Build vs. Buy: Decoding the Tech Dilemma with Industry Experts

The following article summarizes the "Botsplash Build vs Buy" webinar on June 24th, 2025. The webinar featured the following guests,

• Suha Zehl, Founder & Chief Innovation Officer at Z Technology Solutions
• John Stewart, VP of Product at Guaranteed Rate Companies
• Alec Haase, General Manager of AI Hightouch

The webinar centers on the perennial "build vs. buy" decision for technology solutions, particularly in the context of financial services and marketing technology.

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The Evolving Philosophy of "Build vs. Buy"

The panelists unanimously agreed that there's no universal "one-size-fits-all" answer; the optimal path is always contextual and problem-dependent.

• Strategic Differentiation vs. "Table Stakes": Suha Zehl emphasized a core principle: build what differentiates your brand, especially customer-facing elements that showcase unique value. Conversely, buying best-in-class, off-the-shelf products is often the most sensible approach for "table stakes" functionalities (common, non-differentiating features).
• The Hybrid Model: Alec Haase advocates for a hybrid approach, having experienced both sides as a practitioner and now a vendor. He suggests building the core data foundation (e.g., in cloud data warehouses like Snowflake or Databricks), as this data is a key differentiator. Then, organizations should buy specialized tools (like Customer Data Platforms or audience builders) that seamlessly integrate with and activate that proprietary data.
• Velocity and Iteration: John Stewart, from a product development perspective, highlighted the critical need for velocity and rapid iteration, especially in product marketing. This often means buying solutions for short-term problems to quickly test and learn while concurrently pursuing long-term strategic goals through internal builds.

Deconstructing the Decision: Key Factors to Evaluate

The panelists delved into several critical factors that influence the build vs. buy decision:

• Total Cost of Ownership (TCO): A common misconception, according to Alec, is that building is always cheaper. He stressed internal builds' hidden and never-ending costs, including ongoing maintenance, support, and continuous development hours, far beyond initial team costs. John concurred, noting that purchased solutions still require maintenance but are often substantially cheaper and faster than building complex platforms from scratch.
• Speed to Market: Buying typically offers quicker turnaround times, especially for SaaS products with fewer complex backend integrations, allowing faster testing and deployment.
• Internal Capabilities and Opportunity Cost: Suha Zehl brought up the crucial aspect of team capacity and skill set. For smaller organizations, committing to an internal build means missing out on other strategic initiatives, a significant opportunity cost. She also warned against creating single points of failure if only one or two individuals possess the expertise for a proprietary system. John echoed this, stating that internal teams should focus on building products with clear differentiation, not "table stakes" functionalities.
• Flexibility vs. Rigidity: While custom builds offer maximum flexibility, Alec noted that modern off-the-shelf tools are surprisingly flexible and can often be customized to meet specific needs, debunking the myth that buying means rigid solutions. John added that even purchased solutions aren't "set it and forget it"; they require ongoing integration and customization efforts.

Navigating Misconceptions and Mitigating Risks

The experts shed light on common pitfalls and how to avoid them:

• Underestimating Complexity: Suha highlighted that companies often underestimate the complexity of regulatory compliance in industries like mortgage, which can severely hinder proprietary systems when new rules emerge. Similarly, the security aspects of internal builds, including understanding federated AI and secure data handling, are frequently overlooked.
• Lack of Diligence: Aru pointed out a critical disparity: companies conduct rigorous vendor vetting but often cut corners when building internally, potentially exposing sensitive information.
• Building on a "Crumbling Foundation": Suha warned against prioritizing speed over quality in internal builds, where organizations might "clean it up later." This often leads to building on a flawed data platform or architecture, creating more technical debt.
• Superficial Data Analysis: Jordan Bartholomew advised looking beyond high-level metrics (like average star ratings) to understand the underlying sentiment in customer reviews. Ben Shore cautioned against applying generic data trends (e.g., FICO scores) without considering a company's target audience. Sarah Bacha noted that customer-stated preferences don't always align with their behavior, emphasizing the need to analyze qualitative and behavioral data.

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Real-World Successes and Lessons Learned

The panelists provided compelling examples of build and buy strategies in action:

• Successful Builds: Suha shared a case from higher education where an internal student record system succeeded because users were deeply involved in its development, addressing real pain points. Alec recounted how his previous company built proprietary event collection and experimentation tools that were ahead of their time and critical to company growth, proving highly successful for differentiation.
• Builds That Backfired: Suha also detailed a scenario where a mortgage pipeline management tool failed because it was built in isolation, without understanding actual organizational needs, leading to wasted resources and zero adoption.
• Successful Buys & Strategic Partnerships: John Stewart highlighted the value of relational partnerships with vendors. His experience with Adobe Target showed how threatening to leave led to a collaboration vastly improving their recommendations engine. Similarly, the partnership with Botsplash for live chat and SMS is evolving into a collaboration on their internal AI agent, allowing them to test and iterate quickly at the forefront of innovation. Suha shared a success story of implementing a business intelligence solution that, despite initial resistance, ultimately empowered users to self-serve reports, reducing IT resource demands.

The Dawn of AI and Low-Code Platforms: A New Frontier

The conversation shifted to how emerging technologies like AI and low-code/no-code platforms are reshaping the build vs. buy landscape:

• Prototyping Powerhouses: John and Alec agreed that these platforms are excellent for rapid prototyping and ideation, significantly reducing the time to develop minimal viable products (MVPs). Suha noted they are ideal for junior developers to create proofs of concept quickly.
• Current Limitations for Production: However, they concurred that low-code/no-code tools often hit a wall regarding accurate production-level scale, complex integrations, or handling sensitive customer data. Full-scale engineering is still required.
• AI as an Augmenting Force: AI is increasingly used to augment developers (e.g., AI coding assistants) rather than replace them entirely. Suha stressed the ongoing need for "human in the loop" due to AI's propensity to "hallucinate," especially in code, and the necessity of human experience that AI lacks.
• AI Agents for Customer Interaction: John discussed the rise of AI agents for SMS, IVR, and chat, which can reduce costs. While improving rapidly, the human element remains crucial for nuanced interactions, with the ability to route to a live person being paramount quickly.

Conclusion

The webinar underscored that navigating the build vs. buy decision in the age of data and AI is more complex than ever. Organizations must move beyond simplistic cost comparisons to a holistic evaluation that considers long-term strategic goals, internal capabilities, hidden ownership costs, regulatory compliance, security, and AI's transformative yet still evolving capabilities. 

The future likely lies in strategic hybrid models, where internal teams focus on building competitive differentiators while leveraging best-in-class external solutions for foundational and standard functionalities, all while prioritizing data integrity and customer experience.

Thank you to all our enthusiastic attendees for joining us for the webinar. We hope for your participation and continued support in the future.

Check out all of our webinar recaps here.

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