Home Management Key Considerations When Starting An
AI Program For Your Law Firm
Key Considerations When Starting An AI Program For Your Law Firm

Key Considerations When Starting An
AI Program For Your Law Firm


Artificial intelligence has been dominating the legal tech landscape in recent years. By now, most firms have decided that they need to incorporate AI into their practices for one reason or another. Figuring out how exactly to do that, though, is an entirely different question. Once you’ve made the decision to implement an AI program, what do you need to do to get it off the ground and ensure that it’s effective?

Committing to AI and forming a committee isn’t enough. There are certain crucial steps you need to follow to make sure that you implement the right AI program for the needs of your firm and that you get that program up and running in a way that will truly benefit your practice.


Building and Rolling Out an Effective AI Program


Implementing a successful AI program requires a great amount of strategic forethought. To settle on the right program for your firm, it’s crucial to engage in guided design thinking that’s aimed at identifying key areas where the firm can reap the most benefits from AI.

The way to start is by bringing in a neutral outside consultant to run blue sky sessions – the AI discussion should be a two-way street, where the firm’s lawyers can describe what they hope to gain from AI, and the consultants can connect the dots and come back with recommendations of two or three areas where AI will most benefit the firm. Too many firms make the mistake of implementing AI that isn’t targeted at addressing legitimate needs. This only results in the firm incurring unnecessary costs without seeing any tangible benefits or problem solving. The right consultant will help you vet the vendor landscape, considering the positives and negatives of your various options without pushing certain tools.

Once you’ve identified your key areas for AI, it’s important to determine whether or not you have the data to do what it is you want to do. After all, AI is data-driven – if you lack the necessary data, the technologies won’t help you. For example, if a firm decides it wants to invest in performing litigation outcomes, it must have access to underlying data on prior litigation outcomes for predictive software to work. Only by doing thorough due diligence can you know what data you have, how organized or disorganized it is, and what knowledge it contains. If your internal data is insufficient, you may need to consider purchasing additional data to give a full picture.

After you’ve formulated your AI plan, the final step is actually testing it to make sure that it’s working before you do a full rollout. The best way to do that is with a proof of concept, which demonstrates how the new solutions will function, using a subset of the data to determine whether the program works or not. If the proof of concept meets expectations, it should become the basis for a prototype that your employees can test to provide feedback. Skipping the prototyping step means missing out on crucial user feedback that’s necessary to maintaining and optimizing your technology to achieve its greatest utility. Doing a full-scale rollout of untested AI is a sure recipe for failure.


Helping Your Employees Embrace AI


Prototyping your AI program to incorporate feedback from the real people who will be using it goes a long way toward building a successful program that will actually be useful. However, it’s not the end of the story.

Once you’ve gone through development and are doing a full rollout of your chosen solutions, it’s key to train your staff on how to use them. Most importantly, your training needs to be tailored for each group of users. Lawyers may not get the same levels of training as support staff, depending on how the system will be used. Your technology department might get entirely different training to learn the back end of how things work and how to interact with the consultants who implemented the program.

While a fully trained rollout is unquestionably a huge accomplishment, the work doesn’t end there. You need to be constantly maintaining your program behind the scenes and updating it to accommodate your changing business needs. Successful AI isn’t simply a plug-in. A tremendous amount of customization is required to make it successful, with consistent improvements to ensure that it remains useful and cost-effective.

Vigilant communication is critical to the success of any AI program. It’s easy to be caught up in the fanfare when you first decide to implement AI, but it’s just as easy for that fanfare to die down by the time you actually launch your solutions. If you constantly communicate the benefits of your AI program to your employees and keep them engaged in improving the technology by encouraging them to communicate feedback upward, you’re far more likely to reap the benefits of AI.

From the initial stages of building a program to its ultimate full-scale rollout, having an objective, experienced consultant to guide you through the process can mean the difference between wasting money on useless technology and implementing cost-effective solutions that will improve your business. At the end of the day, strategic planning and constant communication are what make an AI program successful. With the right approach, you can ensure that your technology is always working for you, not against you.


Vicki LaBrosse
Vicki LaBrosse
Arup Das on EmailArup Das on LinkedinArup Das on Twitter
Arup Das
Arup Das
Arup Das is CEO of Alphaserve Technologies. He is an expert in institutional level technology governance and operational risk management standards that are prevalent in hedge funds, private equity funds, venture capital funds and global law firms. Mr. Das holds an MBA from Cornell University and sits on its Board of Entrepreneurship; he also has a Masters degree in Computer Engineering from the State University of New York at Stony Brook and a Masters in Analytics from Villanova University where he is an advisor for their Center of Business Intelligence.


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