Real-world use cases and savings

Examples below are grounded in published studies/case studies and always model one role first. Enter hourly labor cost and number of employees with the same role.

Hourly labor cost (€)

Hourly labor cost is used as an indicative gross monthly savings input.

Hourly labor cost (€)

Employee count (same role)

Enter how many employees perform the same activity that AI can replace or significantly offload.

The model uses realistic one-person values only. Large totals always come from multiplying by employee count.

Total potential for selected inputs

Total replaced hours / month355,7 h

Total gross savings / month8 893 €

Total gross savings / year106 710 €

AI chatbot for L1 support (Intercom Fin)

Fin improved from 28% to roughly 46% average resolution rate.

Replaced role: L1 support operator (FAQ / triage)

Replaced activity: Repeated FAQ responses and first-line ticket triage.

Automation mode: The chatbot executes this activity automatically; humans handle only escalated exceptions.

Practical outcome: Fin improved from 28% to roughly 46% average resolution rate.

Model assumption: Single-role model: 170 h/month × 46% = 78 h/month replaced by AI.

Replaced hours / month (single role): 78 h

Replaced hours / month (total): 78 h

Gross monthly company savings: 1 950 €

Gross yearly company savings: 23 400 €

Source: Intercom: AI-first customer service (Fin resolution rate)

AI chatbot for scaled support (Lightspeed)

Published range of 45-65% conversation resolution by Fin.

Replaced role: Support specialist for repetitive cases

Replaced activity: Handling routine questions and first-line triage before escalation.

Automation mode: AI resolves low-complexity cases, humans handle complex and sensitive scenarios.

Practical outcome: Published range of 45-65% conversation resolution by Fin.

Model assumption: Conservative midpoint model: 170 h/month × 55% = 94 h/month replaced by AI.

Replaced hours / month (single role): 94 h

Replaced hours / month (total): 94 h

Gross monthly company savings: 2 350 €

Gross yearly company savings: 28 200 €

Source: Intercom: Lightspeed case study

Agentic workflow for refunds and disputes (Klarna)

Klarna reports AI assistant handled 2/3 of support chats (equivalent to 700 FTE).

Replaced role: Support operator for refunds/returns/disputes

Replaced activity: Handling repetitive refund, returns, and payment dispute requests.

Automation mode: AI agent executes workflow steps autonomously; humans handle exceptions and sensitive cases.

Practical outcome: Klarna reports AI assistant handled 2/3 of support chats (equivalent to 700 FTE).

Model assumption: Single-role model: 170 h/month × 66% = 112 h/month replaced by AI.

Replaced hours / month (single role): 112 h

Replaced hours / month (total): 112 h

Gross monthly company savings: 2 800 €

Gross yearly company savings: 33 600 €

Source: OpenAI customer story: Klarna

AI assistant for customer support team (NBER)

Randomized study on 5,179 agents: +14% productivity (issues/hour).

Replaced role: Customer support agent

Replaced activity: Drafting responses, navigating knowledge base, and handling standard requests.

Automation mode: AI assistant accelerates agent workflows; humans remain in the loop for final decisions.

Practical outcome: Randomized study on 5,179 agents: +14% productivity (issues/hour).

Model assumption: Single-role model: 170 h/month × 14% = 24 h/month effectively replaced.

Replaced hours / month (single role): 24 h

Replaced hours / month (total): 24 h

Gross monthly company savings: 600 €

Gross yearly company savings: 7 200 €

Source: NBER Working Paper 31161

Internal AI assistant for knowledge work (NBER)

Field experiment (66 firms): AI users spent 2 fewer hours per week on email.

Replaced role: Admin / operations specialist

Replaced activity: Email workload, summaries, and repetitive written communication.

Automation mode: AI drafts and summarizes; humans review and make final decisions.

Practical outcome: Field experiment (66 firms): AI users spent 2 fewer hours per week on email.

Model assumption: Single-role model: 2 h/week × 4.33 = 8.7 h/month replaced by AI.

Replaced hours / month (single role): 8,7 h

Replaced hours / month (total): 8,7 h

Gross monthly company savings: 217 €

Gross yearly company savings: 2 610 €

Source: NBER Working Paper 33795

AI assistant for development (GitHub Copilot study)

Controlled experiment: developers completed tasks 55.8% faster.

Replaced role: Software developer

Replaced activity: Repetitive coding, boilerplate, basic tests, and documentation.

Automation mode: AI pair programmer generates suggestions; humans review, integrate, and decide.

Practical outcome: Controlled experiment: developers completed tasks 55.8% faster.

Model assumption: Conservative model: 70 h/month repetitive dev tasks × 55.8% = 39 h/month replaced.

Replaced hours / month (single role): 39 h

Replaced hours / month (total): 39 h

Gross monthly company savings: 975 €

Gross yearly company savings: 11 700 €

Source: The Impact of AI on Developer Productivity (arXiv 2302.06590)