Let’s talk numbers.
Not theoretical projections. Not vendor marketing claims. Real ROI from AI automation projects I’ve built and deployed over the past two years.
Because at some point, every AI conversation needs to answer one question: Is this actually worth it?
Contents
The Cost Equation
Every automation project has three cost components:
1. Build Cost
Design, development, integration, testing, and deployment. For the projects I work on, this typically ranges from £15K-100K+ depending on complexity.
2. Operating Cost
AI API costs, hosting, monitoring tools, and maintenance time. Usually £500-3000/month for mid-scale deployments.
3. Opportunity Cost
The value of what your team could do with freed-up time. This is often underestimated and is usually the largest value driver.
Case Study 1: Customer Support Automation
Client: B2B software company, 2000+ support tickets/month
Before:
- 8 support agents handling tier-1 tickets
- Average resolution time: 4.2 hours
- Cost per ticket: £18
- Annual support cost: £432,000
The Build:
- AI agent for ticket classification, response drafting, and common issue resolution
- Integration with help desk, knowledge base, and product telemetry
- Human escalation for complex issues
- Build cost: £45,000
- Timeline: 6 weeks
After (6 months in):
- 73% of tickets resolved autonomously
- Average resolution time: 12 minutes (autonomous) / 2.1 hours (human-handled)
- Team reduced to 3 specialists handling escalations only
- Annual support cost: £108,000
- Operating cost: £1,800/month
ROI Calculation:
- Annual savings: £324,000
- Operating cost: £21,600/year
- Net annual savings: £302,400
- Payback period: 1.8 months
- 3-year ROI: 1,916%
Case Study 2: Sales Intelligence System
Client: Recruitment agency, 15 sales reps
Before:
- Reps spent 40% of time on lead research and outreach
- Average of 12 qualified meetings booked per rep per month
- Lead research accuracy: “inconsistent”
The Build:
- AI system monitoring job boards, LinkedIn, and company news
- Automated lead enrichment from 8+ data sources
- AI-generated personalized outreach sequences
- Calendar integration for automated booking
- Build cost: £62,000
- Timeline: 8 weeks
After (12 months in):
- Reps spend 10% of time on research (AI handles 75%)
- Average of 47 qualified meetings per rep per month
- Lead quality score improved 34%
- Rep time reallocated to closing deals
ROI Calculation:
- Additional meetings per year: 6,300
- Conversion rate: 15%
- Average deal value: £8,500
- Additional revenue influenced: £8M+
- Operating cost: £2,400/month
- Payback period: 3 weeks
Case Study 3: Finance Operations
Client: E-commerce company, £12M annual revenue
Before:
- 2 full-time finance staff on invoice processing
- 500+ invoices/month across 200 suppliers
- 60 hours/month on reconciliation
- Error rate: 3.2%
- Payment delays due to manual approval bottlenecks
The Build:
- AI invoice processing with OCR and validation
- Automated 3-way matching (PO, receipt, invoice)
- Slack-based approval workflows
- Exception handling with human review queue
- Build cost: £38,000
- Timeline: 5 weeks
After:
- 91% of invoices processed without human touch
- 4 hours/month on reconciliation (exceptions only)
- Error rate: 0.3%
- Average days to payment: reduced from 18 to 4
- Early payment discounts captured: £45K/year
ROI Calculation:
- Labor savings: £67,200/year (1.4 FTE reallocated)
- Early payment discounts: £45,000/year
- Error reduction value: £12,000/year
- Total annual value: £124,200
- Operating cost: £1,200/month
- Payback period: 4.1 months
The Hidden ROI
These numbers capture direct savings. But every project delivered additional value that’s harder to quantify:
Speed as Competitive Advantage
The recruitment agency now responds to new job postings within hours, not days. They’re first in line when companies are hiring—before competitors even know the opportunity exists.
Scalability Without Hiring
The e-commerce company 3x’d their supplier base without adding finance staff. The system scales linearly; headcount doesn’t have to.
Employee Satisfaction
Support agents who used to handle repetitive tickets now handle interesting problems. Finance staff who used to chase invoices now do analysis. Talent retention improved across all three companies.
Data Quality
Automated systems capture clean, consistent data. This enables analytics and insights that weren’t possible with inconsistent manual entry.
When Automation Doesn’t Make Sense
Not every process should be automated. Skip automation when:
- Volume is too low — Automating something that happens 10x/month rarely pays off
- Process is unstable — Don’t automate what you’re still figuring out
- Human judgment is core — Some decisions genuinely need human context
- Integration complexity is too high — If 80% of the project is fighting legacy systems, reconsider
The Investment Framework
When evaluating AI automation investments, I use this framework:
Quick wins (Under £30K, payback under 6 months):
- Document processing and data extraction
- Email classification and routing
- Report generation
- Basic customer inquiries
Strategic investments (£30-100K, payback 6-18 months):
- End-to-end workflow automation
- Multi-system integration
- Customer-facing AI agents
- Predictive systems
Transformational projects (£100K+, payback 12-36 months):
- Core process reinvention
- AI-native product features
- Enterprise-wide platforms
Starting the Conversation
If you’re evaluating AI automation for your business, start with these questions:
- What processes consume the most person-hours?
- Where do errors cost you the most?
- What could your team do with 30% more time?
- What competitive advantage would speed create?
The answers usually reveal where the highest-ROI opportunities are hiding.
Want to calculate the potential ROI of automation for your specific workflows? Let’s run the numbers together.