Part Two: Build vs. Buy vs. Partner: Strategic Decisions for Agentic AI Capabilities

In Part Two of Build vs. Buy vs. Partner we look at the three approaches in more detail. The criteria for choosing each scenario is very dependent on several factors including organizational capabilities, AI expertise, use cases, specific requirements versus speed of deployment and several other factors. Understanding all the relevant organizational context can lead to much more effective approaches to agentic AI deployment. In Part Three of the article we’ll look at the case for hybrid models and methods for phasing the implementation.

The Case for Building In-House

Certain organizational circumstances strongly favor building agentic AI capabilities internally, particularly when the potential competitive advantage justifies the substantial investment required. Understanding when building makes strategic sense requires examining both the conditions that make it attractive and the realistic assessment of what building entails.

Organizations should seriously consider building when their core business processes are unique competitive differentiators that could be enhanced through proprietary agentic AI capabilities. Financial services firms with specialized trading strategies, manufacturers with unique production processes, or service organizations with proprietary methodologies often find that off-the-shelf solutions cannot adequately address their specific needs. In these cases, building allows the organization to create AI systems that directly support and enhance their competitive advantages rather than settling for generic capabilities.

The sensitivity and strategic value of internal data also strongly influences the building decision. Organizations that have spent decades accumulating proprietary datasets, customer insights, or operational knowledge may find that sharing this information with external vendors or partners introduces unacceptable risks. Building internal capabilities allows these organizations to leverage their data assets fully while maintaining complete control over access and usage.

Strong existing AI and engineering capabilities within the organization can make building more feasible and cost-effective. Organizations that already have machine learning teams, robust data infrastructure, and experience deploying AI systems at scale may find that building agentic capabilities is a natural extension of their existing competencies rather than a departure into unfamiliar territory.

The advantages of building extend beyond simple control over functionality. Organizations that build their own agentic AI systems can customize every aspect of system behavior to match their specific processes, requirements, and objectives. This level of customization often enables capabilities and user experiences that would be impossible with packaged solutions designed to serve multiple organizations with different needs.

Building also creates opportunities to develop proprietary intellectual property that can serve as a competitive moat. Organizations that successfully build agentic AI capabilities often discover that these systems become increasingly valuable over time as they learn from organizational data and processes. The resulting AI systems become unique organizational assets that competitors cannot easily replicate or purchase.

Integration with existing technology stacks typically proceeds more smoothly when organizations build their own solutions. Internal development teams understand existing systems, data flows, and technical constraints, allowing them to design agentic AI systems that work seamlessly with current infrastructure rather than requiring extensive modifications or workarounds.

However, building agentic AI capabilities presents significant challenges that organizations must honestly assess before committing to this path. Development cycles for sophisticated agentic AI systems often extend much longer than initially anticipated, particularly for organizations without extensive experience in this specific domain. The complexity of creating systems that can reason effectively, maintain context across interactions, and integrate with multiple enterprise systems often exceeds expectations.

The scarcity of talent with relevant agentic AI expertise is perhaps the most significant challenge for organizations choosing to build. The specific skills required; combining deep understanding of large language models, agent architectures, enterprise systems integration, and domain expertise; remain rare in the job market. Organizations often find themselves competing for the same small pool of qualified candidates, driving up costs and extending hiring timelines.

High upfront investment requirements can strain organizational resources, particularly when considering not just development costs but also infrastructure, ongoing maintenance, and the opportunity cost of internal resources focused on AI development rather than other strategic initiatives. Organizations must realistically assess whether they can sustain these investments through the extended development period required.

Despite these challenges, certain types of organizations have found building to be the optimal strategy. Technology-forward enterprises in financial services often build proprietary trading and risk management agents that incorporate their specific strategies and market insights. Defense contractors frequently develop internal agentic capabilities to meet security requirements that commercial solutions cannot address. Hyperscale technology companies often build their own agent platforms to support their unique scale requirements and to maintain control over core competitive capabilities.

The decision to build requires honest assessment of organizational capabilities, realistic timeline expectations, and commitment to sustained investment. Organizations that choose this path must be prepared for a longer journey but potentially greater long-term strategic value.

The Case for Buying

Purchasing agentic AI capabilities from specialized vendors is the most straightforward path for many organizations, particularly those seeking rapid deployment of proven functionality in common business applications. Understanding when buying makes strategic sense requires examining both the scenarios where it provides clear advantages and the factors that determine vendor selection success.

Organizations should seriously consider buying when their agentic AI needs align with common or commoditized functions that multiple vendors address effectively. IT service management agents that handle ticket routing and basic troubleshooting, customer relationship management copilots that enhance sales productivity, and financial process automation agents are often the areas where vendor solutions provide substantial value without requiring extensive customization.

The need for rapid deployment often drives buying decisions, particularly in competitive markets where speed to market can determine strategic position. Organizations facing immediate operational pressures, regulatory deadlines, or competitive threats may find that vendor solutions enable them to deploy agentic capabilities in weeks or months rather than the years typically required for internal development.

Organizations lacking internal AI and machine learning expertise often discover that buying provides access to sophisticated capabilities they could not realistically develop internally. Vendor solutions typically include not just the agentic AI functionality but also the supporting infrastructure, monitoring tools, and operational expertise required for successful deployment and ongoing management.

The advantages of buying extend beyond simple speed considerations. Vendor solutions provide access to continuous innovation without requiring internal research and development investment. As vendors enhance their platforms with new capabilities, integrate with additional enterprise systems, and improve performance based on learning from multiple customer deployments, buying organizations benefit from these improvements without additional development effort.

Lower initial risk is another significant advantage of buying. Vendor solutions typically come with established performance metrics, customer references, and proven deployment methodologies that reduce the uncertainty associated with agentic AI implementation. Organizations can evaluate vendor capabilities through pilots or proof-of-concept projects before making larger commitments.

Vendors often provide comprehensive support ecosystems including training, integration assistance, and ongoing technical support that can accelerate successful deployment and adoption. Organizations benefit from vendors' accumulated experience across multiple similar implementations, avoiding common pitfalls and following established best practices.

However, buying agentic AI solutions also introduces specific risks that organizations must carefully evaluate. Limited customization options can prevent organizations from fully optimizing agentic capabilities for their specific processes and requirements. While vendor solutions often provide configuration options, they rarely match the complete flexibility available through internal development.

Vendor roadmap dependency creates ongoing strategic risks as organizations become reliant on vendor decisions about feature development, platform evolution, and technology direction. Organizations may find themselves unable to pursue specific capabilities or integrations if they don't align with vendor priorities and business models.

Integration friction often emerges when vendor solutions don't align perfectly with existing enterprise systems, data models, or business processes. Organizations may need to modify their processes to accommodate vendor solution requirements or invest in additional integration infrastructure to bridge compatibility gaps.

Successful vendor evaluation requires systematic assessment of multiple factors beyond basic functionality. Agent architecture and openness determine how well vendor solutions can integrate with existing systems and adapt to changing requirements. Organizations should evaluate vendors' approaches to tool integration, agent communication protocols, retrieval-augmented generation capabilities, and state management across extended interactions.

Multi-agent orchestration capabilities become increasingly important as organizations deploy multiple agentic systems that need to coordinate their activities. Vendor solutions should provide clear approaches for managing agent interactions, preventing conflicts, and ensuring consistent outcomes across different agent types. Finding solutions that incorporate a standardized approach to multi-agent coordination, like the Agent2Agent (A2A) protocol, are the most future proof.

Data control and compliance posture are critical evaluation criteria, particularly for organizations in regulated industries or those handling sensitive information. Organizations must understand exactly how vendors handle data, where it's stored, who has access, and what compliance certifications and audit capabilities are available.

Transparency and auditability features determine how well organizations can understand agent decision-making processes, monitor performance, and meet regulatory or internal governance requirements. Vendor solutions should provide clear visibility into how agents reach decisions and comprehensive logging of agent activities.

Integration capabilities with existing enterprise systems often determine implementation success. Organizations should evaluate how well vendor solutions integrate with their specific enterprise resource planning systems, customer relationship management platforms, and other critical business applications.

The buying decision works best for organizations that can clearly define their requirements, have realistic expectations about customization limitations, and can effectively evaluate and manage vendor relationships. Success requires careful vendor selection but can provide rapid access to sophisticated agentic capabilities with lower initial risk than building.

The Case for Partnering

Strategic partnerships for agentic AI development are a middle path that can combine the benefits of internal control with external expertise, though they also introduce unique complexities that require careful management. Understanding when partnering makes sense requires examining both the scenarios where it provides distinct advantages and the partnership models that have proven most effective.

Organizations should consider partnering when they need to develop strategic co-innovations that require combining their domain expertise with partners' technical capabilities. Industry-specific agent platforms that address unique sector requirements often emerge from partnerships between technology companies with AI expertise and industry leaders with deep domain knowledge. These collaborations can create solutions that neither organization could develop effectively independently.

The need for innovation velocity and shared intellectual property often drives partnership decisions. Organizations seeking to explore new applications of agentic AI while distributing development risks and costs may find partnerships provide access to capabilities and resources they couldn't justify internally. Partnerships can enable experimentation and learning that might be too expensive or risky for individual organizations to pursue alone.

Entering new markets or verticals frequently benefits from partnership approaches that combine complementary capabilities and market access. Technology companies seeking to address specific industries often partner with established players in those sectors, while traditional enterprises looking to enhance their technology capabilities may partner with AI specialists.

Several partnership models have emerged as particularly effective for agentic AI development. Co-innovation labs are formal collaborations where organizations establish shared facilities and teams focused on developing next-generation agentic capabilities. These arrangements typically involve significant commitments from both parties and clear agreements about intellectual property ownership and commercialization rights.

Joint ventures create separate entities specifically focused on developing and commercializing agentic AI solutions. This model provides clear governance structures and can attract additional investment, though it requires substantial commitment and careful coordination between parent organizations.

Embedded go-to-market partnerships allow organizations to integrate their agentic AI capabilities with partners' products and distribution channels. These arrangements can provide rapid market access and customer validation while leveraging partners' established relationships and sales infrastructure.

The benefits of partnering extend beyond simple resource sharing. Successful partnerships enable shared investment that makes ambitious projects feasible for organizations that couldn't justify the full development cost independently. Combining domain expertise from both sides often produces superior solutions that reflect both technical sophistication and practical industry knowledge.

Partnership approaches can create potential for long-term ecosystem advantages as organizations develop complementary capabilities and shared customer bases. These relationships often evolve beyond individual projects to ongoing strategic alliances that provide sustained competitive advantages.

However, partnerships also introduce specific risks that require careful management. Intellectual property ambiguity can create conflicts when partnership agreements don't clearly specify ownership of developments, improvements, and derivative works. Organizations must establish clear frameworks for managing intellectual property created during collaboration.

Governance complexity increases significantly in partnership arrangements as organizations must coordinate decision-making processes, resource allocation, and strategic direction across different organizational cultures and priorities. Successful partnerships require established mechanisms for resolving conflicts and making joint decisions.

Misaligned incentives can undermine partnership effectiveness when organizations have different objectives, timelines, or risk tolerances. Partners may prioritize different aspects of development or have conflicting views about commercialization strategies, requiring ongoing coordination and compromise.

Successful partnerships require careful partner selection based on complementary capabilities, compatible organizational cultures, and aligned strategic objectives. Organizations should evaluate potential partners' technical capabilities, market position, and track record in similar collaborations. Cultural fit often determines partnership success as much as technical compatibility.

Clear governance structures and decision-making processes must be established before beginning substantial development work. Partnerships need defined roles and responsibilities, escalation procedures for resolving conflicts, and mechanisms for adapting to changing circumstances.

Intellectual property frameworks should address ownership of existing assets, developments created during collaboration, and derivative works developed after partnership conclusion. These agreements should consider various scenarios including partnership dissolution, acquisition of either party, and competitive developments.

Partnership approaches work best for organizations that can effectively manage complex relationships, have clear strategic objectives for collaboration, and can commit sufficient resources to make partnerships successful. When executed well, partnerships can provide access to capabilities and markets that would be difficult to achieve independently while distributing risks and costs across multiple organizations.

There’s one more installment of this article. In Part Three we’ll look at hybrid models and phased approaches.