Artificial intelligence (AI) and Machine Learning (ML) are two elements that will have a vital impact on financial planning analysis (FP&A) being leveraged in organizations. However, to realize the economics of scale of AI & ML there are 3 vital elements that need to be present before leveraging these technologies: massive data, minimum 10 years data history and focused algorithms. In this post, I will explore 3 impacts on how AI & ML will change FP&A people, processes and value.
The future of AI and machine learning related to finance and accounting will revolutionize businesses and value of these teams.
Change #1: Data X
What do I mean by Data X? The best way to describe it is through an example. During a hotel stay in Las Vegas, I found myself trying to relax after a long flight. So, I turned on the TV and could select from a large variety of multimedia content (movies, TV shows, songs, etc.). AI & ML will bring the same self-serve and on-demand data to FP&A teams. Think of Data X as Siri or Google Now which helps you get answers, directions, or facts quickly. AI & machine learning will bring the same elements to FP&A by asking, clarifying, or communicating financial or non-financial data in seconds. For example, asking Herman (makes sense if you visited the link above), what was total revenue for the past 6 months?" Or, what is our sales forecast for the next quarter? Data X is the combination of tools, machine learning and providing quick reliable and actionable data instantly. Furthermore, companies such as Microsoft, Tableau, and IBM are already exploring and developing these features and the impact it will have on FP&A will bring transformative change.
Change #2: Chase for 3 I's
The second change AI & machine learning will have on FP&A is around the 3 I's which is introduction, implementation and integration. FP&A will be slow in adopting these tools within our teams and businesses, which means these factors, will be vital to increasing our value proposition. Most organizations will do well in introduction and implementation; however, few organizations will adopt the value and transformative aspects of integration. What do I mean by integration? Integration is where the tools, theory and application of AI & machine learning become entrenched in decision making and business processes. Additionally, AI & machine learning becomes part of the company’s DNA and the basis for business execution. Therefore, implementing the 3 I's will impact FP&A by becoming part of the company culture.
Change #3: Team Evolution
You are probably asking yourself, "Chris this is very general. Come on, man." However, what I mean by team evolution is improving the balance of low & high value activities within FP&A teams. AI & machine learning can eliminate focus on low value activities such as data aggregation, data mining and turning data into information. I am not saying AI & machine learning will take your job but it will align your people/teams on high value activities. Most companies are cash strapped or resource constrained but if AI & machine learning is integrated correctly then FP&A teams will have more time to focus on high value activities such as strategic planning, business partnership, and predictive analytics. Also, gone are the days where doing business the same way you have been for the past 20 or 30 years, expecting the same results and remaining relevant is simply business suicide. Therefore, team evolution and activity balancing will be direct benefits of AI & machine learning.
In conclusion, AI & machine learning is your friend, not your enemy, and it is not going to replace jobs! However, organizations/teams pushing the limits and testing the capacity/value of these tools will benefit tremendously. My 10-year outlook is that tomorrow's FP&A teams/leaders will utilize AI & machine learning as another avenue to accelerate knowledge sharing and data decision making to drive business results. Companies and teams that understand and incorporate these changes in their respective businesses will reap the competitive advantages and scale.
The article was first published in prevero Blog
