Not long ago, a thinking machine was science fiction — the stuff of Isaac Asimov novels and the imaginations of visionary computer scientists. Today, it's how you work. Every major profession — law, tax, audit, risk — now runs on some form of AI, and the conversation has shifted. It's no longer “should we use AI?” It's “which AI can we trust with work that matters?”
The professionals asking that second question are the ones setting the pace for their industries. Adopting AI thoughtfully isn't just a productivity choice anymore — it's what separates the people redefining how their profession works from the people waiting to see how it turns out.
From generative to agentic
In 1956, a group of scientists gathered at Dartmouth College with an ambitious goal to simulate human intelligence using machines. That gathering seeded a revolution that took decades to reach its stride. The breakthrough came in the past 15 years, as growing computing power, massive datasets, and better machine-learning algorithms converged — first into generative AI (GenAI), and now into something more capable still — agentic AI.
It doesn't just answer a question — Agentic AI is an advanced form of artificial intelligence that can plan and execute complex tasks across multiple systems to achieve specific goals. It plans an approach, chooses the right tools, retrieves authoritative content, and adapts mid-task. Then it checks its own work before handing it to you. Where GenAI produces a draft, agentic AI can run an entire workflow. It can research a legal question, cross-reference it against your matter documents, draft the analysis, and flag what still needs your judgment.
That shift changes what “AI you can trust” means. When a system moves from generating text to planning and acting on your behalf, accuracy, transparency, and accountability stop being nice-to-haves. They become the whole point.
Being early to that shift, and demanding the higher standard it requires, is itself a kind of leadership. The professionals who push their teams to adopt agentic AI responsibly are the ones who end up writing the playbook everyone else follows.
Benefits of AI
AI's benefits extend across decision-making, automation, continuous learning, and seamless integration into existing workflows. At its core, AI represents a fundamental shift in how we approach problem-solving and decision-making. In an era defined by unprecedented amounts of data, artificial intelligence serves as our cognitive extension, helping us make sense of complexity that would otherwise overwhelm human capabilities.
Without AI's ability to process and analyze this information, we would be drowning in data while starved for understanding. AI transforms this potential liability into an invaluable asset, extracting meaningful patterns and actionable insights from digital noise.
Enhanced decision-making
No professional can weigh thousands of variables at once. AI can — considering that volume of data in milliseconds to surface the factors that matter most. It's no surprise, then, that 71% of risk and fraud professionals already use GenAI for risk assessment and reporting.
Identity verification is a good example. AI can validate identities across multiple data points simultaneously, catching synthetic identities that traditional checks miss, and adapting to new fraud patterns as they emerge — reducing false positives while catching more real threats.
AI also spots patterns people tend to miss. Researchers have used AI to identify promising new drug combinations by analyzing molecular interactions at a scale and speed no human team could match on their own. This ability to improve the quality and accuracy of work is, in fact, the fourth most common reason professionals say GenAI should be applied to their work.
Automation and efficiency
Automation used to mean assembly-line robots. Today, it means an AI system that reads thousands of documents in hours — work that used to take your team weeks — while you supply the judgment that makes the result usable.
Legal work shows this clearly. Document review and legal research are the two clearest examples, used by 74% and 80% of legal professionals applying GenAI, respectively. Modern AI doesn't just speed up review; it connects relevant precedent and citations across vast case law databases, bringing new accuracy to legal research alongside new speed. Notably, this isn't a niche behavior — both use cases sit among the top five in the segment, each cited by more than half of current GenAI users, meaning adoption is broad rather than a handful of power users driving the numbers. Freed from the routine, your team can spend more time on the strategic and interpersonal work only people can do.
Continuous learning and improvement
Unlike static software, AI keeps learning from new data — which is exactly what compliance-heavy work demands. Tax rules change constantly across jurisdictions, and AI-driven systems can monitor those changes continuously, flag emerging compliance issues, and help automate filing and reporting so small mistakes don't become costly ones. For planning, AI can analyze historical data to surface tax-saving opportunities and model different scenarios before you commit to one.
That same restlessness — never settling for the way things have always been done — is what defines the professionals getting the most out of AI. And professionals are noticing. The share who say GenAI should be applied to their work climbed from 54% in 2024 to 62% in 2025 to roughly two-thirds today, a three-year trend that reads less like a spike of curiosity and more like a growing vote of confidence in tools that keep learning. The tools keep improving. So do the people willing to keep pace with them.
Agentic workflows, built for high-stakes work
This is where today’s AI goes further than the AI of a year or two ago. The latest agentic systems don't wait for step-by-step instructions. They plan multistep work, use the right tools for each step, retrieve authoritative content as they go, and adapt mid-workflow the way an experienced professional would. Think less like a junior colleague waiting on instructions and more like a senior associate managing a matter. It's a shift professionals see coming —77% expect agentic AI to be a central part of their workflow by 2030, and process automation and workflow management already rank as the top use case they envision for it — even though just 15% say their organization has deployed it today, with another 53% planning or considering the move.
That capability now sits inside systems supporting more than one million users, tested across hundreds of real-world workflows under structured expert review. But scale alone isn't the point. The point is what stands behind it — content you can trace, sources you can check, and a clear line back to why the system reached a given answer. You still review the output. What's changed is how much of the underlying work the system can now carry on its own.
Limitations of AI
Understanding AI's benefits means understanding its limits too. That's not resistance to change — it's how you deploy AI responsibly.
Dependency
AI is only as good as the data behind it. Where data is scarce, inconsistent, or biased, an AI system will reflect — and can amplify — those same gaps. Because AI learns patterns from existing data, it can still produce errors when it hits a scenario its training never covered.
Context gap
AI is excellent at pattern recognition and narrow task execution, but it doesn't understand context the way you do. A system that can beat a grandmaster at chess still can't tell you why a parent might let their child win a board game. That gap matters most in complex, real-world situations where nuance changes the right answer.
Ethical considerations
AI systems increasingly handle sensitive personal and financial data, which makes strong safeguards essential to protecting individual rights. Bias, transparency, and accountability all demand ongoing attention — especially as AI takes on more autonomous, multistep work. When a system plans and acts across several steps instead of answering a single question, you need a clear record of what it did and why, and a clear owner for the outcome.
Human oversight isn't a workaround for AI's limits — it's part of how trustworthy AI is built. Your organization must balance the advantages of AI against the responsibility of using it well, protecting fairness and human dignity at every step.
Choosing an AI assistant
That balance is exactly why the right AI assistant matters so much. A capable assistant does more than automate tasks — it improves your decisions, sharpens your efficiency, and gives you back time for the work only you can do. It provides priceless value.
Look first at domain accuracy. Was the assistant built by experts in your field, and can it handle the work you encounter day-to-day? Then weigh user experience, integration with your existing systems, and security. All of it matters, but for work that's reviewed, examined, or defended, one thing matters most — can you trust the answer, and can you show your work if you're asked to?
Getting this choice right is what turns AI adoption into a genuine advantage instead of a shortcut. The professionals reshaping their industries aren't the ones who adopted AI first — they're the ones who chose carefully and built real capability on top of it.
Why CoCounsel
Thomson Reuters built CoCounsel to meet that bar. CoCounsel now powers two complementary approaches. There is platform-wide agentic assistance across CoCounsel Legal, CoCounsel Tax, CoCounsel Audit, and CoCounsel Essentials, plus AI embedded directly inside individual Thomson Reuters products. Same standards, different engine, depending on the job in front of you.
We built CoCounsel to a standard we call Fiduciary-Grade AI™ — the level of accuracy, confidentiality, and accountability that regulated, duty-of-care work demands. In practice, that means every answer is grounded in authoritative content like Westlaw, Practical Law, and KeyCite, plus your own permissioned data, so you can trace a result back to its source instead of taking it on faith.
CoCounsel is built to run entire workflows, not just single tasks — research, analysis, drafting, and review, working together the way your own process does. It doesn't stand still; as agentic AI matures, CoCounsel is evolving with it, so the assistant you rely on today keeps getting more capable, not more complex.
More than one million professionals have already chosen to put their trust in it — not because it was the fastest option, but because it was the one they could stand behind. That's the choice that defines a changemaker — picking the tool that lets you do better work, not just faster work.
As AI reshapes legal, tax, audit, and risk work — and clients expect more than ever — having an AI tool built to your professional standard isn't a convenience. It's the foundation everything else stands on. We're continuously building the technology so you can know today and navigate tomorrow with confidence.
The path forward
Succeeding with AI means understanding both its potential and its limits. AI isn't a solution that runs on its own — it's a tool that dramatically extends what you can do, when you apply it with the right safeguards.
The organizations and professionals who get the most from AI are the ones who choose the right tool for the job and keep their own judgment firmly in the loop. As agentic AI keeps evolving, that combination — powerful technology applied with expertise — is what will set you apart.
Changemakers never stop learning. Neither is the technology built to keep up with them. The question worth asking isn't whether AI will keep transforming your profession. It's whether you'll be the one shaping that change or reacting to it.
