Scaling AI in Your Organisation: 7 Legal Strategies for In-House Counsel

AI in the Legal Function: Why Governance Matters

Artificial intelligence is already reshaping how you deliver legal services. It promises faster analysis, reduced administrative burden and improved decision-making. However, it also introduces new risks. You face increased data exposure, evolving regulatory expectations and uncertainty about how to use AI safely.

As in-house counsel, you are responsible for designing governance frameworks that allow your business to adopt AI without losing control of risk. That includes overseeing data use, managing third-party providers and ensuring your organisation uses AI consistently and responsibly.

AI introduces new tools, but businesses should apply existing governance principles when using them responsibly. You should treat AI as part of your broader risk, compliance, and operational framework and leverage your existing governance mechanisms to ensure the responsible deployment of AI tools.

How to Implement AI Responsibly in Your Organisation

1. Create Visibility Over AI Use Across Your Business

Uncontrolled AI use is one of the most immediate risks. Employees often test tools without approval, which can expose confidential or personal information.

Consider creating an AI register that records every tool used across your organisation. This register should include each tool’s purpose, the type of data it processes and its risk level. Once you have visibility, you can identify shadow AI usage, assess privacy exposure and decide whether to restrict or approve specific tools.

Without this visibility, you cannot manage risk effectively.

2. Define the Business Problem Before Adopting AI

AI is not a solution in itself. If you implement it without a clear purpose, it can increase cost and risk rather than deliver value.

You should identify the specific problem you want to solve. For example, you may want to speed up contract review, improve document search or automate compliance tasks. Once you define the problem, you can assess whether AI is the right tool and whether it fits your existing processes.

If your team cannot clearly explain the problem to solve, revisit your business objectives before proceeding with implementation.

3. Set Clear and Measurable Success Metrics

There are measures of success beyond cost savings alone. A narrow focus on efficiency can create unintended risks, including reduced accuracy or poor decision-making.

Instead, define clear performance indicators such as improved turnaround times, higher accuracy, reduced operational friction or better stakeholder outcomes. You should align these metrics with your existing reporting frameworks so your leadership team can assess AI performance consistently.

Clear performance metrics help you identify issues early and adjust your approach before issues escalate.

4. Establish Practical Governance Guardrails

You need clear rules around how your organisation uses AI. Without them, adoption becomes inconsistent and risk increases.

Your governance framework should clearly define:

• which tools are approved;
• what data can be used;
• how employees should interact with AI systems; and, importantly,
• clear delegations and responsibilities.

You should review and update your policies to reflect AI-specific risks. This includes your risk matrix to accommodate the risk appetite for AI tooling, a data breach response plan for AI-related data breaches, internal AI use policies, and key escalation pathways.

Importantly, AI governance should not sit separately from your existing framework. You should integrate AI into your current compliance and risk systems so your business can scale safely.

5. Strengthen Your AI Procurement and Contracting Framework

As your organisation adopts AI, you will likely rely on more external providers. This creates contractual and data risks that you must manage early.

You should review your precedent agreements and ensure they address key issues such as data ownership, permitted use, training rights and confidentiality. Many AI providers use customer data to improve their models. If you do not address this in your contracts, you risk losing control over sensitive information.

You should also review your procurement approach and ensure it aligns with your organisation’s risk appetite for AI procurement. This will help to ensure long-term consistency across all AI engagements and reduce the likelihood of conflicting contractual terms down the track.

6. Redefine Internal Performance and Accountability

AI changes how your legal team delivers work. You should set clear expectations about how your team should use these tools.

Lawyers remain responsible for their outputs, even when they rely on AI. Your training should set out how you expect lawyers to review and validate AI-generated content before relying on it. This is critical for maintaining quality, accuracy and your legal professional standards.

Creating a culture of transparency is a key enabler to successful AI governance. Encourage your team to share both wins and losses of their AI use. This helps foster a culture of shared accountability.

7. Monitor Emerging AI Regulation in Australia and Globally

AI regulation is evolving quickly, and your obligations will continue to expand under existing privacy, discrimination, consumer protection and procurement frameworks.

Continuing to monitor developments from regulators and government bodies actively. International regimes, such as the EU Artificial Intelligence Act, can provide a strong indication of future regulatory direction in Australia. Aligning AI Strategy With Legal Risk Management

To scale AI effectively, you should align innovation with governance. That means embedding AI into your existing governance systems, rather than dealing with it in isolation.

If you take a reactive or siloed approach, you may face inconsistent use, unmanaged data risks and contractual gaps. Taking a structured approach will allow you to unlock efficiency gains while maintaining legal and commercial control.

You should also recognise that not all AI use cases carry the same risk. High-risk applications, such as those involving personal data or automated decision-making, require stricter oversight. Lower-risk use cases may allow more flexibility. Your governance framework and risk appetite should reflect this distinction.

Key Takeaways

• To scale AI safely, you need clear visibility over all tools used across your business and a defined purpose for adopting them.
• Focus on measurable outcomes that align with your purpose; sometimes this will be beyond just the cost savings.
• Embed AI into your existing governance and other frameworks so that it is not considered in isolation.
• Ensure that data use and risk are properly monitored.
• Ensure your team understands they will be accountable for the quality and validity of any AI outputs.
• Monitor regulatory developments so you can adapt early and avoid compliance risks.

If you need help with your AI governance frameworks, contracting or regulatory support, LegalVision’s experienced team can assist. Joanne Chenn, Legal Operations & Strategic Project Manager at LegalVision, works closely with in-house counsel to develop practical AI strategies, manage risk and ensure your business adopts AI with confidence.