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

Buffalo attorneys should pay attention: the University at Buffalo is launching first‑of‑their‑kind AI‑specialized degrees this fall, bringing interdisciplinary AI education and local research that will directly affect legal workflows from e‑discovery to policy analysis (University at Buffalo announces AI‑specialized degrees).

“New York State's investment in artificial intelligence for the public good is paving the way for generations of New Yorkers to understand and utilize this supercomputing power to its fullest potential.”

At the same time Empire AI's state supercomputing network is funding applied projects that accelerate capabilities (biomedicine, computer vision, and large‑scale data analysis) lawyers will rely on for expert evidence, regulatory compliance, and risk assessments (Empire AI state supercomputing initiative).

To stay competitive and meet New York professional‑responsibility expectations, Buffalo lawyers should pair policy literacy with practical skills - for example, Nucamp's 15‑week AI Essentials for Work bootcamp teaches prompts, tool workflows, and workplace governance for nontechnical professionals (Nucamp AI Essentials for Work bootcamp registration).

Funding snapshot:

Program	Funding
UB AI programs	$5,000,000
Empire AI	> $500,000,000
NIH / DOD support (selected projects)	> $2,000,000
MDA matching grants	$230,000

Artificial intelligence - especially generative AI (GenAI) and large language models - automates language tasks (drafting, summarizing, research) by predicting likely text from vast datasets, and it matters to Buffalo lawyers because it changes how legal work is done, billed, and supervised: faster document review and pinpointed research can reduce costs and respond to client demand, but they also introduce hallucinations, confidentiality exposure, and oversight obligations that New York practitioners must manage.

Use reputable, legal‑grade systems and firm policies: see the Thomson Reuters analysis on preparing firms and selecting professional‑grade GenAI tools for legal workflows for concrete adoption guidance (Thomson Reuters 2025 generative AI report for legal professionals).

For practice areas like patents, the tradeoffs are tangible - efficiency and broader claim drafting versus risks of imprecision and disclosure - discussed in Harter Secrest's review of generative AI for patent drafting (practical risks and benefits of generative AI for patent drafting).

Ethical authorities stress verification and competence: remember the ABA guidance that warns about uncritical reliance on GenAI outputs.

“Because GAI tools are subject to mistakes, lawyers' uncritical reliance on content created by a GAI tool can result in inaccurate legal advice to clients or misleading representations to courts.”

Practically, Buffalo firms should pair staff training and vendor vetting with clear workflow rules (informed client consent, local data controls) and treat AI as an assistant, not a substitute; for detailed tool selection and risk frameworks see the ABA guide to generative AI for the profession (ABA guidance on generative AI tool selection and risks for the legal profession).

Quick reference:

Metric	Value
Professionals who see GenAI as central (TR 2025)	95%
Positive law‑firm GenAI responses 2024 → 2025	51% → 59%
Common lawyer uses	Research, drafting, summarizing, document review, contracts, discovery

Choosing the “best” AI for Buffalo lawyers in 2025 is less about a single product and more about matching tool strengths to firm needs: for transactional and contract-heavy practices, Spellbook's Word add‑in speeds drafting, redlines, and benchmarking without breaking workflows (Spellbook Microsoft Word contract AI for lawyers); for litigation and deep legal research, Casetext's CoCounsel and similar research‑first platforms top lists for citation‑aware, jurisdictional research and document analysis (Grow Law's review of legal AI tools and Casetext CoCounsel for legal research); and for firmwide strategy, Opus 2's procurement and rollout guidance shows why a blended approach (general LLMs for low‑risk drafting, legal‑specific tools for research/contracting, and embedded AI inside existing platforms) produces faster adoption and better risk control (Opus 2 guide to legal AI procurement and rollout).

Key quick comparison for Buffalo firms:

Tool	Best for	Strength	Limitation
Spellbook	Contracts/transactional	Word integration, redlines	Not post‑signature CLM
Casetext/CoCounsel	Legal research & briefs	Citation‑aware, research depth	Cost for small firms
General LLMs (ChatGPT/Gemini)	Drafting, brainstorming	Low barrier, flexible	Data controls, hallucinations

“The best AI tools for law are designed specifically for the legal field and built on transparent, traceable, and verifiable legal data.”

Start with targeted pilots, measure time‑savings and accuracy, and pair any deployment with New York‑focused governance and human‑in‑the‑loop review to satisfy professional responsibility and client confidentiality.

Getting started with AI in Buffalo in 2025 means pairing practical pilots with ethics‑first governance: begin by taking an ethics CLE and vendor‑risk course to understand New York duties of competence and confidentiality, then convene a small governance team to run a narrow, documented pilot with human‑in‑the‑loop review and strict data controls.

Practical first steps are to (1) attend focused training such as BARBRI's CLE on AI ethics, privacy, and supervision to learn Rule‑based obligations and mitigation strategies (BARBRI CLE on AI ethics, privacy, and supervision for lawyers); (2) review guidance on using AI in client conversations and consent protocols from the New York State Bar Association (NYSBA webinar on responsible adoption of AI in attorney‑client conversations); and (3) adopt a documented, risk‑based firm policy template like the five‑pillar playbook to classify tools, forbid inputting confidential data into unapproved systems, require vendor SOC2 and written agreements, and log verification steps (Law‑firm AI policy playbook and rollout checklist from CaseMark).

Remember the ethical anchor: the ABA Model Rules require attorneys to "provide competent representation to a client."

"provide competent representation to a client."

Use this 30/60/90 starter timeline to show progress and evidence of supervision:

When	Action
30 days	Form governance board; audit current AI use
60 days	Adopt risk‑classification policy; approve pilot tools
90 days	Complete staff training; run verified pilot with logs

Start small, document everything, obtain informed client consent where needed, and treat AI outputs as draft work requiring lawyer verification to meet New York professional responsibilities.

Practical AI in a Buffalo law practice means using generative AI where it reliably saves time - contract drafting and redlining, document review and e‑discovery triage, research summaries, deposition/transcript digesting, intake chatbots, and routine correspondence - while preserving attorney oversight, client confidentiality, and New York ethical duties.

Start with narrow pilots (transactional templates, discovery batches, intake flows) and require human‑in‑the‑loop review: use AI to surface clauses, summarize voluminous filings, or draft first drafts, then verify citations, privilege flags, and legal reasoning before filing or advice.

The technical toolkit and limitations are well documented - see the Thomson Reuters breakdown of high‑value use cases and adoption trends for legal teams (Thomson Reuters breakdown of generative AI use cases and adoption trends for legal teams), a practical catalog of tool types and workflows (Briefpoint practical guide to legal AI tools and workflows), and the prompt‑engineering methods that reduce hallucinations and increase accuracy (ContractPodAi prompt engineering guide for legal professionals).

Keep data governance explicit (no confidential client data in public LLMs, SOC2/vendor contracts, logging, and informed client consent) and train staff on prompts, verification checklists, and escalation paths.

The practical tradeoffs in Buffalo: large efficiency gains for routine drafting and review, limited by model hallucination, bias in training data, and privacy risk - so treat outputs as draft work product requiring lawyer validation.

CoCounsel: Bringing together generative AI, trusted content, and expert insights.

Quick reference for common applications and why they matter:

Use Case	Benefit / Adoption
Document review & e‑discovery	Rapid triage, high‑volume accuracy (50%+ adoption)
Summarization & deposition prep	Faster prep, clear highlights for trial teams
Legal research & memos	Speed + citation suggestions (research‑first tools preferred)
Contract drafting & redlines	Template automation, clause benchmarking

New York's AI ethics and enforcement landscape is a patchwork - federal guidance has loosened while state and local rules tighten - so Buffalo lawyers must treat AI adoption as a regulated risk-management exercise, not just an efficiency play.

As recent analysis explains, states and municipalities are imposing duties on employers to assess and mitigate bias, notify impacted people, and keep records, and some proposed laws carry steep penalties for non‑compliance; see a roundup of the 2025 state and local AI legislative landscape for employers (2025 state and local AI legislative and regulatory landscape for employers - Littler).

Remember the central concern:

“Algorithmic discrimination” refers to the use of an artificial intelligence (AI) system that results in differential treatment or impact disfavoring an individual based on protected characteristics...

New York City's Local Law 144 is the most immediate example for Buffalo firms with NYC‑based roles or remote positions tied to NYC offices - it requires independent bias audits, public posting of audit results, and advance notice to applicants (10 business days) before using an automated employment decision tool; full compliance details are published by the city (NYC Local Law 144 automated employment decision tool requirements and compliance - NYC Department of Consumer and Worker Protection).

With federal agency rollbacks, practical best practices are to document vendor SLAs, require human‑in‑the‑loop review, log decisions, and maintain audit trails and training for staff - guidance on employer obligations and actionable mitigations is summarized in recent legal updates (AI and workplace discrimination best practices for New York employers - Husch Blackwell).

Quick compliance snapshot:

Obligation	Key point
Bias audits	Independent, performed ≤1 year before use and at least annually
Notice & disclosure	Advance notice to candidates (typically 10 business days) and publication of audit summaries
Human oversight	Require meaningful human review of adverse or consequential decisions
Penalties	NYC civil fines (hundreds–low thousands); some proposed state bills envision much higher fines (up to six figures)

Buffalo firms should map AI uses, update engagement letters and vendor contracts, train teams on verification workflows, and treat AI outputs as draft work product to satisfy New York professional‑responsibility duties on competence, confidentiality, and supervision.

Procurement and data governance are central to safely adopting AI in Buffalo law firms: treat purchases of AI tools and IT the same as other University or firm procurements - document funding sources, obtain required quotes, route approvals through procurement channels, and vet software with your IT office (the University at Buffalo's Reimbursement and Procurement Team outlines delegate setup in Concur, PCard rules, and CASet vetting for IT purchases) (University at Buffalo reimbursement and procurement guidance).

For AI specifically, require vendor attestations (SOC2, data handling, model training sources), written SLAs on data retention and deletion, and contract terms forbidding unauthorized model training on confidential client data; follow a compliance checklist that maps federal/state rules (GENIUS Act, EU AI Act cross‑impacts, and recent FCC guidance) to procurement steps and audit trails (AI compliance checklist for GENIUS Act, EU AI Act, and FCC guidance).

Operationally, classify data by sensitivity, forbid input of confidential client materials into public LLMs, log model prompts and verifications, and train staff on secure prompt templates - start with a narrow pilot, document human‑in‑the‑loop verification, and use a tested New York research prompt template to limit exposure and improve reproducibility (New York legal research template for secure AI prompts).

Quick procurement checklist:

Required Item	Why it matters
Funding source & account number	Ensures allowable use and auditability
Vendor SOC2 / SLA	Protects client data and limits model training
CASet or IT approval for software	Compatibility and security vetting
Quotes / purchasing thresholds	Compliance with procurement rules

Training and supervision are now core risk‑management functions for Buffalo firms: summer clerks and new associates are arriving with practical GenAI skills but supervisors must bridge those skills to ethical, jurisdictional practice by providing targeted CLE, supervised pilots, and documented verification workflows.

The University at Buffalo's CLE session “What your law clerks know about AI in legal research and writing” is explicitly designed for supervising attorneys and offers 2 NYS CLE credits (1.0 Law Practice Management; 1.0 Ethics), making it a practical first step for firms updating oversight practices (UB Law CLE: What Your Law Clerks Know About AI in Legal Research and Writing - 2 NYS CLE Credits).

Local commentary from UB faculty and clinic directors emphasizes learning foundations first and using AI to augment - not replace - professional judgment, which helps firms define task boundaries and upskill staff without eroding core competencies (University at Buffalo Faculty Spotlight on AI in Business and Startups - Guidance for Legal Professionals).

For scalable, on‑demand options, platforms like myLawCLE provide AI‑focused ethics and practice training that firms can subscribe to as part of a reskilling program (myLawCLE Attorney CLE and AI Training Platform - On‑Demand Ethics and Practice Courses).

Practical workforce impacts include role shifts (routine drafting and triage move to AI‑assisted workflows while junior lawyers focus on verification, complex analysis, and client counseling), a need for written supervision logs, and budgeted CLE time; use the simple reference below to track training choices and credits.

“Learn to walk before you run.”

Training	Format / Key detail
UB CLE: AI for supervising attorneys	Live CLE - 2 NYS credits (1 Ethics, 1 Practice Mgmt)
UB faculty clinics & panels	Workshops + applied clinic experience for students
myLawCLE subscription	On‑demand CLE library - enterprise access for firm training

Hallucinations and fabricated authorities are now an established litigation risk - not a theoretical one - and recent sanctions decisions show New York practitioners can pay a steep price for unverified AI output: courts in Mata, ByoPlanet, and related matters punished lawyers who filed AI‑invented citations or failed to supervise drafting, and judges have repeatedly stressed that the problem is misuse, not the tools themselves (see the detailed Relativity case‑law update for context).

“While the use of AI by itself is not inherently suspect, wholesale reliance on AI without further inquiry or diligence by a lawyer is conduct which a court should deter, as lawyers must always conduct a reasonable inquiry.”

To avoid sanctions and client harm, Buffalo lawyers should adopt a simple checklist: never submit AI‑generated authorities without paper‑trail verification, forbid entry of confidential client data into public LLMs, require vendor assurances (SOC2, no‑training clauses), log prompts and verification steps, train and document human‑in‑the‑loop review, obtain informed client consent where appropriate, and enact rapid remediation and candor protocols if errors surface (the Financial Remedies Journal and other analyses explain how litigants and tribunals have been misled by plausible‑looking but false outputs).

For local adoption aids, consult a Buffalo‑focused compliance and tools guide and integrate verified research workflows into firm policies. Quick metrics to watch:

Metric	Value
Reported cases with AI‑generated fake citations	>230 worldwide
Largest firms using AI (end 2022)	~75%
Large firms exploring generative systems	>60%

Practical next steps for Buffalo firms: run a narrow pilot with logged verification steps, require CLE on AI ethics and competence, update engagement letters and procurement rules, and treat AI outputs as draft work product that a licensed attorney must verify before filing.

Learn specifics from the Relativity case‑law update, the Fabricated Judicial Decisions analysis, and our local AI compliance checklist and Top 10 tools for Buffalo lawyers to make these safeguards operational (see the Relativity Blog AI case‑law update at Relativity Blog AI case‑law update, the Fabricated Judicial Decisions and “hallucinations” analysis at Financial Remedies Journal analysis, and the AI compliance checklist and Top 10 tools for Buffalo lawyers at AI compliance checklist and Top 10 tools for Buffalo lawyers).

Conclusion - Buffalo's legal community stands at a practical inflection point: New York's Empire AI initiative is delivering real, local compute and research capacity that will shape evidence, discovery, and regulatory work for years to come, and firms that pair governance with skills training will gain the advantage.

Empire AI's recent awards and state backing - captured in UB reporting and the governor's launch materials - mean Buffalo lawyers should expect faster, research‑grade tools to surface in litigation and regulatory matters, not just consumer chatbots; see the UB coverage of the $40M award and Empire AI expansion (UBNow coverage of Empire AI $40M award and Beta expansion) and the Governor's initial consortium announcement that seeded the program (Governor Hochul launches the Empire AI consortium).

Market research confirms the payoff: AI can free roughly four hours per week per lawyer and a large majority of firms expect material impact within five years - so the professional response must be practical: documented pilots, vendor vetting, human‑in‑the‑loop verification, updated engagement letters, and targeted upskilling (for example, Nucamp's 15‑week AI Essentials for Work teaches nontechnical prompt and workflow skills).

As Governor Hochul put it:

“With Empire AI, New York is leading in emerging technology and ensuring the power of AI is harnessed for public good and developed right here in this great state.”

Key metrics to track as you plan procurement and supervision:

Metric	Value
Empire AI total public/private backing	>$500M
UB award for Empire AI Beta	$40M
Estimated lawyer time saved (Thomson Reuters)	~4 hours/week
Law firms expecting high/transformational AI impact	79% (Thomson Reuters report)

For practical next steps and firm benchmarking, review the Thomson Reuters Future of Professionals report for legal adoption planning (Thomson Reuters Future of Professionals report - legal executive summary), then codify pilots, training, and client‑consent workflows so Buffalo lawyers can harness AI's benefits while meeting New York professional duties.