To harness AI, law firms need a solid data foundation

The term “artificial intelligence” was first coined in the 1950s. But until recently, adoption of the technology was confined to fantastical Hollywood movie scripts, or to the musings of a select few software engineers and innovative chief information officers. Today, AI is a daily discussion point in our personal and professional lives, sparked primarily by the release of ChatGPT in November 2022.

ChatGPT—and subsequently ChatGPT-4—has left many organizations stunned at the power of generative AI. The discussion now focuses not on if, but when, AI technologies will find their way into the IT road maps of even the most technology-resistant industries and firms.

Specifically, law firms are investing in technologies that harness the power of generative AI to accelerate tasks such as legal research, document review, and deposition preparation, as well contract analysis and editing.

For a 2022 report on legal transformation and technology, the Association of Corporate Counsel surveyed law department leaders and legal operations professionals about their views on next-generation technology investments and what would contribute to improved performance. Surprisingly, AI came in fourth, with 30% of respondents selecting it.

This followed more traditional solutions such as integrated technology and solution providers (53%), cloud-based technology (37%) and all-in-one platforms (34%). In this survey, AI far surpassed other technology investments such as contract management, knowledge management, records management or even workflow automation tools.

It’s important to note that firm size had a significant influence on respondents’ willingness to emphasize investing in AI, with 37% of midsize firms identifying it as one of the most important technology investments, compared with 27% of small firms and 27% of large firms.

But the hype around AI as a truly game-changing technology comes with a sobering reality. Many organizations—especially law firms—are simply not ready. The successful use of AI requires data that is accurately organized and accessible to AI algorithms.

Many organizations have spent the last decade adding point solutions to accelerate their digital transformation, often without a centralized data strategy. Today, many organizations have years of collected data, but the data is unusable in its current state. Common data issues and reasons for this include the following:

• Duplicate data (lack of a single source of truth, multiple instances of the same data)
• Lack of data integrity (human error, manual data entry in multiple systems)
• Hidden or inconsistent data (stemming from a past acquisition, merger or system migration)
• Data downtime (complex, inefficient or nonexistent integrations)
• Lack of resources (a shortage of talent with the skill sets needed to compile, centralize, model and analyze data)

Many law firms have recognized the need to clean up their data architecture, and in the ACC survey, 65% of firms ranked data management/information governance in their top three investments.

Also, among firms for which transformational change is a priority, 70% say that within the next few years, they will focus on data management and information governance, followed by 62% who say automation, and 54% who say performance analytics and reporting.

To prepare for AI by focusing on data strategy, companies need to know where their data is located, where it comes from, who uses it, and for what purpose. Answering these questions may require investing in efforts that lead to data centralization, governance, standardization and a single source of truth.

Steps to take include the following:

• Define, document and understand the company’s data environment (i.e., processes, technology, people).
• Evaluate the efficiency of current processes, including how many people touch the data.
• Evaluate data accuracy and data quality.
• Track how data is being used (for example, create a data and reporting catalog).
• Establish proper governance frameworks to protect both internal and external customers.
• Create standard operating procedures to democratize data.
• Assess internal resource skills, capabilities, and culture to adopt and support a successful data strategy.

Although AI and data management are hot topics, for some firms they remain futuristic ideas with no place in their road map. Of those firms with a limited focus on digital transformation, 26% lack a long-term strategy.

Technology strategy maturity levels

When developing a technology strategy or technology road map, many leaders fail to distinguish different types of strategy or understand their interdependence. Here are some key aspects of baseline technology strategy maturity levels:

• Digital strategy: Process change automation point solutions, application rationalization, system architecture design
• Data strategy: Centralized data repository, data architecture, governance, management
• Reporting and business information strategy: Executive vision/requirements, tools and architecture, governance and security
• AI strategy: Algorithms, platform, integration, culture and ethics

To reach level four—AI strategy—the three previous levels must be mature and accurate.

In a recent blog post, Microsoft co-founder Bill Gates said the development of AI is the most important technological advance in decades—as fundamental as the creation of the microprocessor, the personal computer, the internet and the mobile phone. It’s time for firms to adopt this technology, starting with their digital and data strategies. Those who delay will almost certainly be left in the past.