3. Build vs Buy vs Embed

The Build vs Buy vs Embed decision determines whether to develop custom AI capabilities, procure standalone AI solutions, or leverage AI embedded in your existing enterprise platforms. This is one of the most strategic decisions in AI adoption—it affects cost, time to value, control, maintenance burden, and competitive positioning. The wrong choice leads to wasted investment, technical debt, or missed opportunities.

This isn’t a binary choice. Most organizations pursue a portfolio approach, building where differentiation matters, buying for established use cases, and embedding where platform integration delivers value. The key is making intentional decisions for each use case based on strategic value and capability fit.

The Three Approaches

Build (Custom Development)

What it means: Developing proprietary AI models or applications, either from scratch or by fine-tuning open-source models.

When to build:

• Unique business problem with no existing solution
• Proprietary data provides competitive advantage
• IP protection is critical
• Specialized performance requirements beyond vendor offerings
• Long-term strategic capability you want to own

Advantages:

• Complete control over functionality and data
• Competitive differentiation
• No vendor lock-in
• Can optimize precisely for your needs

Challenges:

• Significant time investment (6-18+ months)
• High upfront and ongoing costs
• Requires specialized talent (data scientists, ML engineers)
• Responsibility for security, compliance, maintenance
• Risk of building what vendors could provide cheaper

Cost profile: High upfront investment, moderate ongoing costs for infrastructure and talent.

Buy (Procurement)

What it means: Purchasing standalone AI solutions from vendors—either general-purpose (ChatGPT, Claude) or specialized products.

When to buy:

• Established use case with proven vendor solutions
• Speed to market is priority
• Lack internal AI expertise
• Use case is not core competitive differentiator
• Want vendor to handle updates and maintenance

Advantages:

• Fast deployment (days to weeks)
• Proven capability (reduced risk)
• Vendor handles maintenance and improvements
• Access to cutting-edge models without research investment

Challenges:

• Ongoing licensing/usage costs
• Limited customization
• Vendor lock-in risk
• Data sharing with third party (unless self-hosted option)
• Less control over roadmap and features

Cost profile: Low upfront investment, ongoing subscription or usage-based costs.

Embed (Platform-Native AI)

What it means: Using AI capabilities built into enterprise platforms you already use (Salesforce Einstein, Microsoft Copilot, SAP Joule).

When to embed:

• Use cases closely tied to existing platform workflows
• Want seamless user experience in familiar tools
• Prefer to consolidate vendors
• Need quick wins without new integrations
• Platform AI meets capability requirements

Advantages:

• Native integration (minimal setup)
• Single vendor relationship
• Often included in existing licenses (or lower incremental cost)
• Familiar user interface
• Vendor maintains compatibility

Challenges:

• Locked to platform ecosystem
• Limited flexibility and customization
• May lag standalone AI providers in capability
• Switching costs if you change platforms
• Feature availability varies by platform tier

Cost profile: Incremental licensing cost (often lower than standalone), no integration investment.

Decision Framework

Use this framework to evaluate each use case:

Step 1: Strategic Value Assessment

Is this use case a competitive differentiator?

• High strategic value → Lean toward Build
• Medium strategic value → Buy or Embed
• Low strategic value (operational efficiency) → Buy or Embed

Does proprietary data provide an advantage?

• Yes, and we want to protect it → Build (with self-hosting)
• Yes, but vendor solutions work → Buy with data controls
• No → Buy or Embed

Step 2: Capability and Maturity Analysis

Does a proven solution exist?

• Yes, and it meets requirements → Buy or Embed
• Partially, needs customization → Buy + configure, or Build
• No established solution → Build (or wait for market maturity)

How mature is the use case?

• Emerging/experimental → Buy (limit investment risk)
• Established in industry → Buy or Embed
• Core to business model → Consider Build

Step 3: Organizational Capability Assessment

Do we have AI development expertise?

• Yes, proven track record → Build is feasible
• Some capability, growing → Buy now, Build selectively later
• No/limited → Buy or Embed

Can we maintain AI systems long-term?

• Yes, dedicated team → Build is sustainable
• Limited capacity → Buy or Embed preferred

Step 4: Time and Budget Constraints

How quickly do we need results?

• Immediate (weeks) → Buy or Embed
• Short-term (3-6 months) → Buy
• Strategic investment (6-18+ months) → Build possible

What’s our budget profile?

• Limited capital, flexible opex → Buy (subscription)
• Capital available, want to minimize opex → Build or self-hosted
• Constrained overall → Embed (leverage existing platforms)

Common Patterns

Pattern 1: Buy Fast, Build Later

Start with vendor solutions for speed and learning, then build custom solutions for strategic differentiators once you understand the domain.

Example: Use ChatGPT API for initial content generation experiments, then build a custom fine-tuned model for proprietary business documents.

Pattern 2: Embed for Productivity, Buy for Innovation

Use platform-embedded AI (Microsoft Copilot, Salesforce Einstein) for user productivity within existing tools, buy specialized solutions for new capabilities.

Example: Deploy Microsoft Copilot for email/document productivity, buy specialized legal AI for contract analysis.

Pattern 3: Buy Generic, Build Differentiation

Procure solutions for generic use cases (customer service, internal chatbots), build custom models for competitive advantage areas.

Example: Use vendor chatbot for general inquiries, build custom AI for proprietary pricing and recommendation engine.

Pattern 4: Hybrid Architecture

Deploy vendor APIs for general use, self-host open-source models for sensitive data processing.

Example: Use Claude API for internal documentation, run Llama on-premise for processing customer PII.

Decision Matrix

Next Steps

Once you’ve determined your Build vs Buy vs Embed approach, the next sections guide you through:

• Provider Selection Criteria (Section 4) — If buying, how to compare vendors
• Deployment Models (Section 5) — Cloud vs self-hosted decisions
• Total Cost of Ownership (Section 6) — Understanding true costs of each approach