Readying data for AI – Start small, think big: a Forrester Research leadership conversation with KOY Inteligência Jurídica

By | 5 minute read | January 8, 2021

With the responsibility to understand 181,000 laws, 159 systems of law, approximately, up to 100 million cases under review, and untold volumes of case documentation, the career of a Brazilian litigation attorney’s career is not for the faint of heart.

As attorney Karla Capela Morais and her colleagues struggled with case management, she knew there had to be a better way. She believed technology could help solve the enormous challenges of tracking individual cases and schedules, managing volumes of unstructured data, and identifying precedents and trends.  Morais took her frontline experience and passion for law to design a case management solution using AI, machine learning and natural language processing. In 2016, she founded legal services provider KOY Inteligência Jurídica (KOY).

KOY, like many who traverse the road to AI adoption and integration find it isn’t necessarily smooth. Travelers are often forced to navigate the potholes and pitfalls of data — too much, not enough, non-standard formats or of unknown origin. In an IBM commissioned study, Overcome Obstacles To Get To AI At Scale, Forrester Consulting reports that firms consider AI initiatives a top priority in digital transformation and a driver of important business outcomes; but inefficient and unruly data, together with talent shortages and a mistrust of AI, significantly inhibit adoption. In fact, 90% of respondents surveyed confess that scaling AI is a struggle. The primary challenges are data quality and governance. Forrester found that 58% of respondents lack data quality, while 40% are plagued by data governance issues.

In a panel conversation featuring Karla Capela Morais, CEO and Founder of KOY and moderated by Srividya Sridharan, VP and Research Director at Forrester, we discussed the importance of data management and governance to the success the legal firm’s AI environments.

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Watch a 0:50 summary above of a leadership conversation with Forrester Research and KOY, and watch a video of the full conversation (23:00) here.

We all agreed no matter the size, location or regulatory environment, data is the organization’s lifeblood. Proficiently collected and effectively governed data provides a healthy foundation for AI. Without that foundation, data quality issues and governance issues can interrupt the AI journey, ultimately impact customer experience, hinder innovation and add unnecessary expense and delays. Getting your data right for AI is a crucial foundational step toward reaping the benefits of AI.

Take advantage of opportunities to learn from AI experts

If you are embarking on an AI initiative, you most likely have a data problem — perhaps several of them. For a significant number of organizations, the traditional ways companies have been doing data management don’t work for this new way of analyzing data with AI and analytics,” said Srividya Sridharan, VP and Research Director at Forrester during our panel discussion. “The data collection and governance systems that previously supported your organization’s operations, customer service, financial planning and strategic initiatives may not work for your AI initiatives.” Why? Many data stores were not designed for use in AI. If you are in this situation, you are not alone. The Forrester study found 52% lack confidence in their ability to successfully leverage data for AI.

Start small then advance your AI solution

Morais compares AI to mathematical logic. If you have bad data, your conclusions and insights will be wrong. “In Brazil, we don’t have structured data sources for lawsuits. We required solid natural language processing capabilities,” she says. “As we deployed AI, our first technology challenge was to unify our data, creating consistent definitions for use across all data sources.” The need for structured data definitions isn’t limited to start up organizations.

In Brazil there is a saying, “done is better than perfect. Even if you don’t have the best data, put it all together to start, then iterate to improve it,” says Morais.

KOY used IBM Watson® Natural Language Processing to ingest its volumes of unstructured data. IBM Watson Natural Language Understanding analyzed text ingested to extract metadata from the content. Concepts, entities, keywords, categories, sentiment, emotion, and semantic roles, are deciphered to increase language understanding. With a data foundation in place, KOY developed Norma, an AI platform that provides its customers with case management services.

Trusted AI tools produce greater results

Powered by IBM Watson Machine Learning and IBM Watson Assistant, Norma continually learns from new case-related information ingested into the KOY AI system and client inquiries with incredible speed.  Where one lawyer requires two hours to read a lawsuit, Norma reads it in six seconds.

There is a persistent perception that AI is competing with humans. Morais disagrees. “Those who work with AI are going to have exponentially better results,” she says.  “When you have integrated, trusted data as your foundation, your team gains new and better insights. It places us in a very strategic position in the industry”

Norma delivers automated intelligent schedule processing, managing thousands of schedules simultaneously for KOY customers. She helps prioritize cases and optimizes process flows by notifying attorneys of upcoming deadlines and relevant information pertaining to their lawsuit stock. Case management and scheduling are vital to the Brazilian legal community because case limitation periods can extend to a maximum of ten years. Norma also helps its customers monitor and reconcile billing and payouts and over the life of their cases. With Norma’s help, KOY customers are achieving increased productivity and revenue performance.

Explore DataOps (data operations) strategy

How can organizations map a process to support their artificial intelligence needs? IBM recommends tracing the organization’s bottlenecks back to the data source. The repeatable process — known as data operations (DataOps) — can help organizations eliminate bottlenecks, especially those related to data never designed or built for AI. IBM DataOps orchestrates people, processes and technology to deliver trusted, high-quality data to data citizens fast. The practice helps enable collaboration across the organization to drive agility, speed and new data initiatives at scale.

The most important point to remember is that you and your organization are not alone. The KOY success story illustrates one customer’s exciting journey to AI. Other paths may better suit your organization’s needs. For insights from other leaders traversing the road to AI, read the Forrester study, Overcome Obstacles To Get To AI At Scale and start your journey today.

Learn how AI can maximize your competitive advantage.

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