Artificial intelligence (AI) is a cornerstone of executive strategy, powering efficiency gains, sharper analytics, and transformative process innovation. Despite the rapid advancements available through AI, many organizations find themselves unable to move forward, believing that their data must be pristine before AI can be utilized effectively. Bifurcated infrastructure, inconsistent metadata, and unclear data ownership slow progress, leading teams to believe they must address their data management fully before pursuing AI at scale.
This belief can create a significant bottleneck, delaying innovation and hindering technological progress. However, what if the very tools being deferred could help solve the data readiness challenge? AI is no longer just the end goal of a data strategy. AI and data management can evolve together, with AI improving the very data foundations it relies on. From automating data discovery to identifying and enriching metadata quality issues, AI can help improve both upstream and downstream data management.
Rather than viewing data readiness as a prerequisite, organizations should see it as a continuous capability that evolves alongside AI. This article challenges the traditional linear approach to AI implementation and introduces a cyclical model where AI and data management evolve together.
Data Readiness & AI: A Mutually Beneficial Cycle
While traditional thinking suggests that data must be perfected before AI can be implemented, the timeline does not have to be inherently linear. Data readiness remains important to implementing AI, but it is essential to recognize that AI and data management can evolve together in a mutually beneficial cycle.
High-quality data enables better AI insights, and those AI insights and systems can, in turn, enhance data management through capabilities like intelligent tagging, anomaly detection, and contextual metadata generation. This symbiotic relationship means organizations should not treat data readiness as a one-off hurdle before AI implementation, but as a continuous process that AI itself can enhance.
Some practical ways AI can enhance data management include:
- Metadata Enrichment & Cataloging: Generative AI (genAI) and classification models can support metadata tagging, summarization, and contextual enrichment of data sets, making it easier for users to discover and understand their data.
- Unlocking Unstructured Data: Natural language processing and computer vision models can extract meaningful entities and relationships from unstructured data like PDFs, emails, and even handwritten notes, expanding the usable data landscape.
- Data Quality Monitoring: Machine learning models can detect anomalies and errors in real time, helping to catch issues before they can cause any downstream impacts.
- Continuous Compliance & Policy Checks: Natural language processing and anomaly detection models can help monitor data flows for sensitive content and policy violations, while generative models can augment this process by contextualizing alerts to assist in identifying false positives. This hybrid approach reinforces governance practices and surfacing issues that may go unnoticed otherwise.
When AI implementation is done effectively, these enhancements to the data processes improve overall data management, which in turn improves the impact of AI-derived insights. For example, in financial trading, AI can flag data quality issues in real-time trading data, leading to improved data processes that make the trading AI itself more effective. It is a two-way street, and companies should seek to improve processes through their implementation.
Embracing this mutual evolution requires a shift in organizational mindset. Companies need to invest in strong data foundations and be early adopters of AI-driven data tools simultaneously. Rather than waiting years to manually perfect a single source of truth, a firm can start deploying AI cataloging or anomaly detection tools now to accelerate its efforts. Letting AI help handle the growing scale and complexity of data allows organizations to work smarter. This must be done with oversight and controls, but organizations that adopt this approach position themselves to innovate faster than those following a linear, chronological pathway.
What This Means
In a rush to implement AI, many organizations treat data readiness as a hurdle to clear—a checklist item on the way to implementation. But that framing underestimates the strategic importance of a strong data program and its role in AI. Data readiness is not just the starting line; it is an ongoing enabler.
As AI becomes embedded across workflows, the relationship becomes cyclical, where better data leads to improved AI insights, and AI, in turn, becomes a lever to enhanced data management. AI is not merely the end consumer of ready data; it is a catalyst for achieving and maintaining that readiness.
This is where the conversation shifts from linear sequencing to mutual reinforcement. AI thrives on structured, high-quality data and, when used wisely, it helps deliver exactly that. No matter what the domain, investing in your data leads to better results, and AI should aid that investment. The two do not work in silos. Instead, they form a feedback loop that continuously sharpens both sides of the knife. Organizations that embrace this perspective position themselves better not just to deploy AI, but to evolve with it.
How Forvis Mazars Can Help
Interested in learning how to get AI working for your data management system? Explore our AI Strategy & Integration page or reach out to a professional at Forvis Mazars.