According to a recent LinkedIn post from Everlaw, the company recently featured insights from Palo Alto Networks Assistant General Counsel Hayden Creque on the use of generative AI in a high-volume procurement legal function. The post describes how Creque’s five-person global procurement legal team, overseeing more than $500 million in annual transaction volume, is using GenAI daily and experimenting with agentic AI to increase speed and efficiency.
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The LinkedIn content highlights use cases such as triaging contracts, synthesizing complex regulatory developments, and drafting routine correspondence, positioning AI as a time-saving assistant for in-house legal teams. It also emphasizes a shift in legal roles from drafting toward more consultative work, suggesting that AI-enabled workflows could enhance the strategic value of legal departments within large enterprises.
The post underscores leadership themes around fostering a culture of innovation, including allowing room for failure while adopting new technologies. It further suggests that the most important capability for modern lawyers is not a static library of prompts but an “engineering mindset” for building and refining prompts, pointing to a skills-based evolution in legal practice.
For investors, this content may indicate Everlaw’s intent to align its brand closely with advanced AI applications and thought leadership in legal technology and e-discovery. Positioning alongside a prominent cybersecurity company like Palo Alto Networks could support Everlaw’s credibility with large enterprise customers and potentially strengthen its competitive stance in the legaltech market over time.
If Everlaw’s platform is perceived as a facilitator of AI-driven efficiency and higher-value legal work, this could support pricing power and customer retention in an environment where corporate legal teams face pressure to do more with fewer resources. However, the post is primarily educational and thought-leadership orientated, and does not provide quantitative data on product adoption, revenue impact, or specific commercial agreements tied to these AI use cases.