The Strategic Importance of Make vs. Buy
Unlike many technology decisions that can be made at a tactical level, AI make vs. buy choices often have strategic implications. They determine:
- The pace of your AI implementation timeline.
- Your organisation’s internal AI expertise development.
- The uniqueness of your AI capabilities relative to competitors.
- Your dependency on external AI ecosystems and vendors.
- Your ability to adapt AI solutions to evolving business needs.
Given these stakes, it’s worth considering the key factors before deciding which approach you take:
Key Factors to Consider
1. Strategic Differentiation
The first question to ask is whether the AI capability in question represents a potential source of competitive advantage. Consider:
Core Capability: Is this AI capability central to your products value proposition or primarily supporting business operations?
Competitive Landscape: Are your competitors building similar capabilities, or is this an area where you could establish a distinctive advantage?
Industry Value: Would proprietary AI in this area fundamentally change your position in the industry?
As a general rule, the closer an AI capability is to your core business differentiation, the stronger the case for building it in-house. Conversely, capabilities that don’t directly drive competitive advantage are often better acquired from vendors who can achieve economies of scale. For example, customer identity verification services typically offer minimal strategic differentiation and have become standardised across industries, making them an ideal candidate for vendor solutions rather than in-house development. On the other hand a retailer’s personalized recommendation engine would have a strong bias toward building in-house.
2. Technical Feasibility
Even with strategic importance, you must assess whether your organization has the technical ability to successfully build the AI capability:
Talent Availability: Do you have (or can you acquire) the necessary data science, ML engineering, and domain expertise?
Data Assets: Do you possess the data required to train effective models, or would you need to acquire or generate this data?
Infrastructure Readiness: Do you have the computing resources and technical infrastructure to support AI development?
Technical Risk: How well-understood is the AI approach needed, and what level of technical uncertainty exists?
Be honest about capabilities—many organisations overestimate their technical readiness for complex AI development projects.
3. Time to Value
The urgency of implementation can significantly influence the make vs. buy decision:
Market Window: How quickly do you need to deploy the capability to capture a market opportunity or respond to competition?
Learning Curve: How long would it take your team to develop the necessary expertise for in-house development?
Implementation Timeline: What’s the realistic timeframe for building vs. buying, including integration time?
Iteration Cycles: How quickly will you need to evolve the capability based on feedback and changing requirements?
When speed is critical, buying often provides the fastest path to initial implementation, though it may limit flexibility for rapid iteration later.
4. Total Cost Dynamics
A comprehensive cost analysis should include both immediate and long-term financial impacts:
Initial Development Costs: What would it cost to build the capability in-house vs. purchase from vendors?
Ongoing Operational Costs: How do maintenance, infrastructure, and personnel costs compare between options?
Scaling Economics: How do costs change as usage scales up? Do vendor solutions offer economies of scale or become prohibitively expensive?
Hidden Costs: What are the potential costs of integration, customization, vendor switching, or building internal capabilities?
Remember that the true cost of any AI solution extends far beyond the initial price tag or development budget.
5. Control and Customization
As with traditional software, AI use cases require different levels of control and customisation:
Integration Needs: How deeply must the AI capability integrate with your existing systems and processes?
Customization Requirements: How specific are your requirements compared to standard market offerings?
Governance Control: How important is direct control over how the AI works, particularly for risk management or regulatory reasons?
Building provides maximum control but at significantly higher cost and complexity. Evaluate whether the level of control you need justifies this investment.
Hybrid Approaches
The make vs. buy decision isn’t always binary. Consider these hybrid approaches:
Buy and Customize: Purchase core technology but customize it extensively for your specific needs.
Build on Platforms: Leverage AI platforms and APIs while building proprietary elements on top of them.
Partner and Co-develop: Work closely with vendors on joint development of capabilities tailored to your needs.
Acquire and Integrate: Purchase smaller AI companies and integrate their technology and talent.
Open Source Foundation: Build on open-source AI technologies while adding proprietary elements.
These approaches can offer “best of both worlds” solutions in many cases.
Conclusion
Maintaining flexibility in your approach, continuously reassessing the balance between building internal capabilities and leveraging external innovation is the key to being successful with your AI projects.
Ultimately, the right decision isn’t about following industry trends or dogmatic preferences for building or buying. It’s about aligning your approach with your specific business context, strategic objectives, and organisational capabilities to create sustainable competitive advantage through AI.
