Organizations are investing in artificial intelligence at an extraordinary pace. Yet only a minority have seen enough of a return to warrant scaling it across the enterprise. Most often, this is because the quality of the information they are feeding the technology is uncertain, and they aren’t sure whether they can trust the resulting output. This places every decision, every action, and every compliance response based on that output at risk. Good governance is the key to avoiding this outcome and to achieving the ROI you require.
According to McKinsey, nearly 90% of organizations say they’re at least experimenting with AI, while only 7% report scaling it across the enterprise. Likewise, Deloitte says 55% of organizations have avoided GenAI use cases because of data-related issues, leading them to enhance data security (54%), improve data quality practices (48%), and update data governance frameworks and/or develop new data policies (45%).
These findings point to a familiar problem that computer scientists gave a simple name long before the AI was a thing: Garbage In, Garbage Out (GIGO). Not only has AI not solved the problem, it has made it worse.
Trust Your Data, Achieve Your Return
Every inaccurate record, outdated policy, duplicate document, or unverified source becomes another opportunity for AI to generate a confident – but incorrect – answer. Addressing this issue is exactly why we developed our Governance Accelerator: to help organizations quickly identify, assess, and improve the quality of the information their AI ingests before it propagates further. It’s also why information governance is one of the highest-ROI programs an organization can invest in, especially in the context of AI.
This is not an issue of technology. Though AI can boost productivity, it can also accelerate poor decisions, regulatory exposure, operational disruption, and reputational damage if the underlying information cannot be trusted. As AI becomes embedded in critical business processes, information quality becomes a board-level risk.
Good governance establishes the provenance, currency, accuracy, and ownership of critical information before AI ever encounters it. It identifies authoritative sources, retires obsolete content, applies consistent metadata, and creates accountability for information quality across the enterprise.
Equally important, governance identifies what AI should not consume, such as unvetted Internet content and uncontrolled internal repositories. Models perform best when they are grounded in curated, trusted information rather than everything an organization happens to possess.
Governance Makes Business Better
Trusted information doesn’t just reduce risk – it improves performance. When employees and AI systems work from authoritative, current, and well-managed information, organizations make better decisions, automate with greater confidence, reduce costly rework, and accelerate compliance activities. That’s where AI begins to generate measurable business value, but it can’t do so if the quality of the source data is unknown or questionable.
For all that AI has changed, there is one fundamental truth that remains constant: the quality of your decisions depends on the quality of your information. Organizations that treat information governance as a strategic control, not merely a compliance exercise, will achieve sustainable returns from AI. Thus, the fastest path to better AI ROI isn’t a better model. It’s better governance.