Key takeaways
- The Precisely Platform brings data management capabilities together on one shared metadata and semantic foundation, with general availability planned for early 2027.
- Precisely AI Studio equips builders with agents, apps, and skills that show what’s possible with the data management capabilities of the Precisely Platform.
- New MCP servers for Precisely products enable AI agents and assistants access to the data management and customer communications capabilities that customers already use.
- AI-ready data is visible, reliable, accountable, and complete, and delivering all four takes unified data management on one foundation.
On October 6, we introduced the Precisely Platform, Precisely AI Studio, and new Model Context Protocol (MCP) servers, opening a new chapter for Precisely built around one purpose: helping organizations make their data AI-ready so they can put AI to work with confidence.
What organizations need to move forward: four characteristics of AI-ready data
At Precisely, we define AI-ready data through four essential characteristics, and organizations need all of them before handing data over to autonomous systems. This is what they look like in practice:
- Make your data visible, so AI can reach critical data from hard-to-reach mainframe and SAP systems without requiring migration to the cloud.
- Make your data reliable, with quality that is measured instead of assumed.
- Make your data accountable, so every decision traces back to the data and the policy behind it.
- Make your data complete, with real-world context on places, properties, businesses, and more, that internal records may not supply.
Over the years, enterprises have assembled a series of individual tools to manage their data, and bolted them together. This approach has always had challenges: when capabilities are added one at a time and stitched together, they don’t share common elements like pipelines, metadata, or security. Organizations have relied on analytics and humans to identify and resolve the resulting data issues.
When AI is introduced in each individual tool, this fragmented approach no longer works. With AI adding uncertainty at multiple points, it becomes impossible for humans or systems to identify the resulting data issues.
Closing those gaps takes a unified data management platform to act as a control plane across an organization’s data ecosystem. With shared metadata and semantics, a unified platform allows a quality rule or business definition to be set once and applied everywhere. It also means managing data where it lives, without forced migration, and staying open to whichever models and tools a team chooses.
The Precisely Platform brings together the capabilities our customers have come to know and love, including data integration, data quality, data governance, location intelligence, master data management, and customer communications on a shared metadata and semantic foundation. The Platform also features built-in data enrichment for the real-world context AI and analytics need.
Why is now the moment for Precisely to make this move?
Precisely has spent decades helping enterprises solve their most complex data challenges.
We put the work into data quality for regulated industries, governance others treated as an afterthought, and the mainframe and SAP systems that still run some of the world’s most consequential transactions.
That work now supports over 12,000 customers in more than 100 countries, including 92% of the Fortune 500.
What has changed is that AI has made that foundation the main event.
This is the new Precisely: a company built around unified data management for AI-ready data. Together, what we announced on October 6 brings that to life.
- The Precisely Platform is the control plane behind all of this, and it’s in preview now with general availability in early 2027.
- Precisely AI Studio allows builders to explore ready-made apps, agents, and skills that show what they can build with Precisely capabilities and jumpstart those solutions.
- New MCP servers connect trusted Precisely capabilities to the AI agents and assistants teams already use.
The response so far has shown that leaders are ready for AI-ready data. Nearly 2,000 data leaders, builders, customers, and partners registered for Precisely Now, exploring this moment and what it takes to put AI to work with trusted data.
People have moved past asking whether AI needs trusted data, and they now want to know how to get there.
What comes next for AI-ready data?
Launch day was just our starting line.
The Precisely Platform moves from preview to general availability in early 2027. The collection of assets in AI Studio will keep growing, and MCP support will be offered for more Precisely products.
Looking for the next best step in your AI readiness journey? Take one AI project that has stalled and ask whether its data is visible, reliable, accountable, and complete. The gaps you find show where to focus first.
Start a free trial of Precisely AI Studio today, join the preview of the Precisely Platform, or watch Precisely Now on demand to see what AI-ready data looks like in practice.
Frequently asked questions
What is AI-ready data?
AI-ready data is data that AI systems can reach, trust, and act on with full context. It is visible where AI solutions need it, reliable in quality, accountable through governance and lineage, and complete with real-world context such as the properties, businesses, or demographics associated with a verified address.
What is unified data management?
Unified data management brings data integration, quality, governance, enrichment, location intelligence, master data management, and customer communications together on one shared metadata and semantic foundation. A quality rule, governance policy, or business definition is set once and applied everywhere, rather than an approach that stitches together multiple tools.
Why are agentic AI projects canceled?
Gartner cites escalating costs, unclear business value, and inadequate risk controls. Many of those problems trace back to data that is out of reach of AI systems, of poor quality, inconsistently governed, or missing context, because AI agents act on data without the review people once provided.
Do you have to migrate data to the cloud to make it AI-ready?
No. Data can be managed where it lives across mainframe, SAP, on-premises, cloud, and hybrid environments, using a control plane that manages data across the ecosystem. That avoids forced migration and the disruption to critical systems that comes with it.
