Build vs Buy for Enterprise AI (2025): A U.S. Market Decision Framework for VPs of AI Product

Understanding the Target Audience

The target audience for this framework includes VPs of AI Product within U.S. enterprises. These executives are focused on integrating AI capabilities effectively into their organizations while navigating complex regulatory environments.

Pain Points

• Pressure for clear ROI from CFOs
• Need for evidence of risk oversight for boards
• Growing regulatory scrutiny

Goals

• Implement AI solutions that drive competitive advantage
• Ensure compliance with existing regulations
• Optimize decision-making processes regarding AI capabilities

Interests

• Strategies for effective risk management
• Best practices in AI deployment
• Cost-benefit analysis of building versus buying AI solutions

Communication Preferences

VPs of AI prefer data-driven insights, structured frameworks, and clear recommendations that can be presented to boards and stakeholders. They favor concise reports and practical examples over marketing jargon.

The U.S. Context: Regulatory and Market Anchors

As the landscape of enterprise AI evolves, U.S. companies face unique regulatory challenges compared to their European counterparts. Key references for U.S. enterprises include:

• NIST AI Risk Management Framework (RMF)
• NIST AI 600-1 (Generative AI Profile)
• Banking and finance regulations (SR 11-7, FDIC/FFIEC guidance)
• Healthcare oversight (HIPAA, FDA regulations)
• FTC enforcement on deceptive practices
• SEC disclosure expectations for AI-related risks

In this environment, the Build vs Buy decision must be evidence-based and communicated effectively to boards and regulators.

Build, Buy, and Blend: The Executive Portfolio View

When making strategic decisions regarding AI capabilities, consider the following:

• Build when capabilities underpin competitive advantage or involve sensitive regulatory data.
• Buy when use cases are commoditized and speed-to-value is critical.
• Blend for most cases, combining vendor platforms with custom last-mile work.

A 10-Dimension Framework for Scoring Build vs Buy

To facilitate informed decision-making, utilize a scoring model based on ten key dimensions:

Utilizing this scoring model allows executives to quantify build versus buy decisions and prepares them for board reporting.

Modeling TCO on a 3-Year Horizon

To ensure accurate comparisons, evaluate total cost of ownership (TCO) over a three-year horizon for both build and buy scenarios:

Build TCO (36 months)

• Internal engineering (AI platform engineering, ML engineering, SRE, security)
• Cloud compute (training + inference with GPUs/CPUs, caching layers, autoscaling)
• Data pipelines (ETL, labeling, continuous evaluation, red-teaming)
• Observability (vector stores, evaluation datasets, monitoring pipelines)
• Compliance (NIST RMF audit preparation, SOC 2 readiness, HIPAA reviews)
• Egress fees and regional replication costs

Buy TCO (36 months)

• Subscription/license baseline + seats
• Usage fees (tokens, calls, context length)
• Integration/change management uplift
• Add-ons (proprietary retrieval-augmented generation, evaluation, safety layers)
• Vendor compliance uplift (SOC 2, HIPAA business associate agreements, NIST mapping deliverables)
• Migration costs at exit, especially egress fees

When to Build (U.S. Context)

Best-fit scenarios for building in-house include:

• Strategic intellectual property: Underwriting logic, risk scoring, financial anomaly detection.
• Data control: Ensuring sensitive data does not pass through external vendor pipelines.
• Custom integration: AI must seamlessly fit into existing systems that vendors may not effectively manage.

However, risks include continuous compliance overhead, talent scarcity, and potential overspending on hidden costs.

When to Buy (U.S. Context)

Best-fit scenarios for purchasing vendor solutions include:

• Commodity tasks: Note-taking, Q&A, ticket deflection.
• Speed: Deployment required within a fiscal quarter.
• Vendor-provided compliance: Vendors aligning with NIST RMF, SOC 2, and HIPAA.

Risks involve vendor lock-in, usage volatility, and potential exit costs. Always negotiate explicit exit clauses in contracts.

The Blended Operating Model

For many U.S. enterprises, a blended approach is the default strategy:

• Buy platform capabilities for governance, audit trails, and compliance guarantees.
• Build custom components for last-mile integration, evaluation datasets, and testing.

This model allows companies to scale while retaining control over sensitive intellectual property and ensuring regulatory compliance.

Due Diligence Checklist for VP of AI

If Buying Vendors:

• Assurance: ISO/IEC 42001 + SOC 2 + mapping to NIST RMF
• Data Management: HIPAA BAA, retention and minimization terms
• Exit: Explicit portability contract language
• SLAs: Latency/throughput targets, U.S. data residency guarantees

If Building In-House:

• Governance: Operate under NIST AI RMF categories
• Architecture: Multi-model orchestration to avoid lock-in
• People: Dedicated LLMOps team with evaluation experts
• Cost Controls: Request batching and retrieval optimization

Decision Tree for Executives

Use the following questions to guide decision-making:

• Does the capability drive a competitive advantage within 12–24 months?
• Do you have governance maturity in-house?
• Would a vendor’s compliance artifacts satisfy regulators faster?
• Does 3-year TCO favor internal amortization versus subscription costs?

These queries can help determine whether to build, buy, or blend solutions.

Example: U.S. Healthcare Insurer

Use Case: Automated claim review and explanation of benefits

• Strategic differentiation: Moderate—efficiency compared to competitor baseline
• Data sensitivity: PHI, subject to HIPAA
• Integration: Tight coupling with legacy claim processing systems
• Time-to-value: 6-month tolerance
• Internal team: Mature ML pipeline, limited LLMOps experience

Outcome: Blend. Utilize a vendor platform with HIPAA BAA and SOC 2 Type II assurance for foundational capabilities, while building custom retrieval layers and evaluation datasets.

Takeaways for VPs of AI

• Leverage a scored, weighted framework for evaluating AI use cases.
• Expect blended estates to dominate, maintaining control over last-mile integration.
• Align strategies with NIST AI RMF, SOC 2, and U.S. sector-specific laws.
• Always model 3-year TCO and include exit clauses in contracts.

In 2025, the Build vs Buy decision should focus on strategic allocation, governance evidence, and execution discipline. By operationalizing this framework, VPs of AI can accelerate deployment while building resilience against regulatory scrutiny.