Key Takeaways
- AI agents fail in production not because the models are wrong, but because the data underneath them is ungoverned, incomplete, or inaccessible to the agent in the first place.
- Precisely announced enterprise MCP server support for multiple products across our portfolio, giving AI agents governed access to data quality, location intelligence, master data, customer communications, mainframe optimization and automation capabilities through a single standards-based interface.
- Existing customers can access these capabilities through the AI tools they already use, with no new tools to learn and no custom integrations to build.
I’ve spent a lot of time over the past year talking with customers who are doing everything right on the AI side and still not getting the outcomes they expected. Their models are capable, infrastructure is in place, and ambition is genuine. Yet, the programs are still stalling or producing outputs that look authoritative and are simply wrong.
The pattern is consistent enough that I’ve stopped being surprised by it. The failure is almost never the model, but the data the model is acting on: addresses that haven’t been verified, records that haven’t been cleansed, master data that no agent can query without a custom integration that someone had to build by hand, then maintain, then rebuild when the team adopted a different AI tool.
Precisely recently announced enterprise MCP (Model Context Protocol) server support for multiple products across our portfolio. This is the capability that connects AI agents and assistants directly to the data quality, location intelligence, master data management, customer communications, mainframe optimization and automation capabilities that our customers already rely on, through a single standards-based interface with no custom integration required.
Today, this support is available across Precisely Automate, Precisely Data360, , Precisely EngageOne™ RapidCX, Precisely Enterworks™, Precisely Spectrum, and Precisely Trillium, with more to come in future releases.
I want to explain the problem it solves, and what it changes for teams that are trying to close the gap between AI ambition and reality.
Why are enterprise AI programs hitting a wall at production scale?
AI agents are being deployed faster than organizations can ensure the quality and governance of the data those agents are acting on.
Here’s what that looks like across functions:
A data steward’s team has invested years in building quality rules, matching logic, and governance workflows inside Precisely Trillium.
Those rules represent real institutional knowledge about what clean data looks like for their organization. When they try to expose that logic to an AI agent, the agent can’t reach it without a custom integration. So, either someone builds the integration, or the agent works around the governed data entirely and acts on whatever it can access. Neither outcome is good.
For location-dependent industries, the version of this problem involves geocoding and address verification. Insurance underwriters, logistics teams, and telecommunications planners depend on accurate location data to make decisions that carry real risk.
The expertise to run geocoding queries has historically lived inside GIS software and with GIS specialists. An AI agent that can’t reach those capabilities cleanly is likely to take one of two paths:
- Route every request through a specialist, adding latency and friction
- Answer from model knowledge, which isn’t the same as a verified address
For product owners and category managers working with master data, it’s a different kind of access problem. Finding and understanding master data means navigating specialized MDM (master data management) structures rather than getting answers in the flow of work. Every decision that depends on trusted product master data moves more slowly than it needs to.
For SAP teams navigating S/4HANA migrations, it’s a workflow access problem. Initiating or interacting with a workflow requires leaving the AI assistant and switching to a dedicated application. The context-switching is at minimum inconvenient, but becomes a true bottleneck for time-sensitive approvals.For analysts and data engineers, it’s a tooling access problem. Preparing data, inspecting a data flow, or re-running one after a fix means leaving the AI assistant and working inside a separate analytics environment. Every handoff slows down the people who are supposed to be moving fastest.
For customer communications teams, the problem is control. Triggering a statement or notice, or checking what a customer has already received, usually means working in a dedicated platform. And in regulated industries, an agent that can’t act within scoped, auditable boundaries isn’t something teams can trust with customer-facing communications.For mainframe teams, knowledge access is the challenge. Decoding a sort message, troubleshooting a failed job, or writing sort, copy and merge logic depends on a shrinking pool of specialists and on documentation scattered across manuals and knowledge base articles.
In each case, the underlying issue is the same: the AI is capable, but it can’t reach the governed enterprise data or workflows that would make its outputs trustworthy. The connectivity and governance problem represents one of the primary reasons AI programs that showed promise in a controlled environment can’t make the transition to durable production use.
What does enterprise MCP server support do?
Model Context Protocol is a standards-based interface that lets AI agents connect to external tools and data sources without requiring a custom integration for each connection. When AI developers talk about “grounding” an agent, this is part of the infrastructure that makes grounding practical at enterprise scale.
Precisely MCP servers are now generally available across multiple products, with support for additional products in future release cycles. Here’s what each of these unlocks:
- Precisely Automate lets AI assistants initiate and interact with SAP automation workflows while maintaining the organization’s own governed business processes.
- Precisely Data360 prepares and analyzes data using Data360 Analyze to create, inspect, update, and run data flows.
- Precisely EngageOne™ connects customer communications workflows using EngageOne™ RapidCX, including triggering new communications and inspecting communication activity and customer engagement history, with all requests scoped to the user and recorded in an audit log.
- Precisely EnterWorks™ enables natural-language search across master data, helping users find and understand data without navigating MDM structures.
- Precisely Spectrum enables trusted address verification, geocoding, data enrichment, and job and dataflow management, without routing through a GIS specialist or building a custom API connection. Teams in insurance, logistics, and telecommunications can get verified location data directly inside their AI assistant.
- Precisely Trillium helps cleanse, match, and validate records using trusted business rules, directly from AI agents and assistants. Data quality work that previously required navigating a specialized console can now happen inside the AI tools teams already use, governed by the rules organizations have already established.
- Precisely Syncsort™ optimizes mainframe sorting by explaining messages, troubleshooting jobs, and generating sort, copy, and merge logic grounded in Syncsort MFX documentation and knowledge base articles. For organizations managing mainframe environments where specialist expertise is increasingly difficult to replace, this reduces the dependency on deep technical knowledge for routine operations. These capabilities will be available in the coming months.
Precisely is the data foundation enterprise agents can rely on. The result is AI that acts on accurate, governed data rather than guessing. If you’re an existing customer, enabling these capabilities requires no new tools and no rebuilt integrations. The capabilities surface in whichever MCP-compatible AI client your teams already use: Claude, Microsoft Copilot, ChatGPT, or any other compatible platform.
How do you know if your data is ready for AI agents to act on?
There’s a useful diagnostic question for any organization deploying AI agents in production workflows: When the agent takes an action, what is it grounded in? If the honest answer is “model knowledge” rather than “our governed enterprise data,” that’s the gap worth addressing before scaling.
Data must be integrated, governed, and enriched to a standard that autonomous AI systems can act on reliably. Getting to that standard requires both mature governance programs and the infrastructure to make governed data accessible to AI agents in the flow of work. MCP servers are part of that infrastructure.
What I keep hearing from data leaders is that their concern isn’t AI capability, but accountability:
- When an agent makes a decision based on a bad record, who is responsible?
- What’s the audit trail?
- How do you demonstrate to a regulator that the data your AI acted on met your governance standards?
These are solvable problems, but only if the AI is connected to governed data in the first place.
The organizations that are getting durable results from AI are investing in the data foundation first, then extending it to meet the specific requirements that agentic systems introduce: tighter lineage documentation, clearer data ownership, quality rules that an AI agent can invoke, not just a human analyst.
Building AI you can stand behind in regulated environments
The stakes are highest for organizations in complex, regulated environments: financial services, insurance, healthcare, utilities. These are organizations where an AI agent acting on an unverified address or an uncleansed record creates major compliance exposure.
That context shapes how we thought about portfolio-wide MCP server support. The goal was to make Precisely capabilities accessible to AI in a way that preserves the governance controls organizations have already established. When a Trillium quality rule governs how a record gets cleansed, that rule should apply whether a human triggers the workflow or an AI agent does. MCP servers make that continuity possible.
MCP servers are available today for Precisely Automate, Precisely Data360, Precisely EngageOne™ RapidCX, Precisely EnterWorks™, Precisely Spectrum, and Precisely Trillium, with Precisely Syncsort™ capabilities to follow in the coming months. This is a portfolio-wide commitment, and we’re working on expanding it. By connecting your AI agents to governed data now, you’ll be better positioned for success as that portfolio grows.
Most of the teams I talk with today are managing several AI initiatives simultaneously, each with different data dependencies. There isn’t a universal right answer for which connection to make first, but what I do believe is that making the first one counts. It establishes the pattern for how AI connects to governed enterprise data, and that pattern compounds.
For organizations in regulated industries, portfolio-wide MCP server support is how AI agents can act on data that meets governance standards, while maintaining the security and governance controls they already have in place.
New customers can request an MCP server demo to get started. For existing customers, Precisely MCP servers are available on a subscription basis to customers with certain select subscription products. Customers are welcome to contact their Precisely account executive for availability and implementation options.
Frequently Asked Questions
What is MCP server support and why does it matter for enterprise AI?
Model Context Protocol (MCP) is a standards-based interface that allows AI agents and assistants to connect with external tools and data sources without custom integration code. For enterprise teams, this means AI agents can access governed data quality, location intelligence, and automation capabilities in the tools they already use, rather than requiring engineering work to build and maintain each connection.
Which Precisely products support MCP servers today?
MCP servers are available for Precisely Automate (SAP automation workflows), Precisely Data360 (data preparation and analysis), Precisely EngageOne™ RapidCX (customer communications), Precisely EnterWorks™ (master data search), Precisely Spectrum (address verification, geocoding, and data enrichment), and Precisely Trillium (data cleansing, matching, and validation). Precisely Syncsort™ capabilities (mainframe sort optimization) will be available in the coming months. MCP servers for additional products will be made available in 2027.
Do existing Precisely customers need to rebuild their integrations?
No. MCP servers connect AI agents to Precisely products without custom integrations. They are available to customers with subscription products; customers can contact their Precisely account executive for availability and implementation options. Existing customers can enable MCP server connectivity without adopting new tools or building custom integrations. Capabilities surface within whatever MCP-compatible AI client the team already uses, including Claude, Microsoft Copilot, and ChatGPT.
Why is this particularly valuable for regulated industries?
Regulated industries require AI systems that act on accurate, governed data, not on model-generated assumptions. Precisely MCP servers connect AI agents to data that has passed through established quality and governance processes, helping organizations maintain their existing security and governance controls in high-stakes environments like financial services, insurance, and healthcare.
