The Complete Guide to Using AI as a Legal Professional in Lawrence in 2025

Lawrence legal professionals should read this guide because Kansas is actively shaping how AI can be used in practice: the Kansas Supreme Court has created a 21‑member Ad Hoc Artificial Intelligence Committee to recommend court and attorney policies (Kansas Supreme Court ad hoc AI committee and recommendations), the executive branch already enforces a statewide generative AI policy for agencies, and the legislature is considering bills that restrict certain platforms - while local rules matter now, for example Shawnee County's Rule 3.125 requires disclosure and certification when filings contain AI content, a compliance step that can prevent sanctions (Shawnee County Rule 3.125 AI disclosure and ethics guidance).

This guide translates those developments into clear steps - ethical checks, vendor vetting, and concrete training - plus a practical upskilling option with Nucamp's 15‑week AI Essentials for Work bootcamp to learn prompts, workflows, and privacy safeguards for everyday law office use (Nucamp AI Essentials for Work 15-week bootcamp registration).

Bootcamp	Length	Early Bird Cost	Registration
AI Essentials for Work	15 Weeks	$3,582	Register for Nucamp AI Essentials for Work (15-week bootcamp)

“Artificial intelligence holds great promise for helping us work more effectively within the court system, but we must make sure we use it responsibly.” - Chief Justice Marla Luckert

Short answer: not in 2025 - but staying put is riskier than change. National studies show legal AI adoption exploded (use jumped from under 20% in 2023 to nearly 80% in 2024), and major surveys find roughly three in four legal experts plan to weave AI into daily work while 65% of firms say “effective use of generative AI will separate the successful and unsuccessful” (see the BridgeTower Media report and Forbes analysis linked here).

That combination - rapid uptake plus measurable productivity gains (automation can save about 4 hours per week and, in some estimates, add roughly $100,000 in billable-equivalent value per lawyer annually) - means Lawrence firms that lack written AI policies, client-disclosure practices, and basic verification workflows will face both competitive and ethical exposure; local rules like Shawnee County's AI disclosure expectations make this concrete.

The practical takeaway for Kansas practitioners: treat AI as an augmenting assistant that must be supervised, documented, and vendor-vetted so it increases client value rather than inviting hallucinations or sanctions - adaptation, not replacement, is the near-term reality.

Metric	Value
Legal professionals using AI (2024)	Nearly 80% (2024 study)
Legal experts planning daily AI use	~73%
Firms saying AI use will separate success	65%
Share of legal work automatable	~44%
Automation time saved / billable impact	~4 hrs/week; ≈$100,000 value per lawyer/year
Observed AI hallucination rate in legal queries	~1 in 6 queries

“lawyers with AI, not AI versus lawyers.”

Generative AI in 2025 is best understood as large language models (LLMs) and natural language processing systems that turn prompts into useful legal outputs - conversational search, concise summaries, and rapid first drafts - so Lawrence attorneys can offload repetitive work and focus on strategy and client advocacy; LexisNexis explains how GPT-style models and transformers power drafting, summarization, and conversational research, while Relativity highlights how purpose-built tools speed document review and let less tech‑savvy lawyers interact with models through natural language (LexisNexis guide to generative AI for lawyers, Relativity guide to generative AI in legal practice).

The practical payoff for Kansas practitioners: use vetted, supervised AI to convert long research and discovery loads into citable, editable summaries and draft templates - then validate citations and privilege manually before filing to avoid hallucinations or ethical exposure.

Top AI Uses (2025 survey)	What it does
Conducting legal research	Pinpoint leading cases and authorities quickly
Drafting communications	Generate memos, emails, and draft motions
Summarizing legal narratives	Create concise overviews of long texts
Reviewing legal documents	Identify key clauses and issues at scale
Drafting/templating contracts	Produce first‑draft agreements and redlines
Reviewing discovery	Automate document review and surface relevant evidence

“You wouldn't think of discovery or litigation necessarily as a creative art... This is where I think the fun is and where the human element is.” - Alison Grounds, Troutman Pepper Locke eMerge

Choosing “the best” AI for a Lawrence law office is less about a single product and more about which of three proven approaches fits firm size, risk tolerance, and workflows: (1) consumer-grade general AIs (ChatGPT, Gemini, Copilot) that are fast and cheap to try but carry data‑control and confidentiality risks; (2) legal‑specific standalone platforms that deliver deep contract analysis, research, or litigation features but often require heavier integration and investment; and (3) AI embedded in existing legal systems that gives targeted generative features inside tools teams already use - usually the fastest route to adoption and the lowest total cost of ownership.

Local firms that need fast, audited drafting and citable research should weigh integrated options: comparison of AI tools for lawyers and three approaches, for example, and the Lexis+ AI legal research and drafting platform, which pairs a dedicated assistant (Protégé) with authoritative content and reports measurable firm ROI, making adoption a business decision not just a tech bet.

So what: picking an integrated or legal‑specific path can cut time‑to‑value and reduce compliance exposure - critical for small Kansas practices balancing limited IT budgets with rising disclosure expectations.

Approach	Strength	Primary tradeoff
General AI (ChatGPT, Gemini, Copilot)	Immediate, low cost pilotability	Data controls & confidentiality risks
Legal‑specific standalone	Deep, task‑focused capabilities	Higher cost, longer onboarding
Integrated AI in existing platforms	Faster adoption, lower TCO	Less flexibility for cross‑functional use

“There are so many tools being introduced right now. So, we rely on different practice groups coming to us to say, ‘Hey, here's something we think could benefit us'.”

Start small, start safe: begin with a time‑boxed pilot that maps which routine tasks (research, first‑drafts, document summarization) will yield the biggest returns and the highest confidentiality risk, then pick the deployment approach that fits your Lawrence practice - consumer tools for fast experiments, legal‑specific platforms for citable research, or integrated features inside existing systems - and require human verification before any court filing.

Use the Wheat Law Library's checklist of AI research limitations to audit training data and contextual risks (Wheat Law Library: AI and Legal Research - checklist of AI research limitations), adopt a written Responsible AI Use Policy that mandates anonymization and staff sign‑off as recommended in practical adoption guides (Adopting AI Thoroughly, Responsibly, and Ethically - practical law firm guide), and measure impact against industry benchmarks (65% of firms report saving 1–5 hours per week after adopting generative AI) so partners can see concrete ROI before scaling (AI Adoption in Law Firms: AffiniPay/MyCase report on time savings and ROI).

The immediate, memorable payoff: a disciplined pilot with mandatory human checks prevents hallucination‑driven sanctions while often freeing enough time to add measurable billable capacity - document the pilot results, lock vendor confidentiality terms, and only then move to firm‑wide rollout.

Step	Action
1. Inventory & Risk	List tasks, mark client‑confidential data and privilege risks
2. Choose approach	Select consumer, legal‑specific, or integrated AI based on budget and compliance
3. Pilot with controls	Run a short pilot; anonymize data; require verification workflows
4. Policy & disclosure	Publish Responsible AI Use Policy and client/court disclosure rules
5. Train & verify	Train staff; require human checking of citations before filing
6. Measure & iterate	Track hours saved, errors found, and vendor SLAs; expand if safe

“any language drafted by generative artificial intelligence will be checked for accuracy, using print reporters or traditional legal databases, by a human being.”

Turn AI into predictable, auditable workflows: triage intake with an automated assistant, push only non‑privileged case files into a secure case Vault, run jurisdiction‑specific searches with a legal research agent, generate a focused first draft, then require human verification before any client communication or court filing.

In practice for Lawrence firms this looks like - (1) intake triage to capture facts and flag privilege, (2) create a case Vault and link your DMS so the AI drafts from firm precedents, (3) run parallel research agents to surface primary authorities and create a timeline, (4) produce a citation‑linked first draft (memo, motion, or discovery), and (5) mandate attorney + paralegal sign‑off with citation checks and source retrieval before filing.

Use tools that explicitly support DMS integrations and citable outputs (for example, Lexis+ AI Protégé and Vault features for drafting, Vaults, timelines and secure deployments, Wheat Law Library AI and Legal Research limitations checklist) and follow the Wheat Law Library's cautions about model limits, bias, and contextual gaps so staff know when to stop relying on the model and verify sources.

For embedded, case‑centric automation that keeps work inside practice management, evaluate platforms with built‑in AI workflows like Neos so summaries, redlines, and timekeeping stay aligned with firm processes.

Protégé Vault feature	Value
Vaults per account	Up to 50
Documents per Vault	1–500
Non‑Vault upload retention	Up to 10 docs auto‑purged after session
My Conversations retention	90 days

“Delivering on the promise of AI for lawyers - Vincent appears to be as close as I have seen in delivering on the promise of generative AI for legal research.” - Bob Ambrogi

Ethics, confidentiality, and compliance are immediate, local concerns for Lawrence lawyers: Kansas practitioners must treat generative AI like any non‑lawyer assistant - verify outputs, avoid entering client‑identifying data, and obtain informed consent when a tool could touch confidential information - because courts and bar authorities are already moving from guidance to enforcement.

National frameworks such as the ABA's AI resolutions and commentary stress human oversight, transparency, and accountability (ABA policy guidance on lawyers' use of AI), while Kansas‑focused analysis notes the practical steps - read privacy policies, anonymize inputs, consult IT, and document client consent - summarized in the July 2024 ethics guidance for Kansas and Midwest firms (Kansas and Midwest generative AI ethics guidance (July 2024)).

Local court rules and cases make the stakes concrete: Shawnee County's Rule 3.125 and recent orders requiring a filing certificate mean a lawyer who fails to verify AI‑drafted language risks sanctions similar to those imposed in Mata v.

Avianca; adopt checklists, lock vendor confidentiality terms, and require attorney + paralegal sign‑off on every citation before filing to turn AI gains into defensible, compliant practice (Shawnee County Rule 3.125 and related court orders explained).

Ethics Action	Why it matters / source
Verify AI outputs & citations	Prevents hallucinations and sanctions (Mata v. Avianca; court orders)
Anonymize inputs & review vendor policies	Protects client confidentiality; recommended in Kansas/Midwest guidance
Disclose/use certificates where required	Comply with Shawnee County Rule 3.125 and judge‑specific orders

“I further certify that no portion of any filing in this case will be drafted by generative artificial intelligence … or that any language drafted by generative artificial intelligence … will be checked for accuracy, using print reporters or traditional legal databases, by a human being before it is submitted to the Court.”

When evaluating and procuring AI for a Lawrence firm, treat vendor conversations like due diligence: insist on documentable security controls, clear data‑handling terms, and evidence of DMS/Vault integrations rather than feature claims alone - use Assembly Software's printable vendor checklist to frame must‑ask questions around security, data migration, scalability, support, and AI practices (Assembly Software legal case management vendor evaluation checklist).

Because Kansas currently lacks a unified ethics rule on AI, local firms must fill that gap by requiring vendors to disclose retention and training practices (for example, confirm whether non‑vault uploads are auto‑purged and how long conversational logs persist - Lexis+ AI documents auto‑purge up to 10 non‑Vault uploads after a session and retains

My Conversations

for 90 days), plus SLAs for breach notification and the right to audit or delete client data (Lexis+ AI security and Vault retention details).

Use the interactive Gavel checklist to score candidates on ethics, compliance, and confidentiality, and require a short pilot that demonstrates real‑world redaction, anonymization, and citation fidelity before signing a multi‑year contract (Gavel AI vendor evaluation checklist for law firms); the practical payoff: a negotiated retention/purge clause and audit rights can prevent client‑data exposure and preserve privilege while unlocking measurable time savings.

Evaluation Criteria	Why it matters	Source
Security & breach notification	Protects client confidentiality and meets ethical duties	Assembly Software legal case management vendor evaluation checklist
Data retention & purge policies	Prevents unintended data training or long‑term logs (e.g., non‑Vault purge, conversation retention)	Lexis+ AI security and Vault retention details
DMS/Vault integration	Keeps work inside firm controls and preserves audit trails	Lexis+ AI security and Vault retention details / Assembly Software vendor checklist
Ethics & compliance scoring	Ensures vendor practices align with local court/bar expectations	Gavel AI vendor evaluation checklist for law firms
Pilot & verification	Validates citation fidelity, redaction, and ROI before full procurement	Assembly Software vendor checklist / Gavel AI vendor evaluation checklist for law firms

Make training and governance the backbone of any AI rollout in Lawrence: form a cross‑functional steering group (model the structure and remit on KU Medical Center's AI Steering Committee to assign a chair, privacy and IT leads, and policy owners), require role‑based training that teaches anonymization, citation verification, and when to stop and escalate (echoing Wheat Law Library's cautions about data privacy, bias, and the limits of model context), and gate vendor selection with a written vendor evaluation checklist and short, measurable pilot that records hours saved, citation errors found, and SLA performance before any firm‑wide purchase; these steps turn abstract ethics guidance into a defensible compliance trail and the concrete ROI partners expect - documented time saved (e.g., 1–5 hours/week in many early pilots), fewer manual redlines, and a negotiated retention/purge clause that preserves privilege.

Use the steering committee to publish a Responsible AI Use Policy, require attorney sign‑off on final filings, and score pilots against security, retention, and audit rights so governance and measurable outcomes travel together.

Governance Element	KU Medical Center Example
Chair	Jeffrey Thompson, Ph.D.
Main purpose	Provide strategic guidance, governance and support for AI use
Key responsibilities	Evaluate alignment with strategy, propose resources, advise on policies and education

“The AI Steering Committee at KU Medical Center aims to provide strategic guidance, governance and support for the use of artificial intelligence technologies.”

Responsible adoption for Lawrence legal professionals means turning abstract guidance into an auditable, low‑risk workflow: publish a written Responsible AI Use Policy that requires attorney verification and local disclosures (e.g., follow Shawnee County filing rules), run a short, time‑boxed pilot using anonymized case data and a vendor checklist to secure retention/purge and audit rights, form a cross‑functional steering group to own governance and role‑based training, and measure outcomes (hours saved, citation errors found) before scaling - pilots in similar practices commonly free 1–5 hours per week while preserving billable quality.

Use the Wheat Law Library's checklist to vet research limits and bias, require human checks of every citation, and consider upskilling key staff with a practical 15‑week course such as Nucamp's AI Essentials for Work to make prompt engineering and verification a firm competency (Wheat Law Library AI and Legal Research guide, Nucamp AI Essentials for Work bootcamp (15-week)).

Immediate Step	Action	Source
Policy & Disclosure	Publish Responsible AI Use Policy; require court/client disclosure	Wheat Law Library / local court guidance
Pilot & Controls	Short pilot with anonymized data; verify citations manually	Wheat Law Library checklist
Vendor Due Diligence	Negotiate retention/purge, audit rights, SLAs	Vendor evaluation checklists / Lexis+ retention notes
Training & Governance	Steering group + role‑based training; measure hours saved	KU steering committee model / Nucamp AI Essentials for Work bootcamp (15-week)

“Artificial intelligence holds great promise for helping us work more effectively within the court system, but we must make sure we use it responsibly.” - Chief Justice Marla Luckert