AI Can Do It, But Should It? Smarter AI Adoption in Legal Tech

Generative AI is Amazing but Should It Be Doing Everything?

Generative AI tools like ChatGPT have transformed how we interact with data. Upload a spreadsheet and suddenly you have instant analysis. Drop in a contract and key parties, obligations, and risks are extracted in seconds. In legal tech, where efficiency & speed is essential, it is no surprise that AI is being embedded into everyday workflows.

But you know, just because we can use generative AI for everything does not mean we should.

The Danger of AI as the Default

AI is quickly becoming the default solution, not just for complex legal tasks but for things that could be handled more simply. Need a quick risk assessment? AI. Formatting a table? AI. Counting how many times a clause appears? AI. However, many of these tasks could be solved with basic NLP, rule-based logic, or even an Excel formula.

AI is an excellent exploratory tool, but we need to ask: once we find utility in it, is it the best long-term solution?

How AI Has Opened the Door to Faster Innovation

One of the biggest shifts AI has enabled is allowing legal professionals to do things that previously required a development team. Development resources are always stretched, with a backlog, competing business priorities, and there can be endless discussions before anything gets built.

Now, generative AI allows teams to explore ideas quickly. Need sentiment analysis on case data? AI can do it in minutes. Want to test a contract clause detection workflow? AI can validate the concept before it even reaches the development queue. It allows firms to experiment and iterate at speed, but once a process is validated, we need to ask, is AI the best way to handle this in the long run?

When to Replace AI with Better Fit Solutions

Once AI becomes part of an everyday workflow, it is time to consider whether a more efficient, sustainable alternative exists.

Take contract analysis, if AI is extracting key dates, parties, and obligations, that is a great quick win. But if this becomes a core workflow, shifting to a structured database with an NLP pipeline could be more efficient, cost-effective, and provide better transparency.

Or take AI-powered spreadsheet analysis. If your firm is repeatedly asking for trends, correlations, or risk exposure, a well-structured Power BI dashboard, a simple web app, or even basic Python scripting might handle it just as well without the ongoing AI overhead.

The ESG Impact of Using AI for Everything

There is also an environmental issue here, AI is mega computationally expensive. Running models at scale consumes energy, and while cloud providers are improving sustainability, unnecessary AI usage still increases emissions and resource consumption.

You often see ChatGPT generating code to run its data analysis, so not only are we using an expensive method to execute the task, but we are also repeatedly generating and running the code from scratch each time. Rather than just making use of existing optimised scripts or structured automation, AI is writing and executing bespoke code on demand, leading to inefficiencies in both compute power and sustainability.

If legal teams are running thousands of AI queries daily for tasks that could be done with simple automation, what is the environmental impact? ESG-conscious firms (of which they all should be) should start questioning whether they need AI for a given task or if they are just using it because it is available.

A Smarter Approach to AI Adoption

This is not about rejecting AI. It is about using it strategically. Legal teams should:

1. Use AI to explore efficiencies since it is a great tool for identifying workflow improvements.
2. Evaluate alternative solutions by assessing whether AI-driven processes that become core workflows could be replaced with a deterministic method, lightweight ML model, or structured web app.
3. Invest in sustainable automation by putting development resources into long-term, scalable solutions if AI proves its value.

That said, AI will remain the best solution in some cases. Sometimes, development resources will not be available. Other times, alternative technology is not feasible. AI will continue to be a key tool for handling those gaps and unlocking efficiency in legal teams.

How Innovation and Development Teams Should Work Together

So rather than development teams simply stepping in to replace AI solutions, innovation and development teams should work closely together to ensure a seamless transition from AI experimentation to robust, scalable solutions. Innovation teams can use AI to test ideas, prove workflows, and gather user feedback, providing a strong foundation before development resources are involved.

From there, development teams can assess the most effective way to build a long-term solution, whether through a structured web application, integrating with existing systems, or deploying lightweight ML models instead of full Generative AI pipelines. This should be a collaborative process where innovation and development teams continually share insights and refine solutions.

To make this work, firms should:

• Ensure innovation and development teams collaborate, so AI does not remain a temporary patchwork solution but instead evolves into a structured, efficient system.
• Establish a clear transition process for moving AI-driven workflows into properly built automation solutions.
• Define criteria for when an AI-driven process should be replaced, considering factors like cost, efficiency, maintainability, and ESG impact.

By fostering a more iterative and collaborative approach between innovation and development, firms can make AI a valuable enabler rather than a long-term dependency.

AI is an incredible enabler, but it should not be a crutch. The real opportunity is not just in using AI but in knowing when to move beyond it and the legal teams that balance AI’s power with practical, efficient alternatives will be the ones who get the most from it without allowing it to take over everything.