Start seeing your data as an asset. It is not an original idea. Douglas B Laney of Gartner built a solid case around this concept in his book Infonomics: How to monetize, manage and measure information as an asset for a competitive advantage. The last decade has witnessed huge growth in AI and ML solutions from start-ups and skunkworks teams within organizations. The economic opportunity for AI globally has been estimated to be between $ 15 trillion and $ 17 trillion by 2030. This figure represents the application of various technologies within AI and covers a broad spectrum. industries and business functions. However, and perhaps more excitingly, this projection ignores the innovations that will emerge through breaking down data silos and turning enterprise data into a true source of intelligence and growth.
To clarify, information is the product of processed data. The data itself is the collection of digital material that has not been sorted into some form of digestible content. Once processed, we have information that can be searched. The idea here is simple and directly analogous to mining. Raw materials are mined from the earth, but they have no value until they are transformed into their final product. You start with Iron Ore, and you end up with the steel frames that make up the Sears Tower (no one in Chicago calls it Willis). For all intents and purposes, the data travels the same way. The challenge that many organizations face is that of coordination and education. Neither can be achieved without management and board support for enterprise-wide artificial intelligence (AI) and machine learning (ML) initiatives. to find new sources of value. Above all, this task must extend beyond the obvious prime avenues for innovation, such as sales and market analysis.
Digital innovation in front-end functions is nothing new, and any organization worth its salt should already introduce AI solutions as a core strategy to focus resources and increase growth. However, the value of legal and compliance data is often misunderstood and rarely realized. As a basic example, regulatory technology (regtech) is an emerging area of ââdigital technology that aims to facilitate regulatory compliance and, where possible, automate it. Regulators around the world face the many challenges of monitoring the events in the industry they oversee to ensure balance with the help of associated rules and laws. Entities under the jurisdiction of these regulators have an obligation to monitor developments in the sector and maintain regulatory alignment. Failure to comply can lead to financial penalties, discrediting and, in extreme cases, serious litigation. The challenge for the regulated is to follow the regulations in real time, while documenting their process in a defensible way.
The full potential of the compliance function can only be achieved by investing in regulatory and supervisory technology solutions that provide near real-time analysis of structured and unstructured enterprise data sets. To be successful, the leadership team must be committed to shifting thought leadership from the role of compliance as an assurance function to reimagining it as a source of strategic analysis and dynamic risk assessment. Regulators themselves have an obligation to monitor all entities under their jurisdiction to ensure that their regulations are followed and that there is no exploitation of customers or other entities. Ultimately, they must verify the compliance of these entities either by verifying their documentation or their process. Constant monitoring of an ever-increasing number of entities and individuals can prove difficult, given limited human and financial resources. By encouraging innovation within organizations’ cost centers, entities can generate value both internally and for regulators themselves.
Despite the many start-ups and the increase in academic research into increasingly exciting forms of deep learning and cutting-edge analysis, there must be a better incentive plan that encourages board support. administration and executive leadership to invest resources in enterprise-wide compliance and regulatory AI. . One possible route could be tax incentives for companies that can demonstrate skunkworks initiatives and a commitment of resources to enhance the digitally intelligent growth of their data ecosystems. It could also take the form of partnering with academic research institutes or start-ups seeking to solve the most important challenges that prevent AI and ML from reaching their full potential. Regardless of how organizations are incentivized, the ultimate goal of introducing these tools is to reduce waste and increase growth in both front-end and non-obvious avenues like compliance functions. rich in data.
To catalyze revolutionary growth, leaders must set bold aspirations, make tough choices, and mobilize resources at scale. Simply put, the ability to rapidly develop, deliver, and scale new products, services, processes, and business models is a muscle that virtually every organization needs to strengthen. There is a positive correlation between innovation and financial performance. This can be most visibly achieved in two areas. The first is the ability to define a bold but plausible aspiration for innovation, based on a clear vision of the economic value that innovation must bring. And second, the ability to make tough choices in resource allocation about the people and funds needed to capture the value of innovation on a scale sufficient to make a difference. It requires an organizational commitment to creating long-term value.
Advances in digital analytics have transformed the way businesses operate. While strategy development will always require creative and thoughtful executives to define aspirations and make bold choices, the right application of AI within the legal function can give leadership an edge. This can be achieved by reducing bias in decision making by calibrating the likelihood that a strategy will succeed before resources are allocated at scale. The Compliance and Risk functions specialize in finding problematic anomalies. It makes sense to give this expertise the opportunity to breathe and innovate in the form of strategic risk advice working towards the same ultimate goals. If they do not evolve beyond insurance policies, the value potential of their expertise will never be fully exploited.
Organizations can also discover new opportunities for growth by supplementing traditional brainstorming methodologies with various forms of pattern recognition. Breaking down data silos in an organization is an essential first step in achieving this goal. Without communication and innovation from the compliance and risk function, it is impossible to say that management has the whole story. Consider the growth of mergers and acquisitions from private and public investors. By enabling knowledge of compliance and risk functions through increased access to AI and ML solutions, private equity firms can perform health checks on their newly acquired entities to understand the critical issues that need to be addressed. resolved early to maximize the likelihood of a clean exit (and minimize losses).
Identifying trends at an early stage by mapping real-time data on the evolution of different lines of business can help the management team take important action before their competition. Management teams would benefit from knowing how the associated trends evolve. They could gain this information through real-time tracking of regulatory changes, monitoring customer sentiment and identifying risks to the provision of products or services. Unknown risks are present in most organizations, but they can be greatly reduced by taking on the role of information governance and enhancing its value as an analytical function. In a world of growing uncertainty, companies need to be dynamic in how they define and manage their digital initiatives.
The imperative for a strategic AI-centric approach is universal, but some companies are already leading the pack with overall improvements in capabilities, talent, leadership and resource allocation. All of this can be linked to better results. Given the resources and tools available today, it is simply not sufficient to leave the traditional functions of cost centers in the area of ââpolicy advice, reactive risk assessment and management. basic data administration. The pandemic has dramatically accelerated the speed at which digital is fundamentally changing business. At the same time, the pandemic has created new vulnerability to future disruptions, as well as new opportunities. To ensure that organizations are less exposed to cyber attacks, business inefficiency, and regulatory risks, the legal function must have the resources to stay ahead of the curve. Catching up with the leaders will become increasingly difficult as the best economic players have already taken more steps than their peers to achieve their AI goals.
Leaving legal and compliance functions out of business intelligence projects would be a big mistake for the organization of tomorrow. Just as law schools increase their students’ exposure to new and emerging technologies, organizations should seriously consider the role of engineers in traditional cost center roles. The challenges created by new and expanding forms of data are only increasing. More than ever, businesses need the ability to act proactively and quickly determine whether they are building or buying. There is a hidden potential that can only be unlocked if the legal functions have the necessary support to build the smart ecosystem of tomorrow.