1. Have a focused purpose: First, identify where AI can really transform the finance function and deliver continuous value. If there are critical processes that consume people's time, involve lots of documents, or are too complex or variable for standard RPA, bring in AI. By analyzing structured and unstructured data, both internal and external, AI also surfaces insights that can make decisions more accurate.
2. Establish robust data management and governance: AI is only as good as the data that it has to work with. With a centralized data foundation, different functions and people work with the same, consistent data sets. But you also need people with data engineering and master data management skills to create and maintain the pipelines going into the lake so that your data is clean and comprehensive.
3. Eliminate bias: AI bias can creep in when decisions made by AI reflect the conscious or unconscious values of the people who designed it or data it's based on, for example, when finance teams make decisions on customers' credit or payment terms.
Our latest study, AI360, shows that 78% of consumers expect companies to actively address bias. Start by recognizing its sources: old data, underrepresented data samples, unwanted influence from business cycles, and assumptions in model selection. Make sure you use current, holistic data sets to create your AI models and understand the behavior of data-business cycles. Continue to monitor the models and test to see if assumptions hold true. And to avoid AI becoming a black box, ensure you can trace the reasoning path behind AI-based decisions.
4. Think through change management: For AI deployment to go off without a hitch, you need to manage the change with your F&A teams. Leaders can minimize bumps in the road by communicating how AI enhances their day-to-day jobs, in addition to enabling them to take on more important roles.
For example, for retail firms, predictive insights and intelligent recommendations help teams quickly and accurately understand the implications of price markdowns on revenue and profit margins so they can make faster and more effective decisions. CFOs can set the example by using AI-generated insights to guide their own strategic choices.
5. Find and nurture the right talent: Applying AI to F&A creates new demands for teams with both business and technical skills. People need industry and functional knowledge to provide essential context and review algorithms. Advanced teams are even hiring behavioral scientists and anthropologists. But they also need technical skills, such as forecasting, data scientists, and engineers, analytics, design thinking, and agile programming. Once you have the right people, they need the right infrastructure to work with. With easy access to intuitive technology at home, a workplace with outdated, clunky systems won't encourage them to stay.
And you need to nurture your talent. Teams once responsible for transactional work may require re-skilling in how to collaborate with AI and use its outputs when, for example, negotiating contracts. According to our study, 75% of workers say they are willing to learn new skills to take advantage of AI. So make sure you provide learning resources.