The Million-Dollar Question: Build Or Buy A Conversational AI Agent?

When it comes to implementing conversational AI agents in your product, the "build versus buy" question isn't just theoretical—it's a strategic decision with major implications for your resources, timeline, and ROI.

As Chief Product Officer at Mindset AI, I've spent years not only building our own conversational AI agent platform but also guiding product and business leaders through this exact dilemma.

The generative AI landscape has matured significantly. With 92% of Fortune 500 companies using gen AI solutions, the days of half-baked beta releases are over. Your customers have seen enough demos to know the difference between genuine value and PR stunts.

I recently hosted a webinar on the build or buy topic: watch it on demand!

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_{The current state of Generative AI}

The AI landscape has changed dramatically in the past two years. We've moved beyond what Gartner calls the "peak of inflated expectations"—that initial period when everyone thought AI could do anything and everything.

Now, we're in what I call the "plateau of productivity." Companies have learned through trial and error what combinations of technologies actually make generative AI valuable. This shift has three important implications for you:

• Higher user expectations: People interact with AI daily and understand what good looks like. They won't tolerate poor experiences.
• The end of beta releases: Using AI as a magical tool without delivering actual value doesn't work anymore. Users want concrete solutions, not promises.
• Back to product basics: It's time to ask fundamental questions again. What problems are we solving? What's the ROI? How do we differentiate?

Importantly, there's a misconception that you need just one AI solution per organization. In reality, different AI capabilities serve different functions—from course authoring to assessment generation to content library agents. Each capability comes with its own maturity level, which should inform your build-vs-buy decisions.

Some AI capabilities are well-established with strong market options, while others remain open for innovation. This creates both opportunities and challenges for product teams deciding where to focus their efforts.

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A framework for build vs buy decisions

When evaluating whether to build or buy a conversational AI agent, you need a structured approach that considers all key factors.

Let's break down the four main considerations:

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1. Build costs

Building AI functionality requires more than just connecting to an API. You need:

• The right team size and composition (data scientists, engineers, UX specialists)
• Realistic timeline estimates (which tend to expand once you discover the hidden complexities)
• A clear understanding of specific use cases before development begins

Many teams underestimate these costs. One organization we worked with spent 1-2 quarters building an AI learning co-pilot before realizing they'd overlooked critical use cases—ultimately requiring them to start over.

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2. Maintenance Costs

Ongoing expenses include:

• Hosting infrastructure
• LLM API usage fees (which can be unpredictable at scale)
• Indexing costs for knowledge bases
• Regular performance tuning and updates

These costs can spike unexpectedly when usage grows, making it difficult to predict your total cost of ownership.

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3. Customization requirements

Not all AI ecosystems offer the same flexibility:

• Some platforms lock you into specific database structures or UI patterns
• Others provide headless APIs that give you more control
• The ability to hook into different LLMs may be important for your use case
• Multi-tenant requirements (e.g., serving different agents to different customers) create additional complexity

Be clear about your must-have customization needs before committing to a build or buy decision.

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4. Reliability considerations

Reliability is often overlooked but becomes critical at scale:

• How will you handle service degradation that affects all users simultaneously?
• What safeguards will prevent bias in AI responses?
• How will you ensure security and compliance with emerging AI regulations?
• Can you provide auditability for enterprise customers?

Underlying all these factors is the ultimate consideration: opportunity cost. What you build or buy today comes at the expense of something else. This reality forces you to be strategic about where you allocate your AI investments.

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For a deeper dive on the build vs buy topic, check out this 30-minute on-deamnd webinar.

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The Hidden Complexities of Building Gen AI Solutions

When you first experiment with AI, hooking up an API to generate text might seem magical—but that excitement wears off quickly. The real challenge lies in the extensive engineering required around that core AI capability.

Let me share what we discovered when building just one conversational AI agent feature:

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Agent building and configuration

What seemed simple grew complex fast. We needed to support multiple agents for different enterprise customers. Each customer wanted their own agent with access to specific content libraries. This required an entire configuration system we hadn't initially planned for.

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Clarification workflows

Basic question-answering wasn't enough. Users get frustrated when AI misunderstands their intent. We had to build systems for AI to ask clarifying questions. Implementing "chain of thought" reasoning required additional engineering. Without these capabilities, AI responses felt disconnected from user needs.

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End-to-end user experience

The conversation window alone needed integrated media players to show relevant content. We built features to jump to specific timestamps in videos, create playlists for multiple content pieces, and provide a seamless way to move from conversation to content consumption.

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The architecture behind It all

Under the hood, we needed systems to ingest and extract data from various content types (text, images, charts). We built knowledge graphs for accurate information retrieval and applied entitlements so users only see what they should. This required pre-processing user queries before sending to the LLM and verifying responses against source material for accuracy.

This "iceberg" of hidden complexity is why many AI projects exceed their initial budget and timeline estimates. What looks like a simple chat interface actually requires multiple interlinked systems working together.

These complexities exist whether you build or buy—but understanding them helps you make more informed decisions about where your team's engineering efforts will deliver the most value.

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Evaluating Gen AI solutions

Whether you're evaluating vendors or assessing your own build options, you need clear criteria for what makes a successful conversational AI agent. Here are the four key areas to focus on:

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1. Accuracy and performance

The fundamental question is whether your solution delivers reliable, valuable responses. Look for:

Context awareness that understands what users are truly asking for. When a user asks about a dress code policy, the AI should clarify which region or department they're in rather than providing generic information.

Chain-of-thought reasoning that shows how the AI reached its conclusions. This dramatically improves user trust and reduces frustration.

Bias detection capabilities that identify and mitigate problematic content. We built systems to track exactly what data our AI has access to and identify potential sources of bias. This becomes crucial when selling to enterprise customers who have vendor management checklists focused on AI ethics.

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2. Security and auditability

This often-overlooked area becomes critical at scale. You need:

Comprehensive reporting on user interactions, question patterns, and AI performance. Without this data, you can't improve your solution.

Source tracking that identifies which piece of content produced each answer. As one enterprise customer pointed out to us, when something goes wrong, they need to track exactly where that information came from and whether it was outdated.

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3. Agent-based architecture

Being "agent-based" is no longer optional in today's AI landscape. A proper agent:

Accesses specific knowledge bases and user data Uses tools (APIs, search capabilities, etc.) Creates strategies to execute tasks Follows complex workflows to deliver value

The most advanced solutions now enable agents to collaborate with each other, orchestrating multi-step processes while keeping the user experience seamless.

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4. Workflows and integrations

Simple question-answering delivers limited value. To create transformative experiences, your solution needs:

Structured workflows that guide users through complex tasks Integration with existing systems to pull and push information The ability to trigger appropriate actions based on user intent

These capabilities elevate your AI from a novelty to an essential productivity tool.

By systematically evaluating potential solutions against these criteria, you'll make more informed decisions about whether to build or buy—and which vendors deserve your consideration.

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ROI considerations

At its core, the build-vs-buy decision comes down to return on investment. Every product investment must deliver clear, measurable value.

If your average developer costs $100-200K annually, every day spent building has a real cost. Using a 50% return benchmark:

• One day of development = $600-800 in expected return
• One month of development = $125,000 in expected return

This calculation becomes sobering when you consider the months required to build comprehensive AI capabilities. For a team of 10-11 people, you need to generate substantial returns to justify the investment.

As Marty Cagan famously said, "If you're just using your engineers to code, you're only getting half the value." When you task engineers with rebuilding capabilities that already exist in the market, you waste their creative potential.

Instead, direct your engineering talent toward areas where:

• Your company has unique domain expertise
• Market solutions are immature or non-existent
• You can create significant competitive advantage

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The business impact quadrant

Every organization falls into one of four categories:

1. Loss-making with low growth (failing)
2. Profit-generating with low growth (viable)
3. Loss-making with high growth (challenging)
4. Profit-generating with high growth (successful)

Your build-vs-buy decisions should move you toward that successful quadrant. By spending six months rebuilding what already exists, you might miss opportunities to create truly innovative features that drive growth.

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Real-world success stories

We've seen this play out with multiple customers. One organization spent nearly two quarters building an AI learning co-pilot before realizing the scope was much larger than anticipated. By switching to our pre-built solution, they redirected their engineering resources toward unique innovations that their competitors couldn't match.

Here are some organizations that made the decision to buy the foundational conversation AI agent platform and customize it to their needs—and make room for other features in their roadmap:

• Training Industry, a leading L&D provider, launched their first AI agent, TIA, without a lengthy development process or deployment issues. TIA was an overwhelming success, with over 1,400 free trials generated, 11% of which converted into paid subscriptions.
• A major online learning platform with over 5,000 courses in its content library deployed an AI agent to help users navigate this huge knowledge bank and find the right answers to their questions. During their proof of concept, they already saw a 900% increase in course enrollment from those who engaged with the agent. After the early full roll-out, they observed a 260% increase in revenue per AI agent user.
• Persimmon Life also experienced fast launch, and they hit 50% of the project target in one month. The FAQ AI agent they deployed was so popular that it freed up 40% more time for regional managers—who previously dealt with the frequently asked questions—so they could focus on higher-impact tasks.

The key is balancing immediate needs with long-term strategy. Buy the mature capabilities that customers already expect, then build the innovations that will set you apart.

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Conclusion

Only you can make the decision whether to build a conversational AI agent completely from scratch or to buy the foundational Platform-as-as-Service. But hopefully, this guide and our on-demand webinar help identify the key considerations.

If you haven’t watched our on-demand webinar, I explain the whole build-vs-buy decision process in 30 minutes.

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