Why Buying Beats Building to Deliver Value at Speed and Scale

AI is transforming business like never before, positioning itself as the most disruptive technological force of the last 25 years. The hype is not just noise—companies are putting their money where their mouth is. U.S. businesses are projected to spend a staggering $300 billion annually on AI over the next five years. This surge in investment is not without reason; the potential to revolutionize business processes, particularly customer service, is undeniable. The McKinsey Institute predicts that AI could cut customer service costs by up to 40%, with some startups boldly claiming reductions of up to 80%.

Customer service stands at the forefront of this AI-driven disruption, with a simple goal: deliver trusted, consumable answers to the right person, at the right time, via the right channel. To achieve this, AI must sift through vast amounts of unstructured data—policies, procedures, product knowledge. This is exactly where AI excels. But there’s a crucial question that businesses face: Should they build a proprietary AI system or buy a modern knowledge management solution that will power their AI investments?

In these early days of AI transformation, the answer is clear: buy.

The Case for Buying a Modern AI Knowledge Management Solution

1. Time to Value: Start Transforming Customer Service in Weeks

Building a proprietary AI-powered customer service system is no small feat. It’s complex, time-consuming, and fraught with challenges like compliance reviews, security concerns, and the need for extensive testing. On the other hand, a best-of-breed knowledge management solution can deliver immediate value. Implementation can take as little as 4 to 8 weeks.

Beyond implementation, modern knowledge management systems come with features that accelerate success—like proactive outreach capabilities that anticipate and resolve customer issues before they even arise. These solutions reduce service requests and boost customer satisfaction. This is why leading knowledge management solutions often deliver a 4X to 8X ROI in just 12 months.

2. Organizing the Mess: AI Is Only as Good as the Content You Feed It

In most businesses, knowledge bases are riddled with outdated information, gaps, and inconsistencies. Simply layering AI over this chaos leads to the infamous “garbage in, garbage out” problem. Gartner has underscored this point, predicting that by 2025, 100% of virtual customer assistant and virtual agent assistant projects that lack integration with modern knowledge management systems will fail to meet their customer experience and operational cost-reduction goals.

The solution? A best-of-breed AI-powered knowledge management system that can:

• Analyze customer interactions to identify pain points and knowledge gaps.
• Use AI-powered tools in expert workflows to generate best-practice responses.
• Deliver correct, consistent, and compliant answers to AI front-end.

3. Tap Into Years of Best Practices

Leaders in AI knowledge management such as eGain have spent decades developing best practices for delivering trusted answers and AI integration. When companies buy these solutions, they gain access to a trove of capabilities and expertise, including:

• Collecting feedback from agents and customers to refine AI-generated content.
• Optimizing AI-driven guidance for complex, error-prone processes.
• Implementing real-time analytics for continuous improvement of AI capabilities.
• Building knowledge content workflows for accurate and compliant responses.

4. A Smart Hedge Strategy for AI Investment

Even if you’re ultimately aiming to develop an internal platform to feed trusted content to your front-end AI models, implementing an off-the-shelf AI knowledge management solution is a smart hedge. This allows you to:

• Realize benefits now while your internal solution is being developed and tested.
• Gain insights into AI adoption patterns across employees and customers.
• Identify high-impact use cases before committing resources to development.

Debunking Arguments for Building Proprietary AI model that Directly Plugs Into Your Knowledge Content Silos

1. Getting Trusted Content to Feed AI Is as Easy as R-A-G

Retrieval-Augmented Generation (RAG) is often touted as a solution to feed AI with trusted content. But when businesses look closer, they’ll realize that knowledge is spread across multiple silos within the organization. For example, one of our clients has over a hundred content silos across departments, all of which need to be tapped into to find the trusted answer. RAG may work for simple prototypes, but in an enterprise with multiple knowledge silos and compliance requirements, it falls short.

Building the complementary infrastructure around RAG—effectively creating a homegrown knowledge hub—might be possible, but it’s not efficient. It’s far wiser to buy a modern AI knowledge hub that seamlessly combines AI with expert knowledge, scaling securely across platforms. The eGain AI Knowledge Hub, for example, integrates workflow, content management, AI guardrails, audit trails, and analytics into one powerful solution, along with hundreds of enterprise-ready best practices.

2. Competitive Advantage: AI Model vs. AI Execution

Many boards and CEOs believe that building a proprietary AI model will give them a competitive edge. Given the rate of improvement in AI model capability and cost, it is hard to imagine that one can keep up with this pace of change by building proprietary models. What that means is the proprietary AI models can easily get overtaken by industry innovation, in terms of capability and cost. Years of effort and millions of dollars of investment could be wasted on proprietary AI projects. In such an environment, we believe it is wise to derive competitive advantage from moving up the value stack and establishing differentiation based on organizing, connecting, and configuring best-in-class fungible AI models within the context of your business workflows.

With the recent DeepSeek launch, it is clear AI models are exciting, valuable, and a commodity. They will rapidly become cheaper, faster, and better. Sustainable advantage will be derived from how quickly companies deploy AI (at scale) in the context of their use cases, data, and processes to solve real business problems. In fact, leveraging multiple models (within a “BYO AI” composable architecture) will allow businesses to maximize value from AI investments. Exactly what an AI Knowledge Hub allows you to do: Bring your AI models, if and when needed, and plug them into modern workflows that combine experts and AI to automate customer service.

3. Risk Mitigation: More Control Doesn’t Equal Less Risk

Some believe that building AI in-house allows them to manage risk more effectively, especially in compliance-heavy sectors. However, it’s not the AI model itself that mitigates risk, it’s the safeguards you build around it. For example, eGain’s solution includes:

• AI confidence score controls, which ensure that low-confidence AI responses never reach customers.
• Non-editable legal and compliance scripts to ensure AI cannot modify required verbatim content.
• Real-time audit trails for transparency and accountability in decision-making.

4. Intellectual Property (IP) Protection: Keeping Your Data Secure

Executives often worry that using third-party AI models could expose proprietary business knowledge. Best-of-breed knowledge solutions mitigate this risk with advanced security protocols and strict contractual agreements that prevent unauthorized learning on proprietary data. These solutions provide a multi-layered approach to ensure your data remains secure.

Conclusion: The Smartest Path to AI-Powered Customer Service

Building AI from scratch is a high-risk, daunting task—especially in the early stages of the AI revolution. Instead of spending years and resources developing a homegrown solution, businesses should opt for a leading AI Knowledge Hub that delivers results in weeks.

By buying rather than building, companies can:

• Realize immediate ROI.
• Ensure AI-generated content is accurate, consistent, and compliant.
• Leverage industry-leading best practices.
• Future-proof their AI strategy with a flexible, scalable knowledge system.

With AI set to redefine customer service, the companies that act fast will emerge as leaders in their industry. The real question is: Will your company lead the charge, or be left behind?
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Ready to revolutionize your customer service with AI? Explore how a modern AI Knowledge Management Solution can deliver results today!

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