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Harvard Business Review

It's Time for Your Company to Invest in AI. Here's How.

How is it that one organization can invest millions in AI capabilities, only to see a competitor achieve better results with a fraction of the spending? This question captures the strategic dilemma facing organizations today: What is the best approach for investing resources toward AI capabilities? When should companies build AI capabilities in-house versus purchasing external solutions? The answer isn't as simple as choosing one over the other. Organizations are rejecting the binary build-or-buy question in favor of more nuanced approaches. According to International Data Corporation (IDC), only 13% of IT leaders plan to build AI models from scratch, while 53% intend to start with pretrained models and augment them with enterprise data. This shift toward strategic implementation—including the growing trend of strategic partnerships—recognizes that success with AI isn't about how much you spend, but how intelligently you invest across build, buy, blend, and partner strategies. As an AI transformation advisor, I've observed firsthand how organizations navigate these decisions while simultaneously reconfiguring their workforces to accommodate new technologies. The urgency of these decisions has intensified as AI adoption accelerates across organizations of all sizes— TriNet's 2024 State of the Workplace report reveals that 88% of SMB employers and 71% of employees are currently using AI in the workplace. The organizations seeing the greatest returns have developed systematic approaches that go far beyond simple cost considerations. A Framework for Strategic Decision-Making The most successful organizations assess each AI capability through a systematic framework. The first question shouldn't be, 'Build or buy?' It should be, 'Does this capability create unique value for our customers in ways competitors can't easily replicate?' This strategic value assessment requires examining three critical dimensions: competitive differentiation potential, organizational readiness, and long-term strategic alignment. Companies that excel at this evaluation process consistently outperform those that make decisions based primarily on upfront costs or technical preferences. When to Build Organizations build when the capability represents core competitive differentiation, their data and domain knowledge create unique barriers to entry, long-term cost efficiency at scale justifies higher upfront investment, or intellectual property protection is essential to their business model. The building approach requires comprehensive planning and systematic execution. Begin with detailed capability mapping —identify all AI capabilities needed, from customer-facing applications to operational systems. For each capability requiring custom development, conduct thorough feasibility assessments examining technical requirements, talent needs, and infrastructure demands. Establish dedicated cross-functional teams combining existing internal talent with strategic hiring. These teams should include not just technical specialists but also domain experts who understand your business context and can ensure the AI solutions address real operational challenges. Plan for 12-24 month development cycles with iterative releases that allow for continuous feedback and refinement. In addition, create robust development infrastructure, including scalable computing resources, comprehensive data pipelines, and machine learning operations (MLOps) capabilities that support the entire machine learning lifecycle. This infrastructure investment often represents 30-40% of total project costs but is essential for long-term success. Finally, define clear success metrics that go beyond technical performance to include business impact measures, such as development velocity, system reliability, user adoption rates, and measurable competitive advantage creation. Establish regular review cycles to assess progress against these metrics and adjust strategies as needed. Risk management becomes particularly critical for build strategies. Develop contingency plans for talent retention challenges, technology evolution, and changing business requirements. And consider how custom-built systems will integrate with future technology acquisitions and ensure your architecture can evolve with organizational needs. JPMorgan Chase exemplifies this comprehensive approach, investing $17 billion in technology in 2024 with significant portions directed toward proprietary AI systems. Their custom-built AI platform for fraud detection analyzes transaction patterns specific to their customer base, delivering tailored risk assessments that off-the-shelf solutions couldn't match. This investment has reduced account validation rejection rates by 15-20% while dramatically lowering false positives—demonstrating how strategic building can create measurable competitive advantages. When to Buy Organizations purchase external solutions when speed-to-market is critical, specialized vendors offer superior expertise, or internal development costs exceed long-term value creation. The buy strategy works particularly well for standardized functions where competitive advantage comes from implementation excellence rather than underlying technology differentiation. Successful purchasing requires sophisticated vendor evaluation processes that examine not just current capabilities but future roadmap alignment and integration flexibility. Develop comprehensive evaluation criteria covering technical performance, security compliance, scalability potential, and vendor stability. Conduct comprehensive vendor assessments including reference checks with similar organizations, pilot testing of key functionality, and detailed analysis of total cost of ownership (i.e., licensing, implementation, training, and ongoing support costs). Pay particular attention to integration requirements and ensure purchased solutions can work seamlessly with existing systems and data flows. Negotiate contracts that provide flexibility for changing requirements while protecting against vendor lock-in, and include provisions for data portability, API access, and performance guarantees. Consider multi-vendor strategies that avoid over-dependence on single providers while creating competitive dynamics that benefit your organization. Remember to develop robust change management processes for purchased solutions. Even off-the-shelf software requires significant organizational adaptation, including user training, process modification, and cultural adjustment. Plan for 6-12 month implementation timelines that include comprehensive testing, user training, and gradual rollout phases. Last, monitor vendor performance continuously through established service level agreements and regular business reviews. Maintain awareness of competitive alternatives and be prepared to make vendor changes when performance or strategic alignment deteriorates. A prime example of this approach is Salesforce's acquisition strategy, where they've purchased specialized AI companies like Einstein Analytics and integrated these capabilities into their core platform. Rather than building every AI feature internally, Salesforce strategically acquires proven technologies and teams, accelerating their AI capabilities while focusing internal development on core CRM innovations that differentiate their platform. When to Blend The hybrid approach—building some capabilities and systems while buying others—works best when some components require customization while others can be standardized, or when organizations want to maintain control over core algorithms while leveraging external infrastructure. Blending strategies have become increasingly popular as organizations seek to balance speed, cost, and competitive differentiation. Successful blending requires sophisticated architectural planning that enables seamless integration between internal and external components. Design modular systems with well-defined interfaces that allow different components to be developed, updated, or replaced independently. Additionally, develop robust APIs and data exchange protocols that ensure smooth communication between internal systems and external solutions. Pay particular attention to data security and compliance requirements, especially when integrating cloud-based external services with internal systems containing sensitive information. Establish clear governance structures that define ownership and accountability for different system components, and create cross-functional teams responsible for integration oversight, performance monitoring, and strategic evolution of the blended solution. Plan for ongoing optimization as both internal and external components evolve. Blended solutions require continuous attention to ensure that updates to one component don't disrupt others and that the overall system maintains coherence and performance. Capital One demonstrates this approach effectively, building their own machine learning platform for credit decisioning—a core competitive function—while purchasing pre-built AI solutions for customer service automation. This hybrid approach has resulted in significant improvements in processing efficiency and customer satisfaction scores, demonstrating how strategic blending can maximize return on AI investments. When to Partner Strategic partnerships represent a fourth pathway that differs from traditional vendor relationships by providing comprehensive solutions that combine technology, expertise, and ongoing service delivery. This approach is optimal when capabilities are essential but non-differentiating, specialized providers offer superior expertise and technology, or organizations need flexible service models that adapt to changing requirements. Strategic partnerships require careful provider evaluation based on multiple criteria including technology capabilities, industry expertise, service quality, and cultural alignment. Look for partners who can provide end-to-end solutions rather than just software licenses, including implementation support, ongoing optimization, and strategic consultation. Establish detailed partnership agreements that go beyond traditional service level agreements to include strategic alignment commitments, innovation collaboration, and mutual performance incentives. These relationships should feel more like extensions of your internal team than external vendor arrangements. Develop integration strategies that allow partner solutions to work seamlessly with your internal systems while maintaining appropriate security and compliance controls. This often requires establishing dedicated communication channels, shared performance dashboards, and regular strategic review processes. Finally, create governance structures that ensure partnership relationships evolve with your organizational needs. Regular strategic reviews should assess not just operational performance but also strategic alignment, innovation collaboration, and long-term value creation. A compelling example is Domino's Pizza's strategic partnership with Microsoft Azure for their AI-powered ordering and delivery optimization platform. Rather than building these capabilities internally or simply purchasing software licenses, Domino's partnered with Microsoft to co-develop AI solutions that optimize delivery routes, predict customer preferences, and automate order processing. This partnership approach allowed Domino's to access Microsoft's advanced AI capabilities while leveraging their own deep understanding of pizza delivery logistics. In doing so, Domino's boosted AI accuracy from 75% to 95% for predicting order readiness using load-time models that factor in labor variables and order complexity. Microsoft benefits by gaining real-world insights that improve their AI services for other retail clients, while Domino's gets enterprise-level AI capabilities without the massive internal investment required to build them from scratch. The Strategic Imperative The organizations seeing the greatest returns from AI have transcended the simplistic build-or-buy debate. They've created decision frameworks that systematically evaluate each capability against strategic value creation, organizational readiness, and long-term competitive positioning. These frameworks recognize that different capabilities require different approaches, and the most successful implementations often combine multiple strategies within a coherent overall architecture. Success requires more than choosing the right approach for each capability—it demands sophisticated execution including robust project management, careful vendor selection, seamless integration planning, and continuous performance optimization. Organizations must develop internal capabilities to manage these complex implementations while making strategic decisions about where to focus limited resources for maximum competitive advantage. The strategic question isn't simply whether to build, buy, blend, or partner; it's how to create organizational capabilities that leverage all four strategies appropriately while developing the decision-making frameworks that ensure each approach delivers maximum strategic value. The companies that master this multifaceted approach will not only optimize their AI investments but create sustainable competitive advantages that justify every investment decision.