When a lawyer or compliance professional relies on AI, the inevitable question turns to who holds the responsibility for any errors that occur? According to the Thomson Reuters 2026 Future of Professionals Report, almost half (47%) of professionals say they do.

That accountability is illusory, however. Professionals are being held responsible for AI-assisted work without the standards or tools needed to exercise that responsibility. Although 74% of professionals say they use AI tools multiple times a week, most lack access to professional-grade systems and clear guidance on what their organization considers acceptable outputs, as well as on critical issues such as validation and human override.

Almost since the beginning of AI, its adoption has outpaced the safeguards needed to preserve professional judgment, leaving many professions caught between accountability and chaos. Indeed, the greatest risk is not a single error, but the erosion of the judgment that accountability requires.

The "standards gap" is the real problem

Consider what happens when organizational leadership articulates an AI strategy but fails to translate it into daily practice. This occurs in one-third of firms and departments with named AI strategies, according to the report. When that gap appears, something predictable follows: professionals improvise. Some seek out unsanctioned tools, while others simply guess at what proper use looks like.

The 34% of respondents who admit to using AI tools their organization hasn't sanctioned are not rebels; rather, they are professionals trying to do good work without clear guidance on how to do it responsibly.

This pattern reveals that the underlying issue isn't shadow AI. In fact, shadow AI is just a symptom. The disease is the absence of clear professional standards for what AI-assisted work should look like in practice.

The report makes clear that professionals largely agree on what AI must provide: protection of confidential data, authoritative and verified content, and reasoning that can be explained and defended. These are the minimum requirements for fiduciary-grade work. Yet two-in-five professionals say they lack access to systems that meet those standards, forcing many to rely on general-purpose tools for work in which precision, authority, and accountability are essential.

What standards should actually look Like

Clear standards are not restrictions on innovation; rather, they are the foundation for responsible innovation — and they need to go beyond the obvious. Let's look at some various standards and their meaning:

Data authority standard ‍

This is professional authority, rather than data protection. When AI cites a source, professionals need to know: Is this based on current precedent or AI extrapolation? Does this account for jurisdictional differences? Has the regulatory landscape changed since the system was trained?

Standards must require that AI outputs distinguish between settled law and evolving areas, and flag assumptions and inferences rather than presenting them as conclusions.

Judgment validation standard‍

This requires that every AI output be accompanied by documentation: What reasoning did it provide? Where did it consult sources? Where must professional judgment be applied?

This is not about transparency for transparency's sake. It is about creating procedures for override and challenge. And these procedures that must not be burdensome, but they must be tracked. When patterns emerge of professionals overriding AI outputs in particular areas, those patterns reveal something important. Either the system needs retraining, or professionals need better guidance on when to trust it.

Professional competency standard

This standard shifts training away from tool functionality and toward judgment development. Most AI training teaches what the tools do, yet virtually none of it teaches the harder and more valuable skills, such as knowing when to trust an output, when to verify it, and when to override it.

Professionals need structured exposure to handle cases in which AI fails — and they need mentorship that protects skill development as AI accelerates routine work. The goal is not to make professionals dependent on technology, but to ensure they strengthen their independent judgment through proper AI use of AI rather than allowing the technology to replace that judgment.

These standards only matter, however, if they are used within the organization and made a part of daily operations. Use professional-grade AI is aspiration, not instruction; however, Before advising a client, verify AI sources and document alternative interpretations is actionable activity.

Indeed, supervised review of all AI outputs; document what you challenged and why is something a junior professional can do on any Monday morning. Standards must be role-specific and embedded in workflows, not added as afterthoughts to compliance checklists.

Strategic case for standards

Here is where most discussions of AI governance miss the mark. Standards are not primarily a risk mitigation exercise. Indeed, they are a competitive necessity.

Take talent, for example. Almost half (48%) of professionals surveyed say they fear a negative impact on their independent judgment development because of AI. Further, legal professionals say they expect the timeline to trusted judgment to extend by nearly two years under current conditions.

However, those organizations that establish clear standards, that define what judgment looks like in an AI-assisted contextm and how to develop it, can retain talent and accelerate development, according to the report. In fact, professionals that get to use professional-grade AI are twice as likely to stay in their jobs, and those without access are twice as likely to look elsewhere.

Or, consider clients. More than three-quarters (78%) say they expect AI-enabled quality improvements from the firms they work with, but only 6% say they are actually getting those improvements now. Standards enable the consistent delivery of quality that clients demand, and the outside firms that can articulate and deliver to the level of those standards will win relationships and a strong competitive advantage. Those that cannot will lose out on both.

Finally, consider the execution gap itself. When 35% of professionals say their organizations have an AI strategy, but have not articulated it into practice, they are forfeiting the ability to demonstrate governance, attract mid-career talent, and satisfy client expectations. Clear standards create a defensible position that allows organizational leadership to demonstrate professional judgment, not reckless automation for its own sake.

Making standards operational

Shadow AI is just a single symptom of missing standards. Fix the standards, and the symptom largely disappears. Leaders must define what proper AI use looks like in, addressing its specific context through role-specific standards embedded in workflows, training, and quality control.

The goal is not to constrain innovation, but to make responsible AI use the default rather than leaving professionals unguided and forced to improvise.

Most importantly, standards must be tied to business outcomes, from talent retention and client trust to competitive differentiation. As AI becomes more embedded in professional work, the organizations that establish clear standards first will be the ones best positioned to preserve professional judgment, demonstrate accountability, and lead the future of AI-assisted services.

You can download a copy of the recent Thomson Reuters 2026 Future of Professionals Report here.