Automate the repetitive and manual work that quietly drains your operating budget
A course focused on cutting operating costs by automating where money leaks — customer support, document processing, data cleanup, internal operations. It goes past plain automation into human-in-the-loop design that protects quality, so you avoid the trap of saving money and losing service.
What does AI cost reduction actually involve?
This course is about cutting operating costs structurally by automating the repetitive work where money leaks — customer support, document processing, data cleanup, internal operations. The fintech company Klarna handled 2.3 million conversations in a single month with an OpenAI-based assistant, automating roughly two-thirds of all customer contacts, projected a $40 million profit improvement in 2024, and had saved $60 million by the end of 2025. Klarna also saw quality drop on complex and sensitive inquiries and restored part of its human support. The course takes that lesson seriously and designs human-in-the-loop automation that holds cost and quality at the same time.
Cost diagnosis
We quantify where the money goes in handling time and labor cost, then rank what to fix first.
Real automation
Participants automate support, document, and data work themselves with no-code tools.
Quality guardrails
Human-in-the-loop review keeps automation from degrading service or creating risk.
Share of customer inquiries Klarna automated with AI
Cumulative savings through the end of 2025
Drop in customer service cost per transaction
Evidence and cases
Why this training matters now
Klarna: two-thirds of inquiries automated, USD 60 million saved
Klarna's OpenAI-based assistant handled 2.3 million customer inquiries in its first month, automating about two-thirds of all contacts. That is work equivalent to roughly 700 full-time agents, later 853, and it cut average response time from 15 minutes to under 2. The build cost USD 2-3 million, but Klarna projected a USD 40 million profit improvement in 2024 and had saved USD 60 million by the end of 2025. Support cost per transaction fell 40% over two years, from USD 0.32 to USD 0.19.
Source: Klarna and OpenAI case coverage (CX Dive, Fini Labs, 2024-2025)
The full-automation paradox: cut cost only as far as quality holds
In early 2025 Klarna restored part of its human support capacity after roughly 5% hallucination rates on complex and emotionally charged inquiries pushed customer satisfaction down and raised regulatory concerns around disputes and account handling. Pushing automation too far to save money creates a hidden cost in quality. Sustainable savings come from a human-in-the-loop design that separates what gets handled automatically from what a person reviews.
Source: Klarna's shift in AI strategy (AI Business / CX Dive, 2025)
Curriculum
8 hours (1 day) — what's covered
Finding the leaks
2HIdentify repetitive and manual work by department | Quantify handling time and labor cost | Build an automation priority matrix
Support and document automation
2HAutomate FAQ and inquiry handling | Extract key terms from contracts and reports | Generate standard response drafts
Data and operations automation
2HAutomate data cleanup, aggregation, and conversion | Generate recurring reports | Connect it all with no-code workflows
ROI and quality control
2HHow to calculate savings and ROI | Design human-in-the-loop review | Guardrails against automation failure and quality loss
Key concepts in the curriculum
A quick look at what this course covers.
Finding the leaks
Diagnosing where the money drains away
Workflow automation
Repetitive work running through an automated pipeline
Human-in-the-loop guardrails
People checking the quality automation produces
Learning objectives
- Identify and quantify the repetitive, manual work that leaks cost
- Automate customer support, document processing, and data work with AI
- Build a way to calculate savings and ROI
- Design human-in-the-loop automation that holds quality
What you take away
- A cost-leak and automation priority table by department
- One automated workflow for support or document processing
- Working output from data cleanup and recurring report automation
- A savings and ROI calculation with a quality guardrail checklist
Expected outcomes
- Lower cost per unit of repetitive, manual work
- Headroom to move people onto higher-value work
- Faster handling and shorter waits for customers
- Savings you can prove as ROI rather than assert
How teams put this to use
Automated first response
AI answers repetitive FAQs and inquiries and passes only the complex cases to a person
Document extraction
Pull the key terms out of contracts and reports automatically
Data cleanup and reporting
Run recurring data cleanup and scheduled report writing on their own
Operations automation
Connect a department's repeating tasks into one no-code workflow
Cutting operating costs with AI automation — FAQ
That is not the goal. As Klarna showed, pushing automation too far can degrade quality and raise costs instead. This course points toward handing repetitive work to AI and moving people onto judgment, relationships, and planning, with the weight on human-in-the-loop design that protects quality.
That is exactly why the first module is finding the leaks. We quantify repetitive and manual work by department in handling time and labor cost, then build a priority matrix on impact and difficulty so the best candidates surface first.
No. Support, document, and data automation run on no-code tools and AI prompts, so no development background is needed. The course is designed for operations, customer service, general affairs, and finance roles.
During the course you measure the handling time and labor cost of the work you automate, then calculate the savings and ROI afterward. The result is a number you can report to your executives, not a vague claim of efficiency.
Klarna saw quality fall on roughly 5% of complex inquiries and restored human handling for them. We build that lesson in by drawing a clear line between what gets handled automatically and where a person reviews, so cost and quality hold together.
Cutting operating costs with AI automation — get started
Tell us your goals and headcount, and we'll design the program around them.