Highlights
- Agentic AI adoption is accelerating fast, with most professionals now expecting it to be central to their workflow by 2030.
- Client expectations for AI-enabled quality have outpaced what most firms and departments are actually delivering today.
- Fiduciary-Grade AI sets a higher bar for reliable, accountable agentic AI in high-stakes professional work.
A year ago, agentic AI was mostly a talking point — something professionals had heard of but few had actually put to work. That’s no longer true. According to the most recent AI in Professional Services Report, today, 15% of professionals say their organization already uses agentic AI tools, and another 53% say they’re actively planning or considering it, Three-quarters of professionals expect it to be central to how they work by 2030.
In other words, agentic AI has quietly moved out of the experimentation phase and into the growth phase. The question for most businesses isn’t whether to adopt it anymore. It’s whether they’re prepared to use it well.
Why the conversation has changed
Generative AI’s rise was fast and visible. Professionals started using tools like ChatGPT to draft, summarize, and research within months of the technology going public, and usage has kept climbing — 74% of professionals now say they use AI several times a week. Agentic AI is following a similar trajectory, just a step behind. Where GenAI reacts to a prompt and produces something, agentic AI takes an objective and runs with it: researching, drafting, checking its own work, and adjusting course across multiple steps with far less hand-holding.
That shift from “creates content” to “completes a process” is what makes agentic AI valuable — and also what makes getting it right harder than getting GenAI right. A generative AI tool that gives an average answer is a minor inconvenience. An autonomous system making decisions across a multistep workflow needs to be trustworthy at every step, not just the last one.
The gap that actually matters right now
Here’s what the data says businesses are getting wrong. It isn’t that they’re moving too slowly to adopt agentic AI — it’s that adoption and value have quietly come apart.
Clients and stakeholders have already raised their expectations: 78% of corporate clients say it’s very important or essential that the firms they work with deliver AI-enabled quality improvements. Only 6% say they’re actually seeing that from most of their providers. That gap hasn’t gone unnoticed — nearly a third of corporate clients say they’re already reconsidering relationships with firms or providers that are falling behind.
Internally, the picture looks similar. More than a third of professionals (34%) admit to using AI tools their organization hasn’t officially sanctioned — usually because the tools they’ve been given aren’t good enough or the strategy behind them isn’t clear, a telltale sign that adoption is outpacing governance. And even among professionals actively using AI at work, 41% say they still don’t have access to tools built specifically for professional work and grounded in verified content. Put those together and a pattern emerges: the challenge for most businesses isn’t getting people to use AI. It’s giving people AI that’s actually worth using.
None of this means agentic AI isn’t worth adopting. It means adoption alone was never the finish line. The organizations pulling ahead right now aren’t necessarily the ones that moved first — they’re the ones that closed the distance between having the technology and getting real value from it.
What “good” agentic AI actually requires
Not every tool marketed as agentic AI is built to handle high-stakes, multistep work responsibly. For professionals in law, tax, audit, compliance, and similar fields — where an error carries real consequences — “almost right” isn’t an acceptable bar.
That’s the thinking behind Thomson Reuters’ Fiduciary-Grade AI™ standard: AI that’s grounded in authoritative, domain-specific content; protected by rigorous privacy and security safeguards; built and continuously refined by credentialed subject-matter experts; and designed to produce reasoning that’s transparent enough to explain and defend under real scrutiny. Just as importantly, taking on more of the work doesn’t mean AI takes on the accountability. That responsibility stays exactly where it’s always been: with the professional.
It’s a useful checklist for evaluating any agentic AI solution, not just Thomson Reuters’ own. If a tool can’t tell you where its answer came from, how it was validated, or what happens when it hits something outside its lane, it isn’t ready for work where the stakes are real.
Where to start
If your organization is somewhere between “we’ve heard of agentic AI” and “we have a strategy that’s actually working,” you’re in good company — most businesses are exactly there right now. The organizations that get ahead from here won’t be the ones that panic about falling behind. They’ll be the ones that take a clear-eyed look at what agentic AI actually is, what it’s good at, and what to demand from any solution before they commit to it.
That’s exactly what our Agentic AI 101: What Your Business Needs to Know e-book was built to do. It walks through the real differences between generative and agentic AI, how professional-grade agentic AI works inside a business, the concrete benefits worth expecting, and — most usefully — the specific questions to ask any vendor before you buy. If you’re trying to move from “we should probably look into this” to a plan you can actually execute, start there.
