Validation risk: When no one is actually checking the work anymore

Most executives believe their organizations are data-driven

Dashboards refresh automatically. Reports reconcile on demand. Metrics move faster than ever. From the outside, it looks like progress.

But inside many mid-market organizations, fewer people can clearly explain where the numbers come from, how they’re produced or whether the assumptions behind them still hold. Validation hasn’t disappeared — it has become implicit.

This is validation risk. And it’s one of the most underestimated threats to decision confidence in today’s business environment.

How validation becomes invisible

Validation rarely fails because someone decides it no longer matters. It fades as the business evolves.

A report that once required manual review becomes automated. A reconciliation that used to raise questions starts to “look right” most of the time. A system output that once triggered discussion becomes familiar enough to trust without explanation.

As teams grow leaner and timelines compress, review quietly turns into reliance. People stop asking how a number was created and start asking whether it feels reasonable. As long as it falls within an expected range, it’s accepted.

Nothing breaks. Nothing triggers alarms. But the organization slowly shifts from knowing to assuming.

What makes this risk more dangerous today isn’t just automation — it’s reach.

A single output can now travel across the organization in minutes. Data generated in one system feeds dashboards, forecasts, budgets and executive decisions across multiple functions. The same number may be reused, reshaped and reinterpreted without anyone retracing its full path.

Once outputs move beyond the function that produced them, validation responsibility becomes unclear. Everyone trusts the number because it came from “the system.” Few people understand which system, which logic or which assumptions still apply.

AI accelerates this dynamic. Outputs arrive polished, confident and authoritative. The faster results appear, the less likely teams are to pause and challenge them.

Where validation risk shows up most often

Validation risk tends to surface where complexity meets pressure — especially in environments where speed, scale or regulation already stretch teams thin.

In manufacturing, for example, this often shows up in forecasting and margin analysis. A plant-level report pulls data from production systems, inventory tools and a spreadsheet used to reconcile timing differences. The numbers have looked consistent for months, so leadership relies on them to plan staffing and pricing.

What goes unnoticed is that a small upstream change — like how scrap rates are captured — never made it into the reconciliation logic. The output still looks reasonable. The assumptions no longer are.

In healthcare, validation issues frequently emerge at the intersection of clinical operations and finance. Dashboards tracking service line performance rely on automated feeds from scheduling systems, billing platforms and payer data.

Over time, changes in coding rules and reimbursement timing alter what those metrics actually represent. Leadership continues to use the dashboard to guide staffing and investment decisions, unaware that the underlying definitions have drifted. The data isn’t wrong — it’s just no longer telling the full truth.

In both cases, the problem isn’t obvious error. It’s unknown reliability — numbers that feel trustworthy because they’re familiar, not because they’ve been revalidated.

The illusion of confidence

One of the most dangerous aspects of validation risk is that it creates false confidence at the leadership level.

Consider a regional bank reviewing credit exposure across its loan portfolio. Automated reports pull from multiple systems and apply risk ratings that have been in place for years. Trends look predictable. Leadership feels reassured.

What goes unexamined is whether those assumptions still reflect today’s borrower behavior, market conditions or underwriting changes. When exposure starts to concentrate in unexpected ways, the warning signs are already buried inside trusted outputs.

This is how organizations get blindsided by issues they “should have seen coming.”

Dashboards look clean. Reports reconcile. Trends feel familiar. Confidence comes not from confirmation, but from repetition.

Why controls don’t travel with the data

Many organizations assume validation is handled through controls — another approval step, another sign-off, another checklist.

But controls tend to stay where they’re designed.

They rarely follow outputs as those outputs move across systems, teams and vendors. Once data leaves the environment where controls were applied, validation responsibility becomes ambiguous.

This is why validation risk isn’t a missing-step problem. It’s an ownership problem.

If no one owns validation beyond initial production, it effectively disappears.

How shadow automation and interdependency compound the risk

Validation risk accelerates when shadow automation and interdependency collide.

In technology-driven organizations, teams often build automated workflows to reconcile data when core systems don’t align cleanly. Over time, those automations become trusted sources feeding forecasts, pricing decisions and customer communications.

When upstream logic changes or behavior shifts, the automation continues running quietly in the background. Outputs still look consistent. No one retraces the logic. Decisions compound on assumptions that are no longer true.

In insurance, similar dynamics emerge across underwriting, claims and financial reporting. Automated models pull from multiple data sources to assess risk and reserve levels. Shadow adjustments appear to handle exceptions or timing gaps.

Those adjustments get reused across reporting cycles and downstream decisions. As portfolios evolve, the same assumptions continue driving results long after conditions have shifted. By the time discrepancies surface, they span multiple functions and reporting periods.

In neither case does a single automation cause failure. The risk emerges because undocumented processes feed interconnected systems, and outputs travel further than anyone realizes.

Why leaders hesitate to challenge the numbers

Questioning data can feel uncomfortable — especially when systems have been in place for years and appear to be working. Leaders worry about slowing decisions, undermining capable teams or reopening processes they assume are stable.

But avoiding those questions doesn’t eliminate risk. It simply delays when it shows up — often when decisions are already locked in and options are limited.

Executives who manage validation risk well don’t interrogate every number. They’re selective about where skepticism matters most.

They focus on outputs that drive commitments — capital allocation, pricing, staffing and growth strategy — and ensure those numbers are still fit for purpose.

They also normalize curiosity instead of compliance. Instead of asking teams to defend results, they ask how outcomes were produced and how assumptions might change under different conditions.

Most importantly, they clarify ownership. Validation doesn’t happen automatically once data leaves its source. Someone must be accountable for understanding how outputs are reused, where they travel and when they need to be re-examined.

What effective validation actually looks like

Strong validation isn’t about checking everything all the time. It’s about being intentional about what matters most.

Organizations that manage validation risk well focus on:

• Identifying which outputs drive high-impact decisions
• Understanding how those outputs are generated and reused
• Periodically stress-testing assumptions as conditions change
• Assigning clear ownership for validation, not just production

Validation becomes a leadership discipline tied directly to decision confidence — not a back-office task.

Addressing validation risk isn’t about going backward. It’s about restoring trust in how decisions are made.

When leaders understand where their numbers come from, how they move and when to question them, they gain something more valuable than speed.

They gain confidence.

And in a business environment shaped by constant what ifs, confidence is what allows leaders to lead.

How Wipfli can help

Validation risk doesn’t stem from bad data. It emerges when assumptions go unchallenged and outputs travel further than leaders realize.

Wipfli works with mid-market executives to identify where critical decisions rely on unvalidated information and where ownership for validation has quietly eroded. Through enterprise risk assessments, data and technology reviews and scenario-based decision support, we help leaders restore confidence in how decisions are made — without slowing the business down.

Learn more in our Wipfli strategy hub

Read next

• Interdependency risk: The domino effects leaders can’t see
  How hidden connections across systems, vendors and teams cause small issues to cascade into enterprise-wide disruption.
• Shadow automation: The risk CFOs aren’t supposed to admit exists
  How undocumented automations and AI shortcuts quietly introduce financial, operational and reputational risk.
• The invisible risk: How small misses compound into enterprise failure (e-book)
  A practical guide to identifying the micro-risks that quietly build into margin erosion, bad data and leadership paralysis. 
• Audit risk management: Be proactive, not reactive
  Why forward-looking risk management helps leaders anticipate disruption instead of scrambling to respond after the fact.