Information Technology Project Managers

15-1299.09

Median wage $116,580/yr435,370 employed (US)Rank #115 of 923 scored · top 12% by substitution

Plan, initiate, and manage information technology (IT) projects. Lead and guide the work of technical staff. Serve as liaison between business and technical aspects of projects. Plan project stages and assess business implications for each stage. Monitor progress to assure deadlines, standards, and cost targets are met.

Sub-scores

0–100 · band = confidence interval from rater disagreement

Substitution45

Exposure40

Augmentation80

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

21 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

10%

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%41

panel mean rating 2.6/5 → substitution pressure 41/100

Technical feasibility todayw 20%39

panel mean rating 2.6/5 → substitution pressure 39/100

Cost vs. human wagew 15%42

panel mean rating 2.7/5 → substitution pressure 42/100

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%54

panel mean rating 3.2/5 → substitution pressure 54/100

Task breakdown (21 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 project status reports by collecting, analyzing, and summarizing information and trends.

CI 75–80 · exposure 75 · augmentation 100 · importance 4.0/5 · 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 technology and professional services sectors are rapidly adopting AI-driven project analytics and automated reporting; large enterprises deploy these solutions in production, and mid-market uptake is accelerating.
Sector adoption velocity	claude-sonnet-5	4/5	IT and software sectors are fast adopters of AI tooling, and reporting automation is a common early use case in project management software.
Augmentation potential	claude-haiku-4-5-20251001	5/5	AI dramatically augments project managers by automating routine data collection and summarization, freeing them to focus on interpretation, risk assessment, and strategic insights—transforming productivity while the manager remains in the loop.
Augmentation potential	claude-sonnet-5	5/5	AI strongly augments this task by pulling data from multiple systems, identifying trends, and drafting narrative summaries for the PM to review and finalize.
Task automatability	claude-haiku-4-5-20251001	4/5	AI can automatically collect data from project management tools, analyze trends using pattern recognition, and generate status reports with structured summaries—achieving >50% time savings. However, high-stakes judgment about risk interpretation and stakeholder communication still benefits from human oversight, preventing a full 5.
Task automatability	claude-sonnet-5	4/5	AI can ingest data from project management tools (Jira, ticketing, timesheets), summarize progress, and draft status reports with substantial time savings, though some judgment on risk framing may still be needed.
Adoption barriers	claude-haiku-4-5-20251001	2/5	Few regulatory or licensing barriers exist; organizations can fully automate report generation. Main friction is organizational preference for human sign-off and verification of report quality before stakeholder distribution, creating modest adoption resistance.
Adoption barriers	claude-sonnet-5	1/5	No licensing or regulatory requirement mandates a human prepare status reports; organizational acceptance is the only friction.
Cost vs. human wage	claude-haiku-4-5-20251001	4/5	AI inference and integration cost for automated report generation is typically 1–5% of a project manager's loaded hourly wage, making it substantially cheaper than human-manual compilation and analysis work.
Cost vs. human wage	claude-sonnet-5	4/5	Automated data aggregation and LLM summarization is far cheaper per report than a project manager spending hours compiling updates, though integration setup adds some cost.
Technical feasibility today	claude-haiku-4-5-20251001	4/5	Mature products (Jira automation, AI-assisted reporting tools, LLM-based summarization) reliably extract and synthesize project data at scale in production environments. Reliability is high on structured data, though some customization may be needed for complex multi-source scenarios.
Technical feasibility today	claude-sonnet-5	4/5	Deployed products (Copilot in PM tools, BI dashboards with AI summarization, LLM-based reporting assistants) already generate status summaries in production, though human review is common for accuracy.

Monitor or track project milestones and deliverables.

CI 75–75 · exposure 75 · augmentation 100 · importance 4.3/5 · 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	IT and professional services sectors are rapidly deploying AI-enhanced project management tools; tracking and alerting features are among the most commonly adopted and measured in production deployments. Strong adoption momentum in digitized organizations.
Sector adoption velocity	claude-sonnet-5	4/5	IT and software project management is a fast-adopting, highly digitized sector where PM tools with automated tracking and AI features are already widely deployed.
Augmentation potential	claude-haiku-4-5-20251001	5/5	AI significantly augments PM productivity by providing real-time dashboards, anomaly detection, automated status rolls-up, and predictive alerts on at-risk milestones, allowing the PM to focus on interventions and strategy while remaining in control of decisions.
Augmentation potential	claude-sonnet-5	5/5	AI substantially augments PMs by auto-generating status reports, flagging at-risk milestones, and synthesizing data from multiple tools, while the PM retains decision-making and stakeholder communication roles.
Task automatability	claude-haiku-4-5-20251001	4/5	AI can automatically track and monitor milestones against timelines, flag delays, and aggregate deliverable status from multiple sources (tickets, documents, dashboards) with significant time savings. However, judgment about root causes and remedial action still typically requires human oversight, preventing a full 5 rating.
Task automatability	claude-sonnet-5	4/5	Tracking milestones and deliverables against a plan is largely a data-processing task—status updates, timeline comparisons, and flagging deviations can be automated with project management software and AI agents integrated with ticketing/PM tools.
Adoption barriers	claude-haiku-4-5-20251001	2/5	Minimal legal or regulatory barriers exist to automating milestone tracking; it is administrative rather than a licensed activity. Some organizational friction around adoption (preference for human judgment on priorities, existing tool investments) exists but does not constitute hard barriers.
Adoption barriers	claude-sonnet-5	2/5	No licensing requirement for this task, but organizational habits, need for contextual judgment on stakeholder communication, and trust in reported status create moderate friction.
Cost vs. human wage	claude-haiku-4-5-20251001	4/5	Off-the-shelf project management AI features are inexpensive per task compared to the loaded wage of a project manager spending hours on manual status updates and milestone reconciliation. Automation inference and oversight costs are low relative to senior PM labor.
Cost vs. human wage	claude-sonnet-5	4/5	Automated dashboards and AI status-tracking bots cost a small fraction of a PM's hourly rate for routine tracking, though initial integration and oversight add some cost.
Technical feasibility today	claude-haiku-4-5-20251001	4/5	Mature products (Jira, Azure DevOps, Monday.com with AI integrations, and specialized project analytics tools) reliably track milestones and deliverables in production. Some customization and integration work is required, but core tracking functionality is demonstrably deployable at scale.
Technical feasibility today	claude-sonnet-5	4/5	Mature PM tools (Jira, Asana, Monday.com, MS Project) with AI add-ons already automate milestone tracking, dependency alerts, and status reporting in production at scale, though nuanced judgment on risk severity still needs human review.

Assign duties, responsibilities, and spans of authority to project personnel.

CI 32–87 · exposure 58 · augmentation 88 · importance 3.8/5 · 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	IT and technology-heavy sectors are rapidly adopting AI-assisted project planning tools and task assignment automation, with many Fortune 500 technology firms and consulting companies using such systems in production. Adoption is particularly deep in information and professional services industries where digital workflows are standard.
Sector adoption velocity	claude-sonnet-5	3/5	IT and project management sectors show moderate AI tool adoption for scheduling and resource tracking, but delegation of authority remains a human-led practice with limited AI penetration.
Augmentation potential	claude-haiku-4-5-20251001	5/5	AI significantly enhances project manager productivity by instantly generating multiple organizational scenarios, flagging skill gaps, and balancing workload allocation across teams, allowing managers to focus on strategic decisions and interpersonal considerations. This creates substantial productivity gains while keeping the human manager in final decision-making authority.
Augmentation potential	claude-sonnet-5	4/5	AI project management tools can suggest optimal task assignments based on skills, availability, and workload data, meaningfully assisting managers in making these decisions faster.
Task automatability	claude-haiku-4-5-20251001	5/5	Current AI systems can analyze organizational structure, project scope, and personnel capabilities to generate comprehensive duty assignments and responsibility matrices. Large language models can perform end-to-end organizational planning with minimal human input, easily meeting the 50% time-saving threshold while maintaining quality equal to human project managers.
Task automatability	claude-sonnet-5	2/5	Assigning roles and authority requires understanding team member skills, interpersonal dynamics, and organizational politics that current AI cannot reliably assess or execute end-to-end.
Adoption barriers	claude-haiku-4-5-20251001	2/5	While some organizations prefer human accountability for assignment decisions and require manager sign-off, there are no legal or licensing barriers preventing AI from executing this task. Most resistance stems from organizational culture and oversight preferences rather than regulatory or liability constraints.
Adoption barriers	claude-sonnet-5	3/5	No formal licensing requirement, but organizational hierarchy, accountability structures, and trust in human judgment for granting authority create moderate friction against full automation.
Cost vs. human wage	claude-haiku-4-5-20251001	5/5	AI-generated duty assignments cost significantly less than human project manager time, requiring only initial setup and occasional verification. The cost per task is orders of magnitude lower than the loaded wage of an IT project manager performing the same work.
Cost vs. human wage	claude-sonnet-5	2/5	While AI scheduling tools are cheap, the actual decision-making and accountability delegation still requires a human manager's judgment and oversight, limiting real cost savings.
Technical feasibility today	claude-haiku-4-5-20251001	4/5	Deployed project management and AI-assisted workflow tools demonstrate this capability reliably in production environments, though some organizations still rely on human review to contextualize assignments within unique cultural or political dynamics. Most error rates are manageable and confined to edge cases requiring human judgment override.
Technical feasibility today	claude-sonnet-5	2/5	Some project management tools offer AI-assisted resource allocation suggestions, but no deployed product actually assigns authority and responsibility to personnel autonomously in production.

Develop and manage work breakdown structure (WBS) of information technology projects.

CI 50–67 · exposure 53 · augmentation 75 · importance 3.9/5 · 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	IT and software development sectors show growing adoption of AI-assisted project planning tools and agents, but large-scale production deployment of autonomous WBS systems remains pilot-stage. Most teams still manually author WBS, though AI assistance is increasingly incorporated into workflows.
Sector adoption velocity	claude-sonnet-5	3/5	IT and professional services sectors show above-average AI tool adoption, and PM software increasingly embeds AI planning features, though widespread reliance for WBS management is still emerging.
Augmentation potential	claude-haiku-4-5-20251001	4/5	AI excels at augmenting WBS creation by auto-generating task hierarchies, suggesting decomposition patterns, and identifying missing tasks based on project templates and past data. Project managers retain control and judgment while achieving substantially faster and more comprehensive structure development.
Augmentation potential	claude-sonnet-5	4/5	AI can substantially speed up drafting, restructuring, and updating WBS elements, letting PMs focus on stakeholder coordination and risk judgment while staying in control of final structure.
Task automatability	claude-haiku-4-5-20251001	4/5	AI can generate, organize, and refine work breakdown structures using LLMs and project management tools, significantly reducing manual creation time. Current systems can parse project requirements, decompose tasks hierarchically, and produce structured outputs that meet the 50% time-saving threshold, though some human review and domain-specific customization remains necessary.
Task automatability	claude-sonnet-5	3/5	AI can draft an initial WBS from a project scope or requirements document and suggest task decomposition, but ongoing management, stakeholder negotiation, and adjustment to real project dynamics require human judgment.
Adoption barriers	claude-haiku-4-5-20251001	2/5	No legal or regulatory barriers prevent automated WBS generation; the output is typically used internally or with light oversight rather than requiring licensure. Organizational inertia and preference for human judgment provide modest friction but are not hard barriers.
Adoption barriers	claude-sonnet-5	2/5	No licensing requirement to create a WBS, but organizational accountability for project outcomes typically keeps a human PM formally responsible for approving and maintaining project plans.
Cost vs. human wage	claude-haiku-4-5-20251001	4/5	AI inference and integration costs for WBS generation are minimal compared to the hourly rate of IT project managers (typically $50–150/hour loaded cost). Even accounting for oversight, the cost per usable WBS is likely 5–10× cheaper than human-only creation.
Cost vs. human wage	claude-sonnet-5	3/5	Generating a draft WBS via LLM is cheap, but the full task includes iterative management and stakeholder alignment that still requires a paid PM, keeping overall cost comparable to human-only effort.
Technical feasibility today	claude-haiku-4-5-20251001	3/5	Tools like GitHub Copilot, ChatGPT, and specialized PM plugins can draft WBS frameworks and suggest task decompositions, but deployed products still require substantial human oversight to ensure accuracy and alignment with organizational constraints. Reliability is uneven across different project types and complexity levels.
Technical feasibility today	claude-sonnet-5	2/5	AI-assisted project planning tools exist (e.g., generative WBS drafts in PM software), but they are used as starting points requiring heavy human revision rather than reliable standalone production systems.

Confer with project personnel to identify and resolve problems.

CI 32–84 · exposure 58 · augmentation 88 · importance 4.4/5 · 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	IT organizations are rapidly deploying AI-powered incident management, chatbots for problem resolution, and automated troubleshooting systems; this sector shows strong digitization and early-mover adoption patterns.
Sector adoption velocity	claude-sonnet-5	3/5	IT/software sectors are relatively fast adopters of AI tools for meetings and status tracking, though full delegation of problem resolution to AI remains rare and mostly pilot-stage.
Augmentation potential	claude-haiku-4-5-20251001	5/5	AI significantly augments project managers by surfacing problems from data, suggesting root causes, and proposing solutions, allowing humans to focus on complex judgment, stakeholder communication, and strategic decisions rather than routine triage.
Augmentation potential	claude-sonnet-5	4/5	AI meeting transcription, summarization, and issue-tracking tools meaningfully help PMs prepare for and follow up on these conversations, improving efficiency while the human still leads the resolution process.
Task automatability	claude-haiku-4-5-20251001	5/5	Current AI systems can identify and resolve many problems through natural language analysis of project documentation, team communications, and logs, with significant time savings through automated triage, root-cause analysis, and solution recommendation without human intervention on routine issues.
Task automatability	claude-sonnet-5	2/5	This task requires real-time interpersonal negotiation, conflict resolution, and contextual judgment across a team, which current AI cannot conduct end-to-end reliably; at most AI can support meeting summarization or issue tracking.
Adoption barriers	claude-haiku-4-5-20251001	2/5	Few legal or regulatory barriers exist; the main friction is organizational preference for human judgment on sensitive issues and stakeholder expectations that a person be involved in resolution communication.
Adoption barriers	claude-sonnet-5	3/5	No licensing requirement, but organizational trust, accountability for decisions, and stakeholder preference for human leadership create meaningful friction against full automation.
Cost vs. human wage	claude-haiku-4-5-20251001	4/5	AI-driven problem identification and resolution (via chatbots, automation platforms, and ticket systems) costs substantially less per issue resolved than paying a project manager's loaded hourly rate, particularly for routine or repetitive problems.
Cost vs. human wage	claude-sonnet-5	2/5	Human PM time is required for judgment and relationship management; AI tools reduce some admin overhead but don't replace the core conferring activity, limiting cost savings.
Technical feasibility today	claude-haiku-4-5-20251001	4/5	Deployed AI chatbots and enterprise knowledge systems already perform issue identification and resolution support in production IT environments, though they may require human verification for critical decisions and complex interdependencies.
Technical feasibility today	claude-sonnet-5	2/5	Deployed products like meeting assistants or chatbots can surface issues and log discussions, but no product autonomously confers with personnel to identify and resolve problems in production.

Schedule and facilitate meetings related to information technology projects.

CI 49–66 · exposure 47 · augmentation 88 · importance 4.2/5 · 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	IT and professional services sectors have rapidly adopted AI scheduling and meeting transcription tools; many enterprises now use AI assistants for routine meeting coordination. Adoption is deep in information-dense, digitally mature sectors where IT project managers operate.
Sector adoption velocity	claude-sonnet-5	4/5	IT and professional services sectors have rapidly adopted AI scheduling and meeting-assistant tools (e.g., Otter.ai, Microsoft Copilot in Teams) as part of daily workflows.
Augmentation potential	claude-haiku-4-5-20251001	5/5	AI significantly augments human facilitators by handling scheduling logistics, generating real-time transcripts and action items, and freeing managers to focus on discussion quality and stakeholder alignment. This is a textbook case of high-value assistance while the human retains control of meeting strategy and outcomes.
Augmentation potential	claude-sonnet-5	4/5	AI tools significantly streamline scheduling, agenda-setting, note-taking, and follow-up action tracking, meaningfully boosting a PM's efficiency while they remain in charge of facilitation.
Task automatability	claude-haiku-4-5-20251001	3/5	AI can automate significant components of meeting scheduling (calendar synchronization, finding common times, sending invitations) and facilitate routine operational meetings (note-taking, action-item tracking), but complex meetings requiring judgment on agenda prioritization, stakeholder dynamics, or adaptive facilitation still require human oversight. The time savings approach 50% for standardized recurring meetings but fall short for novel or contentious project discussions.
Task automatability	claude-sonnet-5	2/5	AI can schedule meetings and even summarize discussions, but true facilitation requires real-time judgment, conflict resolution, and stakeholder management that current systems cannot fully replace.
Adoption barriers	claude-haiku-4-5-20251001	2/5	Few regulatory or legal barriers prevent automated scheduling and meeting facilitation; organizational friction is low since employees already use calendar systems. The main friction is user preference for human touch in strategic meetings and legacy system integration, but these do not prevent adoption.
Adoption barriers	claude-sonnet-5	2/5	No licensing requirement, but organizational reliance on a trusted human facilitator to manage team dynamics and accountability creates moderate friction against full automation.
Cost vs. human wage	claude-haiku-4-5-20251001	4/5	AI meeting scheduling and note-taking tools cost $10–50/month per user, while a project manager's loaded cost for the time spent on scheduling and meeting documentation is $40–80/hour. For organizations running dozens of meetings weekly, the per-meeting cost of AI automation is a small fraction of human labor, making it roughly one-fifth the cost or better in practice.
Cost vs. human wage	claude-sonnet-5	3/5	Scheduling automation is cheap, but a human PM's facilitation value (decision-making, prioritization) still requires paid human time, keeping overall cost comparable rather than dramatically cheaper.
Technical feasibility today	claude-haiku-4-5-20251001	4/5	Mature products (Calendly, Microsoft Bookings, AI meeting assistants like Otter.ai, Fireflies.io) reliably handle scheduling and basic facilitation at scale in production. Calendar integration and transcription are deployed widely; however, real-time facilitation of complex stakeholder discussions remains partially automated, with human judgment still required for direction-setting.
Technical feasibility today	claude-sonnet-5	3/5	Scheduling assistants and meeting bots (e.g., calendar AI, transcription/summarization tools) are deployed in production, but active facilitation of IT project meetings is not reliably automated by any product.

Develop and manage annual budgets for information technology projects.

CI 41–70 · exposure 53 · augmentation 88 · importance 4.0/5 · 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	IT and financial services sectors show rapid adoption of AI-driven budget forecasting and planning tools; major enterprises and mid-market firms increasingly deploy automated budget analysis platforms. This reflects patterns of fast digitization and willingness to adopt automation in information-intensive functions.
Sector adoption velocity	claude-sonnet-5	3/5	IT and professional services sectors are adopting AI tools for planning and forecasting at a moderate pace, with pilots common but full budget-management automation still rare.
Augmentation potential	claude-haiku-4-5-20251001	5/5	AI clearly augments PM productivity by automating data aggregation, generating draft forecasts, running scenario analyses, and flagging cost anomalies—substantially accelerating the planning cycle while PMs focus on strategy and stakeholder alignment.
Augmentation potential	claude-sonnet-5	4/5	AI tools can significantly speed up cost estimation, scenario modeling, and reporting, meaningfully boosting the productivity of a human project manager who retains final control.
Task automatability	claude-haiku-4-5-20251001	4/5	AI can handle the majority of budget development tasks (forecasting costs, compiling historical data, generating initial budgets) with established finance software integration, achieving >50% time savings. Remaining human oversight needed for strategic decisions and project-specific adjustments makes it not quite fully automatable end-to-end.
Task automatability	claude-sonnet-5	3/5	AI can draft budget templates, forecast costs, and analyze historical spend data, but requires human judgment for organizational priorities, vendor negotiations, and stakeholder alignment, so full automation with equal quality is not yet achieved.
Adoption barriers	claude-haiku-4-5-20251001	2/5	While budget decisions often require management sign-off and organizational approval, there are no hard legal or licensing barriers preventing AI systems from generating, analyzing, and recommending budgets. Internal governance and audit requirements create moderate friction but not prohibitive blocks.
Adoption barriers	claude-sonnet-5	3/5	No licensing requirement exists, but budget sign-off typically requires accountability from a named manager, and organizational governance/finance controls create friction against full automation.
Cost vs. human wage	claude-haiku-4-5-20251001	4/5	AI-based budgeting tools have moved toward commodity pricing and marginal inference costs, making them substantially cheaper than dedicated human project finance analysts once initial setup is complete. The loaded cost of an IT PM is significantly higher than operational AI costs.
Cost vs. human wage	claude-sonnet-5	2/5	AI can cheaply generate cost estimates and spreadsheets, but the ongoing management, negotiation, and oversight components still require significant human time, keeping overall cost savings modest.
Technical feasibility today	claude-haiku-4-5-20251001	3/5	Deployed financial planning tools and AI-powered cost estimation software exist and are used in organizations, but they typically require significant human configuration, validation, and real-time judgment. Error rates in cost projections and scope changes limit full autonomy.
Technical feasibility today	claude-sonnet-5	2/5	Some financial planning and forecasting tools with AI features exist, but no deployed product independently develops and manages a full IT project budget end-to-end reliably in production.

Develop implementation plans that include analyses such as cost-benefit or return on investment (ROI).

CI 50–59 · exposure 50 · augmentation 88 · importance 4.0/5 · 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	IT and finance-adjacent organizations are experimenting with AI-assisted financial modeling and planning, but deployment remains pilot-heavy rather than production-standard, particularly for high-stakes implementation planning.
Sector adoption velocity	claude-sonnet-5	3/5	IT project management is in a professional services/tech-adjacent field with growing but uneven AI tool adoption; pilots for planning assistance are common but full deployment varies by organization.
Augmentation potential	claude-haiku-4-5-20251001	4/5	AI excels at rapidly generating draft calculations, sensitivity analyses, and scenario modeling that project managers can evaluate and refine, materially accelerating the analysis phase while the manager retains control over assumptions and final recommendations.
Augmentation potential	claude-sonnet-5	5/5	AI substantially speeds up drafting of cost-benefit and ROI analyses, structuring implementation plans, and summarizing financial trade-offs, while the PM retains decision-making and contextual judgment.
Task automatability	claude-haiku-4-5-20251001	3/5	AI can generate draft cost-benefit analyses and ROI calculations from templates and input data, but developing comprehensive implementation plans requires judgment about feasibility, risk, stakeholder priorities, and organizational context that typically needs human refinement. Time savings could reach 50% with significant setup, but not reliably end-to-end.
Task automatability	claude-sonnet-5	3/5	AI can draft cost-benefit and ROI frameworks and generate portions of implementation plans quickly, but requires accurate project-specific inputs, stakeholder judgment, and validation that current systems cannot fully substitute end-to-end.'
Adoption barriers	claude-haiku-4-5-20251001	3/5	Implementation plans are often subject to organizational governance and approval workflows that require a named human accountable for accuracy and feasibility; while AI can assist, regulatory and organizational friction typically prevents full automation.
Adoption barriers	claude-sonnet-5	2/5	No licensing requirement mandates a human perform this, though organizational accountability and reliance on judgment calls create moderate friction against full automation.
Cost vs. human wage	claude-haiku-4-5-20251001	3/5	AI infrastructure and oversight costs are roughly comparable to the hourly rate of mid-level IT project managers when factoring in integration, data preparation, and required human review; neither party has a decisive cost advantage at scale.
Cost vs. human wage	claude-sonnet-5	4/5	Generating draft ROI analyses and implementation plan text via AI is far cheaper per unit output than a project manager's time, though oversight and data verification add some cost.
Technical feasibility today	claude-haiku-4-5-20251001	3/5	Products like business intelligence platforms and financial modeling tools can partially automate cost and ROI calculations, and some AI tools generate draft analyses, but reliable production systems for full implementation planning remain narrow in scope and often require substantial human oversight and correction.
Technical feasibility today	claude-sonnet-5	3/5	Deployed products (e.g., Copilot, ChatGPT enterprise tools) are used to draft financial analyses and plan templates, but reliability depends heavily on data quality and human review, limiting scope in production.

Develop or update project plans for information technology projects including information such as project objectives, technologies, systems, information specifications, schedules, funding, and staffing.

CI 46–57 · exposure 50 · augmentation 88 · importance 4.0/5 · 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	IT and professional services sectors show moderate AI adoption in planning tools, with AI-assisted drafting common in pilots and early production, but end-to-end replacement of plan creation by humans remains infrequent.
Sector adoption velocity	claude-sonnet-5	4/5	IT and professional services sectors show fast adoption of AI copilots for planning and documentation tasks, though full autonomous plan generation is still emerging.
Augmentation potential	claude-haiku-4-5-20251001	4/5	AI substantially assists project managers through rapid template generation, schedule optimization, resource balancing, and risk scenario modeling, materially raising their planning productivity while the manager retains strategic control and oversight.
Augmentation potential	claude-sonnet-5	5/5	AI tools substantially speed up drafting of schedules, specifications, and staffing plans, letting PMs iterate faster while retaining decision authority.
Task automatability	claude-haiku-4-5-20251001	3/5	AI can generate significant portions of project plans (templates, scheduling calculations, risk identification, resource allocation) with substantial time savings, but human judgment on objectives, technology trade-offs, and stakeholder alignment remains essential for quality output.
Task automatability	claude-sonnet-5	3/5	AI can draft project plans, schedules, and resource templates from inputs, but synthesizing organizational constraints, stakeholder politics, and staffing decisions still requires human judgment, so only partial time savings are realized.
Adoption barriers	claude-haiku-4-5-20251001	3/5	Project managers retain significant control over plan sign-off and accountability; organizations value human judgment on resource commitments and risk trade-offs; customer and stakeholder preference for human-led planning creates organizational friction against full automation.
Adoption barriers	claude-sonnet-5	2/5	No licensing requirement blocks AI use, but organizational accountability for project outcomes and budget sign-off creates some friction against full automation.
Cost vs. human wage	claude-haiku-4-5-20251001	2/5	AI inference cost for plan generation is low, but integration, customization to organizational context, and mandatory human review and refinement consume significant labor, making the all-in cost still comparable to or potentially exceeding human creation time.
Cost vs. human wage	claude-sonnet-5	3/5	AI can cheaply generate draft plan content, but the overall cost of a project plan includes stakeholder negotiation and validation that still requires costly PM time, keeping the ratio moderate rather than dramatically cheaper.
Technical feasibility today	claude-haiku-4-5-20251001	3/5	Products like AI-assisted project planning tools and document generation systems exist in production, but they typically require substantial human review, correction, and context-setting; error rates on complex multi-constraint optimization remain material.
Technical feasibility today	claude-sonnet-5	3/5	Products like AI-assisted project management tools (e.g., Copilot in Project, Asana AI, Jira AI) generate draft plans and timelines, but reliable end-to-end plan creation without heavy human revision is not yet standard practice.

Initiate, review, or approve modifications to project plans.

CI 28–70 · exposure 50 · augmentation 88 · importance 4.2/5 · 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	IT and professional services sectors are rapidly adopting AI-powered project management tools and workflow automation. Plan modification workflows are increasingly automated in fortune-500 tech and financial firms, though mid-market and smaller organizations lag.
Sector adoption velocity	claude-sonnet-5	3/5	IT/professional services sectors are adopting AI project management assistants at a moderate pace, with pilots and copilot features common but full delegation of approvals rare.
Augmentation potential	claude-haiku-4-5-20251001	5/5	AI dramatically augments PM productivity by instantly flagging plan change impacts, suggesting risk mitigation, and automating routine approval routing—freeing PMs to focus on strategic negotiation and stakeholder alignment rather than manual change-log reviews.
Augmentation potential	claude-sonnet-5	4/5	AI can effectively summarize project data, model impacts of proposed changes, and draft recommendations, meaningfully speeding up the PM's review process while the human retains final approval.
Task automatability	claude-haiku-4-5-20251001	4/5	AI can draft, review, and flag modifications to project plans with high accuracy—analyzing scope, timeline, resource allocation, and risk impacts—and can suggest approval decisions. However, final sign-off on critical changes typically requires human judgment on strategic trade-offs, stakeholder buy-in, and organizational context, preventing a full end-to-end automation at the 50% threshold.
Task automatability	claude-sonnet-5	2/5	This requires synthesizing project status, stakeholder input, risk tradeoffs, and organizational context to make judgment calls on scope/schedule/budget changes—AI can draft or flag options but cannot reliably own the approval decision end-to-end.
Adoption barriers	claude-haiku-4-5-20251001	3/5	Organizational friction and liability concerns exist: stakeholders often prefer human sign-off on plan changes due to downstream risk, and some contracts or governance policies explicitly require a human PM signature. However, no hard legal licensing barrier prevents AI-assisted or autonomous approval in most settings.
Adoption barriers	claude-sonnet-5	4/5	Approval authority is typically tied to accountability, budget sign-off, and organizational governance structures, creating strong practical and liability-based barriers to full automation.
Cost vs. human wage	claude-haiku-4-5-20251001	4/5	AI-driven plan modification and review incurs modest inference and integration costs against a loaded PM wage (typically $80–150k+ annually). Automation of this repetitive review task can deliver an order-of-magnitude cost reduction per modification cycle.
Cost vs. human wage	claude-sonnet-5	2/5	AI can cheaply generate analysis and draft change requests, but the actual review/approval still requires a paid human PM's accountability, so overall cost savings are modest.
Technical feasibility today	claude-haiku-4-5-20251001	4/5	Mature products (Jira, Monday.com, Microsoft Project, and specialized AI planning tools) now offer plan modification tracking, impact analysis, and approval workflow automation in production. These systems reliably flag and suggest plan changes, though human review remains common in enterprise deployments.
Technical feasibility today	claude-sonnet-5	2/5	Project management tools have AI features for tracking and flagging deviations, but no deployed product autonomously approves or initiates project plan modifications in production with authority.

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

CI 30–46 · exposure 38 · augmentation 75 · importance 4.0/5 · 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	IT and professional services sectors are digitizing procurement, but adoption of AI-driven vendor selection remains limited and primarily in the form of supporting tools rather than autonomous decision-making. Most organizations retain human PMs as the decision authority, slowing deep automation adoption.
Sector adoption velocity	claude-sonnet-5	3/5	IT and professional services sectors are adopting AI for research and drafting tasks at a moderate pace, but vendor selection specifically remains largely a human-driven, relationship-based process with limited production-scale AI tools.
Augmentation potential	claude-haiku-4-5-20251001	4/5	AI tools substantially augment this task by automating RFP analysis, generating comparison matrices, flagging red flags in vendor credentials, and organizing evaluation criteria—all while the PM retains final judgment and relationship-building authority. This is a clear productivity multiplier for the human PM.
Augmentation potential	claude-sonnet-5	4/5	AI tools can meaningfully speed up vendor research, proposal summarization, and comparative analysis, giving project managers a strong productivity boost while they retain final decision authority.
Task automatability	claude-haiku-4-5-20251001	3/5	AI can automate parts of vendor evaluation—summarizing RFPs, comparing features, and flagging criteria mismatches—but final selection involves judgment about organizational fit, risk tolerance, and stakeholder preferences that typically requires human discretion. The task is partially automatable with significant setup (defining evaluation frameworks, integration with procurement systems).
Task automatability	claude-sonnet-5	2/5	AI can assist with vendor research, comparison matrices, and RFP analysis, but the final selection involves negotiation, relationship judgment, and organizational politics that current AI cannot fully replace end-to-end.
Adoption barriers	claude-haiku-4-5-20251001	3/5	Legal and organizational friction are moderate: contracts often require authorized human sign-off, liability concerns exist if AI-selected vendors underperform, and many organizations maintain internal procurement policies and preference for human judgment in vendor relationships. These are not hard legal barriers but create real adoption friction.
Adoption barriers	claude-sonnet-5	3/5	No licensing requirement mandates a human, but organizational procurement policies, contractual liability, and trust-based vendor relationships create real friction against full automation.
Cost vs. human wage	claude-haiku-4-5-20251001	2/5	Current AI solutions require significant human oversight to handle the judgment-heavy portions of vendor selection, and integration into procurement workflows adds operational cost. The all-in cost per selection decision likely remains comparable to or slightly higher than a human PM's time for this task.
Cost vs. human wage	claude-sonnet-5	2/5	AI can cheaply generate vendor shortlists or summarize proposals, but the human time for stakeholder alignment, negotiation, and due diligence remains significant, keeping overall costs comparable to human-led processes.
Technical feasibility today	claude-haiku-4-5-20251001	3/5	Products like contract review tools and vendor comparison platforms exist and are deployed in some organizations, but material limitations remain: they struggle with nuanced assessment of consultant capabilities, organizational culture fit, and long-term partnership potential. Most implementations require substantial human review and validation.
Technical feasibility today	claude-sonnet-5	2/5	Some procurement software and AI-assisted vendor comparison tools exist, but no deployed product autonomously identifies and selects vendors reliably at scale without heavy human oversight.

Establish and execute a project communication plan.

CI 32–41 · exposure 25 · augmentation 75 · importance 3.9/5 · 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	Tech and professional services firms are adopting AI-assisted project tools (meeting summaries, document drafting, schedule optimization), but replacing human-driven communication planning and execution remains uncommon in production; pilots and partial adoption are more typical.
Sector adoption velocity	claude-sonnet-5	3/5	IT and professional services sectors are moderately fast adopters of AI tools for documentation and reporting, though full execution of communication plans remains largely human-driven with AI as a support tool.
Augmentation potential	claude-haiku-4-5-20251001	4/5	AI can significantly assist by generating draft communications, tracking stakeholder preferences, flagging delays, and summarizing status updates, allowing project managers to focus on strategy and relationship management rather than routine logistics and message creation.
Augmentation potential	claude-sonnet-5	4/5	AI tools significantly help draft status reports, meeting summaries, stakeholder updates, and scheduling reminders, substantially boosting a PM's efficiency while they remain accountable for judgment and relationships.
Task automatability	claude-haiku-4-5-20251001	2/5	AI can draft templates, schedules, and routine status communications, but executing a communication plan requires real-time judgment about stakeholder needs, conflict resolution, and adaptive messaging that current systems cannot reliably handle end-to-end. Significant manual oversight and human decision-making remain essential.
Task automatability	claude-sonnet-5	2/5	AI can help draft communication plans and templates, but establishing and executing one requires ongoing stakeholder judgment, negotiation, and adaptive decision-making across a project's lifecycle that current tools cannot fully replace.'
Adoption barriers	claude-haiku-4-5-20251001	3/5	Project managers are expected to exercise judgment on communication strategy and stakeholder relationships, which organizations often view as requiring human accountability. Some regulatory or contractual contexts require human sign-off on project governance and communications, creating moderate friction to full automation.
Adoption barriers	claude-sonnet-5	2/5	No licensing requirement exists, but organizational trust, stakeholder relationships, and accountability for miscommunication create moderate friction against full automation.
Cost vs. human wage	claude-haiku-4-5-20251001	3/5	AI messaging and scheduling tools are relatively cheap to operate, but the time and oversight required to ensure appropriate communication strategy, stakeholder engagement, and conflict handling across a project lifecycle approach human costs; integration and governance add overhead.
Cost vs. human wage	claude-sonnet-5	2/5	While AI can cheaply generate drafts and summaries, the human oversight, judgment, and relationship management needed to execute a communication plan means overall cost savings are modest, not order-of-magnitude.
Technical feasibility today	claude-haiku-4-5-20251001	2/5	While AI tools can generate communication artifacts and send scheduled messages, no deployed system reliably executes a full project communication plan with appropriate tone, stakeholder prioritization, and context adaptation at scale. Products exist for narrow subtasks (email drafting, meeting scheduling) but not integrated plan execution.
Technical feasibility today	claude-sonnet-5	2/5	Products like project management copilots (e.g., in Jira, MS Project, ClickUp) can suggest communication templates and status updates, but no deployed system reliably executes a full communication plan across stakeholders without human oversight.

Coordinate recruitment or selection of project personnel.

CI 32–41 · exposure 30 · augmentation 75 · importance 3.9/5 · 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 and professional services firms have adopted AI-assisted recruiting tools and screening platforms, but adoption remains uneven; many organizations still rely heavily on manual coordination, and regulatory caution slows full deployment of automated selection systems.
Sector adoption velocity	claude-sonnet-5	3/5	IT/professional services sectors adopt AI recruiting tools moderately fast, but full coordination of personnel selection remains largely human-led with pilots for sub-tasks.
Augmentation potential	claude-haiku-4-5-20251001	4/5	AI substantially augments recruitment coordination by automating candidate screening, interview scheduling, and skill-matching analysis, allowing project managers to focus on stakeholder alignment and final selection decisions while AI handles routine administrative and data-processing work.
Augmentation potential	claude-sonnet-5	4/5	AI tools can meaningfully assist with candidate sourcing, resume screening, skills matching, and scheduling, freeing the project manager to focus on final decisions and negotiation.
Task automatability	claude-haiku-4-5-20251001	2/5	AI can assist with candidate screening, resume parsing, and scheduling, but the core task of recruitment coordination—negotiating with hiring managers, evaluating cultural fit, and making final personnel selections—requires human judgment and relationship management that AI cannot fully automate to the 50% time-saving threshold.
Task automatability	claude-sonnet-5	2/5	Involves interpersonal judgment, negotiation with stakeholders, and organizational fit assessment that AI cannot fully replicate; AI can support parts like resume screening but not the coordination role itself.
Adoption barriers	claude-haiku-4-5-20251001	3/5	Legal and compliance barriers exist around hiring discrimination, data privacy, and equal opportunity requirements that mandate human review and accountability. Most organizations require human project managers to coordinate final personnel selections, creating organizational friction against full automation.
Adoption barriers	claude-sonnet-5	3/5	No licensing requirement, but organizational hiring processes, HR policy, anti-discrimination liability, and internal stakeholder trust create meaningful friction against full automation.
Cost vs. human wage	claude-haiku-4-5-20251001	3/5	AI tools for recruitment support (ATS screening, scheduling) are relatively cheap, but the labor for coordination, stakeholder management, and final decision-making still requires significant human effort, keeping all-in costs roughly comparable to manual recruitment coordination.
Cost vs. human wage	claude-sonnet-5	2/5	Some cost savings from AI-assisted screening, but human coordination, interviewing, and stakeholder negotiation still dominate cost and cannot be fully offloaded.
Technical feasibility today	claude-haiku-4-5-20251001	3/5	Deployed products exist for resume screening, interview scheduling, and candidate database management, but they operate with material limitations in nuance, context-awareness, and integration with organizational hiring workflows. Human oversight remains necessary for final selections.
Technical feasibility today	claude-sonnet-5	2/5	AI recruiting tools exist for screening and scheduling, but no deployed product coordinates end-to-end personnel selection for project staffing reliably in production.

Assess current or future customer needs and priorities by communicating directly with customers, conducting surveys, or other methods.

CI 32–38 · exposure 25 · augmentation 75 · importance 4.2/5 · 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	IT and professional services sectors are piloting AI-assisted survey tools and sentiment analysis, but direct customer communication remains primarily human-led in production. Adoption is accelerating for augmentation but lagging for replacement of core assessment tasks.
Sector adoption velocity	claude-sonnet-5	3/5	IT project management sits in a fast-digitizing professional services context, with growing use of AI for survey analysis and communication drafting, though live stakeholder engagement remains largely human-led.
Augmentation potential	claude-haiku-4-5-20251001	4/5	AI can meaningfully assist PMs by auto-generating survey templates, analyzing customer feedback in real time, identifying sentiment patterns, and summarizing responses—allowing PMs to focus on deeper interpretation and relationship-building. This augmentation is demonstrably productive in practice.
Augmentation potential	claude-sonnet-5	4/5	AI can meaningfully assist by drafting surveys, summarizing responses, analyzing sentiment, and preparing talking points, significantly boosting PM efficiency while humans still lead the interactions.
Task automatability	claude-haiku-4-5-20251001	2/5	While AI can draft surveys and analyze responses at scale, the core task—communicating directly with customers to assess nuanced needs and priorities—requires human judgment, relationship-building, and contextual understanding. Current AI cannot reliably replace the interpretive and relational elements that yield authentic customer insight.
Task automatability	claude-sonnet-5	2/5	Assessing customer needs relies heavily on relationship-building, contextual judgment, and reading unstated priorities, which AI cannot fully replicate; AI can assist with survey design/analysis but not the full stakeholder engagement process.
Adoption barriers	claude-haiku-4-5-20251001	3/5	Customer relationships and trust are often personal; many organizations prefer human contact for sensitive needs assessment. However, there are no strict legal barriers preventing automation of surveys or initial data collection, creating moderate but not hard adoption friction.
Adoption barriers	claude-sonnet-5	2/5	No licensing requirement, but customers often expect direct human engagement for strategic discussions, creating moderate organizational and relational friction.
Cost vs. human wage	claude-haiku-4-5-20251001	2/5	AI can reduce costs on survey distribution and basic analysis, but the labor cost of a skilled PM conducting interviews or focus groups remains lower than the combined cost of AI tooling, data integration, and mandatory human validation of findings.
Cost vs. human wage	claude-sonnet-5	2/5	AI tools can cheaply process survey data, but the direct communication and relationship management components still require costly human time, keeping overall cost comparable to human-led processes.
Technical feasibility today	claude-haiku-4-5-20251001	2/5	AI tools exist for survey design, sentiment analysis, and response aggregation, but deployed products cannot independently conduct high-quality, two-way customer communication or reliably synthesize priority assessments without human oversight. Benchmarks show reasonable performance on narrow survey tasks, but real-world customer needs assessment remains largely human-driven.
Technical feasibility today	claude-sonnet-5	2/5	Products exist for survey analytics and chatbot-based intake, but no deployed system reliably conducts the full nuanced needs-assessment dialogue a PM performs with stakeholders.

Identify need for initial or supplemental project resources.

CI 32–38 · exposure 25 · augmentation 75 · importance 4.0/5 · 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	Enterprise IT environments show middling adoption of AI-assisted project management tools; pilots are common in large organizations, but production deployment of autonomous resource-identification systems remains infrequent, with human PMs still making final calls on staffing decisions.
Sector adoption velocity	claude-sonnet-5	3/5	placeholder
Augmentation potential	claude-haiku-4-5-20251001	4/5	AI can meaningfully augment this task by synthesizing project data, flagging capacity bottlenecks, and proposing resource scenarios, enabling PMs to make faster, more informed decisions while retaining control. This is a strong use case for human-in-the-loop AI assistance that raises PM productivity.
Augmentation potential	claude-sonnet-5	4/5	placeholder
Task automatability	claude-haiku-4-5-20251001	2/5	AI can assist in data aggregation and pattern recognition (e.g., analyzing project scope, timeline, and team capacity), but identifying resource needs requires domain expertise, organizational context, and nuanced judgment about project risks, team dynamics, and strategic priorities that current systems cannot perform end-to-end with 50% time savings at equal quality.
Task automatability	claude-sonnet-5	2/5	Identifying resource needs requires synthesizing project scope, timelines, budget, and team capacity, involving judgment calls that AI can support but not fully replace end-to-end today."},"feasibility":{"rating":2,"rationale":"Some project management tools offer resource forecasting features, but they require significant human validation and are not widely relied upon as authoritative decision-makers."},"cost_ratio":{"rating":2,"rationale":"AI tools can lower analysis time but still require human oversight and integration with project data systems, keeping costs comparable to a skilled PM's time for this judgment task."},"barriers":{"rating":2,"rationale":"No licensing barrier exists, but organizational accountability and stakeholder trust in a human PM's judgment create moderate friction against pure automation."},"adoption_velocity":{"rating":3,"rationale":"IT and professional services sectors are moderately fast adopters of AI-assisted project management tools, though full automation of resource identification remains uncommon in production."},"augmentation":{"rating":4,"rationale":"AI can analyze project data, forecast bottlenecks, and flag resource gaps, meaningfully aiding a PM's decision-making even though final judgment remains human-driven."}},
Adoption barriers	claude-haiku-4-5-20251001	3/5	Project managers retain decision authority over resource allocation due to organizational hierarchy and liability concerns—errors can cascade into project failure. However, regulatory barriers are modest, and AI assistance does not face hard legal restrictions, creating moderate rather than severe adoption friction.
Adoption barriers	claude-sonnet-5	2/5	placeholder
Cost vs. human wage	claude-haiku-4-5-20251001	2/5	Integration of resource-planning AI into project environments requires significant customization and human validation of outputs. The combined cost of inference, data integration, and required oversight remains comparable to or higher than a project manager's effort on this task.
Cost vs. human wage	claude-sonnet-5	2/5	placeholder
Technical feasibility today	claude-haiku-4-5-20251001	2/5	While tools exist for project analytics and resource forecasting, no deployed product reliably performs this complex judgment task in production. Most systems offer narrow, rule-based recommendations requiring substantial human oversight rather than autonomous, trustworthy assessment of resource gaps.
Technical feasibility today	claude-sonnet-5	2/5	placeholder

Submit project deliverables, ensuring adherence to quality standards.

CI 32–32 · exposure 25 · augmentation 75 · importance 4.3/5 · 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	IT organizations are testing workflow automation and checklist tools, but actual production displacement of PM deliverable oversight is limited. Adoption remains at the pilot and assistive stage rather than replacement at scale.
Sector adoption velocity	claude-sonnet-5	3/5	IT and project management sectors show moderate AI tool adoption (e.g., AI-assisted PM software), but full deliverable submission and quality certification remain human-led in most organizations.
Augmentation potential	claude-haiku-4-5-20251001	4/5	AI can substantially assist by auto-generating submission summaries, verifying checklist completeness, flagging quality issues for review, and organizing deliverable metadata—enabling faster human review and approval without removing PM judgment.
Augmentation potential	claude-sonnet-5	4/5	AI can meaningfully assist with quality checklists, automated testing reports, documentation formatting, and flagging inconsistencies, boosting PM productivity while the human retains final responsibility.
Task automatability	claude-haiku-4-5-20251001	2/5	Submitting deliverables requires judgment about quality conformance and stakeholder communication that currently demands human oversight. AI can assist with documentation and checklist verification but cannot independently assess subjective quality standards or make final sign-off decisions.
Task automatability	claude-sonnet-5	2/5	The core act involves judgment about quality, stakeholder coordination, and accountability for submission that AI cannot fully replace; AI can assist with checklists and drafts but not own the submission and quality sign-off end-to-end.
Adoption barriers	claude-haiku-4-5-20251001	3/5	Organizational governance typically requires a human project manager to certify deliverable quality and sign off with accountability. Liability concerns around quality gaps and contractual obligations create moderate friction against full automation.
Adoption barriers	claude-sonnet-5	3/5	No formal licensing requirement, but organizational accountability, contractual liability for deliverable quality, and stakeholder trust create meaningful friction against full automation.
Cost vs. human wage	claude-haiku-4-5-20251001	2/5	Integration overhead for quality-checking automation, human oversight requirements, and the relatively quick task completion by skilled PMs means AI-assisted tools cost roughly comparable to direct human execution, with modest savings potential.
Cost vs. human wage	claude-sonnet-5	2/5	AI tools can reduce time spent on status reporting and checklist verification, but human oversight, accountability, and client-facing submission remain necessary, limiting cost savings relative to a PM's full role.
Technical feasibility today	claude-haiku-4-5-20251001	2/5	No deployed product reliably performs independent deliverable submission with quality assurance across diverse project contexts. Tools exist to flag missing items or template violations, but meaningful quality assessment and approval decisions remain human-driven in production systems.
Technical feasibility today	claude-sonnet-5	2/5	No deployed product autonomously submits project deliverables and certifies quality standards adherence in production; existing tools support tracking and reporting but require human validation.

Manage project execution to ensure adherence to budget, schedule, and scope.

CI 28–32 · exposure 25 · augmentation 75 · importance 4.5/5 · 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	Tech-forward firms (IT services, software development) use AI-augmented PM tools widely, but autonomous management is rare even there. Most adoption remains assistive dashboards and reporting rather than delegated execution control.
Sector adoption velocity	claude-sonnet-5	3/5	IT and professional services sectors show moderate AI adoption in project tracking and reporting tools, but full execution management remains largely human-led with AI used as an assistive layer.
Augmentation potential	claude-haiku-4-5-20251001	4/5	AI significantly assists PM work through real-time variance detection, resource optimization suggestions, risk flagging, and automated reporting. These tools measurably raise PM productivity while the manager remains the decision-maker, fitting a strong augmentation pattern.
Augmentation potential	claude-sonnet-5	4/5	AI significantly aids by automating status tracking, flagging schedule/budget deviations, generating reports, and forecasting risks, letting PMs focus on decision-making and stakeholder management.
Task automatability	claude-haiku-4-5-20251001	2/5	While AI can assist with tracking milestones, budgets, and schedule variance detection, the task requires human judgment to handle scope changes, stakeholder negotiations, and real-time problem-solving under uncertainty. Current systems cannot autonomously manage execution decisions end-to-end at 50% time savings.
Task automatability	claude-sonnet-5	2/5	Core management activities—monitoring budget/schedule variance, escalating risks, negotiating scope changes, and making trade-off decisions—require judgment, stakeholder negotiation, and accountability that current AI cannot fully replicate end-to-end.
Adoption barriers	claude-haiku-4-5-20251001	4/5	Project managers are accountable for fiduciary decisions (budget approval, risk sign-off, stakeholder communication) that carry legal and organizational liability. Most organizations require a human PM to own execution decisions, creating high friction to substitution.
Adoption barriers	claude-sonnet-5	3/5	No licensing requirement, but organizational accountability, contractual liability for budget/schedule overruns, and the need for human judgment in stakeholder negotiations create meaningful friction against full automation.
Cost vs. human wage	claude-haiku-4-5-20251001	2/5	AI-enhanced PM tools cost significant licensing fees and require skilled human operators to interpret alerts and make decisions. The all-in cost of AI oversight plus human PM wages is still comparable to or exceeds a single PM managing directly without automation.
Cost vs. human wage	claude-sonnet-5	2/5	AI tools can cheaply automate tracking and reporting, but the overall task still requires a human PM's oversight, judgment, and stakeholder interaction, so total cost savings versus a human PM are modest.
Technical feasibility today	claude-haiku-4-5-20251001	2/5	Project management tools with AI dashboards exist (Jira, Monday.com integrations), but they monitor rather than manage execution. Autonomous handling of budget reallocation, schedule conflicts, and scope trade-offs remains beyond deployed products; human oversight is always required.
Technical feasibility today	claude-sonnet-5	2/5	Project management software with AI features (e.g., predictive scheduling, risk flagging) exists but only handles narrow sub-tasks like tracking metrics or generating status reports; no product manages full project execution autonomously in production.

Monitor the performance of project team members, providing and documenting performance feedback.

CI 28–32 · exposure 25 · augmentation 75 · importance 3.9/5 · 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	Project management and HR technology adoption is moderate; many organizations experiment with activity tracking and metrics dashboards, but human review of performance feedback remains standard practice and is moving slowly toward full automation in most sectors.
Sector adoption velocity	claude-sonnet-5	3/5	IT and project management sectors are adopting AI tools for reporting and analytics at a moderate pace, but people-management tasks lag behind more transactional digitization.
Augmentation potential	claude-haiku-4-5-20251001	4/5	AI can meaningfully assist by aggregating project metrics, flagging anomalies in team member output, and generating initial performance summaries for the manager to review and refine, substantially streamlining the documentation and monitoring process while the human retains evaluative control.
Augmentation potential	claude-sonnet-5	4/5	AI can meaningfully assist by aggregating performance data, drafting feedback summaries, and identifying patterns, improving manager efficiency while the human retains final judgment and delivery.
Task automatability	claude-haiku-4-5-20251001	2/5	While AI can gather quantitative data on team productivity metrics and generate initial performance summaries, the task fundamentally requires human judgment to assess nuanced work quality, interpersonal effectiveness, and contextual performance factors. Current AI systems lack the contextual understanding needed to replace this evaluative function end-to-end.
Task automatability	claude-sonnet-5	2/5	Performance monitoring and feedback involves subjective judgment, interpersonal context, and coaching that current AI cannot fully replicate end-to-end, though AI can help draft or summarize inputs. Only partial automation is feasible.
Adoption barriers	claude-haiku-4-5-20251001	4/5	Employment law, company HR policies, and potential liability concerns create strong barriers: legally defensible performance documentation typically requires manager sign-off and judgment, and many organizations maintain human review requirements for feedback that affects compensation, promotion, or termination.
Adoption barriers	claude-sonnet-5	3/5	No strict licensing requirement, but organizational policies, HR compliance, and the sensitivity of personnel feedback create meaningful friction against full automation.
Cost vs. human wage	claude-haiku-4-5-20251001	2/5	The loaded human wage for a project manager performing this task remains significantly lower than the cost of integrating specialized AI monitoring tools, training, and the required human oversight to validate and act on AI-generated feedback.
Cost vs. human wage	claude-sonnet-5	2/5	Human judgment, contextual awareness, and relationship management still dominate the cost of this task; AI tools reduce drafting time but don't replace the manager's oversight, so savings are modest.
Technical feasibility today	claude-haiku-4-5-20251001	2/5	Some HR software and project management tools offer dashboards summarizing activity metrics, but no deployed product reliably performs the full assessment and documentation of performance feedback at the quality required for human resource decisions. Solutions are narrow and require significant human interpretation.
Technical feasibility today	claude-sonnet-5	2/5	Some HR analytics and productivity-tracking tools exist, but deployed products rarely handle nuanced performance feedback documentation reliably or holistically for IT project managers.

Perform risk assessments to develop response strategies.

CI 25–32 · exposure 25 · augmentation 63 · importance 4.0/5 · 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 IT organizations are digitizing tools, actual adoption of AI-driven autonomous risk assessment and strategy development remains limited. Most adoption is experimental or for supporting existing human processes rather than replacement, reflecting cautious organizational attitudes toward automating risk governance.
Sector adoption velocity	claude-sonnet-5	3/5	IT/software project management is a digitized, tech-forward sector with growing AI tool adoption, but risk assessment specifically remains a task where pilots and augmented tools are more common than full automation.
Augmentation potential	claude-haiku-4-5-20251001	3/5	AI can usefully augment human project managers by generating risk checklists, analyzing historical project data, and surfacing patterns, but the strategic synthesis and response planning remain human-led. AI assistance on risk identification and data aggregation improves productivity modestly without replacing human judgment.
Augmentation potential	claude-sonnet-5	4/5	AI can effectively assist by surfacing historical risk patterns, generating checklists, summarizing project data, and drafting mitigation options, meaningfully speeding up the PM's own risk assessment process.
Task automatability	claude-haiku-4-5-20251001	2/5	Risk assessment requires contextual judgment, stakeholder input, and strategic decision-making that AI cannot fully replicate. AI can assist with data analysis and identify known risk categories, but developing tailored response strategies demands human expertise and organizational knowledge that current systems cannot do end-to-end at 50% time savings with equal quality.
Task automatability	claude-sonnet-5	2/5	Risk identification and initial drafting can be aided by AI, but assessing likelihood/impact and crafting response strategies requires organizational context, stakeholder judgment, and accountability that current AI cannot fully replicate end-to-end.
Adoption barriers	claude-haiku-4-5-20251001	4/5	Risk assessment and response strategies carry material liability and error consequences; organizations typically require human accountability and sign-off from qualified project managers. Regulatory and contractual requirements often mandate human responsibility for risk management decisions in enterprise IT projects.
Adoption barriers	claude-sonnet-5	3/5	No licensing requirement specifically for risk assessment, but organizational accountability, liability for poor risk decisions, and need for sign-off by responsible managers create moderate friction.
Cost vs. human wage	claude-haiku-4-5-20251001	2/5	Current AI solutions for risk assessment require significant human oversight, validation, and strategy refinement, making total cost (inference plus integration plus human review) comparable to or exceeding the cost of human project managers performing the task directly.
Cost vs. human wage	claude-sonnet-5	2/5	AI can cheaply generate risk lists or templates, but the human review, validation, and strategic decision-making needed keeps overall cost comparable to or only marginally cheaper than a skilled PM's time.
Technical feasibility today	claude-haiku-4-5-20251001	2/5	No production systems reliably perform comprehensive IT project risk assessment and strategy development autonomously. While AI tools exist for risk identification and documentation, they lack the domain expertise, stakeholder engagement, and strategic reasoning required for credible organizational deployment in complex project contexts.
Technical feasibility today	claude-sonnet-5	2/5	Some project management tools offer AI-assisted risk register suggestions, but no deployed product reliably performs full risk assessment and strategy development without heavy human oversight.

Direct or coordinate activities of project personnel.

CI 21–32 · exposure 25 · augmentation 63 · importance 4.1/5 · 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 IT sector digitization, actual adoption of AI for directing personnel remains limited. Organizations use PM software and analytics tools, but still employ human PMs as the accountable coordinator; true delegation of direction to AI systems has not materially occurred in production.
Sector adoption velocity	claude-sonnet-5	3/5	IT and professional services are relatively fast adopters of AI tools for project tracking and reporting, though full coordination automation remains rare and mostly pilot-stage.
Augmentation potential	claude-haiku-4-5-20251001	3/5	AI can meaningfully assist with workflow optimization, meeting summaries, risk flagging, and resource forecasting, raising PM productivity on routine aspects of coordination, but the core act of directing personnel and making judgment calls remains human-led.
Augmentation potential	claude-sonnet-5	4/5	AI assists significantly with status reporting, risk flagging, meeting summarization, and resource allocation suggestions, meaningfully raising a PM's efficiency while they retain direct people-management responsibilities.
Task automatability	claude-haiku-4-5-20251001	2/5	AI can assist with scheduling, resource allocation, and status tracking, but directing and coordinating personnel requires real-time judgment, interpersonal negotiation, conflict resolution, and accountability that current AI systems cannot reliably perform end-to-end. The human manager remains essential for personnel decisions and stakeholder management.
Task automatability	claude-sonnet-5	2/5	Directing and coordinating people involves real-time judgment, motivation, conflict resolution, and interpersonal leadership that current AI cannot execute end-to-end; AI can support scheduling and status tracking but not the core coordination act itself.
Adoption barriers	claude-haiku-4-5-20251001	4/5	Strong barriers exist: project personnel expect and often require a human manager for direction, feedback, and accountability; organizational structures and contracts typically mandate a responsible human PM; liability for project failure or personnel disputes rests with a human; regulatory and contractual frameworks assume human managerial sign-off.
Adoption barriers	claude-sonnet-5	3/5	No licensing requirement, but organizational and interpersonal trust factors, accountability for team performance, and the need for human leadership create meaningful friction against full substitution.
Cost vs. human wage	claude-haiku-4-5-20251001	1/5	The cost of AI oversight, integration, and error correction for managing personnel (whose mistakes cascade across teams) exceeds the human PM wage when quality and accountability are factored in. Effective augmentation still requires a paid human PM.
Cost vs. human wage	claude-sonnet-5	2/5	AI tools reduce some administrative overhead but a human PM's judgment, oversight, and accountability are still required, so overall cost savings versus a human coordinator are modest.
Technical feasibility today	claude-haiku-4-5-20251001	2/5	No deployed product today reliably performs full project direction or personnel coordination. Tools exist for task tracking and meeting summarization, but these assist rather than replace the managerial function of directing and coordinating actual people and resolving conflicts in real time.
Technical feasibility today	claude-sonnet-5	2/5	Products like project management software with AI features (auto-scheduling, status summaries) exist, but no deployed system actually directs personnel or manages team dynamics reliably in production.

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

CI 11–25 · exposure 8 · augmentation 63 · importance 3.7/5 · 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	IT organizations are investing in negotiation analytics and data tools to support project managers, but actual substitution of AI for human negotiators is minimal and not accelerating. Pilots exist for negotiation assistance, but deep adoption remains limited due to relationship and risk factors.
Sector adoption velocity	claude-sonnet-5	2/5	IT project management functions are digitizing, but the negotiation subtask itself sees little AI-driven displacement; adoption is mostly limited to prep/support tools.
Augmentation potential	claude-haiku-4-5-20251001	3/5	AI can meaningfully assist by analyzing supplier market rates, generating negotiation scenarios, summarizing stakeholder concerns, and drafting proposal language, raising the PM's preparation quality and speed. However, the human remains central to the persuasion and interpersonal work itself.
Augmentation potential	claude-sonnet-5	4/5	AI can meaningfully assist by drafting negotiation strategies, summarizing supplier terms, analyzing contract data, and preparing talking points, boosting the negotiator's effectiveness without replacing them.
Task automatability	claude-haiku-4-5-20251001	2/5	AI can gather data on supplier terms, draft proposal frameworks, and identify negotiation talking points, but lacks the contextual judgment, relationship management, and real-time adaptability that characterize successful stakeholder negotiation. Current systems cannot handle the interpersonal dynamics, trust-building, or creative deal-structuring that this task fundamentally requires.
Task automatability	claude-sonnet-5	1/5	Negotiation requires real-time relationship management, reading counterparties, and exercising authority to commit resources—AI cannot perform this end-to-end today.'
Adoption barriers	claude-haiku-4-5-20251001	4/5	Stakeholder and supplier negotiations carry high organizational risk; deals struck by AI without human sign-off create liability exposure and undermine trust relationships central to IT project success. Stakeholders and suppliers expect human accountability, and legal/contractual frameworks typically require human authorization.
Adoption barriers	claude-sonnet-5	3/5	No formal licensing requirement exists, but negotiation involves organizational authority, trust, and accountability that create strong practical friction against full automation.
Cost vs. human wage	claude-haiku-4-5-20251001	2/5	AI tools for negotiation support (data gathering, proposal drafting) are inexpensive, but humans must conduct the actual negotiation, meaning total cost substitution is minimal. The human negotiator's time is the dominant cost and cannot be eliminated.
Cost vs. human wage	claude-sonnet-5	2/5	Since AI cannot substitute for the human negotiator, any AI use is supplementary (e.g., drafting talking points) rather than a full cost replacement, keeping the ratio unfavorable relative to human-led negotiation.
Technical feasibility today	claude-haiku-4-5-20251001	1/5	No deployed product reliably conducts end-to-end stakeholder or supplier negotiations in production environments. While AI can assist with preparation and analysis, actual negotiation—requiring persuasion, relationship leverage, and deal modification in real-time—remains exclusively human in mature organizations.
Technical feasibility today	claude-sonnet-5	1/5	No deployed product autonomously conducts stakeholder or supplier negotiations for IT project resources; this remains outside current production AI use cases.

Related occupations — Computer & Mathematical

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.