Putting the Pieces in Place: Cost and Risk in Your AI Strategy for Conveyancing

In our first article, we introduced the six-pillar AI strategy framework—Vision, Value, Cost, Risk, Adoption, and Transformation—and highlighted why a structured approach is crucial for conveyancers. In our second article, we drilled deeper into the first two pillars, Vision (defining a clear AI purpose) and Value (measuring ROI and ROE). Now, we turn to the next two pillars: Cost (Understanding the Total Cost of Ownership) and Risk (Building a Robust AI Risk Mitigation Framework). Let’s dive into how to tackle these areas with practical steps you can implement right away.

Pillar 3: Cost—Understanding the Total Cost of Ownership (TCO)

Implementing AI isn’t just about purchasing software, developing technology or securing a monthly subscription. The real cost is far broader, encompassing everything from infrastructure upgrades to staff training and ongoing maintenance.

Here’s how to get a firm handle on the financial outlay.

Why TCO Matters

• Holistic Budgeting: By considering all costs upfront, you avoid nasty surprises that can trip up your budgeting later in the year or undermine stakeholder confidence.
• Sustainable Investment: Planning for the total cost ensures your AI initiatives remain viable in the long run, rather than stalling after a few months.

Practical Steps to Manage TCO

1. Map Out All Potential Costs

• Technology & Infrastructure: Will you need new servers, cloud storage, or data integration tools? Assess whether your current systems can handle the data load and computing demands.
• Licensing & Tools: Beyond off-the-shelf AI platforms, consider if there are additional models needed or APIs you need for specific conveyancing tasks (e.g., document scanning, identity verification).
• Training & Talent: Factor in upskilling costs for existing staff or the expense of recruiting specialists (e.g., data scientists, AI ethics officers).

2. Differentiate Between CapEx and OpEx

• Capital Expenditure (CapEx): These are one-off costs such as hardware purchases or major software licences.
• Operational Expenditure (OpEx): Ongoing costs, like cloud subscriptions, support services, and regular software updates.

Having a clear handle on which budget pot each item belongs to will help finance teams plan accordingly.

3.Create a Living Budget

• AI costs aren’t static; as you refine or expand your AI use cases, you may need more computing power or additional analytics features.
• Maintain a dynamic budget document and update it regularly. This ensures you track real costs, rather than working off outdated assumptions.

4. Evaluate ‘Build vs. Buy’

• For some workflows—like automated contract reviews—an external solution might be faster and cheaper to implement than building from scratch.
• Conduct a cost-benefit analysis that includes ongoing maintenance and the ability to customise solutions to your firm’s needs.

Pro Tip: Involve both your finance team and end-users early in the budgeting process. They’ll offer valuable input on hidden costs and operational pressures you might not have considered.

Pillar 4: Risk—Building a Robust AI Risk Mitigation Framework

Conveyancing is highly regulated, with strict rules around data protection, compliance, and professional conduct. As soon as AI enters the scene, new questions arise around bias, algorithmic accountability, and ethical best practices.

Why Risk Management Matters

• Regulatory Compliance: AI deployment, like any digital tool, must adhere to all FCA regulations, and broader laws such as GDPR.
• Client Trust: Mishandling data or misusing AI can erode client confidence and damage your reputation.

Practical Steps to Mitigate AI Risk

1. Define a Governance Structure

• HITL / Humans In The Loop – keeping humans in the loop in any AI tool that is deployed in the Conveyancing process is vital right now. AI tools are incredibly potent, but are not yet a replacement for qualified legal professionals.
• Identify who within the firm is responsible for AI oversight—this could be a dedicated AI Ethics Committee, a compliance officer, or a partner with specialised training.
• Set clear protocols for AI project approval, model validation, and ongoing audits.

 2. Data Security & Privacy Measures

• Data Encryption: Ensure sensitive client information is encrypted both at rest and in transit.
• Access Controls: Limit who can view or manipulate AI-generated outputs, especially when dealing with personal data.
• Data Retention Policies: Clearly outline how long data (like client records or property documents) is stored, and how it’s eventually disposed of.

3. Monitor for Bias and Errors

• Regular Audits: Conduct periodic audits on AI-generated decisions (e.g., risk assessments or eligibility checks) to identify any patterns of bias or inaccuracies.
• Validation Processes: Compare AI outputs with human review, especially in critical stages like contract drafting or AML checks. If discrepancies appear, investigate why.

 4. Have a Contingency Plan

• Establish a clear incident response process in case of data breaches or AI malfunctions.
• Train staff on what to do if the system fails—who to alert, how to log the issue, and how to continue service without the AI tool temporarily.

5. Transparent Communication

• If you’re using AI in client-facing interactions—like chatbots or automated updates—be transparent about it.
• Explain the benefits and limitations so clients are aware that certain communications are automated.

Pro Tip: Formalise your risk approach in an AI Risk Mitigation Framework—a living document that outlines all governance, compliance, and security measures for your AI applications.

Making It Work: Cost and Risk in Tandem

Cost and Risk are deeply intertwined. Overlooking hidden expenses can lead to rushed or inadequate risk measures, while neglecting robust governance can result in fines or reputational damage that far outweigh any AI investment.

• Budget for Compliance: If you’re cutting costs in your AI project, don’t skimp on compliance tools or security features. These are essential line items, not optional extras.
• Plan for Evolving Regulations: UK data and compliance regulations are likely to adapt to keep pace with AI. Set aside resources to update systems and retrain staff as required.
• Model Indemnity: The larger model providers have begun to provide indemnity clauses to increase confidence in the usage of their tools (e.g., ensuring outputs are not trained on copyrighted material etc.)

Your Next Steps

1.Conduct a TCO Analysis

• List every cost linked to your current or proposed AI initiatives—hardware, software, training, compliance tools.
• Classify each cost as CapEx or OpEx.

2. Develop an AI Risk Mitigation Framework

• Outline governance structures, auditing processes, and data security protocols.
• Share this framework with all relevant stakeholders for input and approval.

3. Review and Refine Regularly

• Both costs and risks can shift rapidly. Schedule quarterly or biannual reviews to reassess and update budgets, risk frameworks, and governance policies.

4. Communicate with Teams

• Make sure everyone understands the importance of compliance and the potential risks of AI misuse.
• Offer training sessions to help them spot and report any anomalies.

Looking Ahead

In our next articles, we’ll tackle Adoption and Transformation—the final pillars that help ensure AI becomes part of your everyday operations, rather than just another ‘tech experiment’.