Last week at the Voice and AI Conference, an interesting debate emerged during a session on business leadership in the AI era. The panel featured Andre Yee, founder of several B2B SaaS startups, and Pat Higbie, CEO of conversational AI company XApp. They disagreed on whether businesses should wait for workflow-specific AI tools versus adopting more general AI tools now.
I went into the session aligned with Yee's view - that purpose-built AI applications tailored for specific tasks yield the best results. At Hill & Ponton, we have taken a two-pronged approach to AI adoption. For marketing, we have already adopted generic and specific AI tools widely. You could argue that many workflow-specific tools for marketing already exist. We've reviewed several on the blog like Opus Clip as well as our Prompt Database and Tool Directory. When it comes to client data, however, we have a firm wide policy that prevents anyone from using ChatGPT for fear of leaking PII or other info. This policy was adopted early even though there have been a number of updates to ChatGPT and other tools around privacy and opting out of using data for training purposes.
We started down the path of developing an internal chatbot that could handle client data but shelved it while we focused on the bigger effort around our expert legal AI tool CaseScribe. This tool was meant to compliment the Website Chatbot that we developed and has been very popular with website visitors.
Our current internal push has been around an AI documentation and creation tool purpose-built for legal workflow called CaseScribe. CaseScribe summarizes evidence and medical records to streamline brief and demand letter drafting. We wanted the security and customization of an in-house system before exposing confidential client data to third-party AI services.
However, Higme made a compelling counterargument - don't wait, start developing an "AI-first" workforce now. He believes AI proficiency will become a core competency, so staff should actively expand skills. Delaying widespread adoption misses low-risk AI uses that boost productivity.
Upon reflection, pursuing both approaches in parallel is optimal. We will continue building CaseScribe while identifying quick AI wins. Our plan is to hold monthly meetings to brainstorm and track results as we empower employees to incorporate AI into their workflows. For example, our intake team could use AI for document review and our case managers could leverage AI for evidence summary and organization.
The key takeaway is that purpose-built solutions and general AI adoption are complementary, not competing, strategies. AI expertise develops through hands-on experience. Law firms shouldn't view AI as a future event requiring extensive upfront development. Rotate staff through exploratory AI projects to identify use cases. This organic discovery process yields fresh ideas and builds critical skills. With the right balancing, law firms can maximize their competitive advantage both today and tomorrow.
What do you think? Are you waiting for specialized tools or incorporating AI experimentally now? Share your thoughts in the comments!