As organisations increasingly embrace the transformative potential of Artificial Intelligence (AI), the importance of robust Information Governance (IG) policies cannot be overstated. AI, a technology that mimics human intelligence to generate, classify, and perform tasks such as image analysis, speech recognition, and content creation, relies heavily on the quality and management of information.

IG is fundamental to the effective deployment of AI. It identifies the business, legal, regulatory, and historical value of information and meticulously manages its lifecycle. IG practices—policy creation, discovery, classification, retention, and disposition—ensure that valuable information is accessible while eliminating data and records that pose unnecessary risks or lack value. Essentially, IG provides the high-quality enterprise information that maximises the utility and effectiveness of AI technology.

Then and Now 

AI has a long history, arguably dating back to the late 1940s and 1950s with pioneering computer scientists like Alan Turing and his work on The Imitation Game. The pressing question now is how to utilise AI for maximum benefit while ensuring its applications are safe and reliable. The authenticity, accuracy, compliance, and protection of data leveraged by AI are critical, and this is where Information Governance (IG) plays a vital role.

Noted futurist, author, and inventor Ray Kurzweil predicts that we may reach the singularity—a hypothetical future where technological growth becomes uncontrollable and irreversible—by 2029. He believes that AI and Generative AI can be effectively and safely deployed with the proper rules and controls. Achieving Artificial General Intelligence (human-level intelligence) safely in the near future hinges on the proper management of data, the fuel of these technologies. Embracing IG in AI plans is not just important; it's essential for a positive vision of the future.

Data Hygiene – Principles & Requirements

For IG and associated professionals, ensuring that AI aligns with organizational principles and legal, regulatory, and business (and historical) requirements is paramount. These principles and requirements are designed to mitigate privacy and cybersecurity risks while ensuring a high level of data quality. Data must be authentic, reliable, and properly accessible to deliver business-enabling and accurate results.

Deploying AI or any technology in compliance with organisational, industry, and jurisdictional guidelines fosters trust in the data and its outcomes. This trust translates into confidence in the AI system. As business models and regulations evolve, it is essential to maintain the technology to ensure continued quality and compliance with standards and regulations.

Fortunately, there are numerous AI governance frameworks available to guide the development of robust structures, practices, and implementation approaches. These frameworks help ensure that AI systems are built and maintained with the necessary oversight and integrity.

Use Cases & Alignment

In today's quickly evolving landscape, businesses are often told they must embrace AI or risk being outpaced by competitors who do. So, how are businesses leveraging AI today? Early innovators developed technologies like optical character recognition, speech-to-text conversion, and creative tools for generating music and art. In practical applications, corporations use AI for contract lifecycle management, document conversion, and chatbots. Banks employ AI for fraud detection, financial modelling, and recruitment, among other functions.

Let's explore two specific use cases: contract lifecycle management (CLM) and electronic file reviews (EFR), also known as matter mobility. Epiq’s Ziad Mantoura wrote a very insightful article on generative AI and contract lifecycle management (CLM), highlighting its relevance beyond the Legal department and its longstanding importance in financial services and pharmaceuticals. He discusses the benefits of standardisation and a golden source repository. In the context of information governance, this translates to adherence to rules and classifying content for proper and rapid use.

Another use case is electronic file reviews in legal and other sectors. This function involves discovering, protecting, and managing legal documents, determining what should be released when an attorney or client departs a law firm. Epiq offers professional and technical capabilities that ensure effective information governance and records management. These capabilities include scanning electronic data, indexing, classification, and tagging. This process facilitates proper archiving, retention, disposition, quarantining, and access control based on IG principles.

When AI is implemented with proper requirements and controls, it delivers significant value. The ROI comes from fully automated processing, enabling employees to complete tasks faster and more accurately, thus mitigating risks like human error. Additionally, it reduces costs associated with manual processing, data storage, and data repurposing.

Risk, Issues and Dependencies 

Leveraging advanced technologies like AI undoubtedly comes with risks. One major risk is the need for skilled users who not only know how to operate the tool but also how to interpret its results accurately. Additionally, maintaining the technology and adjusting processes and inputs in response to evolving business requirements present significant challenges. Dependencies, such as the need for robust Information Governance (IG) and Data Governance (DG), provide the foundational principles and requirements necessary to protect the organization.

The goals are clear: enable business operations, mitigate risks, increase employee productivity, and reduce costs. Proper information and data governance act as the foundation of risk management, creating trust and ensuring security. This was emphasised in a recent Epiq panel where governance and risk were highlighted as key concerns. The panel discussed critical aspects such as data origins, storage, and utilisation, underscoring the importance of effective preparation and use of AI.

Moving Forward

Organisations must recognise that bad data leads to bad results. Superior AI prompts, advanced technology, robust processes, and strong controls cannot compensate for poor data quality. While some data scientists argue that more data is better, we know that quality trumps quantity for the most efficient organisations. As AI approaches human intelligence, it can process information faster but won't make better decisions if fed with extraneous and erroneous data.

Information Governance (IG) is critical to the effective implementation of AI. By identifying the business, legal, regulatory, and historical value of content and managing the lifecycle of information, IG ensures that the right information is accessible while minimising the presence of valueless or high-risk data. IG practices—including discovery, classification, retention, and disposition—ensure that only valuable information is retained and utilised.

Addressing the quality of information through sound governance leads to Mastering the Data Surplus: How IG Drives Success in the Digital Era. As emphasized in the article, "To fully harness the transformative potential of information, we must move beyond the noise and adopt an integrated approach that incorporates key relationships with data governance, risk management, privacy, cybersecurity, records management, and regulations—relevant to all organisations."

By committing to these principles, organisations can ensure that their AI implementations are effective, reliable, and secure, ultimately leading to better decision-making and operational success.

To learn more about AI and Information Governance, check out this recent Epiq Advice article, "Mastering the Data Surplus: How IG Drives Success in the Digital Era."