Following this approach will help you integrate AI in a way that truly enhances efficiency and outcomes, rather than just adding technology on top of flawed processes.
By embedding AI into these processes, legal teams can operate faster and more accurately. The key is to redesign the workflow around AI’s capabilities: determine which steps humans should handle, and which steps AI can augment or automate. People will remain central, but “now with different agents, tools, and automations to support them”. For each workflow, clearly define how AI will collaborate with legal staff – for example, AI flags issues and drafts a document, while lawyers make final judgments and edits. This human+AI partnership ensures technology delivers real value and lawyers trust the outcomes.
2. START SMALL BY SEEDING AI INITIATIVES
Implementing AI in a legal team is a journey – it’s wise to start with small, manageable projects and then scale up as you learn. Begin by seeding one or two pilot AI initiatives that address well-defined, high-impact use cases. Microsoft’s guidance emphasizes “Start small but start strategically.” Identify a low risk but valuable process where AI can make a clear difference (for example, automating initial contract reviews or creating an AI legal research memo). By choosing a pilot that is high-impact (saving significant time) yet low-risk (won’t compromise client service or compliance), you set the stage for a quick win.
PILOT PROJECT BEST PRACTICES
1. Define Success Criteria: Set clear objectives for the pilot (e.g. reduce contract review time by 50% or achieve a certain accuracy in issue spotting). This helps in later evaluating the AI’s performance and business value.
2. Secure Leadership Support: Ensure senior legal and business leaders sponsor the pilot. Leadership buy-in provides the necessary resources and signals the importance of AI to the whole team.
3. Empower an AI Champion: Choose a tech-savvy team member to champion the AI project. This person will liaise between the legal staff and technologists, gather feedback, and drive adoption. In successful transformations, companies often “seed the right entrepreneurs and champions across teams” to build momentum from within. Having an internal champion in the legal team who is excited about AI can accelerate adoption and help address colleagues’ concerns.
4. Train and Involve End Users: Provide training sessions for the lawyers and staff who will use the AI tool. Hands-on practice and open discussions demystify AI and build user confidence. Microsoft’s legal AI guide recommends comprehensive training and a period of close monitoring during pilots. Engage users in refining the tool – their feedback on AI outputs is invaluable for improvement.
5. Measure and Document Results: Track key metrics during the pilot (time saved, accuracy improvements, user satisfaction). If the pilot shows positive results – for example, the legal team finds that NDA reviews now take hours instead of days – document these wins. Early success stories will help in securing broader buy-in for expanding AI to other legal workflows.
Starting with a well-managed pilot “seeds” AI success in the organization. It builds a case study that you can showcase to stakeholders, turning skeptics into supporters. After one pilot, iterate and improve: if the AI needed better training data or policy tweaks, address those. Once refined, expand to the next use case. This incremental approach ensures you learn on a small scale before wider deployment, reducing risk and increasing the chances of long-term success.
3. CHOOSING AI SOLUTIONS: INTERNAL VS. EXTERNAL PLATFORMS
A crucial strategic decision is whether to build AI solutions in-house or leverage external platforms/vendors. Legal teams must consider their technical capacity, data sensitivity, and the pace of innovation when making this choice.
Principle: Choose the option that delivers value quickly and securely, with manageable cost and complexity.
· Building In-House (Custom AI): Large legal departments or firms with substantial IT and data science resources might consider developing custom AI models or workflows internally. The advantage is full customization – the AI can be tailored exactly to your unique processes and proprietary data. For example, a firm could train its own AI model on its specific contract database to get highly specialized insights. Building can also be cost-effective if you have a sufficiently large scale to amortize the investment. However, in-house development comes with significant challenges: you need skilled AI engineers, ongoing maintenance efforts, and the ability to keep up with rapid advances in AI technology. This approach tends to make sense only for very large organizations (e.g. global law firms or tech-savvy corporations) that can invest heavily in AI capabilities. Even then, often the in-house build is a “light-touch” layer on top of existing AI infrastructure rather than starting from scratch.
· Buying/Using External AI Platforms: For the vast majority of legal teams, leveraging external AI platforms or vendor solutions is the most practical route. Modern AI tools for legal (contract review software, AI-assisted legal research platforms, document automation tools, etc.) are available as cloud services or off-the-shelf products. The benefits of using external solutions include:
Ø Immediate Capability: You can deploy AI much faster by using an existing platform rather than spending months developing one. Many vendors offer ready-to-use tools that can be configured to your needs.
Ø Lower Cost & Maintenance: Vendors spread development and R&D costs over many customers, making it far cheaper for each client. They also handle ongoing updates – crucial given how quickly AI is evolving. This spares your team the “operational headaches” of maintaining complex AI software in-house.
Ø Expertise and Quality: Reputable AI vendors focus all their energy on building a superior product – incorporating the latest research, employing top AI talent, and rigorously testing their models. Few legal departments can attract or afford comparable AI expertise internally. By buying, you essentially tap into the best available technology on the market.
When evaluating external solutions, do your due diligence: check the vendor’s track record in the legal industry, data security measures (especially important for confidential legal data), integration capabilities with your existing systems, and whether the AI’s performance has been validated. It’s often wise to pilot a vendor solution with real use cases before a full purchase. Additionally, consider a hybrid approach: you might use external AI engines (like a cloud-based Generative AI service) but have your internal team fine-tune them on your own data – thus combining vendor tech with in-house customization.
Ultimately, pick the approach that aligns with your organization’s capacity and risk tolerance. Many mid-sized legal teams find success by starting with external tools to gain quick wins, then later developing more custom solutions as their AI maturity grows.
4. AI GOVERNANCE: RESPONSIBLE & ETHICAL AI USE
Adopting AI in legal operations must be accompanied by strong AI governance to manage risks and uphold professional standards. Legal teams deal with sensitive data, confidential client information, and decisions that carry legal liability – so it is paramount that AI tools are used responsibly, transparently, and in compliance with laws and ethics. Governance in this context means establishing policies, oversight processes, and best practices that guide how AI is selected, deployed, and monitored within the legal function.
Why AI Governance Matters: Organizations that neglect governance often find their AI initiatives falter. A Microsoft report notes that 67% of businesses struggle to scale AI projects beyond pilot stages due to governance gaps. Without clear guidelines, teams may be unsure how to use AI appropriately, leading to either misuse or underuse of the tools. Poor governance also heightens risks – for example, using AI on client data without proper consent or safeguards could violate privacy laws or confidentiality duties. In one case, a major platform faced lawsuits for using personal data to train AI without explicit consent, underscoring the need for careful data governance. Simply put, effective governance enables you to capture AI’s benefits while mitigating its risks.
Key Components of AI Governance for Legal Teams:
· Ethical AI Principles: Adopt clear principles that will guide all AI use in your legal team, consistent with broader corporate values. Google and Microsoft have published widely respected AI principles emphasizing fairness, accountability, transparency, and safety. For example, Google’s AI Principles state that AI applications should “avoid creating or reinforcing unfair bias” and be built for safety. Similarly, Microsoft’s responsible AI guidelines prioritize fairness (AI should treat all people fairly) and reliability & safety (AI should operate reliably and safely). Legal teams should ensure their AI systems do not discriminate or produce biased outcomes (e.g., an AI contract analyzer should work equally well on contracts from different jurisdictions or written in different styles), and that they maintain high standards of accuracy and security. Document these principles and socialize them with everyone using the AI.
· Data Governance and Privacy: Because AI’s output quality and compliance depend directly on the data it’s trained on or processing, enforce strict data governance. Establish rules for what data can be used to train or feed AI systems – client-identifiable data, for instance, might require anonymization or special consent. Ensure AI tools comply with privacy regulations when handling personal data. Microsoft advocates making “data governance by design” part of daily operations, meaning practices like data quality checks, access controls, and retention policies are baked into your AI workflows. In short, treat data as a critical asset: secure it, respect privacy, and ensure data inputs are representative and free of harmful bias to the extent possible.
· Transparency and Explainability: Given the legal field’s emphasis on reasoning and evidence, it’s important that AI decisions or outputs can be explained and audited. Implement measures for AI transparency – for example, if an AI tool flags a clause as risky, there should be an accessible explanation (even if just citing which policy or past case led to that judgment). Internally, keep documentation of how each AI system works, what data and logic it uses, and what its known limitations are. This way, lawyers can trust the tool and also be prepared to defend or double-check AI-generated results.
· Accountability is key: designate an owner (or committee) for AI governance who will periodically review AI usage, outcomes, and compliance with the principles. As Google’s principle puts it, AI should remain “accountable to people”, meaning humans are ultimately in control and can override or appeal AI decisions.
· Risk Management and Monitoring: Incorporate AI into your existing risk management frameworks. Identify potential risks such as erroneous AI outputs, cybersecurity threats (e.g., malicious use of AI-generated content), or regulatory non-compliance, and have mitigation plans. It’s wise to pilot new AI tools in a controlled environment first to observe any issues. Once deployed, continuously monitor performance and have a feedback loop for users to report problems. Set up a periodic review (say quarterly) of all AI tools in use: Are they delivering expected benefits? Are error rates acceptable? Are there any unintended consequences or new risks? As regulations around AI evolve (like the EU AI Act or new laws on AI in legal practice), ensure your governance policies are updated to remain compliant. In essence, governance is not a one-time checklist but an ongoing discipline.
When done right, governance becomes a strategic asset – enabling faster adoption of AI by building trust with stakeholders (clients, courts, and employees) and avoiding costly missteps.
5. EMBRACE GENERATIVE AI ENGINES AND AI AGENTS (WISELY)
Today’s AI landscape offers powerful technologies that legal teams can leverage, chiefly Generative AI models and emerging AI agents. Understanding these tools – and using each for the right tasks – is crucial for maximizing benefits.
· Generative AI Engines: These are AI models (often large language models, or LLMs) that can generate human-like text, summarize documents, draft content, and answer questions. Examples include Claude. Perplexity, GPT 5, and others. In legal work, generative AI engines are game changers for tasks like drafting contracts or briefs, writing correspondence, summarizing depositions, and analyzing large bodies of text. They essentially act as a very smart word assistant that can produce first drafts in seconds.
For instance, Microsoft 365 Copilot, powered by generative AI, can draft a contract in Word based on your instructions, or summarize an email thread in Outlook.
The key advantage of generative AI is its ability to handle unstructured text and create coherent outputs – a valuable skill in law where so much work involves language. However, these models are probabilistic and can occasionally produce incorrect or nonsensical answers (a phenomenon known as AI “hallucinations”). Therefore, they should be used with a human-in-the-loop: treat AI drafts as a starting point that a lawyer will review and edit. With proper verification, generative AI can greatly speed up writing and research tasks, allowing lawyers to focus on refining arguments and strategy rather than rote drafting.
· AI Agents: Taking AI a step further, AI agents are systems that not only generate content but also can take actions and make decisions in a sequence to accomplish a goal. An AI agent might combine an LLM’s language capabilities with the ability to execute multistep tasks, interact with multiple data sources or software, and adapt based on feedback. In a legal context, envision an AI agent that could receive an assignment (e.g., “analyze this set of contracts and prepare a risk report”), then automatically gather the contracts, run analysis (perhaps querying a generative AI for summaries and an analytic AI for financial data), and finally produce a consolidated report – all with minimal human intervention beyond the initial ask. Agentic AI is still in early stages but holds promises for complex workflows. However, it must be approached cautiously. McKinsey research into “agentic AI” deployments found that many early attempts failed because teams built flashy AI agents that ultimately did not improve the overall workflow.
The lesson: it’s not about having an agent for its own sake; it’s about the workflow outcome. Before implementing an AI agent, break down the process and see if automation truly adds value at each step. In some cases, a simpler solution (like a rules-based software or a straightforward predictive model) might handle parts of the task more reliably than a complex autonomous agent. Always ask: “What is the work to be done, and what’s the best tool (or combination of tools) to do it?”. Often, a hybrid approach works – e.g., using a rules-based system for highly structured tasks, generative AI for interpretive tasks, and a lightweight agent to orchestrate the hand-off between them.
For legal teams, practical adoption of AI agents might start with small-scale internal assistants (like a chatbot that can answer employees’ HR questions using your policies, or an agent that auto-routes incoming legal service requests to the right lawyer). As you experiment, keep in mind McKinsey’s advice: “Agents aren’t always the answer.” Use them when they clearly fit a need (for instance, handling high-variance, complex tasks with lots of unstructured inputs), but not when a deterministic, transparent solution would work better. And just like a new hire, an AI agent requires training, supervision, and iteration. One study noted that onboarding an AI agent is more like hiring a new employee than installing software – you need to give it clear instructions, feed it quality data, and continuously provide feedback so it improves over time. In sum, generative AI and agents are powerful tools in the legal AI toolkit. Use generative AI widely for drafting and insight generation (with human review), and approach autonomous agents more selectively, ensuring they are aligned to well-defined workflows and supervised closely.
6. CHANGE MANAGEMENT AND BUILDING AN AI-READY CULTURE
The success of AI implementation in legal operations ultimately hinges on people and culture. Introducing AI will bring changes to daily work, and those changes must be managed thoughtfully.
A guiding principle from experts: The true challenge of AI transformation is not technical – it’s human (Cambridge Spark).
Here’s how legal leaders can lead the change:
· Foster a Pro-AI Mindset: It’s natural for legal staff to have concerns about AI – ranging from skepticism (“Can it really do quality legal work?”) to fear (“Will this technology replace my job?”). Address these concerns head-on. Clearly communicate that AI is a tool to augment their work, not replace them. Emphasize how it will remove drudgery (like repetitive contract markups or endless document searches) and enable lawyers to focus on higher-value advisory work and creative problem-solving. Share success stories and data: for example, highlight that firms using AI report completing tasks in hours that used to take days, freeing up time for strategic thinking. When people see AI as a helpful colleague rather than a threat, they are more likely to embrace it. Reinforce this mindset by celebrating wins where a lawyer–AI collaboration produced great results (e.g., “With the help of our AI research assistant, Jane prepared a thorough memo in 2 hours – a new record!”).
· Lead from the Top and Empower from Below: Change management must be championed at multiple levels.
Leadership should visibly support AI adoption – for instance, the General Counsel and senior partners can talk about AI in town halls, set goals for innovation, and even personally try out the new tools. This top-down commitment creates alignment and signals that AI is a strategic priority. At the same time, change also happens peer-to-peer. Identify and empower internal champions and change agents in various parts of the legal team (litigation, contracts, compliance, etc.). These are enthusiasts who can train colleagues, provide on-the-ground support, and give feedback to the project team. Santander’s Head of AI noted that successful transformations rely on a “network of internal champions” – people embedded in each business line who drive AI initiatives and connect with central tech teams. Consider establishing an “AI ambassadors” program in your legal department: a small group of forward-thinking attorneys and staff who receive deeper training and then help others. This creates grassroots momentum.
· Invest in Skills and Training: To cultivate an AI-ready culture, the workforce needs to feel equipped and confident using AI. Provide ongoing learning opportunities: workshops on using the new AI contract review tool, seminars on AI ethics and risks, and even basic AI literacy courses for all staff. Some legal teams’ partner with vendors or consultants for formal training sessions. Others create internal tutorials and FAQs focused on their specific AI applications (for example, a quick reference guide on “How to use the AI brief drafting assistant effectively”). The goal is to reduce intimidation and build competence. When people understand how to use AI and how it benefits them, they will naturally incorporate it into their routine. Upskilling is especially important for more senior lawyers who might be less tech-savvy – pairing them with junior “digital native” colleagues in training can be a good strategy. Remember, an investment in your people’s skills is an investment in the long-term success of AI in your organization.
· Cultivate a Culture of Collaboration and Feedback: AI implementation is not a one-off project – it’s an evolving journey. Encourage a culture where attorneys and staff continuously share feedback on AI tools: What works well? What issues are they seeing? For instance, if the AI summaries are missing key points, lawyers should feel comfortable reporting that so the system can be improved (e.g., via better prompts or additional training data). Likewise, celebrate innovative uses of AI that employees come up with on their own. Maybe a paralegal found a creative way to use the AI chatbot to simplify a client update process – recognize and broadcast that. This openness creates a sense of shared ownership of the AI transformation. People feel they are part of shaping how AI is used, rather than having it imposed on them.
· Manage Change and Expectations: Finally, apply classic change management techniques: have a clear roadmap, communicate updates frequently, and be transparent about challenges. Set realistic expectations – AI will improve productivity but might not be perfect at first, and that’s okay. By framing the initiative as a learning process for the organization, you reduce resistance to any hiccups along the way. Also, be mindful of workload during transition periods: implementing AI might temporarily require extra effort (training, double-checking AI outputs, etc.), so adjust timelines or provide support to avoid overburdening staff. As small successes accrue, the momentum will build. Over time, with strong leadership and an inclusive culture, the legal team will transition from initial adoption to embedded, everyday use of AI across its activities. When that happens, AI becomes just another trusted part of the team’s toolkit – almost an “invisible” helper woven into how work gets done.
CONCLUSION
Implementing AI in legal operations is a transformative journey – one that can elevate the legal team’s performance when done with care and vision. By redesigning processes around AI capabilities, starting with focused pilot projects, choosing the right platforms, instituting robust governance, and investing in people and culture, legal teams can capture the benefits of AI safely and effectively. It’s important to remember that AI adoption is not a one-time project but an ongoing evolution.
BEGIN NOW, LEARN CONTINUOUSLY, AND SCALE GRADUALLY.
In doing so, you’ll position your legal department to handle growing workloads more efficiently, deliver faster and data-driven insights to your clients or business, and stay competitive in an era when digital transformation is reshaping every industry.
AS MCKINSEY’S EXPERTS PUT IT, REWIRING THE ORGANIZATION FOR AI IS AN ONGOING JOURNEY OF IMPROVEMENT, NOT A DESTINATION.
With each step forward, keep the core principles in sight: maintain ethical standards, align AI initiatives with business (and legal) goals, and carry your team along through open communication and training. With a solid implementation roadmap and a culture ready to embrace innovation, AI will become a powerful ally in your legal team – driving productivity, improving decision-making, and ultimately enabling better legal outcomes.
List of sources:
· McKinsey Global Institute — The Economic Potential of Generative AI: The Next Productivity Frontier;
· McKinsey — The State of AI in 2023: Generative AI’s Breakout Year;
· McKinsey — Generative AI and the Future of Work in America (report + PDF);
· McKinsey — Rewired overview page (book & framework). McKinsey & Company;
· McKinsey — Rewired for value: Digital and AI transformations that work. McKinsey & Company;
· McKinsey — How to implement an AI and digital transformation (“Rewired to outcompete”). McKinsey & Company;
· McKinsey — Why agents are the next frontier of generative AI;
· McKinsey — One year of agentic AI: Six lessons from the people doing the work;
· Microsoft — Responsible AI Standard v2;
· Microsoft — Responsible AI principles & approach hub;
· Google — AI Principles (official);
