The Value of MCP: Why the Future of Integration Isn’t Software-to-Software, It’s Software-to-AI
- Sep 22, 2025
- 4 min read
If you’ve used ChatGPT or another Large Language Model (LLM) recently, you already know how much they can speed up putting information together.
Now imagine if it had access to all your business data, and understood it in the right way, so you could ask anything.
“Show me the equipment that is not being used in the last year, and how much is it costing us to maintain?”
This is the future being enabled by Artificial Intelligence (AI) and the Model Context Protocol (MCP).
MCP is a big change in how digital systems talk to each other.
Instead of endless APIs and fragile integrations, MCP creates a common language that lets software, servers, and AI, share information with context.
For a long time, “integration” meant connecting one system to another: syncing databases, building connectors, and maintaining APIs. It works, but it’s slow to set up and can be expensive. Every time something changed upstream, something else could fail downstream - costing time and money.
AI and MCP changes that. It’s no longer about connecting software to software, it’s about making data and context available to AI that can understand, reason, and act. The AI can connect to each system on its own, grab the data it needs, and pull it together to get the answers needed.
Instead of writing custom integrations, MCP lets us show what the data is, what it means, and how to use it - and the AI takes care of the rest.
From Data Sharing to Context
Integration is shifting. It’s not about how many systems you connect - it’s about how easy it is for AI to use your data. The systems that win will be the ones that make their data simple and clear for AI to work with.
But this shift brings up a big issue: data quality.
If AI depends on accurate, context-rich data, then bad data leads to bad decisions. And most data still comes from people. We’re still a long way from robots doing all the inspecting, recording, and checking that happens in the real world.
Trust Starts with Good Data
The goal isn’t to just give AI access to data - it’s also to make sure people can trust what it does with it. That trust starts with collecting the right data and being able to check what the AI produces against human-based calculations and assessments.
That’s why businesses need to act now to put better tools and processes in place that help people collect clean, accurate data - easily and at the point of activity. Because if AI is working with bad or incomplete data, the results will be wrong.
Digital change also takes time, especially when people are involved. Companies that focus on getting good data today will be far ahead, while others are still dealing with bad systems and paper forms.
Stop Chasing a “Single Source of Truth”
Here’s another mindset that needs to change, and one that has caused a lot of failed digital projects.
Stop chasing the idea of a single source of truth. The old view that everything needs to connect into one big system, like an ERP, doesn’t make sense anymore.
Businesses should think in terms of sources of truth - plural.
What matters is that the data is accurate and reliable, no matter where it lives. Why force everything into one place? What value does that really add?
Accurate and accessible data is of far greater value than where it resides. (assuming security is considered, of course).
Stop wasting time and money (or blocking new ideas) because “it must integrate with our ERP.” It doesn’t. It just needs to be fit for purpose, produce good data, and make that data available for AI to access and use with context.
Integration, Redefined
Integration isn’t about linking one data field to another any more. It’s about connecting accurate, useful data with AI and context, so both people and their AI can make better decisions.
MCP is the framework that makes that possible.
About the Author
Gabe Alves is the Co-Founder and CTO of EXTAG, a Perth-based SaaS company focused on closing the asset management gap and complementing ERP systems across mining, energy, and defence. With more than two decades in IT infrastructure and deep experience in mining and resources, Gabe leads the development of EXTAG’s user-centric platform — trusted by Tier 1 operators for its speed, flexibility, and ability to deliver AI-ready data from the field. A long-standing contributor to Australia’s innovation ecosystem, Gabe is exploring how the Model Context Protocol (MCP) is reshaping digital integration — from software-to-software connections to software-to-AI collaboration — to drive the next phase of intelligent, data-driven mining.
About EXTAG
EXTAG is a powerful software platform designed for asset-intensive organisations to ensure all assets – no matter how minor – are compliant, safe, and ready for use.
Proven in Oil & Gas since 2018, EXTAG’s proprietary platform complements existing systems to deliver cost-effective, ERP-level control of excluded assets – making Asset Managers’ jobs simpler and their results visible.
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