What Does Thought Leadership Mean in the World of AI?

Law firms of all shapes and sizes are increasingly incorporating AI tools. From global firms building closed proprietary systems to mid-size firms investing in subscription-based platforms and small firms using low-cost consumer tools, the trend is firmly established and accelerating. So is the recognition that AI has a lot to offer firms in the legal marketing arena. However, in one particular space — thought leadership publication — the use of AI gets especially thorny.

When a large language model generates an article, the bylined author is neither “thinking” nor “leading.” But AI can help in thought leadership publication by improving the steps that amplify how lawyer-authors think and lead. The right AI tools are already deployed in most firms — they just need to be redirected.

Idea Generation

Effective thought leadership begins with deliberate ideation, before writing even starts. The most influential thought leadership pieces address clients’ current concerns or anticipate potential challenges and risks. These points arise in conversations between lawyers and their clients, in person, and often, on phone calls or in virtual meetings.

Author-lawyers can use meeting takeaways to refresh their memories as they generate ideas. Alternatively, firm marketing professionals can review those same transcripts and recordings to plan thought leadership content accordingly. Today, brainstorming continues even after the conversation is finished.

A bonus of incorporating meeting captures is cross-selling. Marketing professionals may discover similar concerns arising in meetings held by lawyers who practice across industries, practice areas, or geographical locations, and use that information to develop thought leadership with cross-selling in mind.

Consider a firm with multiple practice areas, including commercial real estate, international trade, and construction. An AI review of AI-transcribed meeting notes shows that multiple international trade clients and commercial real estate clients raised concerns over force majeure clauses. Despite differences between the legal issues, shared threads may make for a compelling thought leadership piece. Depending on how the firm decides to cover the matter, a relevant article can be distributed not only to commercial real estate and international trade clients, but also to construction clients who may encounter their own force majeure challenges.

Conflict Checks

Once the topic is settled, the next step is a conflict check. While many firms do not incorporate them into their processes, conflict checks are essential for thought leadership publication (and other content, including blogs and LinkedIn posts).

Ethics rules prohibit lawyers from writing about matters involving their clients (even public matters) without their clients’ permission. Compliance becomes particularly difficult to manage in firms where practice areas or offices operate in isolation. A New York real estate lawyer writing an article about a recent deal, for example, may have no way of knowing that a colleague in Miami represents an insurance company connected to the deal. When the article goes live, or the lawyer posts related thoughts on LinkedIn, the New York lawyer inadvertently violates ethics rules, and the Miami partner faces an unhappy insurance company general counsel.

Luckily, as law firms increasingly integrate AI into conflict check workflows for client intake, they can extend that integration to content and avoid running afoul of the rules. Some firms have embedded AI directly into intake portals. Others continuously monitor ongoing matters with AI and alert teams when conflicts arise mid-representation due to mergers or other changes to corporate structures.

At any stage of representation, AI can streamline conflict checks by using natural language processing or recognizing related entities to find non-obvious connections. That functionality can be quite useful in the publishing workflow, where firms should apply the same conflict check procedures.

Pitches

After a topic is selected and a conflict check is completed, the next step in the thought leadership publication process is the pitch. AI can improve the pitch process by helping firms publish in the right external publications.

AI optimizes the pitch process in two significant ways: editorial fit and timing. Before the query, editorial outlets’ publication histories should be researched; those that have already covered the topic extensively can be excluded from the pitch process (unless pitching a related niche topic, in which case, depending on the circumstances, those outlets may be prioritized). Outlets that will reach the desired audience but have not covered the topic extensively should be targeted. Specific editors may also be targeted with the help of AI. Personalized query letters should incorporate particular editors’ interests and coverage gaps.

Timing of any pitch is also key and can be optimized with real-time AI-driven monitoring. The best queries are the ones that arrive at precisely the right time. Ties to recent decisions or legislative changes make thought leadership pieces timely and relevant — and their pitches more likely to succeed. Ongoing tracking of legislative updates and caselaw, which many firms are already doing, makes the pitch process more efficient and successful.

Writing

When a query is accepted, the next step is writing. Here, depending on firm rules and publication guidelines, AI can play a wide range of roles. In theory, a firm can post an AI-written summary of recent legislation on its website and in an email blast to clients, but that summary is not likely to distinguish the firm or its lawyers. It is also devoid of thinking or leading.

Assume lawyers write the thought leadership pieces. There are ways firms should use AI to help. With grammar checks and thesauruses readily available, no article (or other content) should include grammatical errors or needless repetition of the same word.

AI can also ensure that lawyers cite properly by flagging inconsistently formatted or incomplete citations. AI tools can verify that citations conform to the editorial guidelines of publications as well, which are often different from each other and Bluebook standards.

Post-Publication

Once an article has been written and published, AI can help with the next steps. Email outreach is a good place to start, as most firms are already incorporating AI into their distribution systems. By segmenting contact lists, firms can target clients who may be interested in the topic and avoid sending unwanted emails to uninterested clients. Firms can also personalize emails to avoid the appearance of mass distribution; subject lines or opening lines can be customized with AI tools. Those same tools can format emails for mobile readability.

Readability is also key in LinkedIn posts, another post-publication arena where AI can help. With the right tools and prompt engineering, AI can extract insights from an article and condense them into a short post. AI-generated LinkedIn posts often need significant editing, so human review of any AI-generated post should be part of the process.

A New Way Forward for Thought Leadership

Human involvement is key throughout the thought leadership process. While AI may streamline distribution or sharpen a query, it cannot (at least, not yet) replicate human judgment or insight.

An AI tool cannot call an editor it has worked with in the past to pitch an article over the phone or draft a case study based on recent representation. It also cannot remember advising a client on a broken deal or know what other clients can learn from that experience. But when used properly, AI should make thought leadership more compelling and relevant than ever.