Already, both corporate legal departments and law firms of all sizes have begun experimenting with Gen AI technology, both with public-facing tools such as ChatGPT, as well as more business-friendly tools such as Microsoft鈥檚 Copilot and legal industry-specific technologies. This experimentation has begun to give way to more discrete planning, as well as specific policies and procedures governing the use of Gen AI in the legal workplace.

This shouldn鈥檛 be a surprise. After all, just about everybody in legal knows that some sort of dramatic sea change is coming. Indeed, 70% of legal professionals said they believe AI and Gen AI will have a transformational or high impact on the legal profession within the next five years 鈥� more than any other trend, including the economy, regulation, and activities around environmental, social & governance (ESG) issues, according to the 成人VR视频 Future of Professionals Report, which also showed that Gen AI brings hope of a new efficient future but also fear of job replacement.

Both that hope and fear may be well-founded, as both the scope and pace of change may be greater than some in the legal industry anticipate. While some law firms are approaching Gen AI as an optional, nice-to-have technology, our research has found that change is likely to take place imminently. And within the next 10 years, the legal industry should expect wide-scale business model changes, as well as some disintermediation of lawyers in situations in which consumers eschew legal professionals altogether in favor of a technological option.

The most successful corporate legal departments and law firms won鈥檛 be the ones merely experimenting with Gen AI 鈥� they will be the ones preparing now for a legal industry future that will look a lot different in 2034 than it does today. That鈥檚 why we are embarking on a new blog series discussing Gen AI usage in the legal industry to help explore that future, laying out the scenarios we anticipate for the legal industry in both the immediate and mid-term future.

Our predictions for AI in the legal industry

To understand how the industry could be impacted by AI in the near and longer term, we garnered the views of 成人VR视频鈥檚 technology and legal market experts, including members of TR Labs, who are at the cutting edge of legal AI research and development. We also interviewed a range of experts across industry and academia. Based on these discussions and other research recently conducted by 成人VR视频, such as our Future of Professionals Survey, we have been able to make early predictions for how the legal market will evolve over the next decade.

Already, Gen AI has begun to make in-roads among law firms, corporate legal departments, and other legal service providers and technology companies. That impact will likely occur in three overlapping waves; however, the effect will not be linear. Some firms of course will move faster than others, while even within each firm progress could be diverse, with some practice areas with work that鈥檚 more susceptible to automation 鈥� such as real estate 鈥� radically transforming with AI faster than other practice areas, such as litigation.

This first wave we are already beginning to see now, with widespread AI adoption and experimentation occurring in corporate legal departments and law firms of all sizes. Over the next one-to-three years, the legal industry will increasingly gain access to trusted Gen AI tools from both large technology companies and legal technology providers, letting legal departments and law firms move from pure experimentation to putting AI plans into action. Especially at the beginning, this work will be focused on back-office functions and support staff. Law firms and their clients will begin considering how they can optimize their labor costs, reducing numbers of new associate hires and non-fee-earning staff.

The focus on non-billable work will not last for long, however, particularly as Gen AI technology advances to perform more complex tasks. Looking three-to-five years out, we begin to see changes to the legal business model emerge. AI will allow legal work to be done more efficiently, and corporate legal departments will put increasing pressure on law firms and outside providers to deliver work faster. As less time is spent on legal tasks, the billable hour will no longer be the most cost-effective way to capture value, prompting firms to reimagine their billing models to better share in efficiency savings and capture the value that鈥檚 added through advanced technology.

Already, Gen AI has begun to make in-roads among law firms, corporate legal departments, and other legal service providers and technology companies.

In response, large law firms will seek to capture more revenue by deploying staff, including juniors, to higher value work. They may also use their new law or internal innovations teams to standardize repeatable work that might otherwise be unprofitable. Meanwhile, small- and midsize law firms will be able to use Gen AI to grow their practice without needing to hire more staff. Regardless of size, however, the point is that change is a must: The law firms that don鈥檛 adapt will not be able to keep up with the efficiencies gained by their peers.

Finally, extending the time horizon out five-to-ten years leads to greater automation of legal services 鈥� and, in some cases, partial or full disintermediation of the legal professional through AI. With Gen AI technology capable of tackling even more complex tasks, AI will be a primary driver of day-to-day legal tasks, with legal practitioners acting as supervisors and strategists. The result is a complete overhaul of not only how legal services are completed and billed, but also the mix of players in the legal arena that are most needed.

Tech-enabled services will allow some legal clients in both the public and corporate spheres to sometimes bypass law firms entirely, relying on technology to serve their legal needs. This means do-it-yourself (DIY) legal solutions for consumers and small legal business are poised to proliferate, increasing access to justice in an underserved market. For law firms to survive, they will need to learn how to blend AI with human expertise and relationships, perfecting their service delivery to compete with a clientele that is familiar with and fully expectant of AI-enabled expertise.

Gen AI鈥檚 rollout looks different based on organizational type

However, as noted above, not all parties in the legal industry will fully embrace Gen AI鈥檚 introduction, nor will the technology鈥檚 advancement affect everyone equally. Gen AI will impact differently sized corporate legal departments and law firms unevenly.

At the forefront of the market, corporate legal departments will purchase and access Gen AI in a way that could transform their structure and day-to-day work product. Gen AI usage will likely be enterprise-wide at many companies, with the legal function receiving the benefits of economies of scale and outside technology investment. As corporate legal departments continue to experience cost pressures, however, we anticipate a changing of internal roles within those departments, with fewer paralegal and junior roles as the tasks those roles usually handle become the first to get automated.

Corporate legal departments are also increasingly likely to expect lower costs from their outside providers in turn, particularly for low-stakes work 鈥� which in some cases means replacing current panel firms with lower-cost alternatives, including alternative legal service providers (ALSPs) or technology firms, meaning that these entities must embrace AI or risk losing out on work. And across the board, this client emphasis on costs could lead to a rise in alternative fee arrangements and a further diminishing of the billable hour.

This shift towards cost-cutting and a change of roles is likely to be mirrored across law firms of all sizes. Large law firms generally have greater technological capability and access to larger datasets, and many also have already established new law or internal innovation teams. Niche data could become a key law firm differentiator in the age of AI. Larger firms also may leverage their economies of scale to enable Gen AI-powered technology products that capture work that would not have been profitable without automation.

This shift towards cost-cutting and a change of roles is likely to be mirrored across law firms of all sizes.

Among midsize law firms, the same technology could be used to capture bigger-ticket work. By using AI to automate repeatable, labor-intensive tasks, midsize law firms should be able to take on more clients, including more complex clients, or perhaps cover more practice areas through an expanded scope. In this way, midsize law firms can bill themselves as low-cost alternatives for plain vanilla transactions or disputes.

At small law firms, Gen AI will be similarly used to automate some processes in order to win new business. This could impact small firms鈥� headcount 鈥� particularly the hiring of new paralegals and junior associates 鈥� as small law firms lawyers leverage Gen AI to automate processes that previously fell to these new hires. With time and cost saved, this would allow some smaller firms to take on more cases or even expand their practice into new areas, enabling these firms to grow their book of business without hiring additional staff. This could also help alleviate the access to justice problem that many consumers currently face.

All in all, there remains a lot of speculation around when and how Gen AI will influence the legal profession. Between the technology鈥檚 development, the industry鈥檚 reaction, and outside economic forces, there are a lot of different paths in legal that Gen AI could take.

Regardless of the exact path, it would be smart for law firms, corporate legal departments, ALSPs, and other legal industry players to begin plans for a potential future now, because it鈥檚 reasonable to believe that a Gen AI-impacted future will lead to profound change in the way people work.

In coming weeks, this series from the 成人VR视频 strategy team and the 成人VR视频 Institute will take a closer look at some of the individual practice and subject-area facets of those changes and what those professionals in the legal sphere should expect.

Yet, artificial intelligence (AI) is not a hammer or a one-size-fits-all commercial off-the-shelf design. It is an intelligent data delivery approach for the next generation of products for audit, legal, and regulatory compliance which requires continuous feedback from adaptive learning systems.

As we are very aware, beginning in late-2022 and accelerating throughout 2023, AI captured the imaginations of every executive and researcher across the world, and 2024 will likely extend its innovation iterations and dominance. (Much as over the last two decades, did information engineering, relational databases, cloud computing, blockchain, and more recently, SaaS.) In fact, nary a 2024 go-to-market, legal, supply chain, application systems, audit compliance, customer service, or back-office strategy excludes AI as part of their infrastructure, operational processes, or sought after skills.

Today, we are witnessing bias taking place with the rapid advancements surrounding artificial intelligence solutions, software, chips, and skills. And if people see a current or future problem, AI is the hammer that must be deployed.

However, what ideation, strategy, design, or implementation of AI are we talking about? In the end, the nuances, details, and context of implementation mean as much as the underlying technology advancements. AI in 2024 is about a kaleidoscope of options, designs, and solution sets. As illustrated in above, there is no longer a one-size-fits-all 鈥� e.g., hundreds of generative AI (Gen AI) solutions 鈥� as the number of variables and options require smaller hammers and greater precision.

To reinforce the exponential complexity, a quick AI query about AI returns dozens of forms including Gen AI, general AI, machine learning, deep learning, natural language processing, expert systems, computer visioning, and even robotics. Another AI query for AI designs yields unfamiliar terms of gradient boosting, random forest, decentralized learning, adaptive learning, checkpointing, and dozens more. Adding additional regulatory parameters, we can ask what AI solutions best fit to the European Union鈥檚 AI Act, the Biden Administration鈥檚 AI Executive Order, passed and pending state oversight rules, and numerous foreign government proposals? When assembled across a landscape, an ecosystem of AI capabilities and choices, the idea that AI represents a singularly familiar design and implementation concept is proving to be an early mirage of what is coming, what has arrived, and what will continue to define extraordinary complex interfaces.

The takeaways from the graphic above concentrates on the ideas that: i) AI is based on a continuously adaptable data design; ii) models and outcomes will never be static as they have to learn and be retrained to ensure their operational and customer efficacy; iii) there will be many models, designs, and innovations; and mostly importantly iv) AI will communicate with other AI systems, creating cascading inputs, outputs, and outcomes that demand continuous monitoring and adjustments. It is this last takeaway that represents a step-function shift from tradition.

Previous applications created outputs defined on coded or systemized processes using known controls. AI outputs with systems can learn and often lack transparency, creating black boxes of decision-making which may not be steady state. Over time these learning algorithms can greatly improve, but the converse can also be true.

Now as the AI kaleidoscope turns with innovative and technological advancements, new options are presented demanding new skills, use of humans in the loop, and unplanned system redesign or even retirement that seems improbable for implementations that are less than a year old. So, what should be done? How can leaders across industries and the core disciplines of audit, tax & accounting, legal, and regulatory address controls and designs which are no longer steady state? Even more challenging is the question of how will AI be regulated 鈥� beyond a set of macro-principles of operation 鈥� when the assessment approaches are opaque and iterating continuously?

The problem with current design and implementation methods is that they start with a solution and wrap requirements around the technology. For example, organizations are actively pursuing prompt engineering skills for Gen AI deployments, and others are seeking out federated AI designs due to the perceived robustness of data security and sovereignty capabilities. Yet, how did they arrive at the selection of the solution when the requirements, skills, operational rationale, and implications are still in the preliminary stages?

To this point, the above illustration ignores the technology to be used and instead focuses on the AI components that are likely common to all forms of AI and their architectures. It is worth noting that the 20 items listed above are not comprehensive. They are a starting point for not just which AI and what features should be included, but also for the implications of skills, adaptive oversight, error correction, feedback, and more.

Additionally, this straw-model industry representation creates the foundational building blocks that will be consistent as AI technology moves from embryonic deployments to stacks of interconnected, specialized solutions that comprehensively alter operational models. If leaders and the industry they represent adopt the traditional systems design approaches, every AI system will be process-siloed and data-fragmented.

Now considering the latest AI regulations 鈥� such as those come from the EU and some US states 鈥� the burden of regulatory compliance for the limited AI solutions is viewed as manageable. Now fast-forward to the end of 2024, and the burden likely will not be linear as additional, critical AI initiatives come online. This is why AI deployments require a fresh, tightly coupled set of architectural building blocks before they are selected, which greatly reduces the systemic errors found within today鈥檚 traditional and SaaS applications.

Indeed, AI is exponentially moving beyond point ideas 鈥� accounting, auditing, supply chains, legal, regulatory compliance, etc. 鈥� and being designed for scale within corporations and their industry partners. AI today is where applications were in the 1980s. Yet to arrive at cloud architectures and SaaS solutions took more than two decades; and it took another two decades for as-a-service componentization to create the technology markets of today. AI has been simmering in research labs for the same period of time.

Now, we have momentum and acceptance that AI can be a next-gen business multiplier which not only improves profitability but also how we work and interact. The implications illustrated above is that AI will impact everything 鈥� but its success, its adaption, and its evolution is less about the technology and more about the business case and its requirements.

Moving forward, AI is rapidly becoming a mosaic of options 鈥� a kaleidoscope of building blocks that continually change depending on the innovation and its architecture. A failure to understand the history of creating and provisioning systems represents a larger challenge than skills, technologies, or even stringent oversight 鈥� combined.

Those organizations that holistically address the components illustrated above will create new markets and book outsized returns. Those that chase the technology and address the commonalities afterwards will witness rapid diminishing returns for their efforts as they flounder from one AI trend to the next.

A report from points out that Gen AI has the capacity to democratize expertise widely, transitioning us away from a world where law and government services are only understood by subject matter experts. The key question then becomes how government agencies can effectively utilize AI to benefit the public while simultaneously protecting the public鈥檚 best interests.

Understanding which tools are in use

At the state, county, and municipal levels, an imperative first step involves identifying the Gen AI tools and systems currently in use. For example, enacted in the summer of 2023 mandates that all generative AI and automated decision-making tools be inventoried by the end of this year. Further, state agency AI policies and an AI Bill of Rights will follow in the state in 2024.

Further, have implemented regulations to ensure responsible use of algorithmic tools. In San Jose, an algorithmic tool must receive approval from the city鈥檚 digital privacy office; while in Seattle, it needs to be approved by the purchasing division. San Jose goes a step further by maintaining a , which provides clear explanations of approved AI tools and their use cases in simple language.

Establishing shared values and stressing accountability around AI use

Government agencies face a delicate balance between regulating innovation and fostering progress in the delivery of government services. The , for example, drew a hard line with its decision to establish a six-month pause on all Gen AI use by state agencies earlier this year. A possibly less punitive approach to Gen AI adoption involves establishing a set of common core values to guide the use of this innovative technology.

this fall, emphasizing 10 fundamental values that should govern the application of this evolving technology in state operations. Broadly, these values seek to ensure that Gen AI use empowers employees, furthers agency mission and equity, but also protects privacy and security.

Municipalities and counties which are implementing AI use policies and guidelines are placing a strong emphasis on holding employees accountable for the accuracy of the content they produce, whether with or without the assistance of Gen AI. As Chief Information Officer Santiago Garces noted in reference to the for use of Gen AI, 鈥渢echnology enables our work, it does not excuse our judgment nor our accountability.鈥�

In addition, employees using Gen AI technology in cities like Boston, Seattle, Tempe, Ariz., and San Jose are required to fact-check the accuracy of generated content and disclose the use of AI in content creation.

has a policy reminding staff to treat AI prompts as though they were visible to the public online and subject to public record requests. And the City of Boston鈥檚 policy stresses the significance of protecting resident and customer privacy by never sharing sensitive or personally identifiable information in prompts.

Expanding access to justice and providing mechanisms for safe innovation

A topic that has sparked debate in recent years is the use of Gen AI tools for legal interpretation. In a 2022 publication from the , the potential benefits and risks of these tools are outlined. The publication highlights the shortage of civil legal aid attorneys and the limitations of pro bono work, and it envisions how non-lawyers can greatly reduce the cost and increase accessibility of legal services.

Risks to moving too quickly in this direction include the danger of inherent bias from existing digital records on which AI tools rely, as well as the fact that the ability of Gen AI to recognize patterns does not necessarily apply to the nuanced judgment needed to inform legal advice. Another publication, issued earlier this year, suggests that fields of law with more stability, like trust law, may be better initial candidates for early AI application.

One major finding in this report is that Gen AI use can shift the legal industry away from hourly billing towards flat-fee service provision as document automation is streamlined. This is in alignment with the 成人VR视频 Future of Professionals report findings, which indicates that less credentialed employees can now complete work with AI assistance that previously required credentialed employees at higher hourly rates.

The State of Utah made history this year by becoming the first to launch a legal services , called , which oversees non-traditional legal businesses and legal service providers with the aim of ensuring that consumers have access to innovative, affordable, and competitive legal services. The agency actively supports emerging tools and platforms that offer unique and creative approaches of providing legal services, particularly to historically underserved communities. To prevent harm, entities within the sandbox are audited monthly to measure utilization and any potential harm.

Of course, a major barrier to collaboration between legal service experts and technologists in advancing AI for legal services is the American Bar Association鈥檚 restrictions on non-attorneys owning or investing in law firms. While this restriction is intended to safeguard the independent judgment of attorneys, it adversely hampers innovation and collaboration between the legal and technology sectors.

Innovation and experimentation should, of course, be deployed first in areas where the risk of harm is less. For example, Santa Cruz County鈥檚 AI usage policy explicitly advises employees against using AI tools in critical decisions related to hiring or other sensitive matters where bias could play a negative role.

Despite all the excitement driving adoption and expansion, however, these solutions are black boxes of capabilities. The direct, negative customer impacts of a chatbot gone astray, the impacts of a biased machine-learning (ML) solution disadvantaging a demographic, the brand damage of errors and ensembled AI subsequently cascading outcomes across systems, and the legal and regulatory impacts regarding the di铿僣ulty of auditing grows with every introduced and cross-linked AI system. It will get better 鈥� but only with oversight and adaptive iterations.

To make the situation more challenging, the skills required to audit AI individual and cross-linked systems are emerging and scarce, leading to a patchwork quilt of capabilities. For auditors and compliance personnel, the journey of AI transparency begins not with technology, but with a clear understanding of the goals and objectives of the AI solution, its touchpoints and control limits, and 铿乶ally its data and technology ecosystem.

The situational reality of assessing AI

Regardless of the industry, business leaders are captivated by AI and generative AI in particular, and they are investing heavily to secure their share of a 铿乿e-year $5.5 trillion to $7 trillion global GDP expansion directly attributable to these nascent technologies. However, the implications of adopting various AI and ML solutions and methods introduce into domain auditing teams multiple levels of complexities which less than 18 months ago were just conference topics and not frontline demands.

As illustrated below, what once was known and encased at audit process business-as-usual is now represented by additional demands and skill sets that are highly segmented. Making the situation more challenging is the lack of foundational interest in auditing from college graduates, and the signi铿乧ant exodus of talent experienced by public accounting 铿乺ms.

Across categories A through D, the granular taxonomies encased within the ecosystem showcase the di铿�erent and potential solutions that traditionally reside outside the core competencies of professionally trained audit teams. The intersection between these categories represents common subject matter.

This graphic implicitly addresses a fundamental challenge with the rapid adoption of innovative AI technologies 鈥� where the risks are hidden within not just the algorithms, but within the data; their outputs across silos; the layers of decision criteria; and most importantly, the legal and regulatory exposures of unintended consequences.

For audit sta铿� steeped in traditional approaches, data sampling, and process assessments, the science of AI encased within and across domain systems ushers in a step-function increase in required capability. These upskill demands must either be addressed within the audit team assembled, or matrixed to highly specialized sta铿�, which can e铿僣iently move from sample-based assessment to full data-discovery using audit-de铿乶ed technologies and tools speci铿乧ally developed for AI audits.

Next-gen demands: 鈥淒o no injustice or harm鈥�

For internal or external auditors, the ability to assemble the forensic skills to analyze and assess AI and its conformance to required standards places the audit teams in unfamiliar situations. Teams targeting AI solutions require advanced knowledge of data and computer sciences, machine and deep learning, and the potential for 100% sampling including algorithmic training, testing, and production data. Beyond the 铿乶ancial and domain knowledge of traditional approaches, data source ingestion feeds AI decisions, embedded work铿俹ws, operational rules, and decision alternatives that are spread across disparate systems leading to complex, opaque interdependencies.

To illustrate this shift of demands and the cascading impacts of skills and approaches, the graphic below represents the activity, team, and iterative approaches comparing the traditional mindsets against adaptive, next- gen AI capabilities. The roles and responsibilities for sta铿� analyzing and assessing AI in all its potential forms, the evaluation playbooks, along with balances and controls are all in the process of a permanent shift of features and functions.

Further, this graphic highlights the sampling di铿�erences created by layers of forensically critical AI subcomponents that merge to deliver decisions. As AI grows in capability, these layers must be independently veri铿乪d and validated before the aggregated outputs can be assessed for conformance and remediation.

Think big, start small

Indeed, linking hope to hype is why multiform AI will dominate 2024 budgets, which before 2023 was relegated to sophisticated technology 铿乺ms or academic institutions. Industry demands are outpacing academic theory, leaving a void of capability and science when it comes to common forensic analysis and assessment of domain de铿乶ed AI decision making. Industry leaders, AI scientists, and accounting practitioners are rushing to address the voids of methods, skills, and regulatory implications; however, what can audit and industry institutions do to ensure that their prized, e铿僣ient AI investments continue to advance ethically, accurately, and with transparency?

To begin with, any initial due diligence checklist for AI oversight minimally includes:

• • • robust engagement planning, scope, and objectives;
    • access to artifacts including code, algorithms, and large language model designs;
    • skill set identi铿乧ation and sta铿僴g matrices;
    • data ingestion, sources, and uses (including training, testing, and production data);
    • security, ethical digital sourcing and outputs, and privacy;
    • regulatory compliance and governance;
    • data-decision transparency and results; and
    • controls, monitoring processes.

The above list is non-speci铿乧 and domain-agnostic and should only be used to seed more speci铿乧 plans of action and audit design. The promise of precision, domain application e铿僣iencies, and correlated insights of data-driven AI solutions are here to stay.

Moving forward, AI will not revert back to the edge of computer science. The mainstreaming of AI is now a freight train with momentum and purpose. For AI to improve without harm, industry and audit leaders cannot hope to stand in front of the barreling locomotive waiting for academics and politicians to study the path forward.

Industry and audit leaders must break from the pack and embrace the emerging skills needed for AI oversight. Those that fail to address AI鈥檚 cascading advancements, 铿俛ws, and complexities of design will likely 铿乶d their organizations facing legal, regulatory, and investor scrutiny for a failure to anticipate and address advanced data-driven controls and guidelines.

However, Bennett Borden, Partner and Chief Data Scientist at DLA Piper, warns that this superhero still needs some help. 鈥淭hink more Iron Man than Terminator,鈥� Borden said, adding that generative AI鈥檚 real strength is to 鈥渃ombine legal acumen with generative AI to give a new product or service.鈥�

Borden and other experts in the legal and tax industries converged last week on the 成人VR视频 Institute鈥檚 Generative AI and Emerging Technology Forum to explain how generative AI鈥檚 superpowers translate from the realm of Krypton to solve today鈥檚 real-world problems. After all, the panel noted, generative AI needs a villain to fight against, and any application of the technology needs a real goal that it鈥檚 trying to solve.

鈥淲hy are we doing this?鈥� asked Jeffrey Willinger, Digital Experience Director at advisory and accounting firm Withum. 鈥淲e鈥檙e trying to solve a business problem.鈥�

Another panelist, Peter Geovanes, Chief Innovation & AI Officer at McGuireWoods, called generative AI a 鈥渕ad scientist鈥� that can supercharge both the practice and business of law. On the legal practice side, generative AI could be used to upload an opposing party鈥檚 motions to find inconsistencies in statements or quickly summarize transcripts. On the business of law side, generative AI could scan public news reports and data to predict emerging trends in litigation, such as new regulatory issues in the energy sector for a vehicle manufacturer.

In both cases, however, it鈥檚 the human input that is pointing the AI towards a specific outcome that allows the technology to do superhuman things. 鈥淚 think that changes the whole dynamic of the law firm and the ability to provide value to those clients and prospects,鈥� Geovanes said.

Panelist Arash Nourk, Director of AI Experience Design at 成人VR视频 Labs, agreed and added that generative AI in its current form is not truly 鈥渋ntelligent鈥� 鈥� it cannot, for example, conduct critical thinking or form strategy. The technology is only as good as the data it鈥檚 trained on, Nourk explained, and the data that an organization collects can also be a reflection of the organization itself.

鈥淎s we are training our models based on our firms 鈥� or any professional services organization 鈥� it becomes a continuation of our brand, of the character of our firms and of the signature that we have,鈥� he said.

Borden added that this does not necessarily always mean the character of the firm today, but can also be a reflection of where the firm wants to go. For example, his team at DLA Piper built horizon-scanning tools that look to pinpoint issues for clients before they arise, allowing the firm鈥檚 lawyers to not only aid their clients today with new tools in their belt, but help them identify problems they didn鈥檛 even know they had. 鈥淚t鈥檚 what new thing can we create with this, not just how does it help us do what we do today,鈥� he said.

This also works the other way, Geovanes said, noting that working with a client can also provide opportunities that the firm itself cannot see. 鈥淭hat鈥檚 where we get these really compelling use cases鈥� for generative AI, which in turn provide a competitive advantage for the firm鈥檚 attorneys.

Of course to generative AI, but the panelists all encouraged experimentation so a firm can learn what use cases make sense for the tool. Willinger noted that 鈥測ou don鈥檛 want to have too many guardrails鈥� that can stifle innovation 鈥� but Borden added that generative AI鈥檚 output currently is like a third-year associate in a law firm. 鈥淚t鈥檚 nice, but you鈥檇 never want to hand it to a client,鈥� he said.

Nourk also explained that humans using generative AI 鈥渉ave to understand how the model came about the answer that it did鈥� when explaining its outputs, and many generative AI tools can now feature modified designs where the AI can be asked how a decision was made.

Indeed, it takes some training and conditioning for humans to figure out how to work best with these technology superheroes. 鈥淧eople are used to giving it a very short prompt, but what we鈥檙e finding is that you need to give the AI a backstory and a motivation, like you鈥檙e a director,鈥� Geovanes said. 鈥�鈥業鈥檓 the actor, what鈥檚 my motivation here?鈥� You鈥檙e angry, you鈥檙e desperate!鈥�

But Geovanes noted that this does work. 鈥淭hat really is the difference between getting a world-class answer and something that is very broad.鈥�

As people become more used to giving these prompts, however, generative AI鈥檚 powers could ultimately become more crucial and in-grained in the day-to-day lives of legal and tax professionals. And while it may create an arms race as people explore what these new superpowers can do, the panel said, it鈥檚 not a race that those in professional services can afford to ignore.

鈥淔irms that don鈥檛 lean into this are like dinosaurs the day before the meteor hit,鈥� Borden said.

Chatbots are basic computer programs designed to simulate a conversation with a human user and have become increasingly popular in recent years as a way to provide customer service, answer frequently asked questions, and even provide mental health support. The latest advanced chatbot, called GPT-3 (Generative Pre-trained Transformer), uses advanced artificial intelligence and machine learning to generate responses to user inputs in a way that is designed to be exceptionally human-like and natural.

Because the need for low-income individuals鈥� legal assistance greatly outweighs the number of lawyers who can assist, GPT can step in to help spot users鈥� legal needs, build out and maintain legal navigators, assist legal services organizations with client in-take, and make court processes and forms easier to navigate.

And because most low-income individuals with legal issues don鈥檛 even recognize their problems as legal in nature, GPT can be taught to catch and identify a legal issue as the person seeks advice through a search engine. The person might then be directed to a legal navigator that will share basic legal information to help address their problem. For example, a site might provide a step-by-step guide to getting divorced, explain how to file a claim against an unlawful landlord (after identifying what constitutes unlawful behavior), or provide legal and other support options for domestic violence survivors.

Organizations like the (LSC) and have already made great strides in building out content-rich online guides, which will become even more intelligent, accurate, and efficient by using AI.

鈥淚magine a user being able to ask for help and a chat bot trained from curated, reliable legal information websites providing a plain language explanation with step-by-step guidance,鈥� says Jane Ribadeneyra, the Program Analyst for Technology for LSC. 鈥淥bviously, we will need to be cautious about using these new tools and ensure they don鈥檛 provide authoritative sounding, but incorrect, information to users. But, I believe those challenges will have solutions and new AI-based technologies we haven鈥檛 even imagined are on the horizon.鈥�

Life-changing legal guidance

Indeed, enhanced guidance for those navigating legal issues on their own will be life changing. For those directed to local legal services organizations, for example, GPT can assist with the in-take process to make client qualification, referrals, and communication easier. Many legal aid organizations have limited resources and are unable to serve all of the individuals who seek their assistance. A chatbot could be used to help screen potential clients and gather basic information about their legal issues, allowing legal aid organizations to prioritize their cases and ensure that they are able to serve the most vulnerable populations, while referring out eligible cases for pro bono services.

Legal-focused AI can also assist with legal research and document preparation to resolve cases faster. For example, a chatbot could be programmed to search for relevant legal precedents or statutes and provide summaries of the information it finds. In fact, this technology is already being developed and refined among the legaltech community, and it could also be used to help draft legal documents, such as contracts or pleadings, by providing template language and guiding users through the process of filling in necessary information.

Legal-focused AI can also assist with legal research and document preparation to resolve cases faster.

鈥淲e鈥檝e started building bots for the public to access basic legal information using GPT technology,鈥� says Tom Martin, Founder and CEO at . 鈥淲ith GPT, we can build these bots 10-times faster than with intent-based natural language systems. GPT-powered chatbots are also much more effective in guiding people quickly to relevant information. It鈥檚 funny that systems like , which were state-of-the-art about two months ago, have now been rendered old-fashioned.鈥�

Amanda Brown, Founder and Executive Director of the , agrees that things are changing fast and access to justice and legal work processes will be the beneficiaries. 鈥淣ew AI tools like ChatGPT have the capacity to significantly support access to justice when paired with allied professionals like legal navigators,鈥� Brown explains. 鈥淟awyers and legal navigators trained to use these tools will be able to more efficiently provide user-friendly information and do basic legal drafting, leaning on their legal training to ensure accuracy and completeness. As we look ahead in legal education and the development of new delivery models, training on the use of these tools should be an essential component of curriculum development.”

Further, collaboration among legal professionals and those developing AI tools will be crucial to ensuring accuracy, relevancy, and effectiveness.

Chatbots in the courts

Finally, GPT has a place within the nation鈥檚 courts to make our legal system more approachable and accessible to those pursuing justice. 鈥淐urrently, I am building a few different chatbots for different workflows (criminal, civil, and drug court),鈥� says Judge Scott U. Schlegel of the 24th Judicial District Court in Jefferson Parish, Louisiana. 鈥淓ach is being built to help court users better navigate the justice system.鈥�

Judge Schlegel explains his chatbots have the potential to scale the court鈥檚 limited resources, provide necessary information to lawyers and litigants at the right time, and help set expectations, which is extremely important. 鈥淲e also hope to integrate these chatbots with the clerk of court鈥檚 system so that court users can get case specific information and a database of Louisiana laws,鈥� he adds. 鈥淭he sky is the limit with all of the potential use cases,鈥�

One potential limitation of using GPT to increase access to justice is the risk of providing incorrect or misleading information, of course. While the bots are designed to be highly accurate and generate responses that are similar to those from a human, they will need additional training and may occasionally provide incorrect or outdated information. The chatbots should be regularly reviewed and updated to ensure they are providing accurate and up-to-date information 鈥� a potentially complicated task.

Despite those challenges, new technologies like GPT chatbots have significant potential to increase access to justice for individuals who are currently underserved by our legal system. By issue spotting, providing basic legal guidance and documents, assisting with legal services in-take processes, and helping court processes, GPT would make it easier for individuals in need to understand and navigate their legal issues. While AI won鈥檛 replace lawyers anytime soon, it is a critical tool to narrow our justice gap and should be used responsibly.

For many industries, the early days of the pandemic were a combination of services coming to a screeching halt and rapid innovation for delivery of services in new ways that prioritized safety. An from 2021 indicated that 94% of CIOs experienced a surge in demand for digital services in 2020 that was directly related to the COVID-19 pandemic.

Post-pandemic, we now have increased our use of technology as a society in new areas of our day-to-day lives 鈥� a fact which more than half of individuals interviewed as part of viewed to be directly caused by the COVID-19 pandemic. Participants in this analysis indicated that increased technology use had enhanced individuals鈥� experiences in banking, shopping, and accessing healthcare; however, experiences had not been enhanced at the same pace in accessing government services. Less than half of participants said they felt U.S. government agencies or organizations were using digital technology effectively for service delivery in the wake of the pandemic.

While the pandemic brought the desire for digital services to the forefront, the end goal of digitization in the years since has been to create better online experiences for citizens. This mindset shift has been reinforced at the federal level, too. that focused on rebuilding trust in government noted that improving service delivery and customer experience should be 鈥渇undamental priorities of the federal government.鈥�

Enhancing user experience through digital governance

Digital service delivery enhances user experience by allowing individuals access to information and services on-demand. Legacy service delivery previously limited response times to government business hours, but technology like artificial intelligence (AI) chat bots can respond to frequently asked questions and help guide users through transactions. This makes services more readily accessible, limits the need for redundant staff interactions, and can reduce wait times both by phone and in-person. Predictive software tools also can encourage users toward more self-service by, for example, generating auto-reminders for users in advance of license or application renewal dates.

Omnichannel service delivery has become ubiquitous in the post-pandemic retail environment. In a government environment, modernized service delivery methods prioritize customer preference and convenience.

A report on aptly describes the future of digital government as frictionless, one in which a proactive approach in understanding consumer needs puts customers at the center by predicting future service needs around life events, rather than putting the responsibility on a user to seek out certain services. It can be a base instinct for those working in government to assume that citizens know what solutions they need, but we must reshape this internal construct in order to appreciate that citizens often understand their unique problem, but not the solutions available to them. Government systems which focus on are best positioned to deliver frictionless experiences.

At the local government level, an individual registering for a new state identification following a move may soon need services and information such as vehicle registration, voter registration, animal licensing, parking permits, or information on local waste and recycling management. These services are managed by different governmental departments (an internal construct), however, they are all a potential need for a new resident relocating from out of state (a common life event).

Managing customer trust & privacy

Data-driven service delivery within government can predict customer needs, enhance communication outreach, and help organizations measure policy effectiveness. In addition to enhancing customer user experiences, this can also help local governments realize cost savings. The benefits gained through collecting user data come at a price, however 鈥� users must trust their local governments to protect their data and to utilize it in a way that brings citizens a direct benefit. An analysis of found that participants overwhelmingly felt that data collected by governments should be kept private, even if sharing data could enhance service delivery or reduce costs.

Further, use of strong digital identifiers in government service delivery can bring multiple benefits to users. Governments in have deployed digital identifier programs across segments of their populations to better democratize access to pandemic resources. Digital IDs can reduce the need for in-person authentication, avoid the redundancy of providing the same data multiple times across government agencies, and can serve as an additional layer of verification and security to protect data privacy.

Ensuring customers are not left behind

A shift to digital-by-default service delivery risks leaving those or those with low technological literacy behind. It is crucially important that customer service can always accommodate those lacking the technological resources or knowledge to navigate self-service or on-demand platforms. By enhancing the user experience and empowering self-service for the majority, the user experiences for those who require hands-on, human interaction in accessing government services will still share positive user experiences.

In summary, while the goal of digital governance is rooted in problem-solving and enhancing customer experience, the experience will look different for each unique customer. Past service delivery was a one-size-fits-all approach, but the future of government service delivery needs to be highly personalized to an individual鈥檚 unique problems and preferences.

While 81% of state and county/municipal courts professionals say their courts are still holding some form of virtual hearings, those numbers are down, according to the 成人VR视频 Institute鈥檚 recent . Compared to one year earlier, in which 89% reported using virtual hearings, resulting in an 8-percentage-point drop. That drop is even more steep on the county/municipal court level, resulting in a 12-percentage-point drop over the past year.

Some access-to-justice advocates worry how a shift away from virtual hearings may impact efforts to improve court access to all citizens, particularly as those hearings have been touted as a potential path to opening up availability for underrepresented populations. As the State of the Courts Report suggests, however, the future may ultimately be a mix of in-person and virtual hearings. This, of course, will result in an increased focus on how to truly open up virtual hearings to all, as both constitutional and technology access barriers remain to full acceptance of such hearings.

The mix of the future

The move to go back in-person hearings doesn鈥檛 surprise retired Judge Ronald Hedges. Following a 20-plus year career as a Magistrate Judge in U.S. District Court for the District of New Jersey, Judge Hedges now is principal of his own firm, Ronald J. Hedges LLC, and chairs the American Bar Association (ABA) judicial division鈥檚 court technology committee. In conversations with courts around the country, Hedges says he has found a large appetite to start going in-person once again.

However, what he ultimately sees as the end result of the return to courtrooms is not fully in-person or remote 鈥� the answer may lie somewhere in between. 鈥淵ou’re going to be seeing a mix of proceedings,鈥� he explained. 鈥淵ou’re going to be seeing live proceedings, there will still be some remote proceedings, and there are also going to be hybrid proceedings.鈥�

Hedges points to potential constitutional issues that could come out of virtual jury trials, such as effective cross examination and 6th Amendment issues like the right to confrontation in criminal cases. However, 聽there are other hearings throughout a matter that could lend themselves more towards virtual proceedings, such as initial appearances in a criminal case, which would then make it unnecessary to transport a defendant to the courthouse for a short appearance.

Indeed, in the State of the Courts Report, litigants鈥� first or initial appearances and motion hearings were the top hearing types being conducted virtually for criminal cases, while motion hearings were the top hearing type conducted virtually for civil cases.

This tracks, Hedges notes, as a version of virtual hearings were already in use for some types of early proceedings even before the pandemic. Virtual hearings are primarily used as a matter of convenience, while still affording due rights. 鈥淏efore I left the bench, we started doing telephone conferences鈥� for multi-district litigation, Judge Hedges says. 鈥淏ecause frankly, it didn’t make much sense to me to make 30 lawyers or so truck into Newark, N.J., from across the country for a half-hour program.鈥�

The lingering tech access issue

Many believe that a move towards more of these virtual hearings may help close the access to justice gap. The State of the Courts Report found that more than three-quarters of respondents (76%) believed virtual hearings would improve access to justice, an increase from 55% just one year prior.

Hedges acknowledges this change, but also reserved some skepticism. 鈥淭here is a caveat to this and that is technology, and whether or not courts still have the technology to do what we were talking about.鈥�

The report also reflected this worry. Access for people with lower-levels of digital literacy ranked as the top challenge for those conducting and participating in virtual hearings, with 42% of court professionals ranking it as a top challenge. Access to technical support (34%), access to broadband networks (26%), and access to technology needed to participate (26%) also were tanked within the top five challenges, the report shows.

For Hedges, these challenges need to be addressed before virtual hearings can truly take hold. 鈥淚f courts are going to allow remote proceedings, then I think there has to be something done to ensure that every population has each equal access to that,鈥� he says.

One possible solution that鈥檚 been discussed is giving courts budget increases to allow them to supply technology to parties so they will be able to participate. But Hedges is skeptical of the economic realities of attempting that method. 鈥淚t’s like everything else when you’re talking about resources like this, it’s where鈥檚 the money going to come from?鈥� he explained. 鈥淚f someone is in a rural area鈥� and they don’t have reliable broadband access or 5G access or the like, I don’t see them being given computers so that they can access online court proceedings. That’s just not going to happen.鈥�

Until these constitutional issues are resolved, there may be an upper limit on the effectiveness of virtual hearings, as courts continue to use them for more administrative and procedural proceedings rather than actual trials. There are some lessons that courts have taken from the pandemic, with what type of proceedings will work virtually chief among them, says Hedges.

鈥淎t the end of the day, people saw all of this as being something we had to do at the time, because we didn’t have another way to get trials done,鈥� Hedges explains. 鈥淎nd now there’s a big move to do trials, but I really don’t see much of an interest anywhere in having remote trials. I just don’t 鈥� and I think that’s unfortunate. But I appreciate why it’s done, and I can certainly understand that there just may not be an opportunity to do as much remote anymore.鈥�

For further insight, you can download a full copy of聽, here.

Despite a radically more difficult economic environment and declining valuations for tech companies, smart money continues to find its way into Legal Tech. However, the growth and impact of the sector seems to be lagging in Latin America, especially when comparing it to more mature markets in the United States and Europe.

Data is notably scarce and unreliable in the Latin American region, but various attempts at mapping the Legal Tech sphere do give us a sense of where the market is right now. (Some of these attempts include efforts by ; ; ; ; and .)

The first elephant in the room is Brazil. The country and its legal market play in a league of their own, and Brazil鈥檚 Legal Tech market is no different. But as a rather insular economy, with particular regulatory and linguistic barriers, it operates in a separate sphere and does not form part of a broader regional ecosystem that many observers see emerging in the rest of (mostly Spanish-speaking) Latin America.

Based on our own research and when looking into the above-mentioned market research, we estimate there are currently between 200 and 250 Legal Tech initiatives across Latin America (outside of Brazil), of which 70% are located either in Mexico, Argentina, or Colombia. We should add here that because of the relatively high turnover rate, this number is a fast-moving target because publicly available data doesn鈥檛 allow us to put any figures on the current size of the market.

When categorizing the various Legal Tech projects, both from the private start-up sector, captive initiatives within law firms, and Legal Tech initiatives promoted by the judiciary or other government institutions (estimated at 15% of all initiatives we were able to identify), we see that about one-third of these initiatives are centered around document management and contract automation, e-signatures solutions, and practice management software.

Analysis of the size, maturity, and health of the various Legal Tech ecosystems in Latin America reveal four key factors that directly affect the potential growth and success of these Legal Tech initiatives:

1. 1. 1. Regulatory framework and quality of the judicial systems;
      2. Availability of digitized of public records;
      3. Market conditions (size and maturity of the legal market); and
      4. Access to funding.

One major obstacle that seems to be holding back the coming of age for Legal Tech in Latin America is the lack of a unified legal system across the region. Latin America is made up of numerous countries, each with their own legal systems, institutions, and regulations. Some regionalization attempts in the main trading blocs such as , of the Southern Common Market) and have not yielded the level of legislative harmonization required for easy regional expansion. This can make it difficult for Legal Tech companies to develop products and services that are transferrable across borders, in turn hampering the scalability of their solutions.

This is especially complicated in the Caribbean, where common law legal systems prevail in the English-speaking countries, contrasting with the civil law systems in Spanish-speaking jurisdictions.

Building the foundation for Legal Tech

Apart from these regulatory differences, there are still many countries in Latin America that simply lack specific regulation that is required to have a solid foundation on which Legal Tech solutions can be built. There are notable gaps in terms of privacy and data protection regulation, electronic signatures, e-commerce, and access to public records and information. These laws have been drafted in some countries, but are non-existent in others, and the levels of implementation vary greatly.

For example, the difference between Ecuador, which has drafted and implemented a highly praised data protection law, and other Central American countries, such as Guatemala and El Salvador, that still are waiting to have such laws approved by their congresses. Similar marked differences exist across the other areas of legislation noted previously. And on top of this institutional weaknesses, many countries also lack a culture of legality in social and business relationships.

Finally, the stability (or lack thereof) of the regulatory framework also affects the viability of Legal Tech initiatives. Colombia, for example, is undergoing yet another tax reform process, the 21^st reform the country has had since 1990. Any Legal Tech initiatives focused on that sector need to factor in the volatility that comes with increased politization and political instability that still constrains the region.

Although 70% of Latin American countries have a formal digital transformation and innovation agenda for the public sector, the digitization of the government and judicial institutions lags behind in most countries. Indeed, less than 30% of government procedures can be carried out entirely online in Latin America, and only 7% of citizens performed their last transaction with their government in an online format, according to a study published by the Inter-American Development Bank. And while there is no shortage of plans, a lack of substantial investment and institutional capabilities hamper the execution of this digital agenda.

Understanding the local legal market

These conditions are further exacerbated by an underdeveloped legal market that is still largely dominated by traditional small-scale legal service providers. In the larger economies, international law firms have clearly gained a foothold in the local market, but these firms mostly serve international financial institutions and multinational companies. The lack of strong domestic private sector development, and limited penetration of broadband internet and mobile phone use in Latin America also limits the total addressable market of Legal Tech companies. So even though a market in appearance can be substantial, the actual potential end users of Legal Tech solutions are still limited. Adding to these factors are also cultural barriers. Many people in the region are still hesitant about the use of technology, not only for legal services but for services in general, preferring instead to work with service providers in person.

Finally, another difficulty is the lack of funding for Legal Tech initiatives in the region. Many Legal Tech start-ups in Latin America struggle to find the investment needed to develop and scale their products and services. We have found that most of the appetite of venture capital has gone to financial tech (FinTech) initiatives, often referred to as the big brother of Legal Tech. What is clear is that FinTech is perceived as a better business and has absorbed a lot of the risk capital that ballooned in the region over the last five years. The deluge of investment that poured into late-stage Latin American tech companies in recent years has now dried up, however, and it is likely that access to funding for Legal Tech will suffer as a result.

Still, there are initiatives in the region such as the Global Legal Tech Venture Day held in Bogota and the Magno Foro LegalTech event in Mexico in 2022, that are geared toward the exposure of Legal Tech initiatives to funding. And there is a growing track record of substantial venture capital investment in Legal Tech, such as the headline investment in Chilean company LemonTech by the U.S. investment firm Accel-KKR based out of Silicon Valley.

Indeed, we鈥檝e seen a Cambrian explosion of Legal Tech initiatives in Latin America, but the local context has so far proven much less fertile for these initiatives to flourish and scale up.聽As of right now, the macroeconomic conditions will likely complicate the access to funding in the short term, and without rapid improvement in the quality of regulatory frameworks, institutional set-up, and market conditions, domestic Legal Tech companies will have a hard time competing with larger consolidated players from outside the region that will be looking to expand their business and tap into new markets.

On the other hand, the democratization of artificial intelligence-based solutions such as ChatGPT, and other tools that make Legal Tech more user friendly and accessible, are going to expand the user base and open the door to applications and solutions not yet seen in the region.

So, despite some of the restraints described above, there is still a great opportunity to spearhead the development of Legal Tech solutions in the virtually untapped Latin American market. And those companies that can successfully time the market and navigate its particular conditions will still benefit from a first-mover advantage 鈥� the opportunity is there.

So if courtrooms are looking to innovate, where should they begin? Experts at the National Center for State Courts say based on their conversations, the answer lies not with a universal piece of technology, but rather with refining courtroom technology processes and adopting technology that fits a court鈥檚 specific needs.

Tech adoption, but smartly

Over the past three years, courtrooms have made a lot of strides in adopting virtual court technologies, notes Zach Zarnow, Principal Court Management Consultant with the National Center for State Courts. It鈥檚 just that those gains are often coming from different starting points.

鈥淭he playing field is not level,鈥� Zarnow says. 鈥淭here is so much difference across the country, as some courts that were better resourced are able to use more stuff, other courts that are less resourced have been creative and innovative by borrowing off-the-shelf technology.鈥�

As a result, even assessing how courts adopted technology since the pandemic can be tricky. Lindsay Hafford, Senior Court Management Consultant for the National Center for State Courts, points to how many courts adopted some form of video conferencing platform, such as Zoom, out of necessity in order to hold remote hearings. That could be considered a step forward in technology adoption 鈥� but now, many courtrooms are struggling to integrate video conferencing with what was already installed in courtrooms, such as digital evidence presentation and court recording technologies.

鈥淲hen they added on some of those pandemic-related technologies in order to do remote or hybrid court, those things don’t always talk to each other, so we’ve got courts that are struggling with that,鈥� Hafford explains. 鈥淲hat they thought would be a temporary solution is left on top of the hard-wired, integrated solution that’s in the room, which it’s still just very challenging to work with.鈥�

As a result, Zarnow and Hafford say they don鈥檛 measure technology innovation by type of technology adopted, but rather by how well the court is adopting processes to fit its needs. 鈥淭here isn’t one piece of technology you must have,鈥� Zarnow says. 鈥淭here are outcomes, goals, and priorities that you set as an institution and that you’re required to set by the law that you then work backwards from to figure out which pieces will make that happen.鈥�

For example, there have been plenty of online dispute resolution projects that have been undertaken in recent years due to pandemic pressures, he says, but they largely failed 鈥渂ecause they paid no mind to what the process was, figured this is now going to be online and will therefore just magically be better, and it didn’t work.鈥� In other words, just because a technology is innovative doesn鈥檛 mean its application is innovative.

鈥淭o me, the most forward-thinking [courts] are the ones that start from basic principles and don’t get wooed by the promise of a technology fix-it-all,鈥� Zarnow explains. 鈥淩ather they should start with, what are we trying to do? How are we going to do it? And let the tests be thoughtful. And that is actually innovative.鈥�

The need for a plan

That means, when looking to undertake a court innovation project, the first stop may be not what technology to implement, but rather what problems a court has to solve and whether it has the personnel on staff to solve those problems. Particularly after the past three years, a few easy first questions to ask are, what have we adopted already that鈥檚 working, what鈥檚 not working, and who do we have that would know?

Hafford notes that some court systems, like in Arizona and Minnesota, have put together committees to provide recommendations for how adopted virtual court technologies can be implemented in the future. Other states such as Texas are exploring possibilities for virtual jury selection, Zarnow adds. Yet, all of these plans have something in common: a desire to start by solving simple problems and pick up easy wins.

鈥淵ou see these courts that are going for the typically higher volume, lower complexity types of cases or proceedings that could be more readily done remotely as a first option,鈥� Hafford says. 鈥淎nd I think that’s where our mind goes, that’s an easy kind of win or an easy entry point into this, to maintaining the remote appearance option or requirement even for those types of proceedings.鈥�

Sometimes, improving the user experience doesn鈥檛 even need to happen in the courtroom itself. Hafford adds that one of the major issues that arose with widespread video conferencing usage was simply notifying parties relevant details before the event 鈥� Zoom links, who to contact with connection issues, processes if there鈥檚 a drop in connection, and the like.

鈥淚 mean pre-pandemic, I think I can say most courts in the country weren’t always capturing email addresses for folks. Their systems may not have even had the field to capture the email address,鈥� Hafford says. 鈥淪o you have to talk about doing programming in your clerks鈥� filing system, so you can capture that email address. When you decide that you’re going to send out e-notifications with a link to access that Zoom proceeding for your official court event, that takes a lot of planning and a lot of groundwork that has to be laid.鈥�

That planning is also crucial because court cases have little margin for error. What happens, for instance, if a defendant鈥檚 connection drops, and default judgment is issued before they can reconnect? 鈥淲e’ve got some pretty basic issues that are very critical to the operation of a court proceeding: people in the room not being able to see and hear people that are appearing remotely in a hybrid situation 鈥� we can’t have court like that,鈥� Hafford adds.

For any virtual court technology and innovation projects to really take off, in that case, planning around courtroom processes isn鈥檛 just a nice-to-have, but a must-have in order to achieve buy-in from all parties involved. 鈥淚f those things don’t start to really gel, then I think that we’re going to see more judges say, I really just can’t risk this,鈥� Hafford says. 鈥淎nd attorneys are going to be staying the same thing.鈥�