🤖 Build, Borrow, or Partner? What is your AI strategy?
Should you build custom solutions, borrow third-party tools, or partner with experts in 2025?
In the race to adopt AI, organisations are faced with a strategic choice: should they build custom solutions, borrow third-party tools, or partner with experts?
Each path offers unique opportunities and challenges, and the right choice depends on your business goals, technical capabilities, and timeline.
Here, we explore these options in depth to help you craft a winning AI strategy.
🏗️ Understanding the AI Implementation Stack
Before diving into the strategies, it’s essential to understand the core components of the AI stack:
Solution Types:
Custom-built systems
Third-party platforms
Consultant-led implementations
Hybrid approaches
Key Components:
Foundation Models: GPT-4, Claude, Llama 2
Development Frameworks: LangChain, PyTorch
Infrastructure: Cloud services, GPUs
Expertise: AI engineers, data scientists
Data Resources: Proprietary and publicly available datasets
These elements form the foundation of any AI initiative, influencing the build, borrow, or partner decision.
💪 Build: Creating In-House AI Solutions
Building in-house AI systems means developing and maintaining custom solutions tailored to your specific needs.
✅ Advantages
Complete Control: From development to deployment, you own every aspect.
Customisation: Tailor features and workflows to fit unique business requirements.
IP Ownership: Innovations remain your intellectual property.
Security Oversight: Direct management of sensitive data and compliance.
❌ Challenges
High Costs: Significant investment in talent, infrastructure, and R&D.
Time-Consuming: Development cycles can span months or years.
Talent Gaps: Finding and retaining AI experts is competitive.
Maintenance Burden: Continuous updates and retraining are essential to stay relevant.
This approach is ideal for organisations with strong technical expertise, a long-term vision, and a need for bespoke solutions.
🔄 Borrow: Leveraging Third-Party Platforms
Borrowing involves using external platforms and APIs to access AI capabilities quickly and cost-effectively.
✅ Advantages
Rapid Deployment: Deploy AI-powered features in days, not months.
Proven Technology: Benefit from well-established tools with consistent updates.
Lower Upfront Costs: Pay-as-you-go pricing minimises initial investment.
Ease of Use: Ready-made solutions reduce technical barriers.
❌ Challenges
Limited Customisation: Vendor solutions may not align perfectly with your needs.
Data Privacy Risks: Sharing data with third parties could expose sensitive information.
Vendor Lock-In: Dependency on a single provider can lead to long-term cost and flexibility issues.
Usage-Based Costs: Expenses scale with usage, potentially becoming unsustainable.
This path works well for businesses that need quick wins and don’t require extensive customisation.
🤝 Partner: Collaborating with Specialists
Partnering means working with AI consultants, integrators, or managed services to leverage external expertise for your AI journey.
✅ Advantages
Expert Guidance: Gain access to top-tier knowledge and experience.
Faster Implementation: Leverage pre-built frameworks and best practices.
Risk Mitigation: Reduce errors and optimise for success with external input.
Scalable Resources: Flexibly scale up or down based on project needs.
Knowledge Transfer: Learn from specialists to build internal capabilities.
❌ Challenges
Higher Short-Term Costs: Initial expenses can be significant.
Dependency Risks: Over-reliance on partners could hinder future independence.
Complex Coordination: Effective collaboration requires clear communication and project management.
This approach is particularly beneficial for organisations looking to fast-track their AI initiatives without sacrificing quality.
🎯 Strategic Decision Framework
Choosing the right strategy requires assessing your business and technical landscape.
Key Criteria
Business Goals
What outcomes are you targeting?
How competitive is your market?
What’s your budget and timeline?
Technical Considerations
Do you have the necessary internal expertise?
How robust is your data infrastructure?
Are security and compliance critical concerns?
Resource Constraints
What talent and tools are available?
Can your organisation sustain the effort long-term?
🛡️ Implementation Strategy
Hybrid Approach
Many organisations find success by blending strategies:
Partner for the initial setup and expertise.
Borrow for quick, off-the-shelf capabilities.
Build for core systems that provide competitive differentiation.
Risk Mitigation
Start Small: Pilot projects help validate feasibility and ROI.
Set Metrics: Define success criteria to track progress.
Establish Governance: Create policies for ethical AI use and data management.
Plan for Knowledge Transfer: Ensure your team gains expertise during the process.
The Recommended Approach
Short-Term Strategy
Partner with specialists to kick-start your AI journey.
Borrow third-party platforms for immediate use cases.
Build internal teams gradually to upskill and reduce reliance on external vendors.
Long-Term Vision
Develop in-house expertise to own critical AI capabilities.
Maintain strategic partnerships for innovation.
Continuously evaluate the build-versus-borrow balance to stay agile.
🚀 Conclusion
The decision to build, borrow, or partner in AI implementation isn’t just technical—it’s strategic. A well-thought-out approach that aligns with your organisation’s objectives and resources ensures long-term competitiveness and innovation.
By embracing flexibility, leveraging partnerships, and building internal capabilities, you can create an AI strategy that drives meaningful impact for years to come.
Ready to Start?
Book a free AI discovery call with Factor AI (my AI Consultancy) to explore how we can help you navigate your AI journey and unlock its full potential: https://cal.com/thisfactor/free15min
