A three-level AI readiness guide will help courts safely implement AI through strategic planning, thoughtful project execution, and continuous improvement while managing the technology's inherent risks and complexity
3 key takeaways:
-
-
-
Establish strong governance and principles first — Before implementing AI, courts must create cross-functional oversight committees, define guiding principles that align stakeholders, and develop clear AI use policies with high-quality data governance.
-
Prioritize people-centered implementation — Successful AI adoption requires engaging stakeholders early as co-creators and conducting thorough resource assessments that account for total cost of ownership (including maintenance and compliance).
-
Commit to continuous monitoring and adaptation — AI implementation requires ongoing human oversight to monitor performance, prevent data and model drift, and systematically review governance structures and policies after each project to strengthen courts’ overall AI readiness for future initiatives.
-
-
AI has the clear potential to revolutionize courtroom workflows, but AI itself can carry unforeseen risks. Indeed, AI solutions are complex and opaque, with inherent randomness and risk, says Siva Appavoo, Senior AI Manager in the New Jersey courts.
To help courts leverage AI safely, the National Center for State Courts with support from the State Justice Institute convened 16 experts to create an AI readiness guide, which was featured in a recent webinar by the NCSC-Thomson Reuters Institute AI Policy Consortium. This guide provides practical advice and offers a three-level approach for courts adopting AI: strategic planning (level 1); thoughtful project implementation (level 2); and continuous adaptation (level 3). These three levels guide courts from establishing governance and principles to executing measurable, people-centered projects that enhance trust and further the course of justice.
Establishing governance, principles & policies
To unlock AI’s potential while mitigating hazards, courts must first establish a strong foundation through clear governance, guiding principles, and well-defined policies. More specifically, courts should:
Establish governance with a diverse group of voices — A cross-functional committee sets policy, oversight, and feedback loops. “AI governance is… really the leadership structure for all of the court’s uses of AI,” says the NCSC’s Dr. Andrea Miller, adding that AI Governance Tool in the AI Readiness guide should be used to run a structured 12‑month plan that covers level 1 readiness steps end-to-end.
Define your operating philosophy before you start — Establishing guiding principles are not bureaucratic exercises but rather essential blueprints for successful and ethical AI integration. Without them, courts risk misalignment among stakeholders, the development of systems that do not serve their intended purposes, and the possibility of costly failures. These principles provide a constant reference point, ensuring that as AI projects evolve, the court remains true to its core values and objectives.
Indeed, the overarching mindset that directs actions and choices as part of the governing principles should align stakeholders, manage expectations, and anchor future decisions. “The leading cause of software failures historically has been misalignment among stakeholders and changing or poorly documented requirements,” says Dr. Brittany Johnson-Matthews, Assistant Professor of Computer Science at George Mason University, adding that the same is true for AI projects. “Without these guiding principles [for AI use], there’s the same risk for misalignment among stakeholders.”
Another core tenet of any firm foundation is to set internal rules as part of an AI use policy that provides guardrails and clarity for staff during the transition. And because high-quality, well-governed data is fundamental, pay attention to the quality of the data. “One of the dirty secrets of data science is the data cleansing process,” says Appavoo. “Garbage in, garbage out.”
Finally, pick projects using workflow analysis and by identifying pain points; then use a scoring matrix to evaluate potential projects based on criteria such as impact and feasibility.
Implementing projects that focus on practicality
After foundational planning is complete, the next stage focuses on the practical implementation of AI projects through productive change management, resource assessment, and strategic procurement. Beyond initial deployment, substantial work occurs during this stage.
The most important element in this phase is that successful AI adoption hinges on a strategic, people-centric approach that carefully considers resources and risk. “When people are engaged early and meaningfully, they stop being subjects of change and start being co-creators and co-designers of it,” explains Dr. Sofia Bosch Gomez, Assistant Professor of Art and Design at Northeastern University. “And that sense of ownership is one of the strongest predictors of adoption.”
Indeed, effective change management and prioritizing person-centered design are paramount. Often, this means actively engaging stakeholders, fostering open communication, and providing comprehensive training and support throughout the project lifecycle.
The most important element… is that successful AI adoption hinges on a strategic, people-centric approach that carefully considers resources and risk.
Perhaps the most challenging action in this phase is that courts start moving beyond immediate costs and benefits to better understand the full financial and operational implications of AI projects. This requires an accurate assessment of both tangible and intangible costs, along with clearly defining success metrics.
“What’s really tricky about that is some of those costs are very obvious and simple,” says Dr. Miller. “Some of them are very squishy and hard to estimate, and the same goes for the benefits.”
In fact, at this stage there are common pitfalls around cost, according to Aaron Judy, Chief of Innovation and Emerging Technologies for Maricopa County, Arizona. “Courts sometimes focus only on the upfront purchase price, or the development budget, and they ignore the updates, the retraining, the legal compliance — and that can multiply the total cost of ownership.”
Further, courts need to consider their own capabilities, the practicality of their AI solution, its long-term sustainability, and potential risks such as transparency and vendor dependency. If the decision is to buy a product off the shelf, the procurement process and vetting vendors will be key. “If we don’t clarify who’s responsible when the system makes a mistake, we expose ourselves to reputational and legal risk,” Judy notes.
Continuous improvement and preparing for the next AI initiative
After implementing an AI project, the journey does not end. Indeed, it evolves the critical importance of incorporating those lessons learned back into court operations through post-project review.
“It is not about getting in the game when it comes to AI, it is about staying in the game,” says Appavoo. “The complexity is actually after you productionize a solution — that is what we see.” You have to have a human in the loop, stay on top of things in terms of observability, constantly monitor the performance, constantly check the data or the model are not drifting, or the business context is changing, Appavoo explains.
To help put this into practice, the AI readiness guide has comprehensive feedback checklists courts can use to systematically review the foundational AI program elements for ongoing adaptation. More specifically, the post-project review process should examine whether governance structures remain effective, if guiding principles need refinement, and whether internal policies require updates. This continuous improvement approach transforms each AI implementation into a learning opportunity that strengthens the court’s overall AI readiness for its subsequent initiatives.
You can access the AI readiness guide from the National Center for State Courts and the State Justice Institute here
