40 ASIAN LEGAL BUSINESS – APRIL 2024 WWW.LEGALBUSINESSONLINE.COM THE BACK PAGE AI IS AMPLIFYING THE MISTAKES OF THE PAST BY MARK DANGELO The acronym of AI (artificial intelligence) has become like the air — it is all around us, and touches everything we do. Indeed, the advancements of AI are highly efficient, increase revenues, and leverage humans-in-the-loop. However, when it comes to AI in all its ever-changing, kaleidoscope of forms, its growing functionalities, its demands for data, and its advancing intelligence, who is responsible for creating, managing, and retiring the roadmaps of integration? Simply put, how do all these AI solution pieces fit together or even talk to each other? What happens when there is a need to audit the cascading inputs and outputs or implement error-corrections? Is there any way to identify AI-created data from traditional systems? While uniquely different, AI’s rapid growth is exposing the factures and fallacies of cascading upstream and downstream integrations — and our ability to assess quality, accuracy, and even systems-ofrecord. Indeed, history is repeating itself. The future of tomorrow requires a proactive integration of innovative research tempered by domain market forces, consumer behaviours, AI technology and digital data explosions, all glued together by security, legal, and regulatory requirements. It is a future that demands layers of integrated solutions, all requiring transparency, heterogeneity, and risk-attributions. Yet, even while learning from legacy mistakes, the introduction of AI solution sets still creates both opportunities and challenges. AI-impacted legacy tech: The burden of repairing the existing impacts of fragmented, siloed legacy systems is estimated to be fixed, which could cost up to $2 trillion, with profit and operational losses fast approaching $3 trillion per year. Data “bar codes”: This represents an easy-to-understand solution that is complex in implementation: From where does the data we use to make decisions, impact operations, or report to investors originate? If regulators, auditors, or legal personnel asked for the traceability of the inputs, can the current or future AI solutions meet the due diligence requirements? Interoperability and trust: A core tenant of vendor packages, the idea of (opensource) APIs and data virtualization dominated the last 25 years of systems, even down to mobile applications. Yet, AI introduces production-ready unknowns for scale, validity, performance, and unintended consequences beyond its 2023-24 pilots. Skills and simplicity: While researchers seek to expand the options of AI and its capabilities, the challenges are where the skills will come from to operate, deliver, and integrate data that is doubling in volume every year — not to mention opaque AI systems provided by vendors, skunkworks, startup efforts, and small-scale prototypes. Making architecture even more challenging, over the last 16 months, we’ve seen the traditional segmentation between industry results and research — internal or academic. Today, hundreds of AI solutions are being developed every week, with investment values and M&A actions dominating strategies that represent the front-office fear, uncertainty, and doubt of being left behind. For researchers in industry and academics, their deep understanding of each unique area provides the roadmaps for adaptation in the face of hyperscale and rapid-cycle technologies. This is where corporate leaders who are driven by results must balance what is available with what is possible when the innovation cycles for AI advancements are now measured in weeks and months rather than years. These AI realities, coupled with regulators and their exploding oversight, demand more than the traditional adoption of siloed regulatory technology to create governance solutions. Only with a holistic approach to AI architecture will enterprises and their researchers arrive at a workable and efficient solution to regulation. Regulation is the glue that demands integration, and integration itself is demanded by fragmented solutions. Indeed, solutions ensure that the enterprise can be profitable in the face of opaque and new market forces. Linking them all together will be unfamiliar, but it is the solution that cannot be left to chance. While industry and research personnel want AI to be simple, that represents an assumption that there is total transparency and recourse even if we use natural language interfaces. AI is not a magic button that operates in a vacuum — industry and researchers have already tried this repeatedly, and it is a recipe for future chaos. To ignore rapid-cycle AI progressions as a business leader is problematic, and failing to integrate disparate technological solutions is déjà vu. The complexity and confusion of AI is just beginning, yet the rush to deep research and fast results is bringing back the ghosts of prior step-functional shifts of innovation and computer advancements. Mark Dangelo currently serves as an independent innovation practitioner and advisor to private equity and VC-funded firms developing data meshes/fabrics, stacked/ ensemble AI, MAD data sciences, ethical data governance, and decentralized finance. A version of this piece was originally published by the Thomson Reuters Institute. Reprinted with permission. Asian Legal Business is seeking thought-provoking opinion pieces from readers on subjects ranging from Asia’s legal industry to law firm management, technology and others. Email for submission guidelines.