Determining the effectiveness of AI models at any given moment can be challenging due to the rapid changes in situations and context, which complicate the assessment of actual outcomes. However, when AI is applied to robotics, it becomes somewhat easier to identify errors visually. That is why this comment from Troy Demmer, Co-Founder of Gecko Robotics is so spot on: “Even the best AI applications are only as good as the data they are trained on,” Demmer said. “Trustworthy AI requires trustworthy data inputs – data inputs that are auditable and interrogatable.”
Demmer’s point is clear: No matter how extensive your AI ambitions, a strong data foundation is essential for success. Because as powerful as AI is, it is not meant to fix bad data. A comprehensive data strategy must precede an AI strategy.
Is Your Data AI-Ready?
The concept of AI-ready data emphasizes the importance of having complete, accurate, timely, actionable, trusted, and compliant data as the foundation for all successful AI projects. AI-ready data initiates a closed-loop cycle where high-quality data leads to better insights and improved results. This data is tailored for the specific task at hand and is in the appropriate form for the intended AI use case.
A platform that generates AI-ready data validates and connects three crucial types of data for AI use cases: the source data itself, the data produced by the AI process (such as recommendations and predictions), and the response/outcomes data, which is then fed back into the platform for further tuning.
Producing AI-ready data is a key primary function of a data readiness platform. Making data right and fit for purpose as data is ingested validates data before it is used in downstream processes, or accessed via APIs and native integrations. Your enterprise’s custom models, rules, third-party analytics frameworks, embedded visualizations, LLM & NLP prompt-based workflows and reporting all depend on high-quality data to produce accurate, trusted results.
Whatever a business’s use case for AI, high-quality data improves the accuracy and reliability of AI models, reduces bias, increases trust in outcomes, reduces inefficiencies, and saves cost by reducing errors and unnecessary workflows.
AI Accountability Starts Upstream
Research shows that failing to establish and follow strong data quality and governance practices is a recipe for AI failure. A recent Forrester study found that 68 percent of organizations face significant data quality and integration challenges that directly impact their AI success. And Gartner predicts that by next year, organizations will abandon more than half (60 percent) of AI projects that are unsupported by AI-ready data.
One reason why companies are discovering (the hard way!) that their enterprise data isn’t healthy enough to support AI is because they mistakenly assume the investments they’ve made in master data management (MDM) systems, data clouds, and other technology – even CDPs – checks the box for cleansing and normalizing data. The problem is an overall lack of accountability from any one system as the ultimate truth for clean, accurate, and timely data. A lack of standardization creates downstream data quality issues that ultimately produce AI results that are untrustworthy.
For instance, it is common for organizations to fuel GenAI applications with enterprise data believing that because the systems involve direct interactions with customers that the data produced are self-validating. They believe the health of the data upstream is less of a concern. Organizations suffer from a clean data mirage. They may have the illusion of data readiness, but in reality what they possess is typically not contextually usable, at least not to support AI use cases that depend on having a precise, updated and in-depth understanding of a customer.
AI in Action: Great Data = Great Results
A modern data readiness platform corrects for the problems associated with bad data and AI by ingesting all customer data (behavioral, identity, permission, transaction, etc.) and ensuring both accuracy and timeliness, the former by cleansing and matching all data into a single view (including householding), and the latter through continually updating the profile, in real time, as data is ingested.
Those processes ensure that data is right, while also ensuring that data is fit for its intended purpose, e.g., for high value AI use cases. Being fit-for-purpose means three things. One, data is actionable, meaning it’s in the right form for consumption. Two, it’s trusted, meaning that it is observable – marketers and business users can tune identity resolution for a specific use case and see that the rules are producing the intended break-aparts (as with householding). Three, it is compliant – it is secure, with managed access to permissions and PII data.
For an example of how AI-ready data helps negate downstream issues, consider a GenAI-powered chatbot meant to provide personalized support. If the customer’s identity is poorly matched due to outdated information or poorly cleansed data, the chatbot might reference the wrong account, make irrelevant product recommendations, or fail to recognize the customer has recently completed a purchase or logged a complaint. Conversely, with properly matched, real-time, trusted data, the chatbot engages in a relevant, seamless interaction that reflects the full, current context of the customer’s relationship with the brand.
Amplify AI Possibilities with Strong Data
AI built on a solid data foundation with data that is right and fit-for-purpose can unlock transformative value. But reliable and trusted AI results are only possible with data that is clean, complete, current and contextually usable.
Investing in a strong data-readiness hub isn’t just a technical requirement, it’s a strategic imperative. Organizations that prioritize data readiness are better positioned to scale AI responsibly, improve decision-making, and deliver more meaningful customer experiences. Before launching AI projects, ask yourself if your data is AI-ready. If not, you’re not ready for AI.
