Discover why AI can’t replace your job yet and how Devgraph’s ontology engine, paired with MCP, powers real-time, relationship-driven decisions for software teams. Learn more about the future of enterprise AI.
Ever wonder if AI could take your job? It’s a question I’ve been asked, and one I revisit as the AI landscape evolves. For me, and almost everyone I’ve posed it to, the answer’s a near-unanimous “No way.”
But why? Could that change?
As I’ve wrestled with this, I’ve tried to pinpoint what makes us uniquely qualified to build value for customers - and what LLMs can’t (yet) do. My conclusion? LLMs are fantastic for creative tasks, especially when they’re working on well-known patterns - think generating marketing copy or a quick code snippet. But they struggle with real-time, relationship-driven decision-making. Your job isn’t just writing code or publishing a blog post - it involves navigating the web of people, processes, and tools to deliver for customers. That’s where AI hits a wall.
The stats are sobering: a 2025 MIT report shows 95% of generative AI pilots fail to reach production, often because they can’t handle the live data enterprises depend on. The AI community is fighting back with tools like Model Context Protocol (MCP), a significant step forward that lets AI tap into real-time data and the ability to trigger actions. But MCP, while incredible, focuses on resources (like tickets or commits) and actions (like tool calls). It’s missing the critical relationship data - the connections between people, software, and systems - that drive business decisions. Let’s dive into why AI can’t replace you yet, how MCP moves us forward, and why relationships are the key to enterprise AI.
Ask a developer if AI could do their job, and you’ll hear, “No, it doesn’t get how things connect,” and they're right. LLMs are trained on static datasets, making them great for generative tasks like drafting a function or summarizing a doc. But software teams live in a real-time world: a bug tied to a customer escalation, a PR blocked by a team lead’s approval, a failing build needing a quick resolution. These aren’t just tasks - they’re decisions rooted in relationships between people (devs, PMs), processes (sprints, CI/CD), rules (like “no merges without tests”) and good old tacit knowledge.
The numbers show AI’s struggle:
So, while AI might crank out a perfect script, it doesn’t grasp the human and process-driven relationships that make your job work. That’s why it’s not occupying your desk anytime soon.
The Model Context Protocol (MCP), launched by Anthropic in 2024, is a massive step forward for enterprise AI. It’s an open standard that lets LLMs pull live data (like a Jira ticket’s status) and trigger actions (like rerunning a build via GitHub Actions). For software teams, this means AI can see a failed CI run or a new bug in real time, no stale training data required.
MCP’s strength lies in how it handles resources (like tickets or commits) and actions (like tool calls). It’s a huge win for real-time needs, letting AI fetch live data and interact with tools seamlessly.
MCP is fantastic, but it’s not the whole story. It excels at gathering data about resources and executing actions but doesn’t natively understand the relationships that drive smart and accurate decisions. For example, MCP can fetch a Jira ticket and notify a dev, but it doesn’t necessarily know that the ticket is tied to a customer’s urgent feature, or that only certain developers can remediate the issue based on internal governance rules.
This problem is compounded by heterogenous environments. What happens when one team has standardized on a different tool for CRM capabilities? Without the proper relationship data, the LLM will have no idea how to elicit the correct response.
Those relationships - how entities connect through people, processes, and rules - are what make a decision correct and, in turn, enable meaningful automation when done right.
As we spoke with software teams about their command and control capabilities, we found a giant gap between where they were and where they hoped to be. This became even more pronounced when considering how these teams leveraged AI to achieve their goals.
And, for those who were already experimenting with how AI could be used for real-time data, tool calling, and contextualized decision making the problem, in some ways, became worse. These experiments usually amounted to developers running local MCP servers, for their specific use cases, with no shared understanding of the relationships that drive their team's success. Yes, there were some wins, but they were limited in scope.
This is where our product comes in. Over the past few months we’ve built Devgraph - an ontology engine to complement MCP, designed to map the relationships that will make enterprise AI truly effective. Think of it as a dynamic blueprint that spells out not just the entities (bugs, PRs) and tools (Jira, GitHub), but how they connect: “Ticket #123 is urgent because it blocks Customer X’s feature, and only Dev A can approve the fix per team rules.” Our engine helps AI move from fetching disjointed data to making decisions and/or taking actions that align with your current reality. We remove the LLM's guesswork.
For software teams, this is a huge lift. All of the power of a natural language interface layered with your unique ontology and the power of MCP. You can fully, and simply, customize the knowledge graph to reflect your reality: no undiscoverable spreadsheets and manual effort.
With the ontology engine you may direct the LLM to interrogate multiple, but connected, systems of record to come to a comprehensive answer. Or you may reliably take action against your live systems based upon their characteristics and relationships.
Think about the case "redeploy the analytics application." Without knowing how this is deployed, which application dependencies are required, which environments are available, or who is even authorized to perform such a task, this would be a hard request to satisfy, even for a well-connected LLM. It is impossible without understanding the relationships between all of these seemingly simple entities.
Likewise, the ontology provides rules-based capabilities. Understanding how your environment is changing, and what actions would yield the best results, again, requires relationship data. Questions like "have i met my disaster recovery requirements?" cannot be answered without understanding the characteristics of an entity and its connections.
That’s decision-making that mirrors how you would handle it yourself. Our ontology engine makes these wins more accessible by encoding the relational logic AI requires to be reliable and effective.
Next time someone asks if AI can take your job, point out that LLMs are great at generating content but lost on the real-time, relationship-driven decisions that define software development. MCP is a quantum leap forward, giving AI access to live data and tools. But without relationships, it’s only half the solution.
That’s why we've built the Devgraph ontology engine - to help AI understand the why and how behind your work, not just the what.
