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Follow the “Four Vs” to Achieve Artificial Intelligence Maturity

Everyone should consider learning more about Artificial Intelligence Maturity and the factors that impact this development. Although AI has been around since the 1950s, it seems to be going through an accelerated growth spurt on its way to full maturity, and this transition phase appears to be making some people a little nervous.

Those nerves will undoubtedly calm as specific AI technology gets closer to maturity and as additional industries become more comfortable with AI. To get there, it’s important to acknowledge AI’s strengths and weaknesses. For instance, there are real and valid concerns around AI’s transparency and trustworthiness, but there are also undeniable benefits in the form of measurable and significant efficiency gains and valuable insights for better decision-making.

Take the legal industry, for example. It tends to fly under the radar when it comes to discussions about AI use, but corporate legal departments and law firms are awash in data from invoices, contracts, and law firm “scorecards” – all of which can benefit from AI’s valuable insights. Legal is also a field that deals with sensitive information and millions of dollars derived from fees and contractual agreements, so maintaining transparency and integrity in AI is essential.

In short, the likely monetary, time and strategic benefits to be derived from the use of AI in the legal industry are substantial. That makes it a great use case for AI maturity through what I call the “four Vs:” Variety, Volume, Veracity, and Velocity.

Variety: Collecting data from a range of sources

People mature when they become exposed to new information or skills, preferably from a wide range of sources. Over time, this collected information gets absorbed and hopefully helps individuals form a better understanding of the world around them.

The same thing applies to AI datasets.

Chances of AI’s learning capabilities enhance with better and more-refined ingestion of cleaner, accurately-labelled data.

It’s even better if that information covers multiple scenarios coming from different experiences, providing the AI with a wealth of information from which it can make better decisions. The use of a variety of data also helps minimize bias that can arise when data is from a small number of sources with many similar characteristics.

For example, a corporate legal department with a large panel of preferred law firm partners may select a firm for a specific matter based on the recency of their previous engagements or a long history of partnering with that firm. But the firm might not be the best fit for the matter. A mature AI model can advise on the firms that are better suited for specific cases based on a combination of success metrics contained in the data, as opposed to “gut instinct” or recency bias.

Volume: Ensuring access to a continual stream of data

People also mature as they acquire more experience. A person who has performed the same job for 10 years has a greater volume of experience than someone who just started.

Again, the same principle applies to AI. The more information the AI collects, the more intelligent it becomes, and the more likely it is to provide accurate predictions and recommendations or automatically flag inaccuracies.

Hence, the volume of information the AI ingests is critical to the technology’s success. AI needs a continuous stream of feedback to build its intelligence. In legal industry terms, that means continuously feeding invoices, client information, law firm information, case results, and all other relevant data into the system. The greater the volume of information, the stronger and more mature the AI.

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Veracity: Building and maintaining AI trustworthiness

Trustworthiness and reliability are key signs of maturity. A reliable and trustworthy person is someone others are more likely to depend upon during times of need.

Likewise, the only way for AI to reach its full potential is if users can trust its conclusions. For that to happen, its results must be free of bias, and the way it processes information and its rationale for recommendations must be highly transparent.

Veracity in AI is especially important in the legal industry. Contracts are built on trust, and the industry relies on trusted relationships to get work done. Often, matters are assigned to law firms because of the trust corporate legal teams have built with them over the years. A single wrong decision based on an inaccurate model can create legal and financial jeopardy.

In short, there’s too much on the line. Attorneys need to be able to trust their AI’s recommendations and use them to make informed, strategic decisions that will reap value for the organization. They must communicate that value widely, so everyone involved in making the AI strong develops and maintains trust in the system.

Velocity: Using AI to expedite processes and gain efficiencies

Variety, Volume, and Veracity are all steps on the AI’s maturity journey. Velocity is what organizations get when their AI officially “grows up.”

Once the AI has collected enough data from multiple, varied sources, and it’s deemed trustworthy, the AI can be used to automate mundane tasks and improve operational efficiency. In a legal setting, AI can segment a portfolio of invoices based on compliance risk and identify the one with the highest opportunity for expert review. Attorneys no longer need to check every line item and can instead focus on higher-value tasks using predictive insights from AI to help them along the way.

Over time, the AI will become better at automatically correcting issues and making more accurate recommendations. It will eliminate unnecessary steps, improve operations, identify areas of improvement, and help teams make more accurate decisions. Some decisions will be able to be completely automated, allowing teams to move faster on projects or legal matters.

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When systems get to this point, teams can celebrate the fact that they have a fully grown, mature, and dependable AI system. However, they can’t rest easy. Maintaining the value of the output from an AI model or system requires a constant, proactive strategy to feed in new varieties of data and update data regularly at scale. To sustain high-quality outcomes, expert feedback will be needed. Hence, the role of machine-aided human experts will continue to grow, especially in knowledge-intensive industries.

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