At 抖阴成年, we bring auditors advanced audit technologies. and launched in 2025, offering AI-powered solutions to help audit professionals work faster and with greater confidence.

Partnerships are equally central to our innovation strategy, with an approach centered around methodology. When we first shared our , the focus was clear: help firms modernize without asking them to walk away from the methodology they trust and their existing technology investments.

The role of the integrated ecosystem

Audit is not a single workflow, and it won鈥檛 be solved by a single tool. Our intention is to meet firms where they are 鈥� and give them an experience with best-in-class options from trusted providers that meet our standards for quality and reliability.

An open ecosystem approach makes it possible to bring together specialized capabilities and integrate them into a cohesive experience, with data being automatically shared with partners embedded in PPC. For firms, that means more flexibility in how they adopt new technologies and less need to manage disconnected tools across the engagement.

Today, we鈥檙e seeing that partnership vision come to life inside real, integrated audit workflows.

Over the past several months, we鈥檝e been working closely with a curated set of partners to embed AI-powered tools into our . Those integrations roll into , with new automated capabilities available to firms within our software.

Embedding partners within PPC

The impact is most visible in complex, manual areas of the audit where teams spend the most time today, such as lease accounting, proof of cash, and financial statement review 鈥� and it’s coming to life through our growing network of integrated partners:

: Lease accounting and specialized workflows

Lease accounting continues to be a complex and time-consuming area for many firms. By integrating Crunchafi into audit workflows, teams can automate lease accounting across FASB ASC 842, IFRS 16, and GASB standards, simplifying complex calculations and generating audit-ready schedules and journal entries directly from source contracts.

“Our clients span a wide range of industries and reporting standards, and lease accounting is one of the areas听where听getting the calculations wrong has real consequences,” said Leander Sico, Partner at Hutchinson & Bloodgood LLP. “Having Crunchafi integrated into Guided Assurance means our team is working from audit-ready outputs tied directly to the procedures we’re following. That connection between methodology and software gives us greater confidence in both our team’s execution and the final workpapers.”

: Automated proof of cash

Our integration with Audit Sight brings automated cash analytics directly into the audit workflows – reconciling bank activity against accounting records and producing audit evidence that allows auditors to reduce or eliminate substantive procedures throughout the audit.

“Audit Sight has transformed how we perform substantive testing,” said Mark Welp, Partner at Holbrook & Manter. “By automating testing of routine transactions, our team can focus on higher-value audit work. The recent PPC methodology updates reinforce that this approach is both practical and aligned with professional standards.”

: Financial statement validation

Financial statement validation and version management are some of the most time and judgment-intensive parts of the audit, but much of the underlying work is repeatable. Through our integration with Trullion, we鈥檙e bringing AI-driven automation into math and consistency checks on financial statements, version and prior-year comparisons, and statement structure validation.

“Partnerships drive increased efficiency while enhancing the effectiveness of our audit procedures,鈥� said Mike Reynolds, Partner at Bennett Thrasher. 鈥淭rullion and 抖阴成年 have consistently delivered on the vision, responsiveness, and execution necessary to solve these key components that differentiate market-leading solutions. Trullion鈥檚 financial statement validation capabilities available from 抖阴成年 Guided Assurance creates a fully integrated, AI-powered workflow, reducing friction throughout the entire financial reporting quality control process. We know this is just the tip of the iceberg of what this partnership can unlock and we are excited to see what comes next.”

Across each of these areas, the goal is to reduce manual effort, increase consistency, and keep work anchored in a trusted methodology.

Looking ahead

Audit is steadily becoming more automated and data-driven. The opportunity is to use that shift to improve both efficiency and quality, freeing up time for auditors to focus on risk, judgment, and insight.

By continuing to invest in both our own capabilities and this broader ecosystem, we鈥檙e helping firms evolve their workflows in a way that feels practical, connected, and grounded in the standards they rely on every day. That鈥檚 where we鈥檙e focused, and where we鈥檒l continue to push the pace of innovation in audit.

This post was authored by Corey Wells, General Manager of Audit at 抖阴成年

was听built for听that听purpose.听

Introducing听CoCounsel听Legal Canada听

鈥�鈥�CoCounsel听Legal Canada is the only comprehensive AI solution for Canadian legal professionals that combines advanced AI capabilities with the authoritative depth of Westlaw content and the applied guidance of Practical Law, in a single integrated solution built for the way legal professionals work. Where other tools address parts of the legal workflow,听CoCounsel听Legal Canada is built to handle the full span of it. The result is faster, more听confident听legal work across research, document analysis, drafting, and organizational听know-how.鈥�听

Here is what that looks like in practice:听

• Research that produces work听product.听Canadian legal professionals have had access to Deep Research on Westlaw Advantage, grounded in authoritative Westlaw content. CoCounsel Legal Canada takes that further. Through CoCounsel Legal Canada, Westlaw and Practical Law now are combined into a single query and response, surfacing answers for the user from both premium content sources. Westlaw鈥檚 legal authority and Practical Law鈥檚 applied, expert-created guidance surfaces the law and how to use it, moving from question to strategy to execution in a single workflow.听 听
• Document analysis at听genuine听scale.听Tabular analysis allows legal teams to work through large volumes of documents in ways that weren鈥檛 previously feasible without significant resource commitment. Whether the task is due diligence, disclosure review, compliance assessment, or privilege review, CoCounsel Legal Canada surfaces risks across multiple issues simultaneously, links findings to source documents, and generates draft reports. The results are designed to be reviewed and challenged, because that is how legal work functions.听
• Drafting within existing environments.听Enhanced drafting within Microsoft Word allows lawyers to produce high-quality first drafts without leaving the tools they already use, drawing on Practical Law content and their own organizational precedents. The goal is not to replace professional judgment. It is to compress the distance between instruction and a verified, defensible final draft.听
• An expert library.听Access expert-created prompts and create custom prompts designed to help legal professionals get started faster and work with greater confidence. The library accelerates AI adoption across an organization while codifying best practices, so teams can build capability consistently rather than starting from scratch on every matter.听

鈥�鈥�鈥�CoCounsel听Legal Canada integrates with Microsoft 365, leading document management systems, and HighQ, working within the infrastructure Canadian legal practices have already built rather than requiring parallel workflows or new platforms.听鈥�鈥�听

鈥�鈥�鈥�Because the work of legal professionals doesn鈥檛 stand still, neither does CoCounsel Legal Canada. Looking further ahead, 抖阴成年 will continue to build on its foundation, introducing additional agentic drafting capabilities, ways to enable more efficient lawyer verification of outputs, and next-gen capabilities that respond to a plain-language question by forming a theory and executing a plan at the level of a senior associate, drawing on Westlaw, Practical Law, and firm content throughout the workflow.鈥�鈥�听

“Lawyers don’t want to just operate software, and that’s not what听great听AI should do.听CoCounsel听keeps them in the analytical mindset they were trained for: going back and forth, challenging answers, and steering the work. With sourcing directly from Westlaw and Practical Law,听they’re听not wasting time second-guessing the results.听We’re听seeing adoption from associates to partners across every practice area. When it spreads that quickly, the experience just works.”
鈥� Andrew M. Medeiros, Managing Director of Innovation, Troutman Pepper Locke LLP听

CoCounsel听Legal Canada is built to the standard legal work demands听

Powerful capabilities matter only if professionals can trust the results they produce. Legal work carries liability. Outputs inform advice. Advice affects outcomes. In that environment, 鈥渁lmost right鈥� is not a workable standard.听

鈥�鈥�抖阴成年 uses the term Fiduciary-Grade AI听to describe what AI must be听in order to听function reliably in high-stakes professional environments, and it is the architectural foundation of听CoCounsel听Legal Canada.听鈥�听

鈥�鈥�鈥�鈥�It means outputs grounded in authoritative, curated content, not the open internet. It means privacy and security built into the system鈥檚 architecture, not layered on as policy. It means transparent, traceable reasoning that a lawyer, client, court, or regulator can examine and challenge. And it means the continuous involvement of credentialed subject-matter experts. 抖阴成年 employs thousands of lawyer editors whose work is not incidental to the product鈥檚 reliability. It is the product鈥檚 reliability.鈥�鈥�鈥�听

鈥淭he reality is from what听we鈥檙e听seeing out听there,听it鈥檚听not a fair fight right now.听CoCounsel听nailed it in terms of the user interface and making it easy for even non-technical people like me to use.鈥�
鈥� Ian Hull, Co-Founding Partner, Hull & Hull LLP听

The practices built today define the profession tomorrow听

The decisions being made now (the tools adopted, the workflows built, the institutional knowledge developed around how to deploy AI effectively) are the foundations of what Canadian legal practice looks like on the other side of this transition. Getting there successfully means choosing AI built for professional work, not just productivity, and investing in the workflow integration that turns capability into听a competitive听advantage.听

Canada has something valuable to contribute to the global conversation about what responsible AI adoption in regulated professions should look like. A professional culture built on precision, accountability, and trust is not an obstacle to AI adoption. It is the foundation for doing it right. The legal profession has always been defined by the trust placed in it. The practices that move deliberately now will be the ones defining what excellent Canadian legal work looks like in the years ahead.听

CoCounsel听Legal Canada is available now, and听we鈥檙e听proud to bring it to the professionals who set that standard.听

Ready to see听CoCounsel听Legal Canada in action?听.听

The exam is widely regarded as one of the hardest professional licensing exams in the United States, with pass rates typically ranging from 30% to as low as 2%. Candidates must navigate thousands of pages of CBP regulations, directives, rulings, and procedures, as well as The Harmonized Tariff Schedule which includes 99 chapters covering every tradable product.

The results reflect 抖阴成年 commitment to Fiduciary-Grade AI^TM 鈥� built to meet a higher standard than general productivity tools, to stand up to scrutiny and with outputs that are reliable and verifiable.

Embedded within the existing ONESOURCE Global Trade Management platform, Global Trade Research AI represents a significant development in trade compliance technology. Demonstrating advanced regulatory reasoning, document-based reasoning at scale, and complex synthesis of multiple sources to help with trade-based research tasks. Unlike general-purpose AI tools, the system is trained on over 100,000 pages of authoritative government sources, including Federal Register notices, CBP CSMS messages, and executive orders.

Meeting Strategic Demands in Trade Compliance
The launch comes as trade departments experience unprecedented elevation within their organizations. According to 抖阴成年 2026 , 43% of trade professionals report increased budgets for hiring, while 37% report more frequent involvement in executive decision-making.

“Trade teams are being asked to become strategic partners rather than operational functionaries,” said Ray Grove, head of product, Global Tax and Trade, 抖阴成年. “They need tools that can provide instant, accurate regulatory intelligence to support real-time business decisions.”

The shift reflects broader changes in how organizations view trade compliance. More than three-quarters of legal trade professionals (76%) believe current U.S. tariff approaches represent a permanent change rather than temporary policy tools, according to the report.

And, it reflects growing enterprise adoption of specialized AI tools for professional services. Unlike consumer-focused AI applications, Global Trade Research AI is purpose-built for regulatory compliance workflows and trained exclusively on verified government sources rather than web-scraped content.

Technical Capabilities and Accuracy
Global Trade Research AI processes natural language queries such as “What are the current tariff rates for HTS 8708.29 from Mexico vs. China?” or “What FTA benefits apply to automotive parts from Mexico?”听 The system synthesizes information across multiple regulatory sources and provides cited responses within seconds.

Key technical features include:

• Authoritative sourcing: Every response includes citations linking to primary government documents
• Real-time updates: Knowledge base refreshes as regulations change, with average update times under one business day
• Trade-specific intelligence: Understands HTS codes, duty drawback procedures, customs warehouse operations, and FTA rules
• Integrated platform: Embedded directly within ONESOURCE Global Trade Management workflow
• The system鈥檚 performance across 18 runs of the CBP licensing exam 鈥� spanning three years of changing tariff policy, demonstrates consistent ability to handle complex regulatory scenarios that typically require extensive professional training.

This is Fiduciary-Grade AI in practice: every answer is traceable, every source is authoritative, and the system is built to support decisions where being wrong carries real professional and financial consequence. Trade professionals can access Global Trade Research AI through their existing ONESOURCE Global Trade Management platform, maintaining workflow continuity while adding AI-powered research capabilities.

A 抖阴成年 customer, Crowe is one of the largest public accounting and consulting firms in the United States. The firm’s decision to add Additive to its technology stack reflects both an immediate opportunity to enhance K-1 processing and a larger commitment to building a more connected, data-driven tax operation supported by modern AI tools.

For tax professionals, the challenge of ingesting and processing K-1 documents is often highly manual, time-intensive, and dependent on spreadsheet-based workflows that can slow down downstream processes during compressed compliance cycles. Additive addresses that challenge by using a GenAI-native platform to ingest and structure data from complex K-1 documents efficiently and at scale. That structured output then feeds into Crowe’s downstream partnership calculation engines and connects with other solutions across the firm’s technology ecosystem, including .

Before making its decision, Crowe conducted a rigorous cross-functional pilot of Additive across its tax practice, bringing in specialists from international, private equity, state and local, and global and high-net-worth individual tax services. The pilot helped validate not only the platform’s ability to automate complex data extraction, but also its potential to improve the quality, speed, and consistency of service delivery across multiple tax disciplines.

“The biggest advantage of Additive is how it helps us better support our clients,” said Jeffrey Mull, Partner, Crowe. “By turning complex, unstructured K-1 data into usable information more efficiently, our teams can spend less time on manual aggregation and more time focused on analysis, insights, and getting clients the answers they need, especially during compressed compliance timelines.”

For Crowe, the value of Additive extends beyond solving a single workflow issue. As tax practices become more digital and data-intensive, firms need technology that fits into real professional workflows, works across systems, and helps experienced practitioners spend more of their time where expertise matters most. The firm sees modern AI tools as an important part of how it will continue to innovate and deliver strong client outcomes in an increasingly complex environment.

“Having access to the latest technology is essential to how we continue to innovate and deliver value to our clients,” Mull added. “From an AI transformation perspective, modern tools like Additive help us unlock the value of our data in new ways, improving how we analyze information and generate insights. It also reinforces our commitment to innovation, ensuring that we are not only keeping pace with change but actively shaping how technology is used to improve the client experience.”

For 抖阴成年, Crowe’s adoption of Additive reflects a broader shift underway in the profession. Firms are increasingly looking for AI solutions that move beyond experimentation and solve practical operational challenges while strengthening the quality of professional work.

“Leading firms are looking for AI solutions that fit into real workflows and deliver measurable impact,” said Erica Butcher, General Manager of Tax, Audit & Accounting Professionals at 抖阴成年. “Crowe鈥檚 adoption of Additive shows how firms can take a focused, practical approach to using AI to improve how work gets done and strengthen client outcomes.”

Fiduciary鈥慓rade AI is 抖阴成年 standard for how AI should work in high鈥憇takes professions. It鈥檚 AI designed for professionals with duties of care and regulatory oversight – drawing on our authoritative, domain鈥憇pecific content; protected by rigorous privacy and security safeguards; shaped by subject鈥憁atter experts; and designed to produce transparent outputs that can be verified.

Almost Right is Not Good Enough听

Before professionals operating in high-precision fields can fully embrace deeper AI integration into their everyday workflows, they need to know that the AI they are using stands up to scrutiny and that its outputs are reliable and verifiable.

For generations, professional trust has been defined by standards, certification, and fiduciary duty. When someone carries a designation like CPA in accounting or JD in legal, we understand both their qualifications and the obligations that govern how they must act. If we expect AI to start to take on more meaningful shares of human time, then as we assess a human鈥檚 fitness for purpose for a job, we must also validate an AI鈥檚 fitness for purpose.

High Stakes Professional Work Requires a Different Standard

In regulated professions that prioritize accuracy, accountability, and trust, AI must be built to a Fiduciary-Grade standard. That means real, factual, authoritative sources, traceable reasoning, and transparent outputs that are ready for human review and verification under professional and regulatory expectations.

As AI takes on more responsibility in completing professional work, it does not assume any additional accountability. That accountability remains entirely human. Professionals remain responsible for the judgments made, the advice delivered, and the outcomes that follow. Fiduciary- Grade AI is designed to support human judgment, not replace it, by producing work that can be examined, explained, and defended under real-world professional and regulatory scrutiny.

The Four Principles of Fiduciary-Grade AI听

Fiduciary鈥慓rade AI is defined not just by what it produces, but by what it is allowed to access, retain, and rely upon in generating outputs that inform professional judgment.

AI grounded in authority; with access to the right context.
A Fiduciary-Grade AI system must derive its substantive outputs from authoritative, curated, and domain-specific content, not just information scraped from the open internet, while also operating with the full context required to complete professional work. Every material output must be traceable to a source that a qualified professional can independently locate, cite, verify, and trust. And only when AI agents can access, know, and act on the specific data, knowledge, systems, and tools can they complete the complex, multi-step tasks that professional work demands.

Data privacy and security are imperative.
Where privacy is paramount, Fiduciary鈥慓rade AI is built to protect it. Privacy and security must be structural features of the system鈥檚 architecture, not policy overlays or configurable options.

Built with human expertise, not just human oversight.
Professional workflows must be designed, tested, and continuously refined with meaningful involvement from credentialed subject matter experts in the relevant professional domain. When ambiguity or risk arises, the system must recognize its limits and bring professionals back in rather than generating an output that overstates its reliability, keeping accountability human and outcomes defensible. Fiduciary-Grade AI requires that customers have access to real-time human support to ensure transparency and trust.

Transparent, verifiable reasoning.
By clearly surfacing and referencing the sources it relies on, AI must be able to provide a reviewable trail of what the system did and what it relied on, sufficient to allow a qualified professional, and, where applicable, a regulator, court, or auditor – to evaluate the basis for the output and determine whether the result is reliable and defensible. Making each step in its planning, reasoning, and execution process visible to the user is vital to helping young professionals learn and grow.

This is the standard we build to at 抖阴成年, and the standard delivered through CoCounsel for legal, tax, audit, and compliance professionals. As AI moves deeper into regulated work, the defining question is no longer whether a system can generate an answer – it鈥檚 whether professionals can verify and stand behind the result.

Nowhere is that truer than in Section 101 patent eligibility, one of the most consequential and frustrating challenges in patent practice today, and the problem that brought and 抖阴成年 together to build something new inside CoCounsel Legal.听

Why听Section听101? Why Now?听

Section 101 patent eligibility is a question at the center of most听utility听patent disputes today. It is often a decisive factor in patent litigation, and one of the quickest ways to win or lose a case. The legal test asks whether a听technical听invention is the kind of subject matter the patent system protects, meaning it must be more than a general idea and must听represent听a concrete, technical improvement.听

听In theory, the framework is clear. In practice, it is anything but:听

• Key concepts lack precise definitions, leaving听wide听room听for interpretation.听听
• The analysis is deeply precedent-dependent, and听outcomes hinge on finding the right prior cases among hundreds of fact-specific听decisions.听Missing a key precedent can mean the difference between听a strong argument听and a weak one.听

And all of this unfolds under constant pressure. Clients need answers听fast;听matters are often fixed-fee, and the uncertainty is genuinely difficult to explain.听

For patent owners seeking to assert a patent, understanding its vulnerability under Section 101 is essential before litigation begins.听For defendants, a fast, reliable eligibility assessment can reveal a path to an early win.听For both sides, the current reality often looks the same: assign a听junior associate to research similar cases, spend hours or sometimes days finding the right precedents, and still wonder whether something important was missed.听

This is the kind of problem that demands a fiduciary-grade solution:听one built not听for the average task, but听for the specific, high-stakes reality of patent practice.听

Unmatched IP Expertise, Delivered at Scale听

The听Patent Claim Eligibility Analyzer听was not built by technologists who then consulted practitioners. It was built with practitioners at the center of every decision 鈥� and with a caliber of technical听expertise听on both sides that distinction shapes everything about what the听Patent Claim Eligibility Analyzer听can do.听

抖阴成年 engaged Sterne Kessler through a听forward deployed engineering motion, a model that pairs engineers who combine strong legal backgrounds with deep AI and technical听expertise听and听embeds them directly alongside practitioners.听The Thomson听Reuters听engineering team worked side by side with Sterne Kessler’s IP litigators to deeply understand their workflows, co-build the solution in rapid iterations, and move with听speed and flexibility.听听

The result is a听tool听shaped by the kind of tight, trust-based collaboration that only happens when both sides bring genuine depth to the table.听

Sterne Kessler brings decades of litigation-tested intellectual property听expertise听to this partnership. The firm worked alongside 抖阴成年’ engineers and editorial teams to translate the way experienced IP听practitioners听approach Section 101 鈥� their analytical frameworks, their precedent instincts, their litigation-proven methodologies 鈥� into a repeatable, scalable workflow now inside听CoCounsel听Legal.听

That meant curating听an initial听corpus of approximately 200 highly relevant Federal Circuit Section 101听decisions,听cases selected not by keyword, but by their factual and analytical relevance to the kinds of claims practitioners听encounter听in real matters. 抖阴成年 editorial teams then reviewed and augmented that corpus, applying the same editorial rigor that underpins Westlaw.听

The result is a workflow grounded in practitioner intelligence, trusted legal content,听and engineering听鈥� combined at a depth that general-purpose AI听tools simply cannot replicate.听

Built听for Real IP Work

The听Patent Claim Eligibility Analyzer听reflects how IP work is听actually done.

How the听Patent Claim Eligibility Analyzer听Works

1. Enter a patent claim: Select the听Patent Claim Eligibility Analyzer in听CoCounsel听Legal and paste a claim directly into the chat.
2. CoCounsel applies the same Step 1 / Step 2 logic that courts use:听The听Patent Claim Eligibility Analyzer听structures the analysis the way judges do, first asking whether the claim is directed to a general or abstract idea, then whether it adds a meaningful technical improvement.
3. CoCounsel finds the most relevant court decisions for that specific claim: Using semantic analysis rather than keyword search,听the听Patent Claim Eligibility Analyzer听matches the claim to prior Section 101 cases with similar fact patterns, so practitioners surface the right cases, not just the most听frequently听cited ones.
4. It draws from a curated corpus built by Sterne Kessler and 抖阴成年 editors: The workflow leverages an initial set of approximately 200 highly relevant Section 101 cases, curated by Sterne Kessler and reviewed and augmented by 抖阴成年 editorial teams.
5. It explains why each cited case matters: Rather than listing citations, CoCounsel provides reasoning that connects the claim’s language to the reasoning and outcomes in those cases, giving practitioners a litigation-ready foundation, not just a list of results.
6. Citations link directly to Westlaw: Every source is verifiable. Practitioners can validate citations and continue deeper research as needed, maintaining full accountability for the final work product.

The听Patent Claim Eligibility Analyzer听surfaces both binding and persuasive authority when factually relevant, reflecting how Section 101 arguments are听actually made听in practice.听The goal is not to replace attorney judgment. It is to give attorneys a faster, more consistent, more defensible foundation from which to exercise it, so less time is spent on the research听phase听and more time is spent on strategy, client counsel, and the work that requires human听expertise.听

For patent owners, that means a stronger, faster assessment of a patent’s eligibility risk before litigation begins.听For defendants, it means a rapid, precedent-backed read on Section 101 positions from the outset of a case.听

For both, it means a head start on brief writing, a more consistent work product across matters and experience levels, and greater confidence that no key precedent has been missed.听

A New Model听for Legal Product Innovation听

Beyond the听Patent Claim Eligibility Analyzer听itself, this partnership听represents听something worth examining at a higher level: a fundamentally different model for how legal AI products can and should be built.听

While听building useful legal technology has always required thinking like a lawyer,听the traditional approach to legal technology development听across the industry听follows a familiar pattern. Technologists听identify听a problem, build a solution, and bring it to听market. Practitioners are consulted听but they are听largely recipients听of the finished product.听Expertise听flows in one direction.听

This partnership inverts that model. Sterne Kessler did not simply advise on the听Patent Claim Eligibility Analyzer, they co-developed it, inspired by the firm’s practical methodologies for Section 101.

What began as a co-development initiative has evolved into a scalable market offering available to patent practitioners across CoCounsel Legal. In doing so, it has demonstrated what is possible when practitioners and technologists collaborate.听

That model also opens new possibilities听for how law firms think about their own听expertise. Firms are听evolving听the ways they create value from their knowledge, and this partnership is an example of what it looks like when a firm’s internal intelligence becomes a repeatable, scalable offering.听

CoCounsel听Legal’s architecture听is听designed to enable exactly this kind of听forward-deployed, domain-specific innovation, making it possible to translate specialized听expertise听into scalable, trusted AI experiences. The听Patent Claim Eligibility Analyzer听is the first proof point.听

Additional co-developed workflows are already in development, with the ambition of bringing the same practitioner-built, precedent-grounded approach to other complex areas of patent law, signaling what is possible across specialized legal domains where expertise is the differentiator and where the stakes are听too听high听for approximation.听

The Standard the Profession Deserves听

What makes the听Patent Claim Eligibility Analyzer听meaningful is not just what it does. It is the standard it was built to. Every output is grounded in a curated, editorially reviewed body of case law. Every citation links to a verifiable Westlaw source.听The analytical structure mirrors the reasoning courts听actually apply.听And the workflow is explicitly designed to support attorney judgment, not substitute for it.听

That is听fiduciary-grade AI. It is听not a general-purpose听tool听adapted听for legal work, but a purpose-built solution grounded in authoritative content, shaped by the听expertise听of practitioners who听perform听this work at the highest level, and accountable to the professional standards that patent practice demands.听

The 抖阴成年 and Sterne Kessler partnership was built on that standard. And as the collaboration deepens and expands, it is the standard we听will听keep.听

In law, starting work has never been a constraint. Finishing it accurately, defensibly, and at a professional standard is. We are already seeing that distinction begin to shape how this market is evolving. While AI is expanding where work begins whether that鈥檚 in a general-purpose AI tool, an email, or inside a document workflow, it is not the system that can stand behind the result. In practice, the control point in legal AI is not the interface where work is initiated. It is the system where that work is validated, grounded, and completed.

AI can now draft, summarize, and analyze in seconds. This is changing how legal work begins. But legal work does not end with a draft. It ends when someone can put their name on it. That requires outputs to be grounded in authoritative sources, validated for accuracy, and traceable back to their origin. These are system-level requirements.

There is a growing narrative that AI will replace enterprise systems. What鈥檚 actually emerging is a separation of roles: AI is where work begins; professional systems are where it is executed, validated, and completed. AI assistants are becoming the place where work begins, while professional systems are where that work is executed, validated, and completed. These roles are complementary, but not equal. The layer where work begins is broad, fast-moving, and increasingly interchangeable. In practice, the layer where work is completed is where trust and accountability sit. It is also where meaningful differentiation shows up, because that is the layer responsible for producing outputs professionals can stand behind. Trust is built into the architecture, including the content, the validation, and the way outputs are produced.

As AI becomes embedded across more tools and environments, work can start almost anywhere. The question is where it resolves, and what system ensures it is right. Our expanded partnership with Anthropic, as outlined in our recent announcement, reflects how this is starting to take shape. , connecting that work directly into professional systems helps ensure it carries through to completion with the rigor required in professional settings. This is less about embedding a system into every interface and more about ensuring that wherever work begins, it can be completed in systems designed to stand behind the result.

The most advanced legal organizations are already operating this way. They use general-purpose AI to accelerate early thinking and exploration, and professional systems to complete high-stakes work. This is already happening in firms like . The pattern is not that AI replaces the system, but that the two now perform distinct and complementary roles. AI is not replacing the system. It is changing how work flows into it.

As this architecture evolves, the distinction between where work starts and where it finishes is becoming more important, not less. Work will begin everywhere, but it will not finish everywhere. When that validation layer is missing, the consequences are already visible, from hallucinated citations to filings that cannot withstand scrutiny. Systems that can validate it, ground it in authoritative content, and make it defensible in real professional contexts. The next generation of CoCounsel Legal, now in beta, reflects this shift, with customers increasingly relying on it to complete workflows end to end.

鈥淭he new version of CoCounsel is now one of the first tools I turn to when I want to get work done. Where I once used CoCounsel for specific tasks, I now start nearly everything with it.鈥�
鈥� Sterne, Kessler, Goldstein & Fox PLLC

The next phase of this market is unlikely to be defined by who helps professionals start work fastest. It will be defined by who enables them to finish it with confidence.

Womble Bond Dickinson UK鈥檚 early adoption of CoCounsel Legal, 抖阴成年 advanced fiduciary-grade AI platform for legal professionals, has become a case study in what firmwide AI integration looks like. It required vision, discipline, creative leadership, and a partnership built on radical candor.听

Setting the Stage听

CoCounsel听Legal integrates advanced AI with Westlaw and Practical Law content, as well as a firm’s own knowledge and tools, to support the full breadth of legal work鈥攊ncluding research, document analysis, and drafting鈥攊n a single platform. When Womble Bond Dickinson听committed听to听early adoption, the platform was still in development.听Which听was precisely the point.听

What began as an evaluation of the original听CoCounsel听product evolved into a sweeping, full-scale early adoption initiative, spanning 7 of Womble Bond Dickinson’s UK offices and all 650听timekeepers (including 457 qualified lawyers)听working in听them. The goal was not simply to make the tool available. It was to make it indispensable, transforming AI use from an occasional experiment into a daily professional habit.听

Sam Dixon, Chief Innovation Officer and Partner, led the charge. His vision was anchored in a clear strategic ambition听the firm already had in place: a self-service innovation model in which every lawyer across the business has innovation as part of their role.听CoCounsel听Legal, with its intuitive interface and enterprise-wide applicability, was the right tool at the right moment to听help听make that vision real.听

A Partnership Built on Candor听

What set this initiative apart from a conventional technology rollout was the nature of the relationship between Womble Bond Dickinson and 抖阴成年. It was a co-development partnership defined by openness, mutual accountability, and a shared commitment to getting it right.听

From the outset, both teams committed to saying the quiet parts out loud. When something wasn’t working, they said so. When feedback was uncomfortable, it was shared anyway. Womble Bond Dickinson stress-tested CoCounsel Legal’s capabilities, challenged its assumptions, and provided structured, direct feedback throughout the development process. 抖阴成年 listened, iterated, and acted. As a result, the firm鈥檚 input directly shaped the product before launch, a level of influence that reflects genuine partnership, not merely consultation.听

That bidirectional feedback loop ran throughout the project. The joint team, spanning Womble Bond Dickinson’s innovation and legal technology functions and 抖阴成年 sales, customer success, and product teams, built in multiple touchpoints for sharing insights, surfacing issues, and refining both the product and the rollout approach. Challenges became collaborative problem-solving moments rather than blockers.听

The pilot group itself was carefully constructed to reflect the full diversity of the firm: across practice areas, job roles, seniority levels, technology comfort levels, and demographic backgrounds. This听wasn’t听a pilot of the听willing. It was a deliberate, representative sample designed to surface real-world adoption challenges before the firmwide launch.听

Bringing the Whole Firm Along听

Rather than relying on emails, slide decks, and vendor-led training sessions, Womble Bond Dickinson took a different approach. Sam Dixon travelled to听each of the firm鈥檚 7听staffed听UK offices听and personally led 35听introduction听and training sessions for听CoCounsel听Legal, a campaign that became affectionately known as “Cocoa with听CoCo.” In the middle of a British summer, he served hot chocolate, sat down with colleagues, and walked them through the platform himself.听听

It’s听the听kind of visible, lead-by-example听behavior听that research shows听makes听a real difference.听According to the听2025 抖阴成年 Future of Professionals Report,听professionals who agree that leaders in their听organization听consistently lead by example are 1.7x more likely to be experiencing benefits from AI than those who disagree.听

The sessions were part of a broader, multimodal training program that included use-case specific meetings, pre-recorded leadership conversations reinforcing responsible use, and one-to-one engagement on the floor. Sam didn’t wait for questions to come to him. He walked the offices, asked people directly whether they had used CoCounsel Legal, and if they hadn’t, asked why not. That kind of visible, personal commitment created a ripple effect across the firm.听

The training program also served as a real-time risk management mechanism. Face-to-face conversations surfaced misconceptions early, allowing the team to refine their messaging on the spot. One recurring insight was around Deep Research in Westlaw Advantage, a key capability of CoCounsel Legal that delivers comprehensive legal research in 10 to 15 minutes, the kind of work that might otherwise occupy a lawyer for an entire day.听

Results That Speak for Themselves听

The impact on the quality and efficiency of legal work听at Womble Bond Dickinson听has been significant.听CoCounsel听Legal is now used regularly across practice areas, supporting work that spans听almost the听full breadth of what lawyers do, from legal research to document analysis to drafting.听听

The platform delivers up to 80% efficiency gains on specific tasks, enables junior lawyers to contribute more meaningfully at an earlier career stage, and accelerates client turnaround across the firm.听

A Model for the Industry听

This initiative has not gone unnoticed. Womble Bond Dickinson and 抖阴成年 were recently听recognized听with the听.听

As Sam听Dixon reflected on the decision to commit to early adoption of a product still in development: “It was the vision for the product and where I thought it would go and what I thought its advantages were over competitors. If I didn’t believe in听the听抖阴成年 vision for the product, I couldn’t听have believed听it fit into our vision for the firm’s AI adoption ambitions.”听

鈥淲omble Bond Dickinson exemplifies what it means to lead in the AI era. Sam Dixon and his team didn’t just deploy fiduciary-grade AI, they embedded it into the culture of the firm, making it a natural part of how every lawyer works. That kind of leadership is what separates firms that experiment with AI from those that truly use it to transform how they work,鈥� said Raghu Ramanathan, President of Legal Professionals, 抖阴成年.听

The听trust听between Womble Bond Dickinson and 抖阴成年, and trust in the product, has been听earned through transparency, delivered through partnership, and validated through results. That is the real story here. Womble Bond Dickinson didn’t wait for AI to be perfect before committing. They helped make it better. And in doing so, they built something more valuable than a technology deployment: a firm-wide culture in which AI is not a novelty, but a natural part of how every lawyer works.听

Lawyers have always been accountable for their work. That was true before AI, and it is just as true now. A brief carries your name. An argument carries your judgment. A citation carries your reputation. None of that changes because an AI tool helped you produce it faster.听

That鈥檚听why when firms talk about adopting AI for legal work, the first question听shouldn’t听be about speed or cost savings. It should be: can I听actually verify听what this produces? If you听can’t听trace an output back to its source, check whether that source is still good law, and inspect the reasoning that connected the two, you听don’t听have work product. You have a draft you听can’t听stand behind.听

That standard is not new.听What’s听new is how many lawyers are finding out the hard way that the AI tools they adopted听weren’t听built with it in mind.听

Courts across the United States have now sanctioned attorneys for听submitting听briefs with fabricated citations, false quotes, and mischaracterized precedent 鈥� all generated by AI and not verified by the attorneys. When those tools are built on content scraped from the web rather than authoritative legal sources听maintained听by practicing attorneys, the risk of error is structural. The AI has no way to know whether a case is still good law, whether a statute has been amended, or whether a citation听actually supports听the argument听it’s听being used to make. Verification becomes difficult not because the tools听don’t听show their work, but because the underlying sources听can’t听be trusted in the first place.听

At听Thomson听Reuters,听we听understand听that lawyers听don’t听just need to find the law 鈥� they need to be able to stand behind what they find.听We鈥檝e听always built Westlaw听and Practical Law听with that in mind, and听it’s听the same principle we carried into CoCounsel Legal from the very beginning.听

Built for Verification at Every Stage听

When we designed CoCounsel Legal, we started from a simple premise: a lawyer should be able to verify everything the AI produces before putting their name on it. That meant building听tools that give attorneys everything they need to do that听verification听themselves,听at every stage of the workflow.听

As the research unfolds,听Deep Research听shows you its work in real time, step by step. You can follow the reasoning as it develops,听explore听findings as they听emerge, and听refine听the research with more specificity听by answering听additional听questions.听听

As citations are built, two things work in parallel.听KeyCite听is woven into every stage of the research workflow, flagging cases overruled in part, warning of proposed amendments to statutes, and surfacing cases that are听frequently听cited together even when they听don’t听cite each other. Alongside it,听CoCounsel Legal’s patent-pending citation ledger听tracks every source the AI draws on throughout the research process and confirms that each source was听actually read听and reviewed 鈥� not just referenced.听Together, they give attorneys what they need to听answer the question听that听should听precede every citation听they rely on: does this hold up?听

Before anything goes out, two more layers of review engage.听The Verify function, launched in February 2026, surfaces every assertion made in the research report alongside the relevant source passages and pointers for听additional听research 鈥� giving attorneys everything they need to听verify听before anything goes out the door.听Litigation Document Analyzer听goes further听by听identifying听potential misrepresentations of law throughout an entire brief, your own or opposing counsel’s. Because in litigation, what a document implies about the law matters just as much as what it explicitly says.听

Every one of these capabilities exists for the same reason: because when you use AI to do legal work, you are still the one responsible for it.听听

The Question Every Lawyer Should Be Asking听

Not all legal AI is built the same way.听Some tools are little more than general-purpose foundation models with a legal label applied 鈥� with little ability to confirm whether the underlying sources are current, authoritative, or accurately represented in the answer.听They can be fast. They can be impressive in a demo. But when a client’s matter is on the line and a judge is asking questions, impressive in a demo is not the standard that matters.听

At 抖阴成年,听fiduciary鈥�grade听AI is our standard for how AI should work in听high鈥�stakes professions.听听It鈥檚 AI designed for professionals – built on our authoritative content; protected by rigorous privacy and security safeguards; shaped and validated by subject鈥�matter听experts; and designed to produce transparent outputs that can be verified.听听

We鈥檝e spent decades earning the trust of the legal profession. That history shaped how we built CoCounsel Legal. When your firm is evaluating which AI tools to adopt, the conversation about speed and efficiency matters. But it shouldn’t be the only conversation. Ask how the system handles accuracy. Ask what happens when you need to trace an output back to its source. Ask whether you can actually verify what it produces before your name goes on it. Those questions will tell you everything you need to know about whether a tool was built for legal work or just marketed to it.听

Lawyers have always been accountable for what they put their names on. The right AI gives you the tools to meet that accountability 鈥� and the confidence to know you have.