Public Safety Telecommunicators
43-5031.00Median wage $53,040/yr102,500 employed (US)Rank #465 of 923 scored · top 50% by substitution
Operate telephone, radio, or other communication systems to receive and communicate requests for emergency assistance at 9-1-1 public safety answering points and emergency operations centers. Take information from the public and other sources regarding crimes, threats, disturbances, acts of terrorism, fires, medical emergencies, and other public safety matters. May coordinate and provide information to law enforcement and emergency response personnel. May access sensitive databases and other information sources as needed. May provide additional instructions to callers based on knowledge of and certification in law enforcement, fire, or emergency medical procedures.
Sub-scores
0–100 · band = confidence interval from rater disagreement
Substitution28
Exposure30
Augmentation60
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
18 rated tasks, binned by substitution score.
Position among all scored occupations
Distribution of 923 occupation scores; the marker is this occupation.
Tasks with substitution ≥ 70
0%
Run 1.0.0-draft.1 · computed 2026-08-05 · rater panel: claude-sonnet-5, claude-haiku-4-5-20251001 · intervals span rater disagreement.
Why this score
The five weighted dimensions of the composite, averaged across this occupation's tasks (importance-weighted, panel mean). Exact weights and formulas: /api/v1/methodology.
Task automatabilityw 35%30
panel mean rating 2.2/5 → substitution pressure 30/100
Technical feasibility todayw 20%30
panel mean rating 2.2/5 → substitution pressure 30/100
Cost vs. human wagew 15%36
panel mean rating 2.4/5 → substitution pressure 36/100
Adoption barriersw 20%inverted — strong barriers lower the score20
panel mean rating 4.2/5 (barrier strength) → substitution pressure 20/100
Sector adoption velocityw 10%22
panel mean rating 1.9/5 → substitution pressure 22/100
Task breakdown (18 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.
Maintain files of information relating to emergency calls, such as personnel rosters and emergency call-out and pager files.
66CI 60–72 · exposure 70 · augmentation 75 · importance 4.2/5 · click for rater detail
Maintain files of information relating to emergency calls, such as personnel rosters and emergency call-out and pager files.
66| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Public safety digitization is uneven: larger departments have adopted dispatch software and digital roster systems, but many smaller agencies still rely on manual processes. Adoption is progressing but slower than in information-intensive sectors. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Public safety/emergency dispatch is a traditionally slow-adopting, under-resourced government sector with legacy systems, limiting fast AI integration despite the task's technical simplicity. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI can meaningfully assist operators by auto-populating rosters from source systems, flagging outdated entries, suggesting corrections, and automating routine updates—allowing human operators to focus on verification and exceptions rather than rote data entry. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI-powered database tools, auto-updating rosters, and smart scheduling can significantly reduce manual burden on telecommunicators while they retain oversight of accuracy. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | File maintenance—organizing, updating, and storing personnel rosters and pager information—is highly routine and structured data management that AI systems can execute end-to-end today. Modern document management and database systems can automate roster updates, verification, and archival with minimal human intervention, easily meeting the 50%-time-saving threshold. |
| Task automatability | claude-sonnet-5 | 4/5 | Maintaining and updating structured records like rosters and call-out lists is a data management task well-suited to automation via database systems and AI-assisted tools, with most manual entry/update work reducible. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Public safety agencies have moderate adoption barriers: legacy system integration, regulatory compliance for emergency records (e.g., NIST standards), and organizational resistance to changing operational workflows. However, no single human must legally sign off on file maintenance itself. |
| Adoption barriers | claude-sonnet-5 | 2/5 | No licensure requirement for record-keeping itself, though data accuracy for emergency response creates some organizational caution and need for verification before fully automating. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | Automated file management and database systems cost a fraction of a human operator's time to maintain equivalent records. Once deployed, the per-task cost of updating and organizing files is orders of magnitude below the loaded wage for manual file management. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | Automated database and scheduling tools are far cheaper than dedicating telecommunicator time to manual file upkeep, though initial integration with legacy CAD/dispatch systems adds cost. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Deployed products (enterprise content management systems, HR databases, dispatch software) reliably perform this file maintenance in production across emergency services. Error rates are low for structured data tasks, though integration with legacy emergency systems may require customization. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | Records management software and CAD systems already handle much of this, but integration with AI for autonomous updates/maintenance across disparate legacy systems in dispatch centers is uneven and often still manual. |
Record details of calls, dispatches, and messages.
63CI 55–71 · exposure 70 · augmentation 88 · importance 4.7/5 · click for rater detail
Record details of calls, dispatches, and messages.
63| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Adoption is emerging in larger, well-resourced 911 centers (major metros, some state systems) but remains patchy in small and mid-sized jurisdictions due to legacy infrastructure, budget constraints, and conservative risk-management practices. Growth is steady but not yet mainstream. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Public safety answering points are often underfunded, use legacy CAD systems, and adopt new technology slowly due to procurement cycles, training, and reliability requirements for life-safety systems. |
| Augmentation potential | claude-haiku-4-5-20251001 | 5/5 | AI transcription and auto-population of call details directly augments dispatcher productivity by removing manual typing, allowing them to focus on listening, questioning, and decision-making. This is a textbook case of human-in-the-loop assistance that materially raises throughput and accuracy. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI-assisted transcription, auto-fill of dispatch fields, and real-time call summarization can meaningfully speed up documentation while the telecommunicator retains control over accuracy and prioritization. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | Current AI systems can reliably transcribe spoken calls (speech-to-text), extract key details, and populate structured dispatch records with high accuracy, saving significant time over manual entry. However, minor ambiguities in background noise or field terminology may require human review, preventing a perfect 5. |
| Task automatability | claude-sonnet-5 | 4/5 | Automated speech-to-text and CAD integration can transcribe and log call details, dispatch times, and messages with substantial time savings over manual entry, though verification of critical details still benefits from human oversight. |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | Public safety agencies face moderate barriers: regulatory requirements to preserve full call recordings for evidence, liability concerns if transcription errors lead to misdispatch, and organizational inertia around legacy systems. However, there is no legal mandate requiring human transcription, and many agencies are already piloting automation. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Emergency dispatch records are legally significant (evidentiary, liability-sensitive) and typically require certified telecommunicators to verify and finalize logs, creating strong procedural and regulatory friction against full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 5/5 | Automated transcription and record population cost a fraction of a cent per call after amortized infrastructure; a human telecommunicator's loaded wage is $50–80k annually. AI cost per task is orders of magnitude lower. |
| Cost vs. human wage | claude-sonnet-5 | 4/5 | Automated transcription and logging software costs a fraction of a telecommunicator's loaded wage per call, though integration and quality-assurance oversight add some cost. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Deployed speech-to-text and NLP systems (Google Cloud Speech-to-Text, AWS Transcribe, Cisco) are production-ready in many 911 centers and dispatch operations, reliably converting calls and messages to text and extracting actionable fields. Edge cases and dialects occasionally require correction, but the core functionality works reliably at scale. |
| Technical feasibility today | claude-sonnet-5 | 3/5 | CAD systems with voice logging, transcription, and auto-population of dispatch records are deployed in many 911 centers today, but accuracy on noisy audio, jargon, and codes still causes material error rates requiring human correction. |
Observe alarm registers and scan maps to determine whether a specific emergency is in the dispatch service area.
47CI 25–69 · exposure 50 · augmentation 75 · importance 4.3/5 · click for rater detail
Observe alarm registers and scan maps to determine whether a specific emergency is in the dispatch service area.
47| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Public safety and emergency services remain relatively laggard in AI adoption compared to finance or tech sectors. Most 911 centers still rely on legacy systems and manual verification; automation of triage subtasks is not yet widespread in production. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Public safety/emergency services are traditionally slow to adopt new technology due to funding constraints, legacy infrastructure, and safety-critical caution, though some CAD modernization is underway. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI can rapidly surface location matching and service-area confirmation to a human dispatcher, reducing lookup time and error. The dispatcher retains final authority over dispatch decisions while the AI augments their speed and accuracy on the verification subtask. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI-enhanced mapping, automated address verification, and alarm-correlation tools can meaningfully speed up and improve accuracy of jurisdiction determination while the telecommunicator retains final judgment. |
| Task automatability | claude-haiku-4-5-20251001 | 4/5 | Current AI can reliably parse alarm registers, read maps, and match location data against service area boundaries with high accuracy. The task is largely spatial-computational and rule-based, requiring no subjective judgment, allowing AI to complete it end-to-end with significant time savings. |
| Task automatability | claude-sonnet-5 | 2/5 | While AI can process geospatial data and alarm feeds, this task requires real-time integrated judgment under time pressure with life-safety consequences, limiting full end-to-end automation today.9 |
| Adoption barriers | claude-haiku-4-5-20251001 | 3/5 | While this is a technical subtask, public safety operations have organizational inertia and regulatory compliance frameworks around call handling. Liability concerns (missed emergencies) and the need for human oversight of dispatch decisions create moderate adoption friction. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Public safety answering points are subject to strict regulatory, liability, and certification requirements, and erroneous jurisdiction determination could delay life-saving response, creating strong institutional resistance to full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 5/5 | Map scanning, alarm register parsing, and service-area lookup are computationally cheap operations. AI inference cost is orders of magnitude lower than the loaded wage of a full-time telecommunicator performing this subtask repeatedly. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Specialized emergency dispatch software with mapping integration requires significant licensing, integration with legacy CAD/alarm systems, and human oversight, so cost savings versus a trained telecommunicator are modest at best. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Deployed geolocation services, map APIs, and rule-based matching systems demonstrate reliable performance in production for location verification and boundary checking. However, real-world alarm data may be messy or ambiguous, requiring occasional human verification. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Some CAD systems use automated geo-verification and mapping overlays, but human dispatchers still perform the core verification in virtually all 911 centers; no deployed product fully replaces this step reliably at scale. |
Enter, update, and retrieve information from teletype networks and computerized data systems regarding such things as wanted persons, stolen property, vehicle registration, and stolen vehicles.
40CI 29–51 · exposure 50 · augmentation 63 · importance 4.6/5 · click for rater detail
Enter, update, and retrieve information from teletype networks and computerized data systems regarding such things as wanted persons, stolen property, vehicle registration, and stolen vehicles.
40| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Public safety telecommunications remains a highly regulated, conservative sector with legacy system dependencies and union-protected positions. While some jurisdictions pilot automation, widespread production deployment of fully autonomous data entry and retrieval remains limited; most systems still require human telecommunicators in the loop. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Public safety dispatch centers are typically under-resourced government entities with legacy systems and slow technology adoption cycles, unlike fast-moving information sector firms. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI assistants can significantly enhance dispatcher productivity by auto-populating database queries, suggesting matching records, and flagging relevant information (wanted persons, stolen vehicles) from incoming reports, allowing human operators to focus on triage and verification rather than manual data entry and search. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can assist telecommunicators by pre-populating fields, suggesting relevant lookups, or summarizing retrieved records, improving speed and accuracy while the certified human retains query authority. |
| Task automatability | claude-haiku-4-5-20251001 | 3/5 | Data entry, lookup, and retrieval from structured databases can be largely automated by AI systems that parse incoming information and query databases. However, the task requires judgment about accuracy, prioritization of urgent situations, and handling of ambiguous or incomplete information that still requires human oversight, limiting full automation to roughly 50% time savings. |
| Task automatability | claude-sonnet-5 | 3/5 | Structured data entry/retrieval from databases is technically automatable via system integration and query automation, but this occurs concurrently with live call handling requiring human judgment about when/what to query.deployment for full end-to-end handling remains limited.rating reflects partial automatability of the mechanical lookup portion. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Criminal justice data systems are subject to strict legal and regulatory requirements (NCIC, state records regulations, liability for inaccuracy). Data entry errors can lead to false arrests or constitutional violations, creating significant error-cost asymmetry and requiring human authorization and oversight for sensitive queries. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Access to NCIC and criminal justice information systems requires background-checked, certified personnel under strict federal/state regulations (CJIS), making unauthorized automated access a hard legal barrier. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | Inference costs for database queries and data entry automation are very low compared to dispatcher wages. Integration and oversight overhead is modest, making the all-in AI cost roughly one-tenth or less of the human labor cost for high-volume information processing tasks. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Legacy teletype/NCIC systems require certified operators and secure access protocols; building and certifying an AI system to interface with these restricted networks would carry significant integration, security, and compliance costs comparable to or exceeding human labor for this narrow task. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Modern AI and RPA systems already perform structured data entry and retrieval in law enforcement settings. Deployed products handle database queries and information integration reliably, though integration with legacy teletype systems and validation of criminal records requires careful configuration and human review in practice. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | While database query automation exists broadly in IT, purpose-built products that autonomously interact with NCIC/state law enforcement teletype systems during live dispatch are not deployed at scale; most CAD/RMS integrations still require human-initiated queries. |
Read and effectively interpret small-scale maps and information from a computer screen to determine locations and provide directions.
38CI 25–51 · exposure 42 · augmentation 88 · importance 4.6/5 · click for rater detail
Read and effectively interpret small-scale maps and information from a computer screen to determine locations and provide directions.
38| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Emergency dispatch modernization is slow; most 911 centers remain under-digitized and risk-averse. While some jurisdictions use AI-assisted location lookup, wholesale replacement or agent-based dispatch remains rare due to regulatory caution and the life-safety criticality of the function. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Public safety communications is a traditionally slow-adopting government sector with legacy systems, budget constraints, and cautious rollout of new tech, though computer-aided dispatch systems are increasingly common. |
| Augmentation potential | claude-haiku-4-5-20251001 | 5/5 | AI mapping and location tools significantly augment dispatcher productivity: real-time map overlay, instant address verification, and automated routing recommendations make human dispatchers faster and more accurate without removing human judgment from critical decisions. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI-enhanced mapping, geolocation services, and address-lookup tools already meaningfully speed up how telecommunicators find locations and generate directions, keeping the human in control of final decisions. |
| Task automatability | claude-haiku-4-5-20251001 | 3/5 | AI can extract location data from screens and generate directions autonomously, but interpreting ambiguous caller descriptions, handling non-standard map formats, and real-time decision-making under pressure require human oversight. This covers roughly half the task with significant setup. |
| Task automatability | claude-sonnet-5 | 2/5 | AI can process map data and generate directions computationally, but the task requires real-time interpretation combined with dynamic caller information (their location description, landmarks, distress state) in a live emergency context, which limits full automation.take at equal quality.the ≥50% time savings bar is not clearly met end-to-end.the human element of cross-referencing caller cues is not easily replaced. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Regulatory and safety-critical barriers are substantial: dispatchers must verify caller information, ensure accuracy in emergency contexts where wrong directions cost lives, and maintain human accountability. Liability and the requirement for human sign-off on critical decisions present strong adoption friction. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Emergency dispatch requires trained, often certified personnel who bear legal and safety responsibility for accurate location determination; errors have life-and-death consequences, creating strong human-in-the-loop and liability barriers. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | API costs for geolocation and routing are extremely low (often cents per query), while dispatcher wages are $35k–$50k annually. Even accounting for integration and oversight, the per-task cost is substantially lower than human labor. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Human telecommunicators are already using low-cost mapping software; a fully autonomous AI system would need extensive integration, liability safeguards, and real-time reliability, making all-in cost comparable to or higher than current human-plus-tool costs. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 4/5 | Deployed GPS and mapping APIs (Google Maps, etc.) perform location lookup and direction-generation reliably at scale in production. However, integrating these with emergency dispatch workflows and handling edge cases (unclear addresses,911 caller confusion) requires human validation, limiting pure end-to-end performance. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | GPS and mapping software already assist dispatchers (geolocation lookups, CAD system integration), but no deployed product independently interprets caller-described locations and autonomously provides directions without human oversight in 911 contexts. |
Monitor alarm systems to detect emergencies, such as fires and illegal entry into establishments.
31CI 25–37 · exposure 33 · augmentation 63 · importance 4.3/5 · click for rater detail
Monitor alarm systems to detect emergencies, such as fires and illegal entry into establishments.
31| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Public safety agencies have been slow to adopt AI-driven emergency detection in production; adoption remains mostly in pilot or support-tool phases rather than replacement. Budget constraints, regulatory caution, and organizational resistance to autonomous decision-making in life-safety contexts limit real-world deployment velocity. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Public safety/emergency dispatch is a traditionally slow-adopting government sector with legacy systems and cautious regulatory environment, though some AI-assisted triage tools are being piloted. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI can significantly assist human operators by pre-filtering false alarms, prioritizing incoming signals, and providing real-time pattern detection that raises operator situational awareness and processing speed. Human operators remain in the loop while AI substantially increases their ability to handle volume and detect threats. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help filter false alarms, prioritize signals, and provide decision support, improving efficiency, but the human telecommunicator must remain in the loop for verification and response decisions. |
| Task automatability | claude-haiku-4-5-20251001 | 3/5 | Current AI can detect certain alarm triggers and classify some emergency types from sensor data with reasonable accuracy, but requires human verification before dispatch due to false-alarm rates and complex contextual judgment. Autonomous end-to-end handling of emergency triage still has meaningful error rates that prevent the 50% time-saving threshold from being reliably met. |
| Task automatability | claude-sonnet-5 | 2/5 | Basic alarm triage (signal classification, routing) could be automated, but full monitoring requires judgment on ambiguous or life-critical situations, human callback verification, and real-time decision-making that current AI cannot fully replace end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Strong regulatory and liability barriers exist: emergency dispatch is heavily regulated by FCC and state agencies, dispatchers must be certified/authorized personnel, and legal liability for missed emergencies or false dispatches falls directly on the agency. Customers and municipalities require human accountability in critical life-safety roles. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Public safety dispatch is heavily regulated, requires certified telecommunicators, and carries high liability for missed or misclassified emergencies, creating strong barriers to full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | While alarm-monitoring AI infrastructure has dropped in cost, the integration with existing emergency dispatch systems, continuous 24/7 redundancy requirements, and ongoing human oversight still make the total cost-per-emergency-handled comparable to or higher than staffed positions in most jurisdictions. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | Automated alarm-signal processing software is cheap and already used to filter false alarms, but the human telecommunicator remains necessary for verification and response coordination, keeping overall cost comparable rather than dramatically cheaper. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | AI monitoring products exist and can flag potential alarms in controlled environments, but deployed 911/emergency center systems still rely heavily on human operators for actual emergency confirmation and dispatch decisions. Most operational systems use AI as an aid rather than autonomous detector, with low production-scale true replacement. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Some automated alarm monitoring and triage software exists (e.g., central station automation), but reliable, production-grade AI systems that independently detect and adjudicate emergencies without human dispatchers are not widely deployed in 911/PSAP contexts. |
Test and adjust communication and alarm systems, and report malfunctions to maintenance units.
28CI 25–30 · exposure 25 · augmentation 50 · importance 4.0/5 · click for rater detail
Test and adjust communication and alarm systems, and report malfunctions to maintenance units.
28| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Public safety sectors are slow to adopt automation in system maintenance due to regulatory compliance, liability concerns, and the mission-critical nature of emergency infrastructure; most adoption remains at the monitoring/alerting layer rather than autonomous adjustment. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Public safety and emergency communications sectors are typically slow adopters of AI due to legacy systems, budget constraints, and safety-critical requirements. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI-powered diagnostic dashboards and automated health checks can meaningfully assist technicians by flagging anomalies and recommending tests, reducing time spent on routine monitoring; however, the core adjustment and judgment tasks remain human-centric. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI-based monitoring tools can help detect anomalies and flag malfunctions, assisting telecommunicators in identifying issues faster even though the human still performs testing and coordination with maintenance. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While routine diagnostic testing of systems can be partially automated, the adjustment of communication and alarm systems requires contextual judgment, and determining whether a malfunction warrants maintenance versus user error involves nuanced decision-making that AI cannot reliably perform end-to-end today. |
| Task automatability | claude-sonnet-5 | 2/5 | Testing and adjusting physical/telecom equipment involves hands-on diagnostics and interfacing with hardware/software that current AI cannot fully perform end-to-end without human execution.ed |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Public safety systems are heavily regulated (FCC, state emergency management), and unauthorized adjustments to 911/emergency dispatch infrastructure can have liability consequences; most jurisdictions require licensed telecommunications technicians or certified personnel to adjust critical systems. |
| Adoption barriers | claude-sonnet-5 | 3/5 | No strict licensing barrier for this specific task, but organizational protocols, safety-critical infrastructure, and reliance on specialized technicians create moderate friction against full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Current AI monitoring and diagnostic tools still require significant human oversight, integration, and validation, making all-in costs comparable to or exceeding a telecommunications technician's marginal labor cost for the actual testing, adjustment, and judgment steps. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | Diagnostic software can reduce some labor but equipment testing, physical adjustments, and reporting still require human oversight, keeping costs comparable to human labor rather than dramatically cheaper. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed production systems reliably diagnose, adjust, and report on communication/alarm system malfunctions autonomously; while monitoring and alerting tools exist, they typically flag issues rather than execute adjustments or contextual triage to maintenance units. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Some monitoring/diagnostic software exists that flags alarm faults, but comprehensive testing and adjustment of communication systems is not reliably automated in deployed products for this role. |
Monitor various radio frequencies, such as those used by public works departments, school security, and civil defense, to stay apprised of developing situations.
27CI 25–29 · exposure 25 · augmentation 63 · importance 4.7/5 · click for rater detail
Monitor various radio frequencies, such as those used by public works departments, school security, and civil defense, to stay apprised of developing situations.
27| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Despite tech maturity elsewhere, public safety dispatch remains one of the slowest-adopting sectors due to liability, regulatory conservatism, union protections, and the high cost of failure. Most agencies are in pilot/evaluation phases, not production deployment. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Public safety/emergency dispatch is a traditionally slow-adopting, highly regulated sector with cautious integration of AI tools into live operations. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can usefully assist by providing automated transcription of radio traffic, keyword highlighting, pattern alerting, and call logging, helping humans filter and prioritize incoming information, but the human operator remains central to dispatch decisions. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can transcribe, translate, and flag keywords across multiple radio channels simultaneously, significantly helping telecommunicators track more streams than they could unaided. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can process and classify radio signals and flag keywords or patterns, the task requires sustained monitoring of multiple concurrent channels with contextual judgment about developing situations—determining urgency, jurisdiction, and appropriate response. Current systems cannot reliably handle the full scope of decision-making and situational awareness at 50% time savings equivalent. |
| Task automatability | claude-sonnet-5 | 2/5 | AI can transcribe and flag audio streams, but continuous multi-channel situational monitoring requiring real-time judgment and rapid human response is not yet fully substitutable end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Public safety dispatch is heavily regulated; federal and state agencies mandate human monitoring of emergency frequencies, and liability for missed or misclassified emergencies is severe. Union contracts and legal requirements typically mandate licensed human operators for final dispatch decisions. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Public safety communications carry high liability and regulatory oversight, and humans are generally required to interpret and act on emergency information, creating strong barriers to full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Transcription and monitoring AI infrastructure (hardware, software, integration, human oversight of alerts) remains costly when accounting for the need to verify detections and maintain human supervision for safety-critical environments, making it comparable to or exceeding the loaded wage of a telecommunicator. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | Speech-to-text and alerting systems are relatively cheap to run, but integration, redundancy, and required human oversight for safety-critical monitoring keep costs comparable to staffing, not drastically cheaper. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | AI systems can transcribe and classify radio traffic in controlled settings, but production deployments handling real 911/emergency dispatch radio across diverse channels and accents remain limited. Products exist in pilot/research form but not at the reliability and integration level required for safety-critical dispatch operations. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Some dispatch centers pilot AI-assisted audio monitoring/transcription tools, but reliable production-scale systems that autonomously monitor multiple radio frequencies for public safety are not widely deployed. |
Answer routine inquiries, and refer calls not requiring dispatches to appropriate departments and agencies.
27CI 25–29 · exposure 25 · augmentation 50 · importance 4.5/5 · click for rater detail
Answer routine inquiries, and refer calls not requiring dispatches to appropriate departments and agencies.
27| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Although some 911 centers use IVR for non-emergency call screening, adoption of AI-driven routing remains limited and spotty. Most public safety agencies rely on human operators as a failsafe, and organizational conservatism around life-safety tasks slows transition to AI-first systems. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Public safety/government sectors are historically slow adopters of AI due to regulatory, budgetary, and liability constraints, with pilots existing but production-scale deployment rare. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist by suggesting likely call categories, pre-filling forms with caller information, and offering referral recommendations to the operator, moderately improving throughput and accuracy. However, the operator must still validate and make final routing decisions, limiting the transformation of productivity. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can assist with call transcription, triage prompts, and suggested routing to speed up telecommunicators' decisions, but humans remain essential for judgment calls on ambiguous or urgent calls. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While routine inquiry classification and basic call routing are potentially automatable, the task requires understanding context, assessing urgency, and making judgment calls about appropriate referrals—all of which current systems struggle with reliably. Significant human oversight would still be needed, limiting time savings to well below the 50% threshold. |
| Task automatability | claude-sonnet-5 | 2/5 | While AI voice systems can handle simple FAQ-style routing, distinguishing routine inquiries from emergencies reliably enough to avoid dangerous misclassification limits full automation today.'s a high-stakes triage task that resists full substitution despite partial capability. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Public safety dispatch is heavily regulated and often requires licensed or trained personnel to make life-safety decisions. Legal liability for missed emergencies, union agreements, and regulatory requirements that a human answer or validate critical calls create strong structural barriers to full automation. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Public safety answering points are tightly regulated, often require certified telecommunicators, and misrouting or missing an emergency carries severe liability, creating strong barriers to full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Deployed IVR and chatbot systems are relatively cheap operationally, but they still require significant backend integration, monitoring, and human escalation handling. The need for human oversight and error correction means the all-in cost remains comparable to or exceeds a telecommunicator's loaded wage for the same output quality. |
| Cost vs. human wage | claude-sonnet-5 | 3/5 | AI phone-answering and routing systems are relatively cheap to run, but integration with legacy CAD/dispatch systems, liability review, and human oversight narrow the cost advantage over a telecommunicator's marginal call-handling cost. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | IVR and basic chatbot systems exist for simple call triage, but they have material error rates in understanding nuanced situations and often frustrate callers or misroute calls. No mature production system reliably handles the full scope of routine-versus-urgent distinction and multi-department referral logic that 911/public safety requires. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Some 911/311 systems pilot AI call-routing and chatbots for non-emergency lines, but production deployment for actual public safety telecommunicator call triage remains limited and narrow in scope. |
Scan status charts and computer screens, and contact emergency response field units to determine emergency units available for dispatch.
25CI 25–25 · exposure 25 · augmentation 50 · importance 4.5/5 · click for rater detail
Scan status charts and computer screens, and contact emergency response field units to determine emergency units available for dispatch.
25| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Public safety agencies are laggards in AI adoption; most 911 systems still rely on human operators and legacy computer-aided dispatch (CAD) systems, with only limited pilots of AI-assisted features in forward-looking jurisdictions. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Public safety/emergency services are traditionally slow-adopting sectors with legacy CAD systems and cautious rollout of AI due to life-safety stakes. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI tools can assist by auto-populating unit status from CAD systems, highlighting available units in real-time, or suggesting dispatch recommendations, meaningfully reducing operator workload while the human retains final dispatch authority. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can help surface unit status and recommend optimal dispatch options, aiding decision speed, but the telecommunicator remains essential for judgment and communication. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI could monitor status charts and parse computer screens, the task requires contextual judgment about unit availability, readiness, and appropriateness for dispatch—decisions involving real-time operational awareness that current systems cannot reliably automate end-to-end with 50% time savings at equal quality. |
| Task automatability | claude-sonnet-5 | 2/5 | Monitoring status boards and coordinating live dispatch decisions requires real-time judgment, prioritization under uncertainty, and voice communication with field units that current AI cannot fully replace end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Public safety dispatch has strong regulatory and liability requirements—human oversight of dispatch decisions is often mandated by regulation, and errors carry life-safety consequences that create substantial legal and organizational friction against full automation. |
| Adoption barriers | claude-sonnet-5 | 4/5 | Public safety dispatch is a high-liability, safety-critical function often requiring certified telecommunicators and regulatory/agency oversight, creating strong barriers to full automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | An AI system capable of reliable dispatch availability assessment would require significant integration, oversight, and ongoing monitoring to prevent critical failures; the all-in cost remains comparable to or higher than a trained telecommunicator's loaded wage. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI-assisted dispatch recommendation tools exist but still require human telecommunicators for verification and communication, so cost savings are partial rather than replacing the labor cost outright. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed system today reliably handles the full workflow of scanning dynamic operational dashboards, contacting field units (via radio/phone), and synthesizing availability into dispatch decisions; products exist for narrower elements (status monitoring) but lack the integrated human-in-the-loop reliability required for production emergency dispatch. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Some CAD systems offer automated unit recommendation algorithms, but reliable autonomous determination and live coordination with field units in production 911 centers is not deployed at scale. |
Determine response requirements and relative priorities of situations, and dispatch units in accordance with established procedures.
23CI 20–25 · exposure 25 · augmentation 63 · importance 4.8/5 · click for rater detail
Determine response requirements and relative priorities of situations, and dispatch units in accordance with established procedures.
23| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Public safety is a laggard sector in AI adoption; most 911 centers still rely on legacy computer-aided dispatch systems, and cultural/regulatory resistance to removing human judgment in life-safety decisions slows pilot and production deployment. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Public safety/emergency services are traditionally slow-moving, risk-averse, and under-resourced for AI adoption; pilots exist but production-scale autonomous dispatch is rare. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can meaningfully assist dispatchers by offering rapid location mapping, historical incident patterns, suggested unit recommendations, and call classification hints, but the human operator retains final judgment authority on priority and response strategy. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can meaningfully assist by auto-transcribing calls, suggesting priority codes, pulling location/history data, and recommending unit assignments, letting telecommunicators work faster while remaining in control. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can assist with basic call classification and unit availability checks, the task requires real-time judgment about complex, evolving emergency situations—including assessing threat severity, resource constraints, and dynamic field conditions—that current AI systems cannot reliably handle end-to-end without substantial human oversight. |
| Task automatability | claude-sonnet-5 | 2/5 | Triage and dispatch require real-time judgment on ambiguous, high-stakes human input (panicked callers, incomplete info) that current AI cannot reliably handle end-to-end; partial decision support exists but full automation is not near the 50% time-saving bar without human oversight. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Emergency dispatch is heavily regulated (NENA standards, state/local requirements), carries high liability for errors, and legally mandates trained human operators sign off on critical resource allocation; regulatory and legal barriers prevent full substitution. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Public safety dispatch is heavily regulated, requires certified telecommunicators, and carries severe liability for errors, mandating human authorization and accountability. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Integration of AI dispatch assistants still requires trained human operators to validate and execute critical decisions; the combined cost of AI tools, infrastructure, and mandatory human oversight typically exceeds the wage savings from partial automation. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI could cheaply flag priority levels, but liability and required human decision-making mean the human cost isn't eliminated, keeping all-in cost comparable rather than dramatically lower. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Some AI-assisted dispatch tools exist (e.g., location routing, call categorization), but no deployed system currently performs the core judgment—prioritization and response-requirement determination across varied, ambiguous emergencies—at the reliability required for production 911 centers. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Some CAD systems use AI-assisted triage/recommendation tools, but no deployed product independently determines priority and dispatches units without a human telecommunicator making the final call. |
Learn material and pass required tests for certification.
21CI 3–40 · exposure 17 · augmentation 75 · importance 4.4/5 · click for rater detail
Learn material and pass required tests for certification.
21| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 3/5 | Many public safety agencies now use online learning platforms and AI-assisted study tools, but uptake remains inconsistent across jurisdictions and organizations; certification testing itself remains largely human-administered. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Public safety/emergency dispatch is a traditionally slow-adopting, highly regulated government sector with limited AI integration into certification processes specifically. |
| Augmentation potential | claude-haiku-4-5-20251001 | 4/5 | AI study assistants, adaptive learning engines, and practice-test generators substantially boost study efficiency and retention, helping candidates prepare faster and more thoroughly while humans remain responsible for final certification. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI study aids, practice tests, tutoring chatbots, and adaptive learning tools can meaningfully help candidates prepare for and pass certification exams, improving study efficiency significantly. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | AI can assist with material review and generate practice questions, but the certification exam typically requires proctored human supervision and verification of identity, preventing full end-to-end automation. Learning itself involves human cognitive engagement that AI cannot fully replace at production quality. |
| Task automatability | claude-sonnet-5 | 1/5 | This task is inherently a human learning and certification process; while AI can help someone study, it cannot itself 'learn material and pass required tests' on behalf of the certified individual since certification requires the human to demonstrate competency.','rating':1 |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Certification exams are legally required and must be administered by authorized human proctors who verify identity and test integrity; this hard regulatory requirement prevents full automation of the testing component. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Certification and licensing requirements mandate the individual telecommunicator personally pass tests and demonstrate qualification, a hard regulatory/legal barrier preventing any AI substitution. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 4/5 | AI-driven learning platforms cost pennies per user per session, while a human instructor or tutor costs $20–50+ per hour; the ratio heavily favors AI, though proctoring still requires some human oversight. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no substitutable AI service delivering this outcome, so cost comparison is moot; the human must personally complete certification, making AI substitution cost irrelevant/inapplicable at a 1. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 3/5 | AI tutoring and practice-test systems exist (e.g., Duolingo, exam-prep platforms), but no product reliably handles the full pipeline of learning *and* passing official certification exams without human test administration and verification. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed product performs certification learning/testing on behalf of a person, since the point of certification is to verify the individual's own knowledge and legal accountability. |
Relay information and messages to and from emergency sites, to law enforcement agencies, and to all other individuals or groups requiring notification.
20CI 20–20 · exposure 25 · augmentation 63 · importance 4.7/5 · click for rater detail
Relay information and messages to and from emergency sites, to law enforcement agencies, and to all other individuals or groups requiring notification.
20| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Public safety agencies adopt AI tools as assistive dashboards (CAD, speech analytics) but maintain human operators as the primary decision-maker. Actual displacement remains minimal; adoption is cautious due to mission-critical nature and regulatory scrutiny. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Public safety/emergency services are traditionally slow adopters of AI due to liability, unionized labor, and legacy infrastructure, with pilots more common than widespread production deployment. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist by transcribing calls, flagging high-priority patterns, suggesting routing, and auto-logging routine information. These aids improve dispatcher efficiency and consistency, but the human remains essential for judgment, de-escalation, and exception handling. |
| Augmentation potential | claude-sonnet-5 | 4/5 | AI can meaningfully assist with real-time transcription, language translation, information summarization, and alert routing, helping telecommunicators relay information faster while remaining in control of critical decisions. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI could theoretically capture and log some incoming emergency information, the task requires real-time judgment about routing, prioritization, de-escalation, and handling of chaotic or conflicting information streams that demand human discretion. Autonomous relaying would fail to achieve 50% time saving at equal quality due to liability and decision-making complexity. |
| Task automatability | claude-sonnet-5 | 2/5 | Relaying emergency information requires real-time judgment, prioritization, and handling ambiguous or high-stress human speech; current AI can assist but cannot reliably replace the full decision chain end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Emergency dispatch is heavily regulated (FCC, state/local standards) and law enforcement agencies typically mandate human operators for accountability and liability. Liability asymmetry (missed calls or wrong routing causing harm) creates strong legal and organizational barriers to full automation. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Public safety dispatch is heavily regulated, requires certified telecommunicators, and carries severe liability for errors, making full automation of relaying critical emergency information legally and organizationally restricted. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Current AI integration (ASR, logging, routing hints) reduces some clerical overhead but does not eliminate the core labor cost of a trained dispatcher. The all-in cost of AI assistance plus mandatory human oversight remains comparable to or exceeds the loaded wage. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI transcription/translation tools are cheap, but liability and error-cost in emergency contexts require human oversight, keeping effective all-in cost comparable to or only modestly cheaper than trained telecommunicators. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed AI systems reliably perform live emergency dispatch and multi-party relay at scale. Existing tools (speech-to-text, routing suggestions) assist but do not substitute for the human operator; error rates in critical life-safety scenarios remain prohibitive. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Some CAD/dispatch systems use AI for transcription, translation, or auto-routing alerts, but production systems that autonomously relay critical emergency information without human oversight are not deployed at scale. |
Question callers to determine their locations and the nature of their problems to determine type of response needed.
18CI 18–18 · exposure 25 · augmentation 50 · importance 4.9/5 · click for rater detail
Question callers to determine their locations and the nature of their problems to determine type of response needed.
18| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Emergency services are traditionally slow to digitize and risk-averse; dispatcher automation is rare in production and adoption remains nascent despite technological progress in other sectors. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Public safety/emergency services are a low-digitization, highly regulated, risk-averse sector with minimal AI deployment in core call-taking functions. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist by suggesting likely incident types, auto-filling location fields from caller coordinates, or flagging high-risk keywords to prioritize dispatcher attention, moderately raising operator efficiency while the human remains the decision-maker. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can assist with real-time transcription, language translation, location lookup, and decision-support prompts, improving dispatcher speed and accuracy while human remains in control. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can extract basic information from structured speech (location, incident type), real emergency calls involve ambiguous situations, emotional distress, and nuanced judgment about threat severity that require human interpretation. Current systems cannot reliably handle the full complexity and safety-critical nature of call triage at 50% time savings. |
| Task automatability | claude-sonnet-5 | 2/5 | AI voice systems can capture basic location/nature data, but reliable triage of ambiguous, high-stakes emergency calls with accents, noise, distress, and incomplete info still requires human judgment to meet quality bar at scale. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Legal and regulatory requirements mandate that licensed/trained human dispatchers perform or directly supervise call-taking in most jurisdictions; liability for missed or misrouted calls creates hard barriers to unsupervised automation. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Emergency dispatch is safety-critical and typically requires certified telecommunicators; legal, liability, and life-safety concerns make full automation highly restricted. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Integration, error handling, and human oversight costs for an unreliable system approach or exceed the wage of an experienced dispatcher; the liability and retrain burden make AI economically unfavorable today. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI voice triage could be cheap per call, but liability, accuracy requirements, and need for human oversight/backup keep effective all-in cost close to or above human dispatcher cost. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Chatbots and speech recognition exist but are deployed only in limited, non-emergency pilot contexts. Production emergency dispatch still relies almost entirely on human operators; no mature system performs this task reliably in actual 911 centers at scale. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Some 911 centers pilot AI-assisted call intake or translation tools, but no mature product independently and reliably conducts full emergency intake without a human dispatcher in production at scale. |
Receive incoming telephone or alarm system calls regarding emergency and non-emergency police and fire service, emergency ambulance service, information, and after-hours calls for departments within a city.
14CI 9–20 · exposure 17 · augmentation 50 · importance 4.8/5 · click for rater detail
Receive incoming telephone or alarm system calls regarding emergency and non-emergency police and fire service, emergency ambulance service, information, and after-hours calls for departments within a city.
14| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Public safety is a laggard sector with strong regulatory, liability, and operational constraints. Adoption remains limited to auxiliary tasks (transcription, logging) rather than end-to-end call handling; no major displacement of telecommunicators is occurring. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Public safety answering points (PSAPs) are traditionally slow-adopting, resource-constrained government entities; AI pilots (e.g., translation, call summarization) exist but full-scale agentic adoption is minimal. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist by transcribing calls, flagging keywords related to call type, and suggesting response protocols, improving operator efficiency and reducing transcription load. However, the human operator remains essential for judgment, de-escalation, and accountability. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can assist with real-time translation, transcription, call summarization, and decision-support prompts, improving dispatcher efficiency, though the core interaction remains human-led. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can transcribe calls and classify some routine non-emergency inquiries, the task requires real-time judgment on emergency severity, triage decisions, and human reassurance that current systems cannot reliably provide end-to-end. The safety-critical nature and unpredictability of caller needs prevent 50% time savings with equal quality. |
| Task automatability | claude-sonnet-5 | 1/5 | This task requires real-time triage of life-critical, ambiguous, high-stress human speech with legal and safety consequences; current AI cannot reliably replace the full end-to-end judgment and rapid decision-making required. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Public safety dispatch is heavily regulated; many jurisdictions legally require a certified, licensed human operator to receive and process emergency calls. Liability for misdispatch or missed emergencies creates legal and organizational barriers that prevent direct AI substitution. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Emergency call-taking is tightly regulated, requires certified/licensed telecommunicators, carries severe liability exposure, and legally requires human judgment and accountability for dispatch decisions. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI infrastructure for call processing (transcription, classification, routing) is inexpensive, but the liability, redundancy, and oversight costs for safety-critical dispatch make the all-in cost comparable to or higher than trained telecommunicators, especially when human escalation is required. |
| Cost vs. human wage | claude-sonnet-5 | 2/5 | AI transcription/triage tools are cheap to run, but the liability, oversight, and integration costs of using AI for emergency dispatch keep effective cost comparable to or higher than trained telecommunicators for equivalent quality. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | Deployed systems can handle call routing and transcription, but no production system reliably answers, triages, and responds to emergency calls without a human operator. Chatbots and IVR systems exist but have high error rates on genuine emergencies and caller distress detection. |
| Technical feasibility today | claude-sonnet-5 | 2/5 | Some AI-assisted call-routing and translation tools exist in 911 centers, but no deployed product independently handles full emergency call-taking reliably at scale; humans remain the primary responders. |
Provide emergency medical instructions to callers.
11CI 0–23 · exposure 13 · augmentation 50 · importance 4.9/5 · click for rater detail
Provide emergency medical instructions to callers.
11| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Adoption of AI for autonomous emergency instruction remains negligible; public safety organizations are highly conservative due to safety-critical constraints, union protections, and liability risk. Pilots are rare and production systems essentially non-existent in the sector. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Public safety answering points are notoriously slow to adopt new technology due to regulatory, liability, funding, and legacy infrastructure constraints; AI-driven instruction-giving is not in production anywhere at scale. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist by surfacing relevant medical protocols, suggesting next steps based on dispatcher input, or flagging high-risk symptoms in real time, meaningfully supporting human operators without replacing their judgment. Such augmentation tools could improve response consistency and speed while keeping the human telecom operator firmly in control. |
| Augmentation potential | claude-sonnet-5 | 3/5 | AI can assist by providing protocol prompts, transcription, translation, or decision-support scripts to dispatchers, improving consistency and speed, though the human remains essential for judgment and delivery. |
| Task automatability | claude-haiku-4-5-20251001 | 2/5 | While AI can retrieve and suggest standard medical instruction sequences, the task requires real-time assessment of unique caller situations, emotional de-escalation, and safety judgment that current systems cannot reliably perform end-to-end. Partial automation of protocol lookup or scripting is possible, but autonomy over the full interaction falls well short of 50% time savings at equal quality. |
| Task automatability | claude-sonnet-5 | 1/5 | Providing real-time emergency medical instructions requires split-second judgment, situational assessment via voice cues, and adaptive human empathy under life-or-death stakes; no off-the-shelf system can autonomously handle this end-to-end today. |
| Adoption barriers | claude-haiku-4-5-20251001 | 4/5 | Strong regulatory and liability barriers exist: emergency services are tightly regulated, callers depend on human judgment for safety, and a death or harm resulting from inadequate AI instructions creates extreme liability exposure. Many jurisdictions require trained human operators to provide or verify medical guidance, and 911 dispatch centers face legal accountability. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Emergency medical dispatch is heavily regulated, requires certified training (e.g., EMD certification), and carries extreme liability for errors, making licensed human involvement legally and practically mandatory. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | Integration of AI assistance into a live emergency dispatch environment requires significant oversight, validation, and human telecom infrastructure. The cost of reliable systems, liability coverage, and mandatory human review likely exceeds the savings from partial automation against the relatively modest wage of the task. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | Given liability, error costs, and the need for certified human judgment, any AI system would require extensive human oversight and redundancy, making it not meaningfully cheaper than trained telecommunicators. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | No deployed production system autonomously handles emergency medical instruction calls; existing AI is limited to research prototypes or narrow support tools. Current systems cannot reliably assess medical situations, adapt to caller confusion or distress, or make the safety-critical adjustments required in real emergency dispatch environments. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed production system independently delivers emergency medical dispatch instructions to callers; existing tools are decision-support aids for human dispatchers, not autonomous replacements. |
Maintain access to, and security of, highly sensitive materials.
7CI 3–11 · exposure 5 · augmentation 38 · importance 4.6/5 · click for rater detail
Maintain access to, and security of, highly sensitive materials.
7| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 2/5 | Public safety and classified-material handling sectors are conservative and regulatory-constrained. AI adoption remains limited to monitoring aids and logging; core security decision-making remains in human hands with slow institutional change. |
| Sector adoption velocity | claude-sonnet-5 | 2/5 | Public safety/emergency dispatch is a slow-adopting, highly regulated sector where AI use for security-sensitive access management remains minimal and cautious. |
| Augmentation potential | claude-haiku-4-5-20251001 | 3/5 | AI can assist by automating audit logs, flagging suspicious access patterns, and monitoring system health, which augments human security officers' ability to manage sensitive materials more efficiently within a human-supervised framework. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI can support monitoring/logging or flagging anomalous access attempts, but it plays a peripheral role compared to human-managed security protocols and compliance obligations. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Maintaining access to and security of sensitive materials requires ongoing judgment about who should have access, risk assessment, and decision-making in response to threats or anomalies. Current AI systems cannot independently perform these core functions reliably enough to meet security standards. |
| Task automatability | claude-sonnet-5 | 1/5 | Maintaining access controls and security of sensitive materials (CJIS data, dispatch systems) requires accountable human judgment, physical/administrative safeguarding, and legal responsibility that current AI cannot assume end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Regulatory frameworks (CJIS, classified material handling, HIPAA, SOC 2) legally require human accountability and sign-off on access control and security decisions. Liability and compliance frameworks create hard barriers to full automation. |
| Adoption barriers | claude-sonnet-5 | 5/5 | Access to CJIS and similar sensitive law enforcement data is governed by strict regulatory, background-check, and legal authorization requirements that mandate vetted human personnel. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 2/5 | AI tools for monitoring and logging may reduce some overhead, but comprehensive access control and security systems still require significant human supervision, specialized security personnel, and liability reserves. The AI cost advantage is marginal compared to the human cost. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no viable AI substitute performing this function alone, so cost comparison favors the human role since AI cannot assume the liability and compliance responsibilities involved. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 2/5 | While AI can assist with monitoring systems and flagging anomalies, no deployed product reliably handles the full responsibility of access control and security maintenance for sensitive materials without human oversight. Security breaches carry prohibitive costs, so systems remain in human-supervised mode. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product independently manages security clearance, access control, and custodianship of sensitive law enforcement materials; this remains a human administrative/security function. |
Operate and maintain mobile dispatch vehicles and equipment.
5CI 0–10 · exposure 0 · augmentation 25 · importance 4.6/5 · click for rater detail
Operate and maintain mobile dispatch vehicles and equipment.
5| Dimension | Model | Rating | Rationale |
|---|---|---|---|
| Sector adoption velocity | claude-haiku-4-5-20251001 | 1/5 | Public safety agencies operate in heavily regulated, conservative sectors with long capital cycles and entrenched human-centered dispatch workflows. Adoption of automation for vehicle operation and maintenance remains minimal. |
| Sector adoption velocity | claude-sonnet-5 | 1/5 | Public safety dispatch operations are a low-digitization, physically-grounded sector with minimal AI adoption for vehicle/equipment maintenance tasks. |
| Augmentation potential | claude-haiku-4-5-20251001 | 2/5 | AI could assist with vehicle diagnostics, maintenance scheduling, or route optimization for dispatch planning, but the core task of operating and maintaining the physical vehicles offers limited augmentation potential and remains primarily human-executed. |
| Augmentation potential | claude-sonnet-5 | 2/5 | AI could assist with maintenance scheduling, diagnostics alerts, or equipment tracking, but offers little direct help with the physical operation and upkeep itself. |
| Task automatability | claude-haiku-4-5-20251001 | 1/5 | Operating and maintaining physical dispatch vehicles requires hands-on mechanical work, vehicle operation, and real-time situational awareness in field conditions. Current AI cannot physically operate vehicles, perform maintenance, or manage dynamic roadside conditions at production scale. |
| Task automatability | claude-sonnet-5 | 1/5 | Physically operating and maintaining vehicles and hardware equipment requires manual manipulation, driving, and physical maintenance tasks that current AI cannot perform end-to-end. |
| Adoption barriers | claude-haiku-4-5-20251001 | 5/5 | Public safety dispatch operations are heavily regulated and require licensed drivers, vehicle operators, and maintenance personnel. Legal liability, safety-critical decision-making, and mandatory human certification create hard barriers to automation. |
| Adoption barriers | claude-sonnet-5 | 3/5 | While no strict licensing mandates a human specifically for vehicle maintenance, safety-critical equipment upkeep and organizational protocols create meaningful friction against automation. |
| Cost vs. human wage | claude-haiku-4-5-20251001 | 1/5 | AI has no role in this task, so cost comparison is not applicable; a human operator is required. Any system that could theoretically perform this would require significant hardware investment, making it more expensive than current human-operated dispatch. |
| Cost vs. human wage | claude-sonnet-5 | 1/5 | There is no AI substitute performing this physical task, so cost comparison favors human labor entirely; AI would add cost without replacing the function. |
| Technical feasibility today | claude-haiku-4-5-20251001 | 1/5 | No deployed AI system reliably operates dispatch vehicles or performs vehicle maintenance in production public safety settings. This task remains entirely within human operator domain with no mature automation products. |
| Technical feasibility today | claude-sonnet-5 | 1/5 | No deployed AI product operates or maintains physical dispatch vehicles or their equipment; this remains a human physical/mechanical task. |
Related occupations — Office & Administrative Support
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.