Building vs. Buying AI Solutions: What CFOs Need to Consider

The Fundamental Question 

CFOs evaluating AI agents face the build versus buy decision. Understanding requirements, costs, risks, and timelines for each approach prevents misaligned investments and implementation failures. 

The right choice depends on company size, technical capability, timeline needs, and operational requirements. 

What Building Actually Means 

Building AI solutions means assembling internal data science team, developing custom models and workflows, creating proprietary integrations, and maintaining complete technology stack internally. 

This is not configuring purchased software. This is software development from foundation. 

Build Requirements 

Technical team needed: 

• 2-3 data scientists ($150K-$200K each) 

• 1-2 ML engineers ($140K-$180K each) 

• 1 DevOps engineer ($130K-$160K) 

• 1 product manager ($120K-$150K) 

• Annual team cost: $640K-$890K 

Technology stack: 

• ML development platforms 

• Model training infrastructure 

• Production deployment systems 

• Monitoring and logging tools 

• Annual platform cost: $50K-$100K 

Timeline: 

• Hire team: 3-6 months 

• Develop proof of concept: 2-4 months 

• Production development: 4-8 months 

• Testing and refinement: 2-3 months 

• Total: 12-21 months to production 

Total Year 1 investment: $700K-$1.2M 

What Buying Actually Means 

Buying AI solutions means implementing proven platforms through experienced partners who configure technology to company-specific rules and integrate with existing systems. 

Configuration and integration work, not software development. 

Buy Requirements 

External resources: 

• Implementation partner ($25K-$35K) 

• AI platform subscription ($3K-$6K annually) 

• Voice platform subscription ($2K-$4K annually) 

• Year 1 cost: $30K-$45K 

Internal resources: 

• IT liaison (20-30 hours) 

• Business process experts (40-50 hours) 

• Internal time: $5K-$7K 

Timeline: 

• Partner selection: 2-4 weeks 

• Implementation: 8-12 weeks 

• Total: 3-4 months to production 

Total Year 1 investment: $35K-$52K 

Cost Comparison Over 3 Years 

Build Approach 

Year 1: 

• Team salaries: $640K-$890K 

• Platforms and infrastructure: $50K-$100K 

• Recruiting and onboarding: $50K-$75K 

• Total: $740K-$1.065M 

Years 2-3: 

• Team salaries: $640K-$890K annually 

• Platform costs: $50K-$100K annually 

• Annual: $690K-$990K 

3-year total: $2.12M-$3.05M 

Buy Approach 

Year 1: 

• Implementation: $25K-$35K 

• Platforms: $5K-$10K 

• Internal time: $5K-$7K 

• Total: $35K-$52K 

Years 2-3: 

• Platform subscriptions: $5K-$10K annually 

• Oversight and refinement: $3K-$5K annually 

• Annual: $8K-$15K 

3-year total: $51K-$82K 

Cost difference: $2.07M-$2.97M over 3 years 

When Building Makes Sense 

Criteria for Build 

Company size: Enterprise ($500M+ revenue) 

Mid-market companies lack scale to justify build investment. Exception volume insufficient to amortize development cost. 

Technical capability: Existing AI team 

Building makes sense when company already has data science team for other purposes. Incremental cost is lower. 

Unique competitive advantage: Process IS differentiation 

If exception handling approach is proprietary competitive advantage, building protects intellectual property. 

Volume justification: 500+ exceptions monthly 

At this volume, build cost per exception ($350-$500 Year 1, $115-$165 ongoing) becomes comparable to buy. 

Timeline flexibility: 12-18 months acceptable 

Building requires patience. Urgent operational needs eliminate this option. 

Example: Enterprise software company 

• 800 exceptions monthly across support, renewals, collections 

• Existing 6-person data science team 

• Proprietary customer success methodology 

• Build cost amortized across high volume and existing team 

When Buying Makes Sense 

Criteria for Buy 

Company size: Mid-market ($20M-$500M revenue) 

Scale matches buy economics. Implementation cost proportional to operational value. 

Technical capability: Standard IT resources 

No data science expertise needed. IT handles API integration and system coordination. 

Standard processes: Industry-typical exception handling 

Process follows common patterns. No proprietary methodology requiring protection. 

Volume reality: 30-200 exceptions monthly 

Buy cost per exception ($100-$175 Year 1, $25-$50 ongoing) is economically viable. 

Timeline pressure: 3-4 months to value 

Operational need drives implementation urgency. 

Example: Distribution company 

• 80 AR collection exceptions monthly 

• Standard IT team, no AI expertise 

• Industry-typical collections approach 

• Need results within 90 days 

The Hybrid Reality 

What Most Companies Actually Do 

Year 1: Buy proven solution 

• Implement configurable platform 

• Prove concept and ROI 

• Learn operational requirements 

• Cost: $35K-$52K 

Year 2-3: Evaluate build 

• If volume exceeds 200 monthly and growing 

• If process differentiation emerges 

• If platform limitations constrain value 

• Decision to build becomes data-driven 

This approach: 

• Minimizes initial risk 

• Proves value before major investment 

• Builds organizational capability 

• Defers build decision until justified 

Risk Comparison 

Build Risks 

Technology risk: Custom AI may underperform commercial platforms. No proof of concept before significant investment. 

Team risk: Key staff departure creates knowledge gap. Recruiting replacement takes months. 

Maintenance burden: Ongoing development, bug fixes, platform updates require permanent team. 

Opportunity cost: Team focused on exception handling cannot work on other AI initiatives. 

Sunk cost: If approach fails, $700K-$1M+ investment lost. 

Buy Risks 

Vendor dependency: Platform changes or pricing increases affect operations. 

Capability constraints: Platform limitations may prevent desired functionality. 

Competitive exposure: Implementation partner sees your approach, potentially works with competitors. 

Switching cost: Changing vendors requires reimplementation ($15K-$25K). 

Mitigation: Use standard platforms, own business rules, contractual protections, pilot before commitment. 

The CFO Perspective 

Financial Evaluation 

Return on investment: 

Build approach (3 years): 

• Investment: $2.12M-$3.05M 

• Staff time savings: $220K-$330K (assuming 100 exceptions monthly) 

• Working capital: $180K-$300K (if collections) 

• ROI: Negative unless volume exceeds 400 monthly or competitive advantage quantifiable 

Buy approach (3 years): 

• Investment: $51K-$82K 

• Staff time savings: $220K-$330K 

• Working capital: $180K-$300K 

• ROI: 490%-690% over 3 years 

Budget Allocation 

Build: Requires executive approval, board visibility, multi-year commitment 

Buy: Within operational budget authority, minimal board involvement, flexible commitment 

Strategic Considerations 

Build creates capability: Develops internal AI expertise applicable beyond exception handling 

Buy maintains focus: Operational improvement without technology distraction 

Common Misconceptions 

"Building Gives Us Control" 

Reality: Buying from reputable platforms with data portability provides adequate control. Building creates maintenance burden reducing agility. 

"Buying Creates Vendor Lock-In" 

Reality: Using standard platforms (OpenAI, Anthropic, Twilio) with portable business rules minimizes lock-in. Switching implementation partners costs $15K-$25K versus rebuild cost of $700K+. 

"We Have Technical Team, Should Build" 

Reality: Having technical team does not mean building is optimal use of that team. Opportunity cost matters. Could team create more value elsewhere? 

"Building Is Cheaper Long-Term" 

Reality: Only true at enterprise scale (500+ exceptions monthly). For mid-market, ongoing platform costs ($8K-$15K annually) are far less than team costs ($690K-$990K annually). 

The Decision Framework 

Step 1: Volume Assessment 

Under 200 monthly: Buy is only economically rational choice 200-500 monthly: Buy preferred unless other factors strongly favor build 500+ monthly: Build becomes economically viable if other factors align 

Step 2: Technical Capability 

No AI team: Buy is only option Existing AI team with capacity: Build possible, evaluate opportunity cost Existing team at capacity: Buy maintains team focus on higher-value work 

Step 3: Competitive Advantage 

Standard process: Buy appropriate Moderate differentiation: Buy with custom configuration Core competitive advantage: Build may be justified to protect IP 

Step 4: Timeline Needs 

Urgent (3-6 months): Buy required Moderate (6-12 months): Buy preferred Patient (12+ months): Build possible if justified 

Step 5: Risk Tolerance 

Risk-averse: Buy proven solution Risk-tolerant with budget: Build if other factors align 

The Reality 

Mid-market companies (95%+) should buy AI solutions. Economics, timeline, risk profile, and technical requirements all favor configured platforms over custom development. 

Building makes sense for enterprise companies with existing AI teams, proprietary competitive advantage, very high exception volume (500+ monthly), and patient timelines. 

The build versus buy decision is not about capability. It is about economics, risk, and optimal resource allocation. For most mid-market companies, buying provides better ROI, faster results, and lower risk than building. 

About the Author 

This content is published by ERP AI Agent, a consulting practice specializing in AI agents for mid-market ERP exception processes. 

Published: January 2025 Last Updated: January 2025 Reading Time: 8 minutes