Buy, Build, or Partner?
What TPRM & AI Leaders Need to Know?
Artificial Intelligence (AI) is a business imperative. From content generation to agentic automation to analytics and decision-making, AI is rapidly transforming industries. However, acquiring AI isn’t a one-time purchase — it’s an ongoing process or ecosystem of integrating data, models, infrastructure, and governance into business operations. Organizations must carefully evaluate how they obtain AI capabilities—whether by buying, building, or partnering.
For Third-Party Risk Management (TPRM) professionals, the growing reliance on external AI solutions introduces vendor risks, compliance challenges, and data security concerns. Meanwhile, AI strategists must balance speed, cost, control, and innovation when selecting an acquisition model.
Making the Right AI Acquisition Decision
- If speed matters most → Buy an AI solution.
- If control & differentiation are key → Build in-house AI.
- If customization & efficiency are needed → Partner with AI experts.
AI Strategy & TPRM Must Align
AI acquisition isn’t just an IT decision — it’s a strategic risk & business decision. A hybrid approach is emerging as the dominant trend—organizations buy AI for quick wins, partner for customization, and selectively build when differentiation is critical.
How Organizations Are Acquiring AI Today?
Based on existing software footprints and AI adoption surveys, a clear trend emerges:
- 50-70% of organizations choose to BUY AI solutions for speed, lower costs, and vendor support.
- 20-35% prefer PARTNERING to develop customized AI solutions without building from scratch.
- Only 15-25% fully BUILD AI in-house, typically companies with strong AI expertise and proprietary data.
A hybrid approach is emerging as the dominant trend—organizations buy AI for quick wins, partner for customization, and selectively build when differentiation is critical.
BUY: Fastest Route, But At What Cost?
Organizations purchase or license prebuilt AI solutions from vendors, such as:
- Off-the-Shelf AI Software (On-Prem or Packaged) – Traditional AI software deployed within an organization’s infrastructure.
- Software-as-a-Service AI (SaaS) – Cloud-based AI platforms offering subscription-based access (e.g., Microsoft Copilot, Salesforce Einstein).
- Cloud AI API Integration (Third-Party AI as a Service) – AI capabilities accessed via APIs from providers like OpenAI, Claude, AWS, or Google.
- Embedded AI Features in Enterprise Software – AI-powered enhancements within existing enterprise applications (e.g., AI-enhanced cybersecurity, HR, or ERP systems).
- Prebuilt AI Applications (Task-Specific AI Software) – AI solutions designed for specialized business needs, such as fraud detection or document processing.
- AI-Powered Automation Tools (e.g., RPA with AI) – AI-enhanced automation tools that optimize workflows and reduce manual labor.
- Acquisition of an AI Company for Its Technology/IP – Buying an AI company outright to own its models, technology, and talent.
Key Considerations for TPRM & AI Strategy
□ Risk of Vendor Lock-in – Can you migrate away later, or will your organization become dependent on a single AI provider?
□ Data Privacy & Compliance – Where is your data stored and processed? Are there clear terms regarding data ownership and model training?
□ Regulatory and Legal Risks – Does the AI solution comply with GDPR, CCPA, or industry-specific laws?
□ Customization Limits – Does the AI allow meaningful configuration, or are you constrained by vendor-defined capabilities?
□ Hidden and Scaling Costs – Are there unpredictable costs based on usage?
□ Security & Reliability – What safeguards are in place against AI-generated errors, hallucinations, or security vulnerabilities?
BUILD: Full Control, But High Investment
Organizations develop AI models in-house, such as:
- Train a Foundation Model from Scratch – Creating proprietary AI models from raw datasets, requiring extensive R&D (e.g., BloombergGPT).
- Fine-Tune an Open-Source Model – Customizing open-source models like Llama 2 or Mistral to fit business-specific needs.
- Self-Hosted Pre-Trained Model (Inference Only) – Deploying and running a pre-trained AI model internally.
- Develop Proprietary AI Models Internally – Building AI-powered solutions from the ground up.
- Build AI-Powered Applications from Scratch – Developing AI-driven software solutions tailored to business processes.
- In-House Customization of Open-Source AI Frameworks – Modifying existing open-source AI frameworks for enterprise use.
Key Considerations for TPRM & AI Strategy
□ Development and Maintenance Costs – Do you have the budget for AI talent and infrastructure?
□ Time-to-Value – AI development takes months or years—can your business afford the wait?
□ Security & Compliance – Does your team have the skills to manage AI security, data protection, and regulatory requirements?
□ Ongoing Model Training & Updates – Who will update the AI over time to prevent performance degradation?
□ ROI Justification – Will the investment in AI create a sustainable competitive advantage, or would a vendor solution provide similar value faster?
PARTNER: Balance of Customization & Speed
Organizations collaborate with external AI vendors, research labs, or consulting firms to co-develop AI solutions, including:
- Custom AI Development via an AI Consultancy or Agency – Engaging AI experts to build tailored AI solutions.
- Strategic AI Alliance or Co-Development with a Vendor – Partnering with tech firms to develop AI-driven innovations.
- Managed AI Services (AI as a Managed Offering) – Outsourcing AI model management and deployment to a trusted partner.
- Model Hosting on Cloud AI Platforms (Managed Fine-Tuning) – Deploying custom fine-tuned AI models on cloud platforms like AWS, Azure, or Google Cloud.
- Hybrid AI Approach (Self-Fine-Tuning + API Access) – Combining fine-tuned internal AI models with external API capabilities.
- Joint Venture with AI Technology Providers – Co-investing in AI initiatives for shared benefits.
- AI Research Collaboration with Universities or AI Labs – Partnering with academic institutions for cutting-edge innovation.
Key Considerations for TPRM & AI Strategy
□ Who Owns the IP? – Will your organization retain full ownership of the AI, or does your partner maintain rights?
□ Customization vs. Standardization – Will the AI solution be tailored to your business, or is it a standardized offering?
□ Compliance & Regulatory Adherence – Is the partner’s AI compliant with data privacy laws and security best practices?
□ Flexibility to Transition – If needed, can your organization take over AI operations from the partner in the future?
Recommendations for TPRM Teams
- Strengthen Vendor Due Diligence – Assess AI provider compliance, security, and IP risks.
- Develop AI Risk & Compliance Frameworks – Implement AI-specific risk controls and regulatory monitoring.
- Enhance Cybersecurity Protections – Require vendors to implement encryption, model security, and access controls.
- Negotiate Stronger AI SLAs & Contracts – Define AI model accuracy guarantees, data usage policies, and exit strategies.
- Implement Continuous AI Risk Monitoring – Track AI performance for bias, drift, and security vulnerabilities.
How is your organization managing AI risks?
Drop your thoughts in the comments!
Contact Information
For more information contact me:
Reza Kopaee
