We get asked a version of the same question almost weekly: “When did AI governance actually become real?” Our answer is consistent — it was not a single law, a single enforcement action, or even one headline moment. It was 2025.  

What changed in 2025 was not the presence of AI. AI had already been embedded across products, services, and operations. What changed was the risk model surrounding it. Signals that had been building quietly for years converged at the same time. And when that happens, governance stops being conceptual and starts being operational. 

In the remainder of this article you will find a reflection on what we saw unfold during 2025 and how those signals shape the three priorities leaders should be focused on as they move into 2026. 

What shifted in 2025

For several years, most organizations approached AI governance through intent — responsible AI principles, ethical commitments, high level policy statements, and committees charged with oversight. Those efforts were not wrong, but in 2025, they reached their natural limit. Here is what changed: 

Regulators moved from guidance to enforcement signaling. Not everywhere and not all at once, but enough to make leadership teams take notice. The conversation shifted from “what should we do?” to “what will we have to defend?” 

Insurers began tightening AI-related exclusions and underwriting language. That was a critical signal. Insurance markets do not move on philosophy —  they move on loss data and exposure models. 

Enterprise buyers changed their questions. Instead of asking what organizations believed about responsible AI, they began asking what organizations could prove. What assessments existed, what controls were in place, and who was accountable? 

Shortly after, boards shifted their focus. Their questions were no longer about ethical frameworks — they were about defensibility.  

“Can we explain how this system behaves if something goes wrong?” 

For many organizations, that question exposed an uncomfortable truth: their AI governance posture looked reasonable on paper, but fragile in practice. That realization defined 2025. 

Why this moment feels familiar 

We have seen this pattern before. Information security went through it, privacy went through it, and financial controls went through it. Early stages are principle driven, then frameworks emerge, and eventually, evidence and assurance become unavoidable. 

AI governance crossed that threshold in 2025, which is why management system thinking matters. Standards like ISO 42001, ISO 42005, and ISO 23894 did not appear by accident. They reflect where governance expectations are heading, not where they have been. 

Priority 1 for 2026: Move from AI policy to AI proof

The priority for 2026 is straightforward, even if it is not easy. AI governance must move from policy to proof.  The say-do ratio has to be measured and communicated. 

Written principles still matter, but they no longer carry decision weight on their own. Regulators, insurers, customers, and auditors are asking for evidence of how decisions are made, how risks are assessed, and how tradeoffs are handled over time. 

This includes: 

• Impact assessments tied to real use cases 
• Risk registers that evolve as models and data change 
• Clear records of who approved what and why 
• Evidence that governance is active, not ceremonial 

This is not about creating paperwork — it is about making governance traceable. If you cannot reconstruct a decision six or twelve months later, that gap becomes a liability the moment scrutiny increases. 

What you, as a leader, should do now 

• Identify where AI decisions are being made without durable records 
• Make impact and risk assessments part of normal operations, not special events 
• Design governance as if it will be reviewed by a third party, because eventually it will 

Proof is becoming the currency of trust. 

Priority 2 for 2026: Treat AI assurance as inevitable 

One of the quieter but more important developments in 2025 was the rise of AI assurance expectations. It did not arrive as a mandate but as a question.  

Procurement teams began asking vendors to show evidence of AI governance, boards requested independent views on AI risk exposure, and insurers looked for objective signals of governance maturity. This mirrors exactly how assurance matured in cybersecurity. 

Once assurance enters the ecosystem, it does not disappear — it becomes normalized. AI risk is not confined to a single team or model. It spans internal development, third party services, data pipelines, and downstream use. Over time, self-attestation stops being credible. 

Management systems make this survivable. ISO 27001 showed how assurance can scale without overwhelming organizations, and AI governance is now following a similar path. 

What you, as a leader, should do now 

• Decide where AI assurance belongs within your organization 
• Align AI governance with existing audit and assurance functions 
• Establish expectations for vendor AI oversight before customers force the issue 

By 2026, assurance will be one of the primary ways trust is evaluated.  

Priority 3 for 2026: Use standards to navigate regulatory fragmentation 

If 2025 demonstrated anything clearly, it is that AI regulation will not converge neatly. Different jurisdictions are moving at different speeds. Definitions vary, enforcement models differ— and this fragmentation is not temporary. Waiting for clarity may feel prudent, but it leaves organizations exposed.  

Standards exist precisely for this environment. They provide a stable operating backbone when laws shift. Courts, regulators, and insurers increasingly rely on standards as evidence of due care because they are structured, auditable, and internationally recognized. ISO 42001 does not replace regulation but operationalizes compliance across jurisdictions without requiring organizations to rebuild their programs every time a new rule appears. 

What you, as a leader, should do now 

• Stop designing AI governance around a single regulation 
• Anchor your program in standards and map regulatory obligations on top 
• Be explicit internally that adaptability is the goal, not perfect prediction 

In a fragmented regulatory landscape, standards become more valuable, not less. 

Where this leaves us 

2025 was not the year AI regulation suddenly arrived. It was the year leaders realized that existing governance approaches would not scale. 

2026 will reward organizations that: 

• Build evidence instead of narratives 
• Normalize assurance instead of treating it as exceptional 
• Use standards to absorb change rather than chase headlines 

This is not intended to be a narrative about fear, instead it is about leadership. The organizations that invest now will not be scrambling later. They will move forward with confidence while others are still trying to understand why the earth shifted beneath them. 

At A-LIGN, this is the work we see coming. Not because the market demands it rhetorically, but because the underlying systems are already changing.