Cutting through the AI Hype: Why Legal Teams need AI Experts, not just AI
Avinash Bonu
Head of Legal Tech
Iota Analytics
Jonathan Nystrom
Senior Advisor
Iota Analytics
The explosion of AI solutions in the legal industry has created a cloud of confusion. From off-the-shelf large language models (LLMs) to so-called "legal AI platforms," law firms and corporate legal teams are inundated with choices – each promising efficiency, cost savings, and better outcomes. Over the coming weeks, Avinash Bonu, hands-on technical creator and Legal Tech business unit lead at Iota Analytics and Jonathan Nystrom, a seasoned litigation and discovery technology expert, will take a deep dive into Iota Analytics’ significant experience enabling legal teams to achieve their goals without breaking the bank or risking clients’ trust. Jonathan and Avinash have together seen hundreds of cases from inception through resolution, and have supported legal teams in corporate law, law firms, legal tech companies, accounting and advisory firms, and ALSPs around the world.
“We are here to help dispel some of the confusion that has cropped up around AI in the legal space” says Avinash. “We’ve been doing this so long and so extensively that our hands-on lessons learned have changed how we view this critical work. There is no substitute for making your own mistakes, but if we can help others avoid those mistakes by sharing our experience, we are delighted to do all we can to help the legal community.” “We asked Jonathan Nystrom to help with this series because he was deeply involved in several transitions in the legal sector, from paper-to-eDiscovery, from manual review to TAR, from sampling to data analytics, and so on.”
Picking the wrong AI tools can be at best an expensive misstep and at worst, a long-term reputational risk. Misaligned AI can introduce errors, breach compliance, and disrupt workflows, making legal teams less effective rather than more. But it’s not just about tools: the way you approach AI is often more important than the precise tools and platforms you choose to use.
Legal AI needs to work for legal professionals, not the other way around. Legal professionals of all levels and roles are constantly faced with a barrage of competing priorities vying for their precious time and focus. AI should make their jobs easier and more effective, not create additional worries. At Iota Analytics, we take a different approach built on years of delivering whatever legal professionals need – building AI solutions that are right-sized for legal workflows, delivering precise, explainable results without wasted resources.
The Pitfalls of Generic AI in Legal Work
Many legal teams, eager to modernize, adopt general-purpose LLMs, only to encounter unexpected challenges:
Lack of Accuracy and Explainability – Standard LLMs generate text based on broad internet-trained data, lacking legal precision and often unable to justify their conclusions.
Increased Compliance and Security Risks – Legal work demands strict confidentiality and compliance with regulatory standards. Off-the-shelf AI, with opaque data handling practices, can create unintended liabilities.
High Costs, Low Relevance – Large, generalist AI models demand heavy computational resources, increasing costs while providing only marginal value for legal-specific tasks.
Workflow Misalignment – AI should enhance existing legal processes, not force teams into new, inefficient workflows. Generic models often fail to align with contract analysis, patent research, and case law review.
Right-Sized LLMs: AI That Works for Legal Teams
Instead of forcing legal teams to adapt to AI, we recommend building AI that adapts to legal work. Right-sized LLMs are tailored to harness the power of available large language models in a way that delivers the performance characteristics unique to legal professionals:
Accuracy and Traceability – Every decision is explainable, ensuring that AI outputs can be trusted. Results are validated by 100+ legal experts.
Efficiency and Cost Optimization – Models are lean and focused, reducing infrastructure costs while delivering precise insights tailored for legal teams.
Compliance and Security – Priority is placed on data security and legal industry standards, ensuring that AI solutions meet the strictest confidentiality requirements.
Seamless Legal Workflow Integration – Well-executed legal AI enhances legal processes like contract review, eDiscovery, knowledge management, and compliance audits without forcing disruptive changes.
Real-World Use Cases: AI that Solves Legal Challenges
Well-done legal AI delivers practical value in almost unlimited areas of the practice and business of law. Examples we will examine in this series include:
Patent Analytics and Competitive Intelligence
Challenge: Legal teams need to analyze large patent datasets for prior art searches and competitive monitoring. There is always a mismatch between the overwhelming volume of patents and finite human capacity to make sense of them. Establishing both breadth and depth is critical, yet elusive.
AI-Powered Knowledge Base Creation
Challenge: Lawyers spend hours searching through legal memos, case law, and internal documentation. Clients increasingly refuse to pay for “learning on the job” or “finding information.” Clients expect every legal professional to have perfect recall, contextual awareness, and instant knowledge integration skills. AI can help close this gap for forward-leaning legal professionals.
Contract Review and Risk Analysis
Challenge: Reviewing contracts for compliance, risk, and deviations from standard clauses is time-consuming. Two decades of contract lifecycle management software have made great strides, but the proliferation of contracts and novel language, driven by AI, has made the challenge rise to a new level. No human reviewer can consume, ponder, examine, challenge, and then propose alternatives to language in a way that keeps up with the breakneck speed of modern business.
eDiscovery and Litigation Support
Challenge: Sifting through millions of documents in litigation is overwhelming. Over the past thirty years multiple phases of advancement have empowered legal teams to do more with less, but the amount of “more” has increased exponentially and the expectations of completeness have grown at an equal pace. In other words, thirty years ago most courts and adverse parties accepted “we did our best.” But today, “our best” is expected to be nearly perfect regardless of the time allotted, volume in question, or complexity involved. Meeting these ever-growing demands requires optimal deployment of human and machine assets, and AI is a great force multiplier to accomplish this.
The Bottom Line: AI Built for Legal Teams, Not the Other Way Around
Stay tuned as we cover these topics and more in our series on getting the most out of AI. AI should empower legal professionals, not introduce complexity, compliance risks, and unnecessary costs. Iota Analytics has channeled years of experience into our framework for right-sized AI, CurAIte, and will use it as an example to illustrate these principles in upcoming blogs. Legal teams should be confident in how they integrate AI into their workflows – without compromise.
