AI Pilot Program Pricing

Introduction

AI pilot programs provide critical validation before major implementation investments. These proof-of-concept projects test technical feasibility, validate business value, and de-risk full-scale deployments. Understanding pilot pricing helps organizations make smart go/no-go decisions.

What is an AI Pilot Program?

Purpose

• Validate technical feasibility with real data
• Demonstrate business value with measurable results
• Test organizational readiness and adoption
• Refine requirements before full implementation
• Build internal capabilities and confidence
• De-risk major investment decisions

Typical Characteristics

• Duration: 8-16 weeks
• Scope: Single use case, limited users
• Investment: 20-30% of full implementation cost
• Users: 10-50 pilot participants
• Data: Subset of production data
• Infrastructure: Development/test environment

Pilot Pricing by Organization Size

Small Business Pilot (< 100 employees)

Investment: SGD $75,000-$150,000
Timeline: 8-12 weeks
Use case: Single department, 10-20 users

Scope example - Customer Service Chatbot:

• Design & planning: 2 weeks (SGD $15,000)
• Development: 4 weeks (SGD $35,000)
• Testing with 15 users: 2 weeks (SGD $12,000)
• Evaluation & recommendations: 1 week (SGD $8,000)
• Platform costs: SGD $5,000 Total: SGD $75,000

What's included:

• Use case refinement and requirements
• Data preparation (limited dataset)
• Model development and training
• Basic integration (1-2 systems)
• User interface/experience design
• Pilot user training (10-20 people)
• Performance measurement setup
• Success evaluation and recommendations

What's NOT included:

• Production infrastructure
• Enterprise security features
• Full system integration
• Organization-wide training
• Ongoing support and maintenance

Mid-Market Pilot (100-1,000 employees)

Investment: SGD $150,000-$300,000
Timeline: 10-14 weeks
Use case: Cross-functional, 20-40 users

Scope example - Predictive Maintenance:

• Discovery & planning: 2 weeks (SGD $25,000)
• Data pipeline development: 3 weeks (SGD $45,000)
• Model development: 4 weeks (SGD $65,000)
• Integration with CMMS: 2 weeks (SGD $30,000)
• Pilot deployment (2 production lines): 2 weeks (SGD $35,000)
• Monitoring & evaluation: 2 weeks (SGD $25,000)
• Sensors & edge devices: SGD $20,000
• Platform & infrastructure: SGD $15,000 Total: SGD $260,000

What's included:

• Detailed requirements definition
• Data quality assessment and preparation
• Feature engineering
• Model development and validation
• Integration with 2-3 core systems
• Pilot infrastructure setup
• User training (20-40 participants)
• Success metrics dashboard
• ROI analysis and scale-up plan

Enterprise Pilot (1,000+ employees)

Investment: SGD $300,000-$450,000
Timeline: 12-16 weeks
Use case: Multi-location or complex use case, 30-50 users

Scope example - Fraud Detection System:

• Enterprise planning: 3 weeks (SGD $55,000)
• Data architecture: 2 weeks (SGD $40,000)
• Model development (multiple algorithms): 5 weeks (SGD $110,000)
• Integration (transaction systems, case management): 3 weeks (SGD $75,000)
• Pilot deployment (single region): 2 weeks (SGD $50,000)
• Evaluation & business case: 2 weeks (SGD $35,000)
• Compliance validation: 1 week (SGD $25,000)
• Platform & infrastructure: SGD $35,000
• Security & compliance tools: SGD $25,000 Total: SGD $450,000

What's included:

• Executive stakeholder alignment
• Comprehensive data assessment
• Advanced model development
• Integration with 3-5 enterprise systems
• Pilot infrastructure (secure, compliant)
• Compliance and security validation
• Extensive pilot user training
• Performance dashboard and reporting
• Detailed scale-up roadmap and business case

Cost Drivers

1. Technical Complexity

Simple (1.0x baseline):

• Structured data, standard algorithms
• Pre-built platform capabilities
• Limited customization needed

Moderate (1.5-2x baseline):

• Multiple data sources
• Custom feature engineering
• Moderate algorithm tuning

Complex (2.5-3.5x baseline):

• Unstructured data (images, text, video)
• Novel algorithms or deep learning
• Significant custom development

2. Data Readiness

Good data (1.0x baseline):

• Clean, accessible, well-structured
• Sufficient historical examples
• Good coverage and quality

Fair data (1.3-1.7x):

• Some quality issues
• Gaps requiring remediation
• Limited historical data

Poor data (2.0-3.0x):

• Significant quality problems
• Extensive cleaning required
• Missing critical data elements

3. Integration Complexity

Minimal (SGD $10,000-$25,000):

• 1-2 modern systems with APIs
• Standard data formats

Moderate (SGD $30,000-$75,000):

• 2-4 systems, mixed modern/legacy
• Some custom integration work

Complex (SGD $80,000-$150,000):

• 4+ systems including legacy
• Custom integration layers required
• Real-time data requirements

4. User Count & Geography

Single location, 10-20 users: Baseline Multiple locations, 20-40 users: +20-30% Multi-region, 40-50 users: +40-60%

Pilot Phases & Timeline

Phase 1: Planning (Weeks 1-2)

Activities:

• Use case refinement
• Success criteria definition
• Data assessment
• Technical approach design
• Project planning

Deliverables:

• Pilot plan document
• Success metrics framework
• Data requirements
• Technical architecture

Cost: 10-15% of pilot budget

Phase 2: Development (Weeks 3-8)

Activities:

• Data preparation and cleaning
• Feature engineering
• Model development
• Integration development
• Testing and validation

Deliverables:

• Trained AI model(s)
• Integrated pilot system
• Test results and validation
• User interface

Cost: 50-60% of pilot budget

Phase 3: Pilot Deployment (Weeks 9-12)

Activities:

• Pilot user training
• System deployment (limited scope)
• Monitoring and support
• Data collection
• Issue resolution

Deliverables:

• Operational pilot system
• Trained pilot users
• Performance data
• Issue log and resolutions

Cost: 20-25% of pilot budget

Phase 4: Evaluation (Weeks 13-14)

Activities:

• Results analysis
• ROI calculation
• Lessons learned documentation
• Scale-up recommendations
• Business case development

Deliverables:

• Pilot results report
• ROI analysis
• Scale-up plan
• Go/no-go recommendation

Cost: 10-15% of pilot budget

Success Metrics

Technical Metrics

• Accuracy: Model performance vs. baseline
• Reliability: System uptime and availability
• Speed: Response time and latency
• Scalability: Performance under load

Thresholds for success:

• 15-25% improvement over baseline
• 95%+ uptime during pilot
• Sub-second response for most queries
• Linear scaling demonstrated

Business Metrics

• Efficiency: Time saved per transaction/process
• Quality: Error rate reduction
• Cost: Operational cost savings
• Revenue: Revenue impact (if applicable)

Thresholds for success:

• 20-40% efficiency improvement
• 25-50% error reduction
• Positive ROI projection for scale
• Measurable revenue lift (if revenue use case)

User Adoption Metrics

• Usage: Active user rate
• Satisfaction: User feedback scores
• Confidence: Trust in AI recommendations
• Resistance: Identified barriers

Thresholds for success:

• 70%+ of pilot users actively engaged
• 4+ out of 5 satisfaction rating
• 60%+ following AI recommendations
• No insurmountable adoption barriers

Scaling Decisions

Go Decision Criteria

Must achieve ALL of:

1. Technical feasibility proven
2. Positive ROI projected (typically 3-5x)
3. User adoption demonstrated
4. No significant technical blockers
5. Organizational readiness confirmed

Scaling investment:

• Small business: 3-4x pilot cost for full deployment
• Mid-market: 3.5-5x pilot cost
• Enterprise: 4-6x pilot cost

Example:

• Pilot: SGD $180,000
• Full implementation: SGD $720,000 (4x)
• Total program: SGD $900,000

No-Go Decision Criteria

Any of these RED FLAGS:

• Technical performance below targets
• ROI projection negative or marginal (<2x)
• User resistance insurmountable
• Data quality issues unfixable
• Integration complexity prohibitive

Value of no-go:

• Avoided SGD $500,000-$2,000,000 in failed implementation
• Pilot cost: SGD $150,000-$300,000
• ROI: 3-7x on avoided waste

Modify & Retry Decision

Consider when:

• Results mixed but promising
• Issues identified but solvable
• Scope adjustments could improve outcomes
• Pilot revealed better use case

Additional investment: SGD $50,000-$150,000 for pilot iteration

Common Pilot Mistakes

1. Scope Too Ambitious

Mistake: Trying to prove multiple use cases in pilot Impact: Diluted focus, inconclusive results Fix: Single use case, limited users, focused scope

2. Unrealistic Timeline

Mistake: Expecting full results in 4-6 weeks Impact: Rushed development, poor quality Fix: Realistic 10-14 week timeline for meaningful validation

3. Using Poor Quality Data

Mistake: Piloting with unrepresentative data Impact: Results don't translate to production Fix: Use real production data subset, even if requires cleanup

4. Insufficient User Involvement

Mistake: Technical proof without real user testing Impact: Missing usability and adoption issues Fix: 10-50 real users actively using system for 3-4 weeks

5. No Clear Success Criteria

Mistake: Vague goals like "see if it works" Impact: Can't make objective go/no-go decision Fix: Quantitative targets defined upfront

Conclusion

AI pilot programs are smart investments costing SGD $75,000-$450,000 to validate concepts before full-scale commitments. Key success factors:

1. Right-size scope: Single use case, 10-50 users, 10-14 weeks
2. Clear success criteria: Quantitative technical, business, and adoption metrics
3. Real-world testing: Production data subset, actual users, meaningful duration
4. Honest evaluation: Objective go/no-go decision based on results
5. Learn and adapt: Capture lessons for full implementation

Organizations using pilots reduce full implementation risk by 60-75% and achieve 2.1x higher success rates (75% vs 35%).