In a recent enterprise AI podcast episode, Saurabh Kumar, co-founder and CEO of Rezolve.ai, sat down to discuss one of the most critical decisions facing enterprises today: should organizations build or buy when it comes to AI-powered employee support? The conversation with host, Robert O'Brien, an AI and Automation Solutions Consultant at Rezolve.ai, wasn't just theoretical—it emerged from Kumar's real-world experiences with CIOs and IT leaders who'd attempted the DIY route, only to discover a familiar pattern of promising demos that never reached production, spiraling budgets, and projects that fell behind as the AI industry advanced at breakneck speed.
This in-depth discussion reveals the hidden challenges, cost multipliers, and strategic considerations that every technology leader should understand before committing resources to internal AI development.
In this blog, we cover how enterprise teams consistently underestimate the complexity of building AI solutions in-house and what can be done about it. While the first 60% appears straightforward, the final stretch demands enterprise-grade features, domain expertise, and continuous innovation that specialized platforms already provide at a fraction of the cost and time.
Watch the full podcast episode to hear Kumar's complete insights and real client stories that didn't make it into this summary.
Watch the full podcast episode to hear Kumar's complete insights and real client stories that didn't make it into this summary.
The LinkedIn Post That Started the Conversation
Before diving into the podcast insights, it's worth examining the LinkedIn post that sparked this entire discussion. Kumar's observations resonated so strongly with enterprise leaders because they captured a universal experience:
The post's viral reception among enterprise leaders validated what many had experienced but few had articulated so clearly. The podcast conversation expands on each of these five critical points with additional depth and real-world examples.
The 60% Trap: When Demos Meet Reality
The allure of building AI internally is undeniable. Large companies naturally deploy their massive engineering talent to tackle new challenges, and AI seems no different from previous technology waves. The first phase feels deceptively simple—create a bot with sample content, demonstrate basic question-answering capabilities, and watch stakeholders light up with excitement.
But Kumar, drawing from his experience as both buyer and builder before founding Rezolve.ai, explains why this initial success becomes a trap:
"When you take a simple bot that does employee support, the first 50 to 60% is easy—it's easy to give a bot some sample content and make it answer those questions. It demos great."
The problems emerge when teams attempt to scale beyond the demo. Integration with SharePoint reveals content access challenges. Some information shouldn't be accessible to the bot due to privacy concerns. Conflicting information confuses the AI system. Content locked in PDFs or complex table formats proves difficult to parse. Geographic personalization requirements add another layer of complexity.
Each issue seems manageable individually, but collectively they create what Kumar calls "the death of a thousand cuts"—a gradual erosion of the project's viability as it encounters real-world enterprise complexity.
The Hidden Cost Multiplier
Cost overruns in DIY AI projects aren't just common—they're dramatic. Kumar describes scenarios where projected $100,000 initiatives balloon into five, seven, or even ten times the original budget. The root cause isn't poor project management; it's fundamental misunderstanding of what enterprise-ready AI requires.
The initial budget calculation typically assumes linear progress: two engineers for three months, extrapolated across the entire project. This approach ignores three critical factors:
Enterprise Infrastructure Requirements: Beyond the core AI functionality, production systems demand access control, analytics dashboards, observability tools for AI behavior monitoring, compliance reporting, and audit trails. These aren't nice-to-have features—they're organizational requirements that can represent 40-50% of the total development effort.
Exponential Complexity in the Final Third: The last 20-30% of development doesn't follow the same patterns as the initial phases. Enterprise integration challenges, edge case handling, and performance optimization under real-world load create exponentially more work.
Technology Evolution Pressure: The AI landscape evolves so rapidly that teams often find their chosen architecture outdated before project completion. This forces expensive pivots or acceptance of technically inferior solutions, creating ongoing technical debt.
The Enterprise Must-Haves Nobody Talks About
During demos, the conversation focuses on the exciting capabilities—natural language processing, intelligent routing, automated responses. The unglamorous but essential features rarely make the presentation slides, yet their absence kills more projects than any technical limitation.
Kumar draws a perfect analogy: "It's for the same reason I don't think of an oil change when I drive my car. It's not core to what you do or what you want to experience—you want to just get in the car and drive. But the car needs new tires, it needs an oil change."
These enterprise "oil changes" include:
Governance and Auditability: AI decisions need explanation trails, especially in regulated industries. Every recommendation, escalation, or automated action must be traceable and explainable.
Security and Access Control: Enterprise environments aren't clean, well-defined systems. They're messy amalgamations of legacy processes, varied user permissions, and complex data hierarchies that require sophisticated security models.
Analytics and Performance Monitoring: Leadership needs visibility into AI performance, user satisfaction, resolution rates, and system utilization. These dashboards don't build themselves.
Integration Complexity: Modern enterprises use dozens of interconnected systems. AI solutions must integrate seamlessly without disrupting existing workflows or creating security vulnerabilities.
DIY teams consistently underestimate these requirements because they're focused on the core functionality. It's only during implementation that the full scope becomes apparent—usually too late to revise budgets or timelines.
Racing Against Industry Innovation
Perhaps the most overlooked challenge facing DIY teams is the speed of AI evolution. While internal teams struggle with basic implementation challenges, the broader industry has already moved from simple RAG (Retrieval-Augmented Generation) systems to agentic AI, orchestration platforms, and multi-agent systems.
Kumar emphasizes the magnitude of this challenge:
"The pace of change in AI is the fastest I've ever seen any technology evolve in my few decades in technology."
This isn't hyperbole—it's the reality of competing with hundreds of well-funded startups and major technology companies all innovating in the same space.
Specialized AI platform vendors maintain dedicated teams whose sole focus is tracking and integrating the latest developments. They monitor architectural changes like MCP (Model Context Protocol) and A2A (Agent-to-Agent) communication, evaluate new language models as they're released, and continuously optimize their platforms based on learnings from multiple enterprise deployments.
Check out our LinkedIn video on the Model Context Protocol:
Internal teams, by contrast, typically consist of a few engineers juggling multiple projects, getting pulled into production support issues, and working with partial resource allocation. They simply cannot compete with the innovation velocity of specialized vendors.
The Domain Expertise Multiplier
Even if internal teams solve the technical challenges, they face an often-overlooked hurdle: domain expertise. Building effective AI solutions requires deep understanding of the workflows, edge cases, and optimization opportunities within specific business domains.
DIY teams start with a blank slate. Every workflow, feature, and integration point must be designed, documented, and implemented from scratch. They rely on internal stakeholders who, while knowledgeable about their needs, aren't software product managers or AI specialists.
Purpose-built platforms like Rezolve.ai, which focuses on IT and HR support automation, accumulate domain expertise across hundreds of client implementations. They've already encountered and solved the edge cases that will eventually surface in any enterprise deployment. Their platforms come pre-configured with industry best practices, common workflows, and integrations that would take internal teams months or years to discover and implement.
Real-World Casualties
The theoretical problems become concrete when Kumar shares examples from his client conversations:
"Very recently we were talking to a client that's invested close to a million in a DIY project for something that did not go live, and we were able to help them go live in a few short weeks."
This pattern repeats across enterprises of all sizes. Promising internal projects consume significant resources, generate excitement among stakeholders, then quietly fade away as complexity mounts and deadlines slip. The sunk cost fallacy keeps some projects alive longer than warranted, consuming additional resources before eventual cancellation.
The human cost extends beyond financial waste. Internal teams experience frustration and career risk when high-visibility AI projects fail to deliver promised outcomes. IT leaders face credibility challenges that can impact future technology initiatives.
Meanwhile, business users continue struggling with inefficient support processes, waiting for solutions that may never materialize.
The Strategic Decision Framework
For CIOs and CTOs facing the build-versus-buy decision, Kumar offers straightforward guidance rooted in decades of software industry evolution:
"If you have a specific need that is somewhat common across the industry, use a product that's purpose-built—use an AI platform that's purpose-built for it."
The decision matrix is simpler than many organizations realize:
Build When: The use case is genuinely unique to your organization, requires proprietary algorithms or data models, or represents a core competitive advantage that justifies significant ongoing investment.
Buy When: The need is common across your industry, requires rapid deployment, demands enterprise-grade security and compliance, or falls outside your organization's core competencies.
Most employee support, HR operations, and IT service desk requirements fall squarely into the "buy" category. These functions, while critical to operations, rarely represent sources of competitive differentiation that justify massive custom development efforts.
The Path Forward
The enterprise AI landscape continues evolving rapidly, creating both opportunities and risks for organizations attempting to navigate independently. The gap between demo-quality prototypes and production-ready enterprise solutions remains substantial, despite advances in underlying AI capabilities.
Companies like Rezolve.ai are addressing this gap by providing agentic AI-powered service desks that integrate directly with platforms like Microsoft Teams and Slack, delivering enterprise-grade functionality without the complexity of custom development.
For technology leaders, the message is clear: focus internal development resources on areas of genuine competitive advantage and leverage specialized platforms for common enterprise functions. The build-versus-buy decision isn't just about initial costs—it's about opportunity cost, time to value, and organizational focus.
As Kumar notes, the fundamental software industry wisdom remains unchanged:
"The only place I would recommend you think of building something yourself is where you have a unique proposition that is generally not available widely in the market."
Key Takeaways
- The 60% trap: Initial AI demos are deceptively simple, but enterprise requirements create exponential complexity in the final development phases
- Hidden cost multipliers: DIY projects regularly exceed budgets by 5-10x due to underestimated enterprise infrastructure requirements, non-linear complexity growth, and rapid technology evolution
- Enterprise must-haves: Governance, security, analytics, and integration capabilities represent 40-50% of total development effort but are rarely included in initial project scoping
- Innovation velocity gap: Internal teams cannot match the development pace of specialized AI platform vendors who dedicate entire teams to tracking and implementing the latest advances
- Domain expertise advantage: Purpose-built platforms accumulate years of industry-specific knowledge and best practices that DIY teams must discover through expensive trial and error
- Strategic focus principle: Build only when addressing truly unique organizational needs; buy when requirements are common across the industry
Conclusion
The build-versus-buy decision for enterprise AI represents more than a technology choice—it's a strategic decision about resource allocation, risk management, and organizational focus. While the allure of custom development remains strong, the evidence overwhelmingly favors specialized platforms for common enterprise functions.
Organizations that recognize this reality early will deploy AI solutions faster, more reliably, and at significantly lower total cost than those attempting the DIY route. The question isn't whether to adopt AI for employee support and enterprise operations—it's whether to build competitive advantage through focus or lose it through distraction. The choice has never been clearer.
FAQs
Q1: What specific features does Rezolve.ai offer for enterprise IT and HR support?
A: Rezolve.ai provides autonomous Level 1 employee support with hundreds of prebuilt IT Helpdesk skills, reducing service desk tickets by around 35% in 3 months through agentic AI automation.
Q2: How quickly can organizations deploy Rezolve.ai compared to building internally?
A: Based on client examples shared in the conversation, Rezolve.ai can help organizations go live in a few short weeks, compared to internal projects that may consume millions of dollars without reaching production.
Q3: What platforms does Rezolve.ai integrate with?
A: Rezolve.ai integrates directly with Microsoft Teams and Slack, providing AI-powered service desk functionality within existing collaboration platforms.
Q4: What makes Rezolve.ai different from general-purpose AI platforms?
A: Unlike other GenAI platforms, Rezolve.ai is an IT and HR focused product that is purpose built to auto-resolve tickets and elevate employee experience, rather than requiring custom development for enterprise-specific workflows.
.webp)
.jpg)
.png)
.png)