• Written by Azhar Aliyar, Director

A new financial planning system is installed, training is provided, and yet the finance team continues to rely on manual processes and spreadsheets to inform their decisions. The system's capabilities go unused, and the organisation fails to realise the benefits it was expecting. This scenario is all too common, and it highlights the importance of understanding how to drive adoption and change in finance teams.

Attaching new capability to existing routines

One of the primary reasons capable systems go unused is that they are not integrated into the existing workflows and routines of the finance team. Simply providing training and expecting teams to adopt new systems is not enough. New capabilities need to be attached to routines that already exist, such as monthly budget reviews or quarterly forecasting processes. This approach ensures that the new system is used consistently and becomes an integral part of the team's daily activities.

A shift supervisor, for example, might check the maintenance log as part of their daily routine. Similarly, a finance team could use a new system to inform their monthly budget reviews, using the system's analytics and insights to identify areas for improvement. By attaching new capabilities to existing routines, organisations can increase the likelihood of successful adoption and change.

Who needs to be able to ask, not just who needs the answer

Most people get wrong the idea that only senior finance professionals need access to advanced analytics and insights. However, this approach is misguided, as it neglects the needs of other stakeholders who may require access to financial information to inform their decisions. A more effective approach is to consider who needs to be able to ask questions, not just who needs the answer. This might include managers, team leaders, or even external partners and suppliers.

By providing access to financial information and analytics to a broader range of stakeholders, organisations can empower more people to make informed decisions and drive business outcomes. This approach also helps to ensure that financial information is used consistently across the organisation, reducing the risk of errors or misinterpretation.

The finance data this depends on

The effective use of enterprise AI in finance depends on access to high-quality financial data, including general ledger, budget, open commitments and purchase orders, receivables and terms. This data provides the foundation for analytics and insights, enabling finance teams to identify trends, patterns, and anomalies that might inform their decisions. Without access to this data, AI systems are unable to provide accurate or meaningful insights, limiting their effectiveness.

Organisations that are considering the use of enterprise AI in finance should first ensure that their financial data is accurate, complete, and up-to-date. This might involve implementing new data management processes or investing in data quality initiatives. By prioritising data quality, organisations can ensure that their AI systems are able to provide reliable and actionable insights.

What it changes about variance explanations and budgets

One of the key benefits of using enterprise AI in finance is that it can help to identify and explain variances in financial performance. Traditionally, variance explanations have arrived after the period, providing little opportunity for corrective action. However, with the use of AI-powered analytics, finance teams can identify variances in real-time, enabling them to take prompt action to address any issues. This approach can also help to identify budgets that appear healthy but are actually overcommitted, providing a more accurate picture of financial performance.

By using financial analytics and insights, organisations can gain a deeper understanding of their financial performance and make more informed decisions. This approach can help to reduce the risk of financial errors or misinterpretation, providing a more accurate picture of financial performance.

Measured with variance to budget, commitment coverage, and days sales outstanding

The effectiveness of enterprise AI in finance can be measured using a range of key performance indicators (KPIs), including variance to budget, commitment coverage, and days sales outstanding. These metrics provide a clear picture of financial performance, enabling organisations to assess the impact of their AI initiatives. By tracking these KPIs, organisations can identify areas for improvement and make adjustments to their AI strategies as needed.

For example, a category manager might compare the performance of different suppliers using metrics such as days sales outstanding. By using these metrics, organisations can gain a deeper understanding of their financial performance and make more informed decisions.

What this does not do

While enterprise AI can be a powerful tool for finance teams, it is not a replacement for human judgement and expertise. AI systems are unable to provide context or nuance, and they may not always be able to identify the underlying causes of financial variances. In these situations, human intervention is still required, and organisations should not rely solely on AI to inform their decisions. If you are considering the use of enterprise AI in finance, please contact us to discuss your options and determine the best approach for your organisation.