Where Enterprise Software Companies Should Actually Invest in AI

TL;DR

Enterprise AI investments should focus on three areas: internal documentation via context7-style implementations, custom log analysis MCP servers, and unified alerting systems. These practical tools make developers faster and happier, not replace them.

Everyone's talking about AI agents. Agent to agent communication. MCPs. The buzzwords keep piling up. But where does any of this actually make sense for enterprise software companies? I've been thinking about this a lot. The answer isn't some moonshot project. It's boring, practical developer tools that solve real problems.

The Real Opportunity: Developer Tools

Here's what I think enterprise companies should build.

1. Context7 for Internal Repositories

One of the biggest problems with AI coding assistants is hallucinated code. Your assistant writes something that looks right, but it's based on outdated training data. Deprecated APIs. Old patterns that don't work anymore. Context7 solves this problem. It's an MCP server that acts as a real-time documentation wiki for AI assistants. Instead of relying on stale training data, Context7 dynamically injects the latest, version-specific official documentation directly into the AI's context. Your assistant looks up the current docs instead of guessing. If you maintain an open source library, you should add your repo to Context7. Your users will get better code suggestions instantly.

But here's what enterprises should think about: why not do the same thing for your internal codebase? Every company has internal libraries, shared utilities, and custom frameworks. New developers spend weeks figuring out how things work. Senior developers answer the same questions over and over. AI assistants are useless because they don't know your code.

Build a context7-style implementation for your internal repos. Index your code, your README files, your internal wikis. Then expose it through an MCP server. The result? Your AI coding assistants become experts on your codebase. They know your patterns. They understand your conventions. They can point developers to the right code. Here's the best part: developers might not need to write documentation anymore. The system documents itself. The MCP server becomes the source of truth.

2. Custom Log Analysis MCP Server

Every company stores logs somewhere. Kibana. Logstash. Splunk. Datadog. Whatever tool you use, you've built up knowledge about how to read those logs. You know your trace ID patterns. You know which user fields matter. You know how to drill down from a 500 error to the actual code that broke. This knowledge lives in your senior engineers' heads. When they leave, it leaves with them.

Build an MCP server that understands your logs. Not a generic log viewer. A custom implementation that knows your system.

When a developer asks "why did this request fail?", the MCP server can:

• Find the relevant log entries using your trace ID conventions
• Follow the request across services
• Identify the actual error buried in the noise
• Connect to your context7 MCP to find the code that caused it
• Suggest potential fixes based on similar past issues

Debugging goes from hours to minutes. You could even build monitors from this same system. Spot patterns before they become incidents.

Connect this to your alerting bots and on-call rotations. When a production bug hits, the agent can generate a synopsis before the developer even opens their laptop. What broke, likely causes, relevant code, and similar past incidents. Production investigations start from something instead of nothing.

Why This Matters

None of this is flashy. You're not building autonomous agents that replace developers. You're building tools that make developers faster.

That's where the real value is.

AI agents are good at tedious work:

• Reading logs
• Cross-referencing documentation
• Summarizing incidents

Let them do that work. Let your developers focus on actually solving problems.

Extend This to Where It Makes Sense

The same concept applies beyond code. Think about all the knowledge scattered across your company:

• Internal documents in Confluence, Notion, or SharePoint
• Design specs in Figma, Balsamiq, or Draw.io
• Jira tickets for past incidents, user stories for new features, and product requirements
• Architecture decisions in Google Docs

Build MCP servers for all of it. Let your AI assistants pull context from your design files when a developer asks about the expected UI behavior. Let them reference the original product requirements when someone wonders why a feature works a certain way.

The goal is simple: make developer life easier.

Every minute a developer spends searching for context is a minute not spent building. The productivity boost from the right tools outweighs the investment many times over.

The Implementation Path

Start simple:

1. Pick one internal library. Build a context7-style index for it. See if developers find it useful.
2. Build a read-only MCP server for your logs. Just searching and summarizing at first.
3. Connect one alerting system to your new tools. Generate incident summaries for your team.

Each step delivers value on its own. You don't need to build everything at once.

The Bottom Line

Agent to agent communication, MCPs, and all the AI buzzwords do matter. But not in the way the hype suggests. The value isn't in replacing developers. It's in giving them better tools. Build the boring stuff. Index your internal docs. Make your logs searchable. Unify your alerting. Each of these makes your developers faster and happier.

That's how you capture the value of AI in the enterprise.

Thoughts? Hit me up at [email protected]