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.
Based on studies from McKinsey, BCG and Bain, this simulator provides you with a tangible estimate of the time and savings that AI can generate. Simply specify the number of agents and their hourly cost.
This simulator, based on studies by McKinsey, BCG, and Bain, provides you with a concrete estimate of time and costs that can be optimized. Simply enter the number of agents and their hourly rate.
🕒 Temps économisé
💰 Économies estimées
⚠️ Les résultats sont des estimations indicatives basées sur des études (McKinsey, BCG, Bain). Ils ne constituent pas un engagement contractuel.
📚 Sources & Methodology
The results provided by this simulator are based on a synthesis of data from benchmark studies published between 2023 and 2025. They aim to illustrate the potential gains offered by artificial intelligence in the realm of customer service, without constituting a contractual commitment.
🔍 Sources Used
McKinsey & Company
The Economic Potential of Generative AI
The study estimates that between 30% and 45% of customer service tasks are technically automatable thanks to generative AI. The simulator adopts a conservative assumption of 20%, reflecting a realistic short-term automation level.
👉 Read the Study
Bain & Company
Retail Efficiency Rewritten
Bain observes a reduction in operational costs ranging from 17% to 38% among companies that have integrated AI solutions. The simulator uses a weighted average of 28% to reflect a realistic ROI in a customer service context.
👉 Read the Analysis
Boston Consulting Group – BCG
AI at Work: Friend or Foe?
According to BCG, AI can enable up to 5 hours of weekly savings per agent. The calculator uses a cautious estimate of +3 hours per week, corresponding to a common situation of gradual deployment.
👉 View the Report
📚 Sources & Methodology
The results provided by this simulator are based on a synthesis of data from benchmark studies published between 2023 and 2025. They aim to illustrate the potential for gain offered by artificial intelligence in the field of customer service without constituting a contractual commitment.
🔍 Sources Used
McKinsey & Company (2023)
The economic potential of generative AI
The study estimates that between 30% and 45% of tasks related to customer service are technically automatable thanks to generative AI. The simulator adopts a conservative hypothesis of 20%, reflecting a realistic level of automation in the short term.
👉 Read the study
Bain & Company (2025)
Retail Efficiency Rewritten
Bain notes, in companies that have integrated AI solutions, a reduction in operational costs ranging from 17% to 38%. The simulator relies on a weighted average of 28% to reflect a realistic ROI in a customer service context.
👉 Read the analysis
Boston Consulting Group – BCG (2024)
AI at Work: Friend or Foe?
According to BCG, AI can enable up to 5 hours of weekly savings per agent. The calculator uses a conservative estimate of +3 hours per week, corresponding to a common scenario of gradual deployment.
👉 See the report
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.