CASE STUDIES / RESULTS
Real AI Case Studies That Reduce Pain and Create Capacity
These examples show how businesses are using AI to reduce repetitive work, improve response, create visibility, and open new revenue opportunities.
Pain Identified
We find the leaks, bottlenecks, and friction slowing your business.
AI / Automation Applied
We implement the right automation, integrations, and systems to remove the drag.
Business Outcome
You get more capacity, better results, and new opportunities for growth.
Important Note About These Case Studies
Some examples are public case studies from well-known companies and others are published implementation case studies. They are shown to demonstrate how AI can create business value, not to claim these results as OutSource AI client wins.
1
PUBLIC CASE STUDY
IKEA Turned AI Support Efficiency Into New Revenue Capacity
Retail / Customer Service / Interior Design
The Pain: High volumes of repetitive customer service inquiries consumed support capacity and limited how people could be used for higher-value work.
The AI Move: IKEA used AI-led customer service tools to absorb common support questions and reduce repetitive workload.
The Outcome: Employees were retrained into higher-value customer-facing roles, including remote interior design support. Public reporting highlighted a major new revenue channel.
Why It Matters: AI is not only about cost reduction. It can free people from repetitive work so they can move into roles that generate more customer value and more revenue.
Before
Support teams buried in repetitive inquiries
After
More capacity for complex support and design services
8,500
employees retrained
€1.3B
remote design revenue
2
PUBLIC CASE STUDY
Klarna Used AI To Handle Repetitive Customer Service At Scale
Fintech / Customer Support
The Pain: Customer service teams were dealing with very high volumes of repetitive questions across markets and languages.
The AI Move: An AI assistant was used to resolve a large share of common customer support conversations.
The Outcome: Faster resolutions, lower repeat inquiry volume, and stronger operating leverage.
Why It Matters: AI customer support creates value by speeding up answers, improving consistency, and freeing humans for more complex issues.
Before
Longer resolution times
After
Faster issue resolution
2.3M
conversations handled
700
FTE equivalent workload
11 min →
under 2 min
3
PUBLIC CASE STUDY
Salesforce Used AI To Re-Engage Leads And Book Meetings
Sales / Lead Qualification / CRM
The Pain: Large numbers of leads were never worked fast enough, leaving money on the table and opportunities untouched.
The AI Move: An AI sales agent autonomously followed up on leads, re-engaged overlooked opportunities, and booked meetings.
The Outcome: More leads were reactivated, more replies were generated, and meetings were booked automatically.
Why It Matters: Many companies do not need more leads first. They need better follow-up on the leads they already have.
Before
Ignored and stale leads
After
AI follow-up and autonomous meeting booking
68,000
leads re-engaged
156,000
emails sent
800
meetings booked
4
IMPLEMENTATION CASE STUDY
A Boutique Law Firm Cut Intake Time With Structured AI Intake
Legal Services / Client Intake
The Pain: Email-based intake created delays, missing documents, and too much manual admin before new matters could even start.
The AI Move: Intake was moved into a structured client portal with automated collection, cleaner handoffs, and standardized steps.
The Outcome: Matter opening became faster, new-file admin dropped sharply, and the client intake experience became more consistent.
Why It Matters: Many businesses lose momentum after the sale because forms, documents, reminders, and next steps are still handled manually.
Before
1 to 2 weeks with email-driven intake
After
Under 5 days with structured intake portal
60%
faster matter opening
80%
less new-file admin
70%
one-session intake completion
5
IMPLEMENTATION CASE STUDY
AI Recruiting Support Reduced HR Onboarding Time At Scale
HR / Recruiting / Enterprise Operations
The Pain: High-volume recruiting and onboarding created delays, manual follow-up, and too much pressure on HR teams.
The AI Move: A 24/7 AI recruiting assistant handled application flow, scheduling, follow-up, and onboarding support.
The Outcome: Sourcing moved faster, onboarding time dropped sharply, and interview activity increased.
Why It Matters: AI can create real capacity in HR and operations by taking over repetitive communication and process-heavy workflow steps.
Before
Manual recruiting and onboarding delays
After
24/7 AI support and faster HR throughput
30%
lower sourcing time
75%
lower onboarding time
3x
more interviews scheduled
6
IMPLEMENTATION CASE STUDY
Real-Time Dashboards Replaced Manual Spreadsheet Reporting
Real Estate / Leadership / Business Intelligence
The Pain: Leadership relied on too many spreadsheets and disconnected reports, making it hard to see what was happening in real time.
The AI Move: Reporting was centralized into automated dashboards and a clearer business intelligence system.
The Outcome: Reporting became automated, visibility improved, and leadership gained faster decision-making support.
Why It Matters: You cannot fix what you cannot clearly see. AI and automation become more valuable when leaders finally get one clear view of the business.
Before
40+ manual Excel reports
After
Centralized automated dashboards
$104K
annual savings
12+
dashboards
100%
automated reporting
What These Case Studies Have In Common
Repetitive Work
AI handles repetitive tasks so teams can focus on higher-value work.
Slow Response
Automation reduces lag in lead handling, support, and follow-up.
Disconnected Systems
Connected tools improve visibility and reduce duplicate work.
Capacity Creation
The best AI outcomes create room for growth, not just efficiency.
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