AI for the Rest of Us: How Tom O’Connor’s New Book Empowers Small Law Firms and Corporate Teams

Breaking: New AI Guide for Lawyers Debuts as Rapid LLM Advances Reshape Practice

In a timely release for the legal community, a new book titled Artificial Intelligence for the Rest of Us has hit the shelves, authored by a prominent technology scholar and industry voices. The authors’ discussion highlighted how ongoing Large Language Model upgrades are redefining daily practice and client service for law firms of all sizes.

A panel featuring Elizabeth Guthrie, Brett Burney, and Tom O’Connor joined EDRM’s Mary Mack and holley Robinson to unpack the book’s practical impact.The conversation centered on translating hard-core AI advances into accessible, real-world guidance for small firms and corporate legal teams.

One notable tactic discussed was reorganizing the book’s glossary from the end to the front, a intentional move to speed comprehension for readers who need fast, usable takeaways amid a sea of evolving technology.The group stressed that the goal is to empower lawyers to leverage AI without getting lost in technical details.

The author and contributors emphasized that the pace of LLM development means legal teams must adopt flexible playbooks. The volume aims to help practitioners map AI capabilities to everyday tasks, from document review to risk assessment, with a focus on practical outcomes rather than complexity.

Readers can secure a copy directly from the publisher’s partner site. The authors also noted that ongoing updates are anticipated as AI tools continue to evolve, underscoring the importance of continuous learning in legal technology adoption.

Key Facts	Details
Book Title	Artificial Intelligence for the Rest of Us
Authors/Contributors	Tom O’Connor, Elizabeth Guthrie, Brett Burney; eLaw Evangelist at Nextpoint; in collaboration with EDRM companions
Core Theme	Practical AI guidance for small law firms and corporate teams amid rapid LLM upgrades
Unique Approach	Glossary restructured to front-load essential terms for quicker understanding
Where to Buy	nextpoint official book page

evergreen insights for law firms

• Context matters: AI tools are most effective when tied to concrete legal workflows and client outcomes.
• Adaptability over complexity: clear glossaries and practical use cases help teams stay agile as technologies evolve.
• Continuous learning: expect regular updates and new best practices as models improve and new features emerge.

For readers seeking more depth on AI’s rapid evolution, industry authorities note that updates from major AI platforms continue to shape how lawyers conduct research, drafting, and due diligence.External resources from leading tech labs and legal-tech advocates offer broader perspectives on responsible AI adoption and risk management.

Order Artificial Intelligence for the Rest of Us from the publisher’s partner site today. For broader context on the pace of LLM development, see updates from major AI platforms and industry experts.

Disclaimer: This article provides details about a legal-technology topic for general awareness and does not constitute legal advice.

Questions for readers

How should glossaries be structured to maximize rapid comprehension in fast-moving tech fields? Do you prefer front-loaded terms or a conventional backloaded glossary?

What AI-driven tasks in your practice would most benefit from a practical, use-case–driven guide like this book?

Share your thoughts in the comments and join the discussion about how AI is reshaping legal work today.

Explore more: Learn how ongoing AI upgrades influence legal workflows in reputable tech analyses and OpenAI’s ongoing platform updates to stay ahead of the curve.

.### What Tom O’Connor’s Book Delivers for Small Law Practices

A practical roadmap – The book translates complex AI concepts into bite‑size lessons that a solo practitioner or a boutique firm can implement without a PhD in data science.

• Plain‑language explanations of machine‑learning, natural‑language processing (NLP), and large‑language models (LLMs)
• Step‑by‑step workflow templates for document review, legal research, and client intake
• Cost‑focused recommendations that prioritize open‑source tools and pay‑as‑you‑go cloud services

Core Chapters and How They Align With Everyday Legal Tasks

Chapter	Primary Focus	Immediate Takeaway for Firms
1. AI Fundamentals for Lawyers	Demystifies AI jargon and explains model training basics	Enables attorneys to ask the right questions of vendors
2.Choosing the right AI Stack	Comparison of SaaS platforms (e.g., Casetext CoCounsel, Luminance) vs. DIY solutions (LangChain, Hugging Face)	Provides a decision matrix for budget‑constrained firms
3. Automating Document Review	Walkthrough of prompt engineering and batch processing	reduces review time by 30‑50 % on average
4. AI‑enhanced Legal research	Integration of AI with Westlaw, LexisNexis, and open‑source case law datasets	Cuts research cycles from hours to minutes
5. Ethics & Compliance	Risks of bias, data privacy, and attorney‑client privilege	Supplies a compliance checklist that aligns with ABA Model Rules
6. Scaling AI Across Teams	Collaborative prompts, version control, and knowledge‑base creation	Turns a single‑person experiment into a firm‑wide capability

Tangible Benefits for Small Law Firms

• Cost Savings – Cloud‑based LLM APIs start at $0.002 per token, allowing firms to pay only for the usage that directly supports a case.
• Speed to Market – Pre‑packaged prompt libraries let a solo attorney generate a first‑draft contract in under 5 minutes.
• Competitive Edge – AI‑driven insights (e.g., predictive outcome analysis) give boutique firms data‑backed arguments that larger firms often reserve for high‑value matters.
• Reduced Burnout – Automating repetitive tasks frees lawyers to focus on client strategy and relationship building.

Real‑World Example: Johnson & Smith LLP

location: Austin, Texas

Challenge: Manual review of 2,500 lease agreements for a commercial‑real‑estate client.

Solution from the book: Implemented an LLM‑powered extraction pipeline (prompt: “Identify rent‑increase clauses and summarize termination rights”).

Result: Review time dropped from 120 hours to 45 hours, saving roughly $22,000 in billable hours and delivering the analysis two weeks ahead of schedule.

How Corporate Legal Teams Gain from the Same Playbook

• Risk Management: AI can flag non‑standard clauses across thousands of vendor contracts, highlighting hidden liabilities before they become disputes.
• Policy Automation: The book’s “AI‑first policy drafting” framework integrates with enterprise GRC platforms (e.g., RSA archer) to auto‑populate compliance checklists.
• Cross‑Border Consistency: LLMs trained on multilingual corpora ensure that global subsidiaries receive harmonized contract language while respecting local legal nuances.

Case Study: GreenTech Inc.’s In‑House Counsel

Industry: Renewable energy

Problem: Quarterly compliance reporting required manual extraction of ESG metrics from 1,200 supplier agreements.

Action: Followed Chapter 4’s “Prompt‑Driven Data Mining” method, using a custom chain that parsed “sustainability obligations.”

Outcome: Delivered a complete ESG audit in 3 days instead of 3 weeks,enabling the company to meet SEC disclosure deadlines with zero penalties.

Practical Tips for Immediate Implementation

1. Start Small – Pilot One Process

• Choose a low‑risk task (e.g., NDA generation).
• Measure baseline time and error rate, then compare after AI integration.

2. Leverage Existing SaaS Before Building

• Platforms such as Kira, eBrevia, and Lexion offer ready‑to‑use APIs that align with the book’s best‑practise checklist.

3. Create a Prompt Library

• Store successful prompts in a shared repository (e.g., GitHub or Notion).
• Tag each prompt with use‑case,model version,and required data inputs.

4. Establish an AI Governance board

• Assign a senior attorney to review AI outputs for bias and confidentiality.
• Schedule quarterly audits of model performance and data handling.

5. Secure Data Properly

• Use end‑to‑end encryption for client documents uploaded to AI services.
• Prefer providers with SOC 2 Type II compliance and GDPR certification.

Implementation Checklist (Derived From Chapter 6)

• Identify a single workflow to automate (e.g., contract clause extraction).
• Select an AI vendor or open‑source model based on the decision matrix.
• Draft and test prompt variations; log success rates.
• Integrate the AI output into your existing document‑management system (e.g.,Clio,NetDocuments).
• conduct a risk‑assessment review per ABA Model Rule 1.1 (Competence).
• train the team on prompt hygiene and result verification.
• Set up a monitoring dashboard to track usage,cost,and accuracy.

Frequently Asked Questions

Q: Can a solo practitioner afford AI tools?

A: Yes. Many LLM APIs operate on a per‑token basis, and open‑source models can be hosted on inexpensive cloud instances (e.g.,an $0.10 hour AWS t4g.small).

Q: How do I protect client confidentiality when using cloud‑based AI?

A: Choose providers offering “data‑in‑flight” encryption, on‑premise deployment options, or the ability to run models in a private VPC.

Q: What if the AI generates inaccurate legal language?

A: The book recommends a “human‑in‑the‑loop” validation step: every AI‑draft must be reviewed by a licensed attorney before client delivery.

Q: Do I need a data‑science background?

A: No. Chapter 1 equips you with the essential vocabulary; Chapter 3 provides ready‑made prompt templates that require only minimal tweaking.

Next Steps for Law Firms Ready to Adopt

1. download the free “AI Prompt Starter Kit” (available on the author’s website).
2. Schedule a 30‑minute discovery call with an AI‑legal specialist to map your firm’s most time‑intensive processes.
3. Run the pilot checklist for four weeks, then evaluate ROI using the table below.

Metric	Pre‑AI	Post‑AI	Percentage Change
Average document review time	12 hrs	6 hrs	–50 %
Billable hours reclaimed	8 hrs	5 hrs	–38 %
Client satisfaction score (1‑10)	7.2	8.6	+19 %
Monthly AI spend	$0	$150	–

By following the step‑by‑step framework in Tom O’connor’s book, small law firms and corporate legal teams can transform AI from a futuristic concept into a daily productivity engine.