This article compiles benchmarks from IT Systèmes client projects, compares them with available public studies, and outlines the method for measuring the actual productivity gains delivered by an AI agent. Vendor promises of 80% gains don’t hold up for even three months in production: here’s what you should look for instead.

The four dimensions of productivity gains from an AI agent

1. Direct time savings on repetitive tasks

This is the most obvious benefit and the easiest to measure. AI agents reduce the time spent on repetitive tasks, but the extent of this varies greatly depending on the process.

The benchmark study remains the one by Brynjolfsson, Li, and Raymond published by the NBER (Working Paper 31161, 2023, updated 2024). Conducted on 5,172 customer support agents at a Fortune 500 company, it measures an average increase of 14% in the number of tickets resolved per hour, with a peak of 34% for the least experienced agents. This is one of the few randomized studies on the subject: gains reported beyond this range generally pertain to highly targeted cases, not the average outcome of a deployment.

Source: NBER Working Paper 31161, Brynjolfsson, Li, Raymond (2023)

Example: Level 1 IT support. A simple ticket (password reset, VPN access, printer issue) takes an average of 15 to 20 minutes to resolve manually. With an AI agent connected to the knowledge base, resolution time drops to 1 to 3 minutes for standard cases. According to Gartner analyses reported by the IT trade press, 40 to 60% of Tier 1 volume can be automated when the agent is properly deployed. Based on our projects, this ratio starts at the lower end of the range and increases over time.

A concrete example. For an IT department that handles 500 Level 1 tickets per month, automating 50% of that volume frees up approximately 75 to 100 hours of technician time each month—the equivalent of one part-time position that can be redeployed to Level 2 support or preventive maintenance.

Example: Invoice processing. According to APQC and IOFM benchmarks, manually processing an invoice takes 10 to 15 minutes and costs €10 to €15. Automated OCR + ML processing reduces this to 1 to 3 minutes for approximately €2, representing an 80% to 90% reduction in time. OCR + ML accuracy reaches 95% to 99% for standard formats.

Sources: APQC (American Productivity & Quality Center), IOFM (Institute of Finance & Management)

2. Improved quality through reduced errors

Manual data entry comes with hidden costs: detection, correction, rework, and sometimes customer dissatisfaction. Industry benchmarks show an error rate of 1% to 3% for manual data entry, compared to 0.1% to 0.5% for automated data entry (sources: IOFM, APQC). The error rate is reduced by a factor of 5 to 10 depending on the quality of the OCR and subsequent human quality control.

Quantitative example: Accounting department. A department that processes 1,000 invoices per month with a 2% error rate (20 errors) spends approximately 8 to 10 hours per month on corrections. With an error rate of 0.3% (3 errors), the time spent on corrections drops to 1 or 2 hours. That’s 6 to 9 hours saved per month on this aspect alone, in addition to the time saved on the initial processing.

A secondary impact that is often overlooked. Consistent quality improves the reliability of financial reporting and reduces friction between accounting, procurement, and management control. While difficult to quantify, this benefit is clearly evident in practice.

3. Improved responsiveness

An AI agent responds instantly, day and night. In B2B support benchmarks, the time to first response can drop from several minutes to just a few seconds for cases covered by the knowledge base. This improvement is dramatic in case studies, though more modest on average across the entire volume.

For an internal IT support team, a ticket submitted at 10 p.m. no longer has to wait until 9 a.m. to be addressed. The benefits are primarily felt in terms of user experience: less user frustration, less loss of individual productivity due to waiting, and a better reputation for the IT department.

4. Scalability benefits

An AI agent can handle increasing volumes without a proportional increase in marginal costs. If your support tickets rise from 500 to 2,000 per month, the agent can handle the load without requiring you to hire three additional technicians. This is likely the most underestimated business benefit: avoiding the need for additional hiring.

For a growing small or medium-sized business, avoiding 2 to 3 new hires translates to savings of €100,000 to €180,000 per year in payroll costs (gross salaries + recruitment + training), depending on the job profiles. These figures are estimates provided by IT Systèmes based on the average gross costs of a support technician in the Île-de-France region; they should be adjusted to fit your specific situation.

Benchmarks by type of AI agent

Level 1 AI helpdesk/IT support agent

Automatic resolution rate: 40% to 60% of Tier 1 volume in standard deployments (Gartner analysis), with the potential to reach 70% in mature environments
Average resolution time: 1 to 3 minutes, compared to 15 to 20 minutes for manual processing
Availability: 24/7 versus standard hours
Cost per ticket: According to MetricNet (2024), a Tier 1 ticket costs an average of about $22, compared to over $100 for a Tier 3 ticket

→ Read the case study: Level 1 IT Helpdesk Agent — Automating Routine IT Support

AI Accounting Agent / Invoice Processing

Reduction in processing time per unit: 80–90% (from 10–15 minutes to 1–3 minutes)
OCR + ML accuracy: 95% to 99% for standard formats (APQC, IOFM benchmarks)
Error rate: 1–3% for manual processing to 0.1–0.5% for automated processing
Cost per invoice: from €10–15 to approximately €2 (sources: APQC, IOFM)
Return on investment: typically 3 to 9 months for a well-defined AP automation project

→ Read the case study: Internal Support AI Agent — Automating IT and business requests in the enterprise

HR Onboarding AI Agent

Tasks that can be automated: creating AD and Microsoft 365 accounts, application provisioning, sending administrative documents, scheduling training sessions
Time to operationality: D+0 instead of D+2 to D+5, depending on the maturity of the manual process
Time saved through onboarding: 4 to 8 hours, depending on the scope of automation (estimate by IT Systems based on our client projects)

→ Read the case study: Employee Onboarding Agent — Automating the onboarding process for new employees

Legal research AI agent

Reduction in search time: 50% to 75%, depending on the complexity of the database (estimate by IT Systèmes)
Comprehensiveness: simultaneous analysis of thousands of documents, whereas a manual search is limited to a few dozen

→ Read the case study: AI-powered research assistant for law, audit, and consulting firms

Key performance indicators to track to measure gains

Operational KPIs

Automatic resolution rate: the percentage of tasks processed without human intervention
Average processing time: before and after deployment
Volume processed: tickets, invoices, documents per day or per month
Error rate: before and after
Availability: Agent uptime (minimum target: 99.5%)

Business KPIs

Productive hours saved: per week, month, year
Reassigned FTEs: number of full-time equivalents redeployed to higher-value tasks
Recruitment costs avoided: savings from scaling up
ROI: (annual revenue – project cost – maintenance) / total cost
Break-even period: number of months to reach break-even

Customer satisfaction KPI

NPS / CSAT: Customer Satisfaction Score
Time to first response: time until the first response
Time to resolution: time to full resolution
Human infection rate: target below 30%
Dropout rate: an indicator of frustration

How to Maximize Your AI Agent's Earnings

1. Establish a baseline before deployment

Before rolling out anything, carefully monitor the current situation for at least 3 months:

Average time to resolve a support ticket
Accounting error rate
HR hours spent on a comprehensive onboarding process
Current NPS / CSAT

Without a baseline, the gains reported after deployment are meaningless. This is the most common mistake: you don’t know where you started, so you don’t know where you ended up. A Power BI, Tableau, or Grafana dashboard that tracks the same KPIs every week after go-live prevents fruitless debates six months down the line.

2. Continuously optimize

AI agents improve with use, but not on their own. A weekly analysis of failures is essential:

Why didn't the agent resolve this ticket?
What information was missing from the knowledge base?
What change to the prompt or business logic would prevent this type of failure?

Based on our project feedback, an automatic resolution rate that starts at around 30–40% in month 1 can reach 60% by month 6 with consistent maintenance of the knowledge base. Without this consistency, the numbers plateau.

3. Expand the scope gradually

Once the agent is up and running in an initial environment, expanding it to similar scenarios costs only a fraction of the initial development cost. A helpdesk agent that handles password resets effectively can easily handle VPN access requests, printer issues, or Microsoft 365 questions. That’s where the ROI comes in.

Main sources:

Brynjolfsson, Li, Raymond, *Generative AI at Work*, NBER Working Paper 31161, 2023 (updated 2024)
APQC (American Productivity & Quality Center), AP automation benchmarks
IOFM (Institute of Finance & Management), AP benchmarks
MetricNet, Service Desk Benchmarking 2024
Gartner analyses reported by the IT trade press
Feedback on IT Systems Client Projects

Would you like to assess the ROI of an AI agent in your specific context? Contact our IT Systems experts →

FAQ – Productivity and ROI of AI Agents

How many hours can you actually save per week with an AI agent?

The figures vary widely depending on the process, the maturity level of the implementation, and the quality of the knowledge base. The benchmark study (NBER, Brynjolfsson 2023) reports an average productivity gain of 14% in customer support, with a peak of 34% for the least experienced agents. AP benchmarks (APQC, IOFM) show an 80% to 90% reduction in processing time for invoice entry. For an IT support department that handles 500 Level 1 tickets per month, automating 50% of the volume represents approximately 75 to 100 technician hours per month.

Are productivity gains immediate or gradual?

Both. Some of the benefits are immediate upon go-live (automatic resolution of simple tickets, invoice OCR). The rest build up over time, as the knowledge base grows and failure cases are corrected. Based on our projects, a resolution rate that starts at around 30–40% in month 1 can reach 60% by month 6 with consistent weekly optimization.

How can you accurately measure the ROI of an AI agent?

Three factors to quantify: time savings (hours saved × hourly labor cost × 12 months), quality gains (hours spent correcting errors avoided + hidden costs), and avoided costs (no need to hire additional staff to accommodate growth). Simple formula: ROI = (annual gains – project cost – maintenance) / total cost. The break-even point for an AP automation project typically falls between 3 and 9 months, according to IOFM benchmarks.

What automatic resolution rate can be expected?

According to Gartner’s analysis, 40 to 60% of Tier 1 IT workloads can be automated when the agent is properly deployed. For invoice processing, OCR + ML accuracy ranges from 95% to 99% for standard formats. Be wary of vendor claims of 90%+ accuracy right from deployment: these are demo results based on filtered cases, not production averages.

Are AI agents really replacing jobs?

In most of the projects we undertake, no positions are eliminated. Employees are reassigned to higher-value tasks: projects, support, and analysis. The most common outcome is absorbing growth without proportional hiring. That said, this is a business choice, not a technical inevitability: the same technology can also be used to reduce headcount, and it would be dishonest to claim otherwise.

How can I convince my management with concrete figures?

A business case consists of four key points: current baseline (total cost of the process measured over three months), projected savings (based on sourced benchmarks, not vendor promises), investment (project + annual maintenance), and ROI timeframe (6 to 12 months for a well-defined project). Quantified example: an IT support operation costing €90,000/year, with 50% of the workload automatable, can generate approximately €45,000 in annual savings for an investment of €40,000 to €50,000. ROI is achieved in 11 to 14 months.

‍