From AI Ambition to AI Results: How Data Quality Unlocks the Strategy Everyone Is Missing

For more than a decade, organizations invested heavily in business intelligence to create clarity, consistency, and control. Dashboards expanded. Metrics were standardized. “One version of the truth” became the gold standard for financial, operational, and regulatory reporting. That model worked when data’s primary role was to explain the performance numbers after the fact.

That era is ending.

Artificial intelligence and evolving ESG reporting regulations are changing the role of data itself. Data is no longer simply presented for human interpretation; it is acted upon, evaluated, and increasingly relied on to make decisions at scale. Systems now influence outcomes continuously, often without waiting for review cycles, explanations, makeshift bandages, or sign offs.

AI and ESG are forcing business data to do something it hadn’t been designed to do: act autonomously and defend itself publicly.

This shift raises a critical and dangerous question for leaders: Are decisions already being influenced by data that would not withstand scrutiny if no one were there to explain it?

For many organizations, the honest answer is no.

Why Data Quality and Data Governance Suddenly Matter More Than Ever

In a once booming business intelligence and analytics-driven world, governance was about consistency and control, making sure reports reconciled, definitions were aligned, and stakeholders trusted the numbers they reviewed. Those reviews were periodic. Controls were structured around quarterly close cycles, annual audits, and version approvals. As long as the trusty dashboard matched the data from the excel reports, KPI tickers were within range, and the margin of error was manageable, governance was considered successful. Over time, “it looks good to me” became the digital stamp of approval; enough to move forward, enough to stop asking questions.

Then AI entered the scene and changed the equation.

AI certainly won’t wait for your next review meeting, it’s already acting on today’s data. It’s doing its job of making recommendations continuously, often autonomously, and those recommendations increasingly influence real business outcomes as intended. At the same time, sustainability data has moved out of internal reporting and into the spotlight of regulators, investors, customers, and the public. In both cases, data is no longer simply being reported. It is being acted upon and judged by everyone who sees it.

That shift exposes a critical reality many organizations are now confronting: governance designed for reporting does not automatically translate to governance suitable for AI, or sustainability decision making for your business. What worked when humans interpreted numbers at the end of the process breaks down when machines are expected to act on data at scale.

Today, AI quality and governance must answer far more consequential questions:

Why did the system recommend this action?
What data influenced that outcome?
How confident was the signal?
Can we defend this decision to auditors, regulators, or investors without relying on “someone who knows the story”?

Answering those questions requires a fundamental shift in thinking from centralized “truth management” to trust based governance. In this new model, data sources are weighted rather than assumed equal, confidence is measured rather than implied, and explainability is built directly into the operating model instead of retrofitted after something goes wrong or questioned. Governance moves from static documentation to living controls that evolve as data, models, and business conditions change.

Success in the next phase of AI won’t come from better algorithms, but from harder questions. Leaders who hit the breaks on the AI locomotive to ask whether their enterprise data could be trusted to make decisions- not just tell a story- avoid a future where automation crashed or outpaced accountability.

The rest will learn that lesson after the fact.

This is why data governance is no longer a compliance checkbox; it’s the last line of control before decisions are executed without human intervention. It has become the foundation that determines whether AI can scale safely, whether sustainability metrics can withstand scrutiny, and whether leaders feel confident automating decisions instead of slowing them down. Strong governance doesn’t constrain innovation; it enables it by replacing uncertainty with trust.

If AI, regulatory reporting, or sustainability initiatives are already on your roadmap, the real question isn’t whether you should move forward. It’s whether your data governance is ready to support the decisions that will follow. It’s the pivotal moment where we are seeing leaders stop experimenting and start building with confidence.

AI Changes the Definition of Data Quality

Many organizations mistake polished analytics and top of the line software, for readiness. Unaware that within those glistening data lakes, lurks a sticky swamp where the AI data used for financial reporting and sustainability will act on the data beneath the data prepped for dashboards; flaws and all. At the report level, financial close processes run smoothly. Reports reconcile. ESG metrics are collected on a regular cadence. From the outside, everything looks ready.

The risk is mistaking polished analytics for decision readiness.

Experience has demonstrated, traditional reporting with BI and analytics preparation were designed to explain what happened, not to support automated decisions. Data pipelines were optimized to stabilize results and reduce noise giving end users what they asked for not what they needed to hear. To keep things moving forward, anomalies were removed. Details were aggregated. Manual adjustments were accepted with minimal documentation. Ambiguity was resolved through human interpretation, conversation, and institutional knowledge. That approach worked, because people were always there to make sense of the numbers.

AI requires the opposite.

AI systems must understand why the anomalies occur, not smooth them away. They must trace changes back to source systems and business processes, not rely on summaries. They must evaluate the confidence of each signal, not assume accuracy based on reconciliation alone. When data prepared for BI reporting is reused for AI models or sustainability analytics, critical context is often missing, or worse, unintentionally distorted.

This is why we are hearing organizations experiencing AI hallucinations, financial and ESG restatements, and the erosion of stakeholder trust even when their dashboards look clean. The issue isn’t the sophistication of the models. It’s not that the AI failed at the job. It’s like hiring a new employee and giving them reports instead of judgment ready information to help them think.

AI then throws us another curveball at the plate: AI also fundamentally changes the definition and direction of data quality. Traditional frameworks focused on accuracy, completeness, and consistency. Those dimensions still matter, but they are no longer sufficient as a stand alone player in the game. AI demands every move within the data that is explainable, traceable, timely, and representative of real operating conditions. We see that models must show the full stats- which inputs influenced an outcome, how current the data was, and whether the signal was strong enough to justify action.

This creates a challenge data and analytics teams within the business were never asked to solve: data quality is no longer static. It must be monitored continuously. Data drift, shifting business behavior, and evolving regulations can quietly degrade AI performance over time unless governance and controls are actively embedded in the operating model. AI data quality is not about perfection. It’s about decision confidence. Once data begins driving decisions instead of dashboards, “good enough” becomes an unacceptable risk.

Where Organizations can benefit from Eliassen’s Enterprise Data Management Services

Most organizations don’t need another tool. They need clarity.

If leaders don’t know whether their data is decision ready, AI will decide that for them. Working to obtain the best governance frameworks that can support AI learning, sustainability transparency, and regulatory defensibility at the same time, without adding friction or slowing the business. Our customers who are succeeding are focusing on operating models that bring finance, risk, IT, and sustainability teams together around shared accountability, rather than leaving ownership fragmented across silos.

We see this repeatedly: When decisions become automated and disclosures become public, fragmented data governance doesn’t just slow progress, it exposes the organization, because the data was never designed to withstand autonomous decision making or external scrutiny at scale.

When AI produces recommendations that cannot be clearly explained, leaders hesitate to act. When sustainability metrics cannot be confidently defended, credibility erodes with regulators, investors, and the public. In both cases, the issue isn’t ambition or technology, it’s trust. Trust in the data, trust in the governance, and trust that outcomes can stand on their own without heroic manual effort or institutional knowledge filling the gaps.

In working with organizations to get this right they understand a simple truth: AI and sustainability share the same dependency. Both require high quality, well governed data that is explainable, traceable, and resilient under scrutiny. Both demand operating models built with the expectation that business data will be questioned, by machines, auditors, and stakeholders alike. Both break down when governance exists only on paper rather than in practice.

A Final Thought for Leaders

AI and financial regulatory reporting are forcing organizations to confront a reality analytics once masked: data is no longer just interpreted, it is acted upon and judged. If governance cannot explain, defend, and adapt that data, the risk is not falling behind technologically. It is losing trust.

The organizations that succeed will not be those with the most advanced tools. They will be the ones that recognized early that trust is the real unlock, and invested in making their data safe to act on before decisions became automated and accountability became external.

If you are unsure whether your data can be trusted to make decisions without human interpretation, that uncertainty is the signal. That is where meaningful AI readiness truly begins.

A Reality Check for Leaders

If you answered “yes” to any of these, your enterprise data is report ready, but not decision ready for AI.

If you’re questioning whether your data is ready to drive automated decisions, now is the time to act. Eliassen is partnering with forward-looking organizations to launch their enterprise data journey enhancing AI & Automation outcomes, turning uncertainty into confidence and analytics into actionable intelligence. We close the data trust gap quickly, defensibly, and without slowing your business.

Do key reports or metrics still require explanation to be trusted?
Do the numbers change and vary by department or depend on who pulls them?
Do you know where the numbers are coming from or do adjustments play a material role in producing “accurate” results?
Are data issues usually discovered during close, audit, or reporting deadlines?
Have AI pilots stalled due to data concerns rather than model performance?