Customer service: 86% autonomous resolution
Intercom published in 2024 the results of its Fin agent powered by Claude. On a sample of 5 million tickets processed, 86% were resolved without human intervention, with a stable satisfaction score compared to human responses. Unresolved tickets are escalated with full context, reducing human processing time by 40%.
Methodology: calculated on "definitive" resolution, not just first contact. Conditions for replication: well-structured knowledge base, CRM integration, escalation governance.
Finance: 384% ROI on AP/AR (Billtrust)
Billtrust reported a ROI of 384% over 24 months on automating its AP (Accounts Payable) and AR (Accounts Receivable) processes with AI agents. Gains come primarily from:
- 43% reduction in monthly close time
- 62% reduction in reconciliation errors
- 78% of incoming invoices automated (OCR + classification + posting)
This figure is replicable in France with similar configurations, with a 3 to 4 month deployment cycle. See our Finance page.
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Identify my use case →Procurement: 500% ROI (Globality)
Globality, a procurement management specialist, published a ROI of 500% on its sourcing agent among several Fortune 500 clients. The gain comes from reducing sourcing time (from weeks to hours) and optimizing negotiated prices through automatic market analysis.
HR: −33% time-to-hire, −40% cost/hire
Several LinkedIn and SHRM 2024-2025 studies converge on the following gains for HR functions equipped with pre-qualification agents:
- −33% time-to-hire (automated sorting and first contact)
- −40% cost-per-hire (reduced recruiter hours per candidate)
- +18% sourcing quality (better calibrated match score)
Note: these gains depend on CV database quality and strict bias-prevention governance. See our HR page.
Development: 24× return per developer
Anthropic reports a return of 24× per developer on Claude Code. Detailed analysis in our dedicated article.
Sales: 192% pipeline ROI
A 2025 Gartner study notes an average ROI of 192% on AI agent-powered lead qualification tools over 12 months. Gains: automatic prioritization, multi-channel follow-up, sequence personalization.
How to read these figures
All these ROIs are sourced — but they are not mechanically transferable. Each organization starts from a different baseline. A good business case measures your baseline BEFORE, projects a realistic gain, and commits to a tracking indicator.
How to read these figures
Calculation methodology
Three traps on agent ROI:
- Confusing gross gain and net gain — you must deduct integration, monitoring, and governance costs
- Forgetting the learning curve — month 1 ROI is not month 12 ROI
- Omitting hidden revenues — some gains are qualitative (customer satisfaction, retention)
Conditions for replication
To reach these ROI levels, three conditions are necessary: an engaged business sponsor, measured baseline data, and LOOP™-type governance that prevents drift after deployment.
Key takeaways
Agentic AI ROIs are real. They are documented. They are replicable — under conditions. The typical attainment cycle is 6 to 12 months. The initial investment typically represents 15 to 20% of the projected annual gain.
ROI is not automatic. It is the product of a method. That's exactly what Koneetiv offers: a proven method to go from intention to measured result.
Gain categories beyond time saved
An AI agent's ROI never reduces to just time saved. Three other gain categories are often overlooked but essential:
Quality gains
Agents reduce data entry errors, omissions, and inconsistencies. In an accounting team, an agent that reduces invoice error rate from 4% to 0.5% generates a direct gain on customer relations and cash flow. This gain doesn't appear in time saved, but is often greater than it.
Speed gains
An agent responds in seconds where a human responded in hours. This acceleration transforms customer experience, monthly close speed, or product time-to-market. These gains have strategic value that is difficult to quantify but central.
Scale gains
An agent absorbs volume peaks without hiring. For a seasonal business, it's the difference between capturing demand or losing it. For a scale-up, it's the ability to grow without multiplying teams.
ROI calculation traps
Many organizations inadvertently distort their ROI. The most common traps:
- Forgetting the hidden cost of monitoring — a monitored agent costs approximately 10 to 15% of its operating cost in supervision
- Ignoring governance costs — classification, quarterly review, registry updates
- Not integrating the learning curve — month 1 ROI is always lower than month 12
- Overestimating the baseline — if the "before" time is estimated roughly, the gain is illusory
Golden rule of business cases
A good business case is built before deployment, not after. The baseline must be measured in a documentable way, with representative samples and a reproducible method.
How Koneetiv builds the business case
Our approach to the business case has three steps:
- Week 1 — Measured baseline: sampling 50 to 200 real cases, measuring time spent, identifying errors and exceptions
- Week 2 — Modelling: projecting gain over 12, 24 and 36 months with three scenarios (pessimistic, realistic, optimistic)
- Week 3 — Commitment: signing a success indicator contracted with the sponsor
Target ROI by function
In summary, here are the orders of magnitude Koneetiv targets for a first deployment at 12 months:
- Finance: 3× to 8× (AP/AR, reconciliations)
- HR: 2× to 5× (CV screening, pre-qualification)
- Legal: 4× to 10× (contract analysis)
- IT: 8× to 24× (Claude Code)
- Operations: 3× to 7× (customer service)
- Sales: 2× to 5× (lead qualification)
Client cases we rarely talk about
Beyond public figures, here are three real deployments — anonymized — that show how ROI is built in real life.
Case 1: Industrial group, AP automation
A finance department of an industrial group (€800M revenue) deployed an automatic supplier invoice processing agent. Initial volume: 80,000 invoices per year, 6 FTEs mobilized. Result at 12 months: 72% of invoices processed without human intervention, 4 FTEs reassigned to internal control missions, payment delay reduced by 22%. Measured ROI: 6.2×.
Case 2: SaaS scale-up, level-1 customer support
A SaaS scale-up (120 employees) deployed a level-1 support agent connected to its knowledge base. Volume: 2,500 tickets per month. Result at 9 months: 63% of tickets resolved without human, stable customer satisfaction (82%), savings of 3 FTEs. Measured ROI: 4.8×.
Case 3: Law firm, contract analysis
A mid-sized law firm (30 lawyers) deployed a commercial contract analysis agent. Volume: 400 contracts per month. Result at 6 months: 90% of analysis time saved, detection of 3.2× more anomalies than human review alone. Measured ROI: 9.4×.
Deployments that don't reach their promised ROI
In the interest of intellectual honesty, we must also talk about underperforming deployments. We have observed these on these causes:
- Sponsor change mid-project — the new sponsor doesn't share the previous one's vision
- Unstable source data — the agent loses performance with each format change
- Underestimated cultural resistance — users bypass the agent
- Deferred CI/CD integration — gains remain confined to a limited scope
How to replicate these ROIs in your organization
No ROI is magical. It is the product of a method, the right conditions, and disciplined execution. Here are the six conditions we consistently observe in deployments that hit their target.
First, an engaged business sponsor, named and accountable. Second, a quantified baseline, rigorously measured before deployment. Third, a well-bounded use case with sufficient volume to generate improvement data. Fourth, LOOP™-type governance that prevents drift. Fifth, continuous monitoring that detects deviations early. Sixth, a culture of iterative improvement rather than "big bang" projects.
These conditions are not optional. The absence of one halves the ROI. The absence of two generally leads to project failure.