Presented by Certinia
The early excitement around generative and agentic AI has now turned into a practical, often frustrating reality. CIOs and tech leaders are asking why their pilot programs, designed to automate even the simplest workflows, aren’t delivering the magic promised in demos.
When AI fails to answer a basic question or complete a task correctly, the tendency is to blame the model. We believe that LLM is not "Intelligent" Sufficient. But that blame is wrong. AI doesn’t struggle because it lacks intelligence. It struggles because it lacks Context.
In the modern enterprise, the context is stuck in a maze of disconnected point solutions, brittle APIs, and latency-ridden integration – a “franken-stack” of disparate technologies. And especially for service-centric organizations, where the real truth of business resides in the intersection between sales, delivery, success, and finance, this fragmentation is existential. If your architecture locks out these functions, your AI roadmap is doomed to failure.
Cannot travel through Context API
For the last decade, the standard IT strategy was "best of breed." You bought the best CRM for sales, a separate tool for managing projects, a standalone CSP for success, and an ERP for finance; Paired them with APIs and middleware (if you were lucky), and declared victory.
For human workers, this was annoying but manageable. A person knows that the project status in the project management tool may be 72 hours behind the invoice data in ERP. Humans have the intuition to bridge the distance between systems.
But AI doesn’t have intuition. There are questions in it. When you ask an AI agent to “add staff to the new project we won for margin and usage impact," It executes a query based on the data it can access right now. If your architecture relies on integration to move data, AI is running late. It looks at the signed contract, but not the resource constraints. It looks at revenue targets, but not churn risk.
The result is not just a wrong answer, but a convincing, plausible-sounding wrong answer based on a partial truth. Acting on it creates costly operational pitfalls that go far beyond failed AI pilots alone.
Why does agentic AI need platform-native architecture?
That’s why the conversation is going on here and there "Which model should we use?" To "Where does our data live?"
To support a hybrid workforce where human experts work alongside duly enabled AI agents, the underlying data cannot be linked together; It must be native to the main business platform. A platform-native approach, typically built on a common data model (e.g. Salesforce), eliminates the translation layer and provides the single source of truth that good, reliable AI requires.
In the native environment, data resides in a single object model. Scope change in delivery is revenue change in finance. There is no sync, no latency, and no loss of position.
This is the only way to achieve real certainty with AI. If you want an agent to autonomously staff a project or forecast revenue, it will need a 360-degree view of the truth, not a series of snapshots taped together by middleware.
Protecting the side door: API as an attack surface
Once you solve for intelligence, you also have to solve for sovereignty. The argument for a unified platform is typically framed around efficiency, but an increasingly important argument is security.
In a best-of-breed Franken-stack, every API connection you make is effectively a new door you have to lock. When you rely on third-party point solutions for critical functions like customer success or resource management, you’re constantly moving sensitive customer data out of your main system of record and into satellite apps. this movement Is risk.
We have seen this in recent high-profile supply chain breaches. The hackers did not need to storm the castle gate of the main platform. They simply walked in through the side door by leveraging persistent authentication tokens of connected third-party apps.
A platform-native strategy solves this through security by inheritance. When your data resides on a single platform, it inherits that platform’s huge security investments and trust boundaries. You’re not moving data to a different vendor’s cloud just to perform analysis. Gold never leaves the safe.
Fix the Architecture, Then Curate the Context
The pressure to deploy AI is immense, but placing intelligent agents on top of non-intelligent architecture is a waste of time and resources.
Leaders often hesitate because they fear their data is wrong "Clear enough." He believes that before deploying even a single agent he will have to go through every record of the last ten years. On the fragmented heap, this fear is valid.
A platform-native architecture transforms the mathematics. Since data, metadata, and agents live in one house, you don’t have to boil an ocean. Simply ring-fence specific, trusted fields – like active customer contracts or current processing schedules – and tell the agent, ‘Work here. Ignore the rest. By eliminating the need for complex API translations and third-party middleware, a unified platform allows you to ground agents in your most trusted, connected data today, eliminating disruption without waiting for the ‘perfect’ state that may never arrive.
We often fear that AI will hallucinate because it is so creative. The real danger is that it will fail because it is blind. And you can’t automate a complex business with fragmented visibility. Deny your new agent workforce access to the full context of your operations on a unified platform, and you are building a foundation that is doomed to fail.
Raju Malhotra is Chief Product & Technology Officer at Certinia.
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