Calculate the ROI that AI can generate for your customer service in 2026.

Estimate in seconds the time savings and cost reductions generated by AI. Enter your number of agents and their hourly cost.

Data sourced from reports by McKinsey, Gartner, and Deloitte (2024–2026).

Calculateur d'économies

Combien votre service client peut-il économiser avec un chatbot ?

Six paramètres que vous connaissez déjà. Le taux d'automatisation est estimé à partir de repères marché récents et prudents.

Nombre d'agentsEffectif du service client (ce canal)12
Coût chargé d'un agent / anSalaire brut + charges (repère France : 40–55 k€)46 000 €
Conversations écrites / mois3 000
Durée moyenne de traitement (DMT)Temps agent moyen par contact8 min
Demandes simples / répétitives (niveau 1)62 % de demandes simples × 80 % résolues par le bot = 50 % du volume total automatisé62 %
Conversations traitées en parallèleUn agent gère plusieurs chats à la fois× 3

Base de calcul : un agent traite en moyenne 1 500 h par an (hors pauses, formation, absences). Estimation indicative — pour un chiffrage précis (tarif, ROI net), parlons-en.

ViaDialog
En automatisant 50 % de vos conversations, vous libérez l'équivalent de 0,5 agents à temps plein.
💶 Argent économisé / an
24 337 €
Temps économisé, contacts automatisés, coût par contact, vue mois / an et projection 3 ans.

Methodology

The calculator is based on a simple logic: the actual cost of a contact. Cost of a contact = fully loaded agent cost ÷ 1,500 productive hours/year × average handling time (AHT), divided by the number of parallel conversations managed by a chatbot.


Automatable volume = share of simple requests × bot resolution rate. Annual savings = automated contacts × cost per contact; for the callbot, pre-qualification is also added (reduced AHT on transferred calls).


Saved hours are converted into FTEs. This is an indicative result, excluding qualitative gains (24/7 availability, reduced wait times, customer satisfaction).

Market Benchmarks

Chatbot: ~80% resolution rate on simple queries.

Callbot: ~70% resolution rate on tier-1 calls.

Baseline: 1,500 productive hours / year / agent (net of breaks, training, and absences).

Sources

Gartner (2024) — Median cost of contact ≈ $13.50 via assisted channels vs. $1.84 via self-service.

McKinsey (2025) — AI can address up to 60% of volume; ~30% target workforce efficiency.

ContactBabel (2025) — Average cost of an inbound call ≈ $7.16.

Gartner (March 2025) — Agentic AI will resolve 80% of routine inquiries by 2029, delivering ~30% cost savings.

Gartner (Dec. 2024) — GenAI tools will resolve 40% of customer service inquiries by 2027.

Deloitte (2025) — 40% to 60% of simple inquiries are resolvable via self-service.

BCG (2025) — More than 50% productivity gains achieved in contact centers.

AFRC (2026) — +24% automated calls on pilot use cases, +20 pts satisfaction rate.

Ipsos·BVA (2025) — 31% of French consumers currently utilize chatbots; 72% demand transparency regarding whether they are interacting with an AI.

FAQ – AI ROI Simulator for Customer Service

How is the ROI of AI calculated in this simulator?

+

The calculation is based on the average time saved per agent through automation of repetitive tasks (call routing, qualification, basic responses, post-call actions). The simulator uses ratios from McKinsey, BCG and Bain studies, then converts that time into financial savings based on your hourly cost.

What is the average productivity gain with AI in a contact center?

+

Research indicates 3 to 15 hours saved per agent per week, depending on the level of automation (callbots, voicebots, self-service, agent assist). The simulator applies a realistic, conservative scenario to provide a reliable ROI estimate.

Why does AI generate significant cost savings?

+

AI automates low-value, repetitive actions: call triage, information retrieval, standard answers, CRM updates, conversation summaries. Agents can then focus on complex interactions — improving productivity and reducing operational costs.

Is the ROI calculation reliable?

+

Yes. It is based on real-world data observed in contact centers: productivity improvements, reduced handling times, automated workflows. The estimate is conservative and does not include indirect gains such as improved CSAT, NPS, FCR or reduced churn.

Does AI improve only productivity, or also service quality?

+

Both. AI reduces wait times, increases availability, improves consistency, and provides real-time guidance to agents. Most organizations observe +10 to +20 points in user satisfaction.

Which tasks can be automated by AI in customer service?

+

• Call routing & greeting • Request qualification • Standard responses • Post-call actions (notes, summaries, CRM updates) • Self-service and FAQ automation • Multi-channel conversation handling These tasks represent 25–50% of total volume depending on the organization.

Does AI replace human agents?

+

No. The goal is to augment, not replace. AI handles simple, repetitive interactions so agents can focus on complex, value-added conversations.

How long does it take to achieve positive ROI?

+

In most cases, ROI becomes visible within 1 to 3 months, because time savings occur immediately once automation begins.

Does the simulator calculate annual savings?

+

Yes. It displays monthly savings, and projections over 6 or 12 months can be easily extrapolated. A more advanced calculator can provide 12–24 month ROI forecasting upon request.

Which industries benefit the most from AI in customer service?

+

Industries with high volume and repeat patterns: • Insurance • Banking & Finance • Retail & e-commerce • Public sector • Healthcare • Telecom & Utilities These sectors typically generate the fastest ROI.

Why does sovereign AI improve ROI reliability?

+

Local, secured and controlled infrastructures offer: • Faster deployment • Full GDPR compliance • Lower operational risks • High data quality • More accurate automation 👉 Result: more stable and predictable ROI.

What information do I need to enter to get my ROI estimate?

+

Only two fields: • number of agents • average hourly cost The simulator instantly computes time saved, monthly savings, projections and ROI.

Can AI really reduce operational costs by 20–40%?

+

Yes, depending on the automation level: • ~20% with partial automation • Up to 40% with advanced automation + agent assist + reduced post-call tasks The simulator uses conservative values to remain credible.

Is AI useful for small teams too?

+

Absolutely. Even with 2 to 5 agents, AI can save hundreds to thousands of euros/dollars per month. Smaller teams often see the fastest ROI, because every hour saved has a stronger impact.

How do I move from estimation to real ROI?

+

The next step is to analyze: • incoming volume • frequent reasons for contact • automatable tasks • agent pain points • user journeys • workload distribution This allows you to define a concrete AI scope and a measurable ROI.

Calculate the ROI that AI can generate for your customer service in 2026.

Estimate in seconds the time savings and cost reductions generated by AI. Enter your number of agents and their hourly cost.

Data from McKinsey, BCG and Bain reports (2023–2025).

Data derived from McKinsey, BCG and Bain reports (2023–2025).

Calculateur d'économies

Combien votre service client peut-il économiser avec un chatbot ?

Six paramètres que vous connaissez déjà. Le taux d'automatisation est estimé à partir de repères marché récents et prudents.

Nombre d'agentsEffectif du service client (ce canal)12
Coût chargé d'un agent / anSalaire brut + charges (repère France : 40–55 k€)46 000 €
Conversations écrites / mois3 000
Durée moyenne de traitement (DMT)Temps agent moyen par contact8 min
Demandes simples / répétitives (niveau 1)62 % de demandes simples × 80 % résolues par le bot = 50 % du volume total automatisé62 %
Conversations traitées en parallèleUn agent gère plusieurs chats à la fois× 3

Base de calcul : un agent traite en moyenne 1 500 h par an (hors pauses, formation, absences). Estimation indicative — pour un chiffrage précis (tarif, ROI net), parlons-en.

ViaDialog
En automatisant 50 % de vos conversations, vous libérez l'équivalent de 0,5 agents à temps plein.
💶 Argent économisé / an
24 337 €
Temps économisé, contacts automatisés, coût par contact, vue mois / an et projection 3 ans.

Methodology

The calculator is based on a simple logic: the actual cost of a contact. Cost of a contact = fully loaded agent cost ÷ 1,500 productive hours/year × average handling time (AHT), divided by the number of parallel conversations managed by a chatbot.


Automatable volume = share of simple requests × bot resolution rate. Annual savings = automated contacts × cost per contact; for the callbot, pre-qualification is also added (reduced AHT on transferred calls).


Saved hours are converted into FTEs. This is an indicative result, excluding qualitative gains (24/7 availability, reduced wait times, customer satisfaction).

Market Benchmarks

Chatbot: ~80% resolution rate on simple queries.

Callbot: ~70% resolution rate on tier-1 calls.

Baseline: 1,500 productive hours / year / agent (net of breaks, training, and absences).

Sources

Gartner (2024) — Median cost of contact ≈ $13.50 via assisted channels vs. $1.84 via self-service.

McKinsey (2025) — AI can address up to 60% of volume; ~30% target workforce efficiency.

ContactBabel (2025) — Average cost of an inbound call ≈ $7.16.

Gartner (March 2025) — Agentic AI will resolve 80% of routine inquiries by 2029, delivering ~30% cost savings.

Gartner (Dec. 2024) — GenAI tools will resolve 40% of customer service inquiries by 2027.

Deloitte (2025) — 40% to 60% of simple inquiries are resolvable via self-service.

BCG (2025) — More than 50% productivity gains achieved in contact centers.

AFRC (2026) — +24% automated calls on pilot use cases, +20 pts satisfaction rate.

Ipsos·BVA (2025) — 31% of French consumers currently utilize chatbots; 72% demand transparency regarding whether they are interacting with an AI.

FAQ – AI ROI Simulator for Customer Service

How is the ROI of AI calculated in this simulator?

+

The calculation is based on the average time saved per agent through automation of repetitive tasks (call routing, qualification, basic responses, post-call actions). The simulator uses ratios from McKinsey, BCG and Bain studies, then converts that time into financial savings based on your hourly cost.

What is the average productivity gain with AI in a contact center?

+

Research indicates 3 to 15 hours saved per agent per week, depending on the level of automation (callbots, voicebots, self-service, agent assist). The simulator applies a realistic, conservative scenario to provide a reliable ROI estimate.

Why does AI generate significant cost savings?

+

AI automates low-value, repetitive actions: call triage, information retrieval, standard answers, CRM updates, conversation summaries. Agents can then focus on complex interactions — improving productivity and reducing operational costs.

Is the ROI calculation reliable?

+

Yes. It is based on real-world data observed in contact centers: productivity improvements, reduced handling times, automated workflows. The estimate is conservative and does not include indirect gains such as improved CSAT, NPS, FCR or reduced churn.

Does AI improve only productivity, or also service quality?

+

Both. AI reduces wait times, increases availability, improves consistency, and provides real-time guidance to agents. Most organizations observe +10 to +20 points in user satisfaction.

Which tasks can be automated by AI in customer service?

+

• Call routing & greeting • Request qualification • Standard responses • Post-call actions (notes, summaries, CRM updates) • Self-service and FAQ automation • Multi-channel conversation handling These tasks represent 25–50% of total volume depending on the organization.

Does AI replace human agents?

+

No. The goal is to augment, not replace. AI handles simple, repetitive interactions so agents can focus on complex, value-added conversations.

How long does it take to achieve positive ROI?

+

In most cases, ROI becomes visible within 1 to 3 months, because time savings occur immediately once automation begins.

ViaDialog Logo

The AI-powered omnichannel customer service platform

Products

Resources

Industries

Company

152 Boulevard Pereire, 75017 Paris

DESIGNED
AND BASED
IN FRANCE

logo entreprise française
logo entreprise française
logo entreprise française
logo entreprise française
logo entreprise française
logo entreprise française
logo entreprise française
logo entreprise française
logo entreprise française

© 2026 ViaDialog

Designed and hosted in Paris 🥐

Legal Notice
Privacy Policy
English