Jack Wicks is senior product manager at Agiloft with a maths degree from the University of Warwick. As part of Legal IT Insider’s Career Insights series, which features the most interest submissions we receive, we asked Wicks to tell us about his day job.
How would you describe your current role?
I am the senior product manager of Agiloft’s AI innovation team, responsible for steering the addition of generative AI functionality into our data-first agreement platform. I collaborate closely with our legal knowledge experts, data scientists, and software engineers to ensure our customers’ voices resonate throughout our technology. I am a firm believer in never forgetting what makes contractual agreements work – agreements between people – so any contract technology needs to be human–centric, self-administrable, and connected.
How important would you say AI is to the wider enterprise legal technology market?
We’re all AI businesses now, whether we build, buy, or benefit from the use of legal technology. AI is a force multiplier, and it is revolutionizing the enterprise legal technology market by automating mundane tasks, improving efficiency, and providing insights that were previously unattainable. AI-powered tools can accelerate and simplify routine activities but also improve efficiency by identifying patterns and trends in data, which can then be used to predict legal outcomes and improve risk management. Moreover, AI can derive invaluable insights from large datasets of legal information, which can help relevant teams within a business to make better, more data-driven decisions. As AI continues to develop, it is likely to play an even more important role in the market.
What has been your biggest achievement in your current role?
Given the diverse contracting processes of our users, identifying where first to integrate generative AI into Agiloft’s core platform presented our team with a formidable challenge, so there is no doubt that our most significant achievement has been ensuring that our customers can benefit from AI as a force multiplier and still tune their version of our technology to their organization’s needs and processes.
What are the biggest challenges?
AI is a massive part of every conversation right now, and one of the biggest challenges in that environment is managing expectations around what AI can do. Part of our role in the AI innovation team is to think critically about the best use cases for AI at the current stage of its evolution. The rapid pace of AI innovation also poses a significant challenge. What we develop today might quickly become obsolete, as new models emerge. Anticipating these advancements and embedding flexibility into a product’s design is crucial. As is ensuring that the features we do incorporate seamlessly evolve with model improvements. There is no rest for teams at the forefront of AI innovation.
Looking back, what piece of advice do you wish you had received when you started out your career?
Looking back, I wish someone had told me how important cultivating and sustaining customer relationships is. We can chase our numbers and hit our KPIs, but if our customers aren’t happy, we’re done. Many of our customers invest considerable effort in configuring their platform, fostering pride of ownership. That often means those customers are willing to share invaluable insights and support our product development initiatives, so establishing solid bonds with those users is crucial. I have become incredibly passionate about culture and the relationships our team builds with our customers: the business world is relationship-driven, and transparency is a cornerstone of any good culture.
How do you see legal AI evolving?
A gradual reduction in human involvement characterizes the future of legal AI in contract management. As AI gains broader acceptance and trust, its applications will permeate the entire contract management cycle, minimizing the burden on legal teams for repetitive, manual tasks. This transformation will empower legal professionals to focus on more sophisticated issues, allowing them to make a more significant strategic contribution to their organizations’ key goals.
Advancements in generative AI, particularly developing cost-effective, faster models with improved accuracy, pave the way for AI’s widespread adoption across the entire contract management lifecycle. However, large datasets still pose challenges for large language models, so more work needs to be done on finding ways to apply other technologies to that process to refine information and help algorithm-builders to distinguish between meaningful and irrelevant data points.
Are there any barriers to that evolution?
Several barriers impede the evolution of legal AI. The main obstacle is the imperative need to guarantee the security and privacy of sensitive legal data, a challenge heightened by the inherently confidential nature of legal information. This limits the availability of relevant information to train models on, making the challenge of building high-quality algorithms that much harder.
Deploying AI to all areas of the contract management lifecycle hinges on fostering increased trust. We can only earn trust by building a reliable track record and ensuring our approach towards using AI is human-centric and rational. AI is an enabling technology that will supplement human decision-making, not replace it, but I am confident responsible AI implementations will prevail.
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