans conduct the actual negotiation.
Task automatability	claude-haiku-4-5-20251001	4/5	AI agents can handle much of the negotiation workflow—gathering requirements, drafting proposals, simulating counterarguments, and executing routine terms—with minimal human oversight, though complex multi-party negotiations or high-stakes decisions typically require final human approval.
Task automatability	claude-sonnet-5	2/5	Negotiation requires real-time relationship management, trust-building, and adaptive persuasion tactics that current AI cannot reliably replicate end-to-end, though AI can draft talking points or analyze offers.PLACEHOLDER
Adoption barriers	claude-haiku-4-5-20251001	3/5	While there are no strict legal barriers to AI assistance in supplier negotiations, organizational culture often prefers human-led negotiations for relationship-building and trust, and stakeholders may resist fully automated negotiation systems; oversight and sign-off requirements add friction.
Adoption barriers	claude-sonnet-5	3/5	No licensing requirement bars AI involvement, but organizational trust, liability for bad deals, and stakeholder preference for human relationship-holders create meaningful friction.
Cost vs. human wage	claude-haiku-4-5-20251001	4/5	AI inference and integration costs for negotiation assistance are typically a small fraction of the loaded salary of a project manager, especially when handling routine supplier or resource negotiations that would otherwise consume hours of human time.
Cost vs. human wage	claude-sonnet-5	2/5	While AI-assisted preparation is cheap, the actual negotiation still requires a skilled human, so total cost savings versus a human negotiator are minimal today.
Technical feasibility today	claude-haiku-4-5-20251001	3/5	Deployed AI systems (LLMs, agentic workflows) can draft negotiation scripts, manage email exchanges, and track terms, but production-grade systems handling end-to-end stakeholder negotiations with legal/financial consequences remain limited; most deployments are semi-autonomous with significant human judgment required.
Technical feasibility today	claude-sonnet-5	1/5	No deployed product autonomously conducts stakeholder or supplier negotiations in production; negotiation agents remain research/demo stage with humans firmly in control of actual deal-making.

Identify project needs such as resources, staff, or finances by reviewing project objectives and schedules.

CI 32–51 · exposure 38 · augmentation 75 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	3/5	Professional services and information sectors are piloting AI-assisted project tools, but widespread production deployment of autonomous need identification is limited. Adoption is growing but remains in early stages outside tech-forward firms.
Sector adoption velocity	claude-sonnet-5	3/5	Project management is a professional services function with growing AI tool adoption (e.g., AI features in PM software), but deep autonomous adoption for this specific judgment task remains limited.
Augmentation potential	claude-haiku-4-5-20251001	4/5	AI-powered dashboards and analysis tools demonstrably assist project managers by surfacing resource conflicts, timeline risks, and budget correlations. Humans remain in the loop for final approval, but AI significantly accelerates the discovery and scoping phase.
Augmentation potential	claude-sonnet-5	4/5	AI can effectively assist by analyzing schedules, flagging resource gaps, and summarizing objectives, significantly speeding up the specialist's identification process while keeping them in control.
Task automatability	claude-haiku-4-5-20251001	3/5	AI can analyze project documents, schedules, and objectives to identify routine resource gaps and staffing patterns with moderate accuracy, but identifying nuanced financial needs or contextual dependencies requires human judgment and domain knowledge. Partial automation is feasible, but not full end-to-end execution at 50% time savings.
Task automatability	claude-sonnet-5	2/5	Requires synthesizing organizational context, stakeholder input, and judgment about tradeoffs that AI cannot fully access or reason about autonomously, though it can assist with parts like schedule analysis.'
Adoption barriers	claude-haiku-4-5-20251001	3/5	Project managers hold fiduciary responsibility for budget and resource accuracy; organizations typically require human sign-off on identified needs before capital allocation. Liability and organizational governance create material friction, though not absolute legal barriers to automation.
Adoption barriers	claude-sonnet-5	2/5	No licensing requirement blocks AI use, but organizational trust, accountability for resource/budget decisions, and need for contextual judgment create moderate friction.
Cost vs. human wage	claude-haiku-4-5-20251001	2/5	AI tools require subscription licenses, API costs, and continuous integration oversight; the loaded cost of a senior project manager conducting this analysis is still competitive when accounting for implementation and validation labor. AI is not yet substantially cheaper all-in.
Cost vs. human wage	claude-sonnet-5	2/5	AI tools can reduce time on data aggregation but still require significant human oversight and judgment, keeping costs closer to comparable rather than dramatically cheaper.
Technical feasibility today	claude-haiku-4-5-20251001	3/5	Project management tools with AI features exist (e.g., Asana, Monday.com, Microsoft Project) that can auto-flag scheduling conflicts and suggest resource allocation, but these typically require significant setup and human review. Production systems perform narrowly; comprehensive need identification remains unreliable without human oversight.
Technical feasibility today	claude-sonnet-5	2/5	Some project management tools offer AI-assisted resource forecasting, but no product reliably performs full needs identification without heavy human review and correction.

Develop or update project plans including information such as objectives, technologies, schedules, funding, and staffing.

CI 25–55 · exposure 38 · augmentation 75 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	2/5	Adoption of AI for plan drafting is emerging in IT and consulting sectors, but organizations still rely on experienced PMs for the integration and validation step; displacement has not yet reached production scale.
Sector adoption velocity	claude-sonnet-5	3/5	Project management sits within professional services/tech sectors with moderate AI tool adoption, but most usage remains pilot-stage augmentation rather than deep production automation.
Augmentation potential	claude-haiku-4-5-20251001	4/5	AI significantly assists by generating schedule templates, identifying resource gaps, simulating scenarios, and drafting risk registers, allowing PMs to focus on stakeholder negotiation and strategic tradeoffs rather than rote planning assembly.
Augmentation potential	claude-sonnet-5	4/5	AI substantially speeds up drafting of schedules, budgets, and staffing plans, letting the human focus on strategic decisions and stakeholder alignment while staying in the loop.
Task automatability	claude-haiku-4-5-20251001	2/5	While AI can assist in generating draft schedules, budgets, and risk lists, developing comprehensive project plans requires integrating complex organizational constraints, human judgment about feasibility, and stakeholder alignment that current systems cannot reliably perform end-to-end. The task demands cross-functional validation and business context that exceed current AI autonomy.
Task automatability	claude-sonnet-5	3/5	AI can draft plan sections (schedules, staffing outlines, budget templates) from inputs, but synthesizing organizational context, negotiating resources, and judgment calls on tradeoffs still require significant human involvement.'
Adoption barriers	claude-haiku-4-5-20251001	4/5	Project plans typically require sign-off from senior management, compliance review, and direct stakeholder buy-in; organizational and contractual norms strongly favor human accountability and judgment on budget, scope, and schedule commitments.
Adoption barriers	claude-sonnet-5	2/5	No licensing requirement for plan authorship, but organizational accountability and stakeholder trust create moderate friction against fully automated plan sign-off.
Cost vs. human wage	claude-haiku-4-5-20251001	2/5	Current AI tools (generative models, scheduling plugins) require significant integration, human oversight, and rework to produce usable output, keeping total cost per plan close to or above hiring a junior PM or contractor to do the work from scratch.
Cost vs. human wage	claude-sonnet-5	3/5	AI tools reduce drafting time but licensing, integration with existing PM systems, and required human oversight keep costs roughly comparable rather than order-of-magnitude cheaper.
Technical feasibility today	claude-haiku-4-5-20251001	2/5	No mature production systems reliably generate complete project plans autonomously. Tools like AI-assisted scheduling and cost estimation exist but require substantial human review and correction; they remain narrow supports rather than deployable replacements for the full planning task.
Technical feasibility today	claude-sonnet-5	3/5	Project management software increasingly embeds AI features (auto-scheduling, risk flagging, draft generation) but these are assistive add-ons, not autonomous plan creators used at scale without heavy human review.

Plan, schedule, or coordinate project activities to meet deadlines.

CI 25–51 · exposure 38 · augmentation 75 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	2/5	Adoption of AI-native PM tools lags information-sector norms. Most organizations integrate incremental AI features into legacy platforms rather than replacing the PM role; pilots are common but production displacement is minimal. Sectors like construction and traditional manufacturing adopt even more slowly.
Sector adoption velocity	claude-sonnet-5	3/5	Project management and professional services sectors are moderately fast adopters of AI-assisted planning tools, though full replacement of coordination functions remains rare and mostly pilot-stage.
Augmentation potential	claude-haiku-4-5-20251001	4/5	AI effectively augments project planning by generating initial schedules, flagging resource conflicts, and auto-updating timelines—all while the PM retains control. The productivity lift from AI-assisted timeline generation, scenario modeling, and bottleneck visualization is substantial and measurable in current tools.
Augmentation potential	claude-sonnet-5	4/5	AI tools meaningfully assist with generating draft schedules, tracking dependencies, flagging risks, and summarizing status, notably raising a project manager's efficiency while they retain control of decisions.
Task automatability	claude-haiku-4-5-20251001	3/5	AI can automate portions of scheduling (timeline generation, resource allocation, dependency mapping) and coordinate routine notifications, but strategic deadline negotiation, stakeholder alignment, and dynamic replanning in response to unexpected delays require human judgment. Current systems achieve roughly 40-50% time savings on structured planning work.
Task automatability	claude-sonnet-5	2/5	AI can help draft schedules and identify dependencies, but coordinating stakeholders, resolving conflicts, and adapting plans to shifting real-world constraints still requires substantial human judgment and communication that current systems cannot fully replace.
Adoption barriers	claude-haiku-4-5-20251001	4/5	Organizational inertia is high: established PM methodologies (Agile, Waterfall, PMI frameworks) embed human sign-off and judgment. Stakeholder preference for human-led planning and client accountability requirements mean that humans must validate and take responsibility for schedules; AI cannot legally or contractually own project commitments.
Adoption barriers	claude-sonnet-5	2/5	No licensing requirement for scheduling itself, but organizational accountability, stakeholder trust, and the need for someone to own outcomes create moderate friction against full automation.
Cost vs. human wage	claude-haiku-4-5-20251001	2/5	Integration costs (training, data setup, oversight of AI recommendations) and ongoing supervision remain substantial relative to the wage of junior project coordinators. For senior PMs managing high-value projects, AI tooling amortizes better but does not yet reach an order-of-magnitude saving.
Cost vs. human wage	claude-sonnet-5	2/5	AI scheduling tools reduce some manual effort but still require significant human oversight, data input, and stakeholder management, so overall cost savings versus a human PM are modest rather than order-of-magnitude.
Technical feasibility today	claude-haiku-4-5-20251001	3/5	Project management tools with AI-assisted scheduling (e.g., Microsoft Project, Asana, Monday.com) exist in production, but they perform narrowly: timeline generation and basic resource leveling. They struggle with complex multi-project constraints, stakeholder prioritization, and real-time exception handling where humans still drive decisions.
Technical feasibility today	claude-sonnet-5	2/5	Project management software with AI features (e.g., automated scheduling suggestions, risk flags) exists, but reliable end-to-end coordination of activities and deadlines in production remains narrow and supervised, not autonomous.

Recruit or hire project personnel.

CI 32–41 · exposure 30 · augmentation 75 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	3/5	Large tech, finance, and professional-services firms are increasingly adopting AI-assisted recruiting (screening, scheduling), but most mid-market and small organizations still rely on manual hiring. Pilots are common but full automation remains rare because final hiring decisions remain human-led.
Sector adoption velocity	claude-sonnet-5	3/5	Recruiting technology (ATS, AI resume screeners, chatbot schedulers) has moderate adoption in HR/project management contexts, but full automation of hiring remains uncommon and cautious due to legal risk.
Augmentation potential	claude-haiku-4-5-20251001	4/5	AI substantially assists recruiters by automating resume screening, ranking candidates, scheduling interviews, and highlighting relevant qualifications. These assistive features demonstrably raise recruiter productivity and allow them to focus on relationship-building and final selection decisions.
Augmentation potential	claude-sonnet-5	4/5	AI meaningfully assists with drafting job descriptions, screening candidates, scheduling interviews, and summarizing candidate qualifications, significantly speeding up the recruiting workflow while humans retain final decision authority.
Task automatability	claude-haiku-4-5-20251001	2/5	AI can automate parts of recruitment (resume screening, initial filtering, job posting) but cannot autonomously conduct interviews, assess cultural fit, or make final hiring decisions. The full end-to-end task requires human judgment on interpersonal factors and strategic fit, limiting time savings to well below 50% for equivalent quality.
Task automatability	claude-sonnet-5	2/5	AI can assist with drafting job postings and screening resumes but the core judgment of interviewing, evaluating fit, and hiring decisions requires human judgment that current AI cannot reliably replace end-to-end.
Adoption barriers	claude-haiku-4-5-20251001	3/5	Some organizational and liability friction exists: companies prefer human accountability in hiring decisions, employment law creates documentation and discrimination-risk concerns, and hiring teams often resist algorithmic decisions. However, no hard legal requirement prevents automation of the task itself, only strong preference for human oversight.
Adoption barriers	claude-sonnet-5	3/5	Hiring decisions carry legal and discrimination liability risks, often requiring human oversight and documented decision-making, plus organizational preference for human judgment in personnel selection.
Cost vs. human wage	claude-haiku-4-5-20251001	3/5	AI screening tools reduce some clerical and initial-filter costs, but full recruitment still requires experienced recruiters for interviews, negotiations, and decision-making. All-in costs (tool licensing, integration, human oversight) roughly match or only modestly undercut the cost of human recruiters for comparable outcomes.
Cost vs. human wage	claude-sonnet-5	2/5	AI tools reduce some screening costs but the full recruiting/hiring process still requires substantial human time for interviews, negotiation, and final decisions, keeping overall cost comparable to human-driven processes.
Technical feasibility today	claude-haiku-4-5-20251001	3/5	AI-powered recruiting tools exist in production (resume screening, candidate ranking, interview scheduling), but they perform narrowly and often require significant human oversight. Error rates and bias issues mean organizations still rely heavily on human recruiters for the critical judgment phases.
Technical feasibility today	claude-sonnet-5	2/5	ATS and AI screening tools exist and are widely deployed for initial resume filtering, but actual recruitment/hiring decisions still rely on human interviewers and decision-makers in production settings.

Identify, review, or select vendors or consultants to meet project needs.

CI 30–32 · exposure 25 · augmentation 75 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	2/5	Adoption is slow and primarily in large enterprises with mature procurement platforms; most organizations still rely on manual vendor evaluation and selection processes. Pilot adoption of AI-assisted vendor screening exists but production displacement is limited.
Sector adoption velocity	claude-sonnet-5	3/5	Project management and procurement functions are adopting AI tools for research and drafting at a moderate pace, though full vendor selection automation remains uncommon.
Augmentation potential	claude-haiku-4-5-20251001	4/5	AI substantially assists PMs by automating vendor research, initial screening, comparison matrices, and flagging relevant candidates for manual review—raising PM productivity in the evaluation phase while the PM retains final selection authority and relationship management.
Augmentation potential	claude-sonnet-5	4/5	AI tools can significantly speed up vendor research, comparison drafting, and criteria weighting, meaningfully augmenting the human decision-maker's efficiency.
Task automatability	claude-haiku-4-5-20251001	2/5	While AI can gather and summarize vendor information and identify candidates matching basic criteria, final vendor selection requires nuanced judgment about fit, risk, reliability, and organizational relationships that goes beyond data matching. Current systems cannot reliably replace the full evaluation and decision process.
Task automatability	claude-sonnet-5	2/5	AI can assist with research, comparison matrices, and drafting RFPs, but final vendor selection involves negotiation, relationship judgment, and organizational context that current AI cannot fully replicate end-to-end.
Adoption barriers	claude-haiku-4-5-20251001	3/5	Organizations typically retain final vendor selection authority within the PM and procurement teams due to risk, liability, and strategic relationship concerns. However, these are organizational norms and preferences rather than hard legal requirements, creating moderate friction but not prohibition.
Adoption barriers	claude-sonnet-5	3/5	No licensing requirement, but organizational procurement policies, vendor relationship management, and accountability for spending decisions create moderate friction against full automation.
Cost vs. human wage	claude-haiku-4-5-20251001	2/5	AI-assisted vendor research tools cost significantly less than manual research labor, but full end-to-end vendor selection still requires substantial human oversight and decision-making, keeping total cost near or above that of experienced project managers handling the task themselves.
Cost vs. human wage	claude-sonnet-5	2/5	AI can cheaply generate vendor shortlists or comparisons, but human review, negotiation, and due diligence still require significant paid time, keeping overall cost savings modest.
Technical feasibility today	claude-haiku-4-5-20251001	2/5	Products exist for vendor database searching and RFP matching (e.g., procurement platforms with basic filtering), but production deployment for actual vendor selection is limited and typically used only for early-stage screening. The task requires contextual judgment that deployed systems do not reliably handle.
Technical feasibility today	claude-sonnet-5	2/5	Some procurement software and AI-assisted vendor sourcing tools exist, but they mostly support screening and data aggregation rather than reliably completing the selection decision in production.

Confer with project personnel to identify and resolve problems.

CI 25–32 · exposure 25 · augmentation 75 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	2/5	While project management tools are widely digitized, actual adoption of AI for live problem-resolution conferencing remains minimal. Most adoption focuses on scheduling, status tracking, and document generation—not autonomous conflict resolution or stakeholder negotiation.
Sector adoption velocity	claude-sonnet-5	3/5	Project management software increasingly embeds AI (status tracking, risk flagging) in professional services and tech sectors, though the interpersonal conferring itself remains human-led.
Augmentation potential	claude-haiku-4-5-20251001	4/5	AI can strongly augment this task by pre-analyzing project data, surfacing issues before meetings, generating options for resolution, and documenting outcomes—all while the PM retains control of the conversation and decision-making. These assistive functions meaningfully raise PM productivity.
Augmentation potential	claude-sonnet-5	4/5	AI can prep issue summaries, flag risks from project data, and suggest resolutions, meaningfully speeding up the human's ability to identify and address problems.
Task automatability	claude-haiku-4-5-20251001	2/5	While AI can help identify and classify problems through logs and data analysis, the core task requires real-time interpersonal negotiation, judgment, and trust-building with humans. Current AI cannot reliably conduct multi-party problem resolution meetings or replace the human judgment needed to prioritize competing concerns.
Task automatability	claude-sonnet-5	2/5	This requires real-time interpersonal negotiation, judgment about team dynamics, and authority to resolve conflicts, which current AI cannot fully replicate end-to-end.
Adoption barriers	claude-haiku-4-5-20251001	4/5	Organizations typically require a human project manager to own accountability for problem resolution and stakeholder alignment; clients and teams expect human judgment and presence in these critical meetings. Trust and liability for unresolved issues create strong organizational friction against full automation.
Adoption barriers	claude-sonnet-5	3/5	No licensing requirement, but organizational trust, accountability for decisions, and interpersonal rapport create real friction against full automation.
Cost vs. human wage	claude-haiku-4-5-20251001	2/5	AI-assisted tools (documentation, diagnostic summaries) remain cheaper than human PM labor but cannot yet handle the full task. The integration cost for partial automation still leaves most of the human cost in place, offsetting any savings.
Cost vs. human wage	claude-sonnet-5	2/5	Human project managers add negotiation and accountability value that AI tools cannot substitute for, so AI mainly supplements rather than replaces this labor cost.
Technical feasibility today	claude-haiku-4-5-20251001	2/5	No production AI system reliably conducts independent problem-resolution conferences with project teams. Chatbots can assist with symptom analysis, but deployed products cannot replace the human facilitator role required to resolve conflicts and gain buy-in from diverse stakeholders.
Technical feasibility today	claude-sonnet-5	2/5	AI meeting assistants and chatbots can surface issues or summarize discussions, but no deployed product reliably conducts the actual problem-resolution conversation with personnel.

Monitor the performance of project team members to provide performance feedback.

CI 25–32 · exposure 25 · augmentation 63 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	2/5	Enterprise adoption of AI-driven performance monitoring remains limited; most organizations still rely on human managers conducting reviews, with only pockets of early-stage metric automation in tech-forward firms. Regulatory and cultural headwinds slow deployment.
Sector adoption velocity	claude-sonnet-5	3/5	Project management and professional services sectors are adopting AI analytics tools at a moderate pace, though performance feedback specifically remains largely human-led.
Augmentation potential	claude-haiku-4-5-20251001	3/5	AI can helpfully surface data (attendance patterns, output trends, flagged anomalies) to assist a manager preparing feedback, raising the quality of the human conversation. However, the augmentation is confined to data presentation rather than transforming the feedback interaction itself.
Augmentation potential	claude-sonnet-5	4/5	AI can track productivity metrics, summarize contributions, and flag issues, giving managers useful data to inform and personalize feedback conversations.
Task automatability	claude-haiku-4-5-20251001	2/5	Monitoring raw metrics (work logs, time tracking, output counts) can be partially automated, but providing meaningful performance feedback requires contextual judgment, understanding of individual circumstances, and nuanced communication—areas where current AI lacks reliable capability. The core value-add of feedback (motivation, development, accountability) remains fundamentally human.
Task automatability	claude-sonnet-5	2/5	Evaluating human performance requires contextual judgment, interpersonal nuance, and accountability that current AI cannot reliably replicate end-to-end, though it can aggregate metrics to inform the process.4
Adoption barriers	claude-haiku-4-5-20251001	4/5	Performance feedback carries high liability (employment disputes, discrimination claims) and often legally requires a licensed manager's sign-off; organizations face cultural and HR policy friction around algorithmic performance assessment, and employees have strong preferences for human-delivered feedback that affects their career and compensation.
Adoption barriers	claude-sonnet-5	3/5	No licensing requirement, but organizational norms, HR policy, and trust/liability concerns around performance evaluation create moderate friction against full automation.
Cost vs. human wage	claude-haiku-4-5-20251001	2/5	Monitoring infrastructure (time-tracking, analytics platforms) costs money and requires human review and judgment anyway; the total cost of an AI-assisted system plus necessary human oversight is not yet cheaper than a project manager spot-checking team performance, especially given the supervision needed to prevent unfair or inaccurate feedback.
Cost vs. human wage	claude-sonnet-5	2/5	AI tools can cheaply generate data summaries, but the actual judgment-based feedback delivery still requires manager time, so overall cost savings are limited.
Technical feasibility today	claude-haiku-4-5-20251001	2/5	While dashboard tools and analytics platforms can flag metrics and generate summary reports, no deployed product reliably synthesizes behavioral observation, context, and personalized feedback at the quality expected in professional settings. Existing systems are narrow (task completion rate only) and lack real-time behavioral sensing.
Technical feasibility today	claude-sonnet-5	2/5	Some HR/PM software offers analytics dashboards and sentiment tracking, but no deployed product autonomously monitors and delivers performance feedback to team members reliably.

Submit project deliverables to clients, ensuring adherence to quality standards.

CI 25–32 · exposure 25 · augmentation 75 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	2/5	Despite high digitization in professional services, AI adoption for project submission workflows remains limited to assistive tooling (drafting, checklist support). Production automation of client deliverable sign-off is rare; organizational inertia and risk-aversion in client-facing processes are strong.
Sector adoption velocity	claude-sonnet-5	3/5	Project management in professional services is adopting AI tools for tracking and reporting at a moderate pace, though final client-facing sign-off remains largely manual.
Augmentation potential	claude-haiku-4-5-20251001	4/5	AI can meaningfully assist by auto-generating quality checklists, flagging missing components, formatting submissions, and summarizing deliverables for PM review. These capabilities substantially raise a PM's throughput and catch errors before client submission, even though the PM retains final authority.
Augmentation potential	claude-sonnet-5	4/5	AI can strongly assist with quality checklists, drafting deliverable summaries, and flagging inconsistencies, significantly speeding up the human's review and submission process.
Task automatability	claude-haiku-4-5-20251001	2/5	While AI can help format and organize deliverables, and flag basic quality issues, the task fundamentally requires human judgment to verify that complex project outputs meet client-specific standards and expectations. End-to-end automation would demand AI to independently assess quality sufficiency and sign off on client-facing submissions, which remains unreliable.
Task automatability	claude-sonnet-5	2/5	The judgment-based work of verifying quality standards and client-facing submission requires human accountability and contextual judgment that current AI cannot fully replace end-to-end.'
Adoption barriers	claude-haiku-4-5-20251001	4/5	Significant barriers exist: client trust often requires a named human contact; liability for quality failures typically rests with the PM or firm, not the system; and professional service norms strongly favor human accountability on client-facing submissions. Many contracts explicitly require human certification of deliverables.
Adoption barriers	claude-sonnet-5	3/5	No licensing requirement exists, but liability for deliverable quality and client trust create meaningful organizational friction against full automation.
Cost vs. human wage	claude-haiku-4-5-20251001	2/5	AI systems for document preparation and quality checking have non-trivial inference and integration costs, and human oversight remains mandatory. The cost advantage is marginal compared to a project manager's loaded wage when factoring in required review time.
Cost vs. human wage	claude-sonnet-5	2/5	AI can cut some administrative overhead but human review and client relationship management still dominate the cost, so overall savings are modest.
Technical feasibility today	claude-haiku-4-5-20251001	2/5	No production systems today reliably handle the full submission task, which involves evaluating quality against context-dependent client requirements, managing exceptions, and maintaining accountability. Tools exist for document preparation and basic checklist automation, but not for autonomous quality assurance at the submission gate.
Technical feasibility today	claude-sonnet-5	2/5	Some tools assist with document assembly, checklists, and status tracking, but no deployed product autonomously certifies quality and submits deliverables to clients reliably at scale.

Assign duties or responsibilities to project personnel.

CI 25–30 · exposure 25 · augmentation 63 · click for rater detail

Panel ratings per dimension for this task
Dimension	Model	Rating	Rationale
Sector adoption velocity	claude-haiku-4-5-20251001	2/5	Most organizations still rely on human project managers for duty assignment; while some enterprises use planning tools and dashboards, autonomous duty assignment remains pilot-stage. Adoption is slow, particularly outside large tech firms.
Sector adoption velocity	claude-sonnet-5	2/5	Project management software is widely used but AI-driven task assignment features are still early-stage add-ons with limited deep production adoption for actual delegation decisions.
Augmentation potential	claude-haiku-4-5-20251001	3/5	AI can assist by analyzing workload distribution, identifying skills matches, or flagging resource conflicts, helping managers make better decisions faster. These assistive features are increasingly common in project management software but do not transform the core task since human judgment remains necessary.
Augmentation potential	claude-sonnet-5	4/5	AI can effectively surface team member availability, skill matches, and workload data to help a manager make faster, better-informed assignment decisions.
Task automatability	claude-haiku-4-5-20251001	2/5	Assigning duties requires understanding organizational context, individual capabilities, project constraints, and interpersonal dynamics that current AI systems cannot reliably determine end-to-end. While AI could generate task lists or suggest assignments, the judgment-heavy aspects of matching people to roles and managing team dynamics fall outside reliable automation today.
Task automatability	claude-sonnet-5	2/5	Assigning tasks requires judgment about people's skills, workload, interpersonal dynamics, and organizational context that current AI cannot reliably assess or infer end-to-end.'
Adoption barriers	claude-haiku-4-5-20251001	4/5	Strong organizational and accountability barriers exist: managers are typically held responsible for personnel assignments, and organizations prefer human judgment for fairness, morale, and legal/HR reasons. Authority for staffing decisions typically remains vested in licensed or designated managers.
Adoption barriers	claude-sonnet-5	3/5	No formal licensing requirement, but organizational trust, accountability for team performance, and interpersonal factors create real friction against delegating this to AI.
Cost vs. human wage	claude-haiku-4-5-20251001	2/5	AI tools for resource planning exist but still require significant human oversight, setup, and validation, making them only moderately cheaper than direct human assignment. Integration costs and the need for human judgment keep the cost advantage small relative to a project manager's time.
Cost vs. human wage	claude-sonnet-5	2/5	While software costs are low, the human oversight, judgment calls, and relationship management needed make AI-alone assignment not meaningfully cheaper than a manager doing it directly.
Technical feasibility today	claude-haiku-4-5-20251001	2/5	No deployed product reliably performs full duty assignment in production; tools exist for scheduling and resource allocation but these are narrow and typically require human validation of personnel assignments. Real-world deployment of autonomous duty assignment remains minimal.
Technical feasibility today	claude-sonnet-5	2/5	Some project management tools offer AI-suggested task assignments based on skills/availability, but these are narrow-scope suggestions requiring heavy human review, not autonomous reliable assignment.

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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.