Five questions we received after our Demystifying AI webinar
As more questions reached us after the end of the webinar, We thought it would be worth-while addressing them through an article. We understand that AI is a new topic for most of us working in the office of finance. AI is becoming more accessible and natively supported by most CPM solutions. Finance departments need to make informed decisions on how AI is set to influence their work and role in the organization as whole.
Hopefully the answers below can further clarify the potential added value that AI has in store for you.
- Does the CPM solution determine the “best fit” algorithm automatically or does a data scientist need to identify it?
Answer: A major development in tFivehe CPM technology field is that software can now be simply fed with data and it can identify the “best fit” algorithm by itself. It does that by picking an algorithm from the library that explains best the relationship between provided parameters. For the few percent of cases with very advanced requirements, a data scientist can still develop a custom algorithm and upload it to the platform. For most companies, especially those at the beginning of the AI journey, the out-of-the-box feature is more than enough and certainly a great first step.
- How many variables on average are needed for a correct prediction?
Answer: The number of independent variables depends entirely on the complexity of the business’s market forces. An AI driven forecast implementation should be regarded more like a journey than an exercise to get the perfect fit right away. Starting with a few common sense and easily available variables that influence your market is enough (ex: GDP, unemployment, consumption trends etc) to start seeing improvements compared to a manual forecast process. With time, the team can experiment with more variables and further improve the model.
- Does a black swan event as COVID impact forecast/ budgeting powered by AI?
Answer: Black swan events such as the Covid pandemic are impossible to predict during a regular corporate forecast, but companies using AI driven forecasting are much better at reacting to such events. In crisis times, given the uncertainty, one single forecast may be insufficient to mitigate the array of possible risks. In these cases, the best practice is to create multiple scenarios by adjusting some of the levers in your forecast model. After a short while, a measurable driver of the crisis that impacts your company’s performance can be identified (such as COVID infections rate) and incorporated into the forecast model, from then on things can resume to business as usual. As for budgeting, AI used right can enable more swift and coherent re-allocation of resources in crisis situations and avoid paralysis by analysis types of situations.
- Have companies begun to take black swan events more into consideration after experiencing the COVID pandemic?
Answer: Before COVID, only a small percentage of companies were planning for more than one scenario. As the pandemic struck, the interest has since risen across the board. In most organizations the finance department was expected to play a significant role in containing the impact of the disruptions and safeguard the value of the company. Scenario planning is an effective tool to mitigate risks associated with low risk & high impact events, both before and after the occurrence of the event. AI can make scenario generation easier, more accurate and less time consuming, giving the finance office time to model an appropriate plan of action.
- If you need at least 5 years of historic data, does it only work for “stable” companies?
Answer: There are many ways in which data can be considered “unstable”. If the data is not consistent across the organization in terms of definitions and timing, a certain amount of cleaning can be performed. On the other hand, if the company has been acquiring new businesses or making changes in the product portfolio, existing models for similar products or high-level models can be leveraged until there is enough data for a more granular approach. In most cases there is a way to start using AI at less “stable” companies, but each case has to be assessed individually.
Similar to other business functions that have already heavily adopted AI, it’s a certainty that the Office of Finance will go the same path. The gains in time and accuracy are making the use of AI in finance processes a compelling proposition. If you have more questions, wish to discuss how AI can impact your processes or set up a webinar for your team, please reach out Vadim Stoian.
Vadim is a Senior Consultant at Satriun. He has demystified the hype of AI in Finance and can explain in easy and clear words what AI actually is and what it can bring you.Vadim Stoian