At Trust ’25, Precisely has launched an AI ecosystem for data integrity. It provides organisations with a way to integrate their use of AI-driven analytics into other data practices. Ultimately, Precisely sees this as a way to improve trust in AI through the quality of trusted data. In doing so, it brings AI into line with its other trusted data initiatives.
Josh Rogers, chief executive officer at Precisely, said, “We’re at a critical juncture where AI isn’t just transforming technology, it’s reshaping entire business models and market dynamics. To navigate this rapid change and succeed, enterprises need the utmost flexibility and agility to deliver optimal value from their AI initiatives.
“Our mission is to empower customers with trusted data, no matter which AI technology stack they choose. We’re building a connected AI ecosystem for data integrity that delivers accurate, consistent and contextual data at scale, giving enterprises the flexibility to innovate with confidence.”
Data integrity continues to be a major challenge
Organisations increasingly rely on the need to analyse vast amounts of data for every decision. But to do that, they need data they can trust and rely on, something which is a challenge for many businesses. Data is spread across multiple platforms, is incomplete, often duplicated and untrusted.
All of that has led to a general distrust of the data organisations hold. That level of distrust was evident in a recent Data Integrity Trends and Insights report from Drexel University’s LeBow College of Business. In it, 67% of respondents admitted they don’t completely trust the data they use for decision-making. Worse, only 12% say their data is of a sufficient quality and accessibility for AI.
Compounding that problem is a general lack of observability in data quality. The recent Precisely/BARC Observability for AI Innovation report shows that just 26% of organisations have optimised their data quality observability. On the positive side, 58% do have some observability in place.
The problem is not just about the integrity of the data that organisations hold. When they have deployed AI, they have discovered that it tends to make things up. That further damages the trust in data.
What is Precisely doing about it?
In February, Precisely launched a series of enhancements to its Data Integrity Suite. Among those was an AI Manager, which brought Generative AI (GenAI) capabilities to the product. It relies on customers connecting their LLMs to the Data Integrity Suite, with AWS Bedrock being the first supported model.
This latest announcement of an AI ecosystem for data integrity goes much further. It will support multiple AI models, tools and policies. This provides a way to bring AI and existing data together to enforce data integrity rules and improve areas such as compliance.
It now supports over 30 AI models, including AWS Bedrock, Google Vertex, Microsoft Azure Open AI and others. Effectively, this is a Bring-Your-Own-AI-Model offering. There is also support for vector data integration. The company has also launched its own Model Context Protocol (MCP) server. This allows AI systems to connect to Precisely’s own data and location APIs.
As expected from Precisely, it is a multi-platform solution that covers everything from mainframes to cloud platforms. That is important. IBM mainframes and IBM i systems are still the system of record for many financial institutions and governments. But access to that data can be challenging. This announcement sees tight ecosystem integrations, no matter where the data sits.
The company has also launched a range of AI agents in data pipelines. This will speed up automation and integration of data.
Enterprise Times: What does this mean
While many organisations are concerned about AI hallucinations and data quality for AI, Precisely is doubling down on its tooling. It is welcoming AI as part of that data integrity journey by bringing it into a trusted ecosystem.
It is offering organisations a way to increase quality and enrich the data that AI has access to. Importantly, that addresses the concerns that customers have voiced over the last year. But this is more than just about the underlying data to build AI models from, it’s about ongoing maintenance of that data. The MCP server access to its APIs allows continuous data cleansing and enrichment of AI data.
It will be interesting to see how quickly customers start to bring their models into the AI ecosystem. Given the adoption rate of AI and the risks it brings to data quality, it will be a surprise if Precisely doesn’t see a significant take-up.
