Artificial intelligence (AI) is blazing through finance departments like wildfire, particularly for financial planning and analysis (FP&A) use cases. Adoption is high, with a recent NVIDIA survey reporting that 91 per cent of financial service companies are either assessing or actively using AI to automate tasks and improve operational efficiency.

It’s not surprising that AI is so popular when it comes to FP&A workflows. AI tools can free teams from the drudgery of repetitive tasks and turbo-charge predictions and analysis, empowering finance personnel to focus more on high-value tasks and strategic decision-making.

AI-driven automation can reduce human error to make forecasts more reliable. At the same time, machine learning (ML) can scan massive datasets to unlock deeper insights and spot patterns that indicate emerging risks. With AI and ML, finance professionals can plan real-time scenarios, boost operational efficiency, and enhance risk management for greater resilience.

What’s more, AI is now readily accessible. User-friendly AI chatbots like ChatGPT, Gemini, and Microsoft Copilot have low entry barriers and are effective at routine tasks like data retrieval and analysis. There are few real obstacles to AI adoption and many potential benefits.

However, there are still many pitfalls that can undermine your ability to actualise the promise of AI. If AI isn’t implemented correctly, you can end up with confused personnel, unreliable insights, skewed forecasts, and possibly even serious security incidents and compliance issues. Following best practices for AI implementation in your FP&A processes is vital, without cutting corners. Here are some tips for successfully adopting AI in your FP&A workflows.

Define an AI strategy

AI is still a shiny new object, but it’s a mistake to rush in blindly and adopt every AI tool you see. Take a step back to establish a coherent AI strategy before you implement new solutions and processes.

It’s best to begin by identifying which aspects of your workflow would most benefit from AI automation or ML analysis. Consult your finance stakeholders about the workflows that should be prioritised for automation and areas where they feel they’re struggling to gain insights and spot opportunities.

Then you can define those tools that would drive the greatest value for your teams. Set specific advantages that you expect to gain from introducing AI, together with KPIs and metrics that you’ll track to measure success.

Ensure the data foundations are solid

AI isn’t a magic wand. You can’t wave it over shaky data and expect it to generate valuable insights or fix your data analysis problems. The adage of “garbage in, garbage out” applies to AI data analytics just as much as to manual analysis, and Gartner notes that poor data quality is often cited as a primary reason for slow AI adoption among finance teams.

It’s important to validate your data collection and preprocessing pipelines before introducing AI. Review your data governance policies, and check that there aren’t any siloes that could hamper AI tools from accessing the data they need.

Keep humans in the loop

For all the many benefits of AI, it can’t take over FP&A processes entirely. Humans are still needed for several tasks that aren’t suitable for AI or ML tools. For example, while AI can help with data storytelling, finance professionals need to communicate the insights that AI produces and turn them into a coherent narrative.

Strategic decision-making is another area that needs to remain human-led, and finance personnel are needed to manage relationships with stakeholders in other departments. Compliance and ethics are areas that are growing in importance as AI becomes the norm and that should remain under human management.

Moreover, AI and ML are known to be prone to hallucination. Human verification of AI and ML outcomes is vital to make sure that crucial financial decisions, predictions, and forecasts aren’t based on mistaken assumptions.

Double down on security

One of the biggest problems with AI is the issue of security. Many finance teams hesitate to embrace AI solutions out of concerns that they could undermine data privacy or weaken data security. Data security is important, as handling large amounts of sensitive information requires robust protection measures. These concerns are well-founded, too – last year, Samsung banned employees from using third-party GenAI tools after ChatGPT leaked sensitive data.

International regulations are also catching up with AI and establishing requirements around data privacy and security. It’s important to build clear policies around data use, set up and regularly review access permissions, and establish logging and monitoring to track unauthorised use or data access.

Consult international best practices for AI-related data privacy, because they are likely to strongly inform evolving compliance regulations and put their recommendations into practice.

Foster an AI culture

The best AI tools in the world won’t be much use if your finance teams avoid actually using them. Many employees are nervous that AI could take over their jobs and/or distrust the tech, which leads them to ignore AI-powered insights. Using AI tools effectively also requires digital literacy and technical skills that may be lacking among your employees.

To overcome this hurdle, invest in building an AI culture. Reassure your workers that AI isn’t a threat, and present your new solution as a copilot that will improve their productivity. Train employees for the skills you need, although you may need to hire new AI talent. It’s best to start with user-friendly, intuitive tools for a gentle learning curve.

You’ll also need to educate finance teams to trust the AI. Strive for transparency in AI processes to minimise the “black box” effect. Encourage them to verify AI findings at first, to help them grow comfortable with working with the outcomes.

AI can transform FP&A – but only with the right approach

It takes careful forethought and rigorous implementation to actualise the promise that AI holds to revolutionise FP&A strategies. If you cut corners or ignore basic principles, you’ll see sub-optimal results and possibly complete failure. It’s worth investing in preparing the ground to see your AI solutions succeed.

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