John Fuggles
Are you ready for AI? Something perhaps lawyers and finance leaders are asking. But even within one organisation that question has different meanings and, likely, will get a different response.
Within the firm, those responsible for the overall efficient operation of the business may well consider how the current strategy aligns with the wider company ambitions for AI. How better to optimise all processes, which technologies to use across the business, and taking an altogether broader view of the question, and the needs of the business.
Others may take a focus more on the skills and people element. What training is needed, both from a practical application point of view but also from the perspective of governance and risk, security and policies more generally. When and how to use AI as much as what will be the benefits of. What about bias, and business ethics?
What can be done to remove the mundane, do the ‘heavy lifting’ and automate. How to spot outliers and manage these versus those that sit within the parameters permitted. Allowing the law firm to bring value-add insight to the business.
Very often AI readiness is measured through the lens of “what could we do” more than “are we ready?” Being more focused on the latter at the start will deliver greater benefits later. And to do that we need a process and a mechanism for scoring.
Our recommendation is a simple five-point scoring system (1 = not started, 5 = already there) mapped against short, medium and long-term objectives. This is targeted squarely at the finance function as VantagePoint focuses on leading finance change. Overlayed with a measure of risk and mitigations. This total view will highlight what can be done now, what needs to be addressed first and where the gains can be made now, with a view still on the long-term objective.
The tables below are intentionally easy to use and complete and will give you a good idea of your AI readiness. No two people in the same team or department will complete them the same so it’s wise to have multiple people filling this out and then use the different scores to form a debate on what the collective answer should be
| Strategic Alignment | Evaluation Question | Scoring Criteria (1–5) |
| Finance AI Vision | Does the firm have a clear AI vision aligned to business strategy? | 1 – No AI vision. 3 – Informal discussions. 5 – Clear documented AI vision and roadmap |
| Executive Sponsorship | Is there C-level commitment and sponsorship for AI initiatives in finance? | 1 – No sponsorship. 3 – Some support. 5 – Executive championing AI use in finance |
| Use Case Prioritisation | Have finance-relevant and non-finance AI use cases been identified and prioritised? | 1 – No use cases identified. 3 – Brainstormed but not ranked. 5 – Prioritised list aligned to business impact |
| Cross-Functional Collaboration | Is the finance function collaborating with other teams e.g. data/IT/AI ? | 1 – Siloed. 3 – Occasional interaction. 5 – Regular integrated planning sessions |
| Data Readiness | Evaluation Question | Scoring Criteria (1–5) |
| Data Availability | Are required data sources accessible and consolidated? | 1 – Disparate. 3 – Partially integrated. 5 – Centralised, able to be interrogated |
| Data Quality & Accuracy | How reliable and accurate is your finance data? | 1 – Frequent errors. 3 – Occasional data issues. 5 – High-quality, verified data |
| Master Data Management | Are chart of accounts, cost centres, hours, billing methods etc. standardised? | 1 – Inconsistent. 3 – Mostly aligned. 5 – Fully standardised |
| Data Governance & Security | Are there data controls and compliance policies for all data? | 1 – No formal governance. 3 – Informal guidelines. 5 – Documented and enforced policies |
| Process and Automation | Evaluation Question | Scoring Criteria (1–5) |
| Process Standardisation | Are core processes standardised? | 1 – Highly variable. 3 – Some standardisation. 5 – Fully standardised |
| Use of RPA/Automation | Is robotic process automation (RPA) or rule-based automation in use? | 1 – No automation. 3 – Limited use. 5 – Multiple automation initiatives |
| AI Opportunity Mapping | Have you mapped which processes are AI-suitable (forecasting, reconciliation, AP, AR)? | 1 – No mapping. 3 – High-level list. 5 – Detailed AI opportunity assessment completed |
| System Integration Readiness | Can AI solutions be integrated into existing systems? | 1 – No integrations. 3 – Some integration options. 5 – Fully integrated |
| People and Skills | Evaluation Question | Scoring Criteria (1–5) |
| AI Awareness | Do team members understand AI concepts and their relevance to their role? | 1 – No understanding. 3 – Some awareness. 5 – Teamwide training programmes in place |
| Data Literacy | Are you comfortable working with data and analytics tools? | 1 – Low data literacy. 3 – Basic statistics knowledge. 5 – Skilled in predictive models |
| Change Management Capacity | Is there a culture of innovation and openness to AI change? | 1 – Resistant to change. 3 – Slow adoption. 5 – Keen for experimentation and innovation |
| Upskilling programmes | Are there learning programmes to build AI skills? | 1 – No programmes. 3 – Ad hoc learning. 5 – Regularly training with measures |
| GRC | Evaluation Question | Scoring Criteria (1–5) |
| AI Risk Assessment | Have you assessed risks related to using AI? e.g. bias, errors, hallucinations. | 1 – No risk assessment done. 3 – Informal discussion. 5 – Risk framework exists and in use |
| Regulatory Compliance | Are AI systems aligned with regulations? | 1 – No awareness. 3 – Some controls exist. 5 – Clear AI compliance framework |
| Model Auditability | Are AI model decisions explainable and auditable? | 1 – No visibility into AI models. 3 – Partially. 5 – Fully auditable models |
| Ethics & Accountability | Are ethics, fairness, and accountability embedded in AI programmes? | 1 – No ethics framework. 3 – Discussed but not enforced. 5 – Documented principles and accountability |
What does this all mean?
<50 and you are a long way from ready.
51-70 and you clearly have some good first steps but there is more you can do
71-85 is a great position to be in. Some shaping required to prepare you
86+ you are well placed and should be investing in tools and systems to accelerate the use of AI in your finance function