Right-Sizing AI: Cutting Costs and Boosting Efficiency in Legal Tech
Avinash Bonu
Head of Legal Tech
Iota Analytics
Jonathan Nystrom
Senior Advisor
Iota Analytics
In the rapidly evolving world of legal technology, artificial intelligence has become a powerful tool for streamlining workflows, improving research, and enhancing decision-making. However, the rush to adopt AI often leads firms to deploy large, mismatched models that drive up costs without delivering proportional value.
The Cost of Mismatched Models
Many organizations opt for off-the-shelf AI solutions that come with high recurring license fees, substantial storage requirements, and intensive processing demands. These costs can quickly spiral out of control, particularly when the AI models are not optimized for the specific legal challenges they aim to address. Unused capabilities, excessive computational overhead, and bloated infrastructure lead to wasted resources. “Unchecked runaway expenses can add tens of thousands of dollars a day to project cost”, says Avinash Bonu, Head of Legal Tech at Iota Analytics.
Beyond financial expenses, inefficient AI models can slow down workflows, and thwart the legal team’s ability to accomplish its goal. Examples include missing cases in legal research, missing connections in contract analysis, and missing entities in case assessments. Large-scale AI systems require immense computing power, which not only increases energy consumption but also contributes to higher carbon footprints – a growing concern in corporate sustainability efforts. Additionally, even though larger AI models might offer broader capabilities, they also tend to hallucinate more – producing outputs that sound plausible but aren’t reliably grounded in verifiable data. “Ironically, smaller models exhibit fewer hallucinations,” says Avinash. “They anchor more tightly to the input data rather than generating content through broader inference. With well-crafted prompts, we’ve found it easier to steer smaller models toward controlled, evidence-based outputs.”
The Case for Right-Sizing AI Models
Rather than defaulting to one-size-fits-all AI solutions, legal teams can benefit significantly from custom-built AI models tailored to their specific use cases. Right-sizing AI ensures that firms invest only in the necessary computational power and storage, significantly reducing costs while maintaining—or even improving—performance.
By designing models suited to legal-specific tasks, organizations can achieve:
Reduced Costs – Eliminating unnecessary AI capabilities means paying less for licensing fees, cloud infrastructure, storage, and energy consumption.
Improved Efficiency – Custom AI models can deliver faster, more precise results without the excess baggage of generalized solutions.
Better Compliance and Security – Tailored AI deployment architecture can be built with industry-specific regulations in mind, ensuring compliance with legal standards and reducing risks associated with data privacy breaches.
Enhanced Interpretability – Smaller, domain-specific models are often easier to interpret and audit, which is crucial in legal settings where transparency is paramount.
Strategies for Building Custom AI Solutions
Organizations looking to optimize their AI infrastructure should consider the following steps:
Can this be done better without AI? - Rules-based systems remain one of the most powerful tools in a legal team’s arsenal. Is there a well-defined set of rules around the problem you are hoping to address?
Assess Specific Use Cases – Identify the exact needs and challenges the AI model should address, avoiding unnecessary functionalities.
Select the Right Model Size – Choose a model with the appropriate complexity and scale for the legal tasks at hand.
Leverage Transfer Learning – Utilize pre-trained models fine-tuned on legal data to reduce training costs and improve accuracy.
Implement Efficient Processing Pipelines – Streamline AI workflows to minimize computational waste and maximize performance.
Monitor and Update Models Regularly – Continuously evaluate AI performance and make necessary adjustments to maintain efficiency.
A Smarter Approach to AI in Legal Tech
“I have built more than 40 legal AI solutions,” says Avinash, “and each requires careful consideration of every item on the list above. That is where the human element of expertise comes in to get the most out of your AI project.” Building custom AI solutions may seem like a daunting task, but advances in AI development frameworks and model fine-tuning make it more accessible than ever. By carefully assessing their needs, legal teams can work with AI specialists to build right-sized models that deliver real value without unnecessary financial and infrastructure burdens.
Moreover, embracing AI efficiency aligns with the broader movement toward sustainable and responsible AI. Companies that right-size their AI solutions not only cut costs but also contribute to reduced energy consumption and lower environmental impact.
As AI continues to reshape the legal industry, the firms that take a strategic approach - prioritizing efficiency over excess - will reap the greatest benefits. Right-sizing AI is not just about cutting costs; it's about making smarter investments in technology that truly serves its purpose while supporting long-term innovation and sustainability.
If you’d like help thinking through your AI project, please contact Avinash directly. He’ll walk you through CurAIte, the focus of this blog series.
