I attended a presentation by an amazing speaker from IBM where he spoke to finance leaders about how great decisions are integrated decisions and business analytics is no longer a “nice to have” but more a “need to have”.
Finance organizations have always been tasked with process improvements year over year. These improvements are in processes such as procure-to-pay, where AI can detect fraudulent invoices; record-to-report (general accounting), where AI powered workflow can reconcile subledger transactions and perform risk-based reconciliations; financial and operational planning and analysis where AI can evoke algorithmic models to assess correlations and identify/adjust anomalies to gain efficiency through intelligent automation and improve quality leading to forecast accuracy. FP&A teams can leverage AI to generate baseline forecasts to apply consistent logic across the forecast process and automate processing in overnight hours enabling your finance staff to be more productive when they start their day. Over the last few years, we have all thought about, asked / been asked about, and even experienced some type of AI.
As a finance leader, integrating AI can give your finance team superpowers! Analytical and business performance superpowers that is. AI can be a key change agent for the way work gets done to drive business results. It frees finance teams from tedious tasks, empowers their productivity and more strategic contributions. On average, AI adopters deploy AI across 40% of finance function workflows, which lets teams focus on higher-value, more strategic activities, such as readjusting planning decisions quickly in real time.
By infusing AI into finance processes organizations have realized benefits such as reduced close cycle times, reduction in uncollectable AR balances. Within financial planning, infusing AI increases forecast accuracy by detecting anomalies and reducing sales forecast errors.
Every superpower deserves a super team.
Over the years I have spoken to many organizations who are managing their financial and operational planning processes through spreadsheets. Some through hundreds of disconnected and siloed spreadsheets. These organizations are rarely able to realize process improvements, and many tend to build more and more silos and replicated databases.
To be successful, finance organizations must evolve their planning process and become integrated. Integration facilitates collaboration with the other departments in the organization (i.e. sales, marketing, promotions, pricing, operations) is key. These organizations need to shed the stigma of a finance generated top-down forecast. Many refer to this forecast as the “Finance Forecast” and ignore as it does not reflect reality. Organizations need to become collaborative in the planning process.
Collaborative forecasts integrate the knowledge of the other teams in the organization. They provide the ability to respond quickly to changes in market conditions because teams are all communicating with each other.
Once the planning and forecasting processes are collaborative, then it is time to infuse AI into the process. Infusing AI into your process has been proven over and over to increase forecast accuracy. By having systems in place to do the heavy lifting in your planning and forecasting processes, you empower your workforce to move from data mining to data analyzing and more value-add activities such as strategic growth initiatives. The infusion of AI in your process removes the bias in your predictive baseline by letting the system do the heavy lifting. The removal of manual intervention and human bias increases forecast accuracy.
What Ai is the right AI and where do I start?
Embrace Change! You will never achieve progress if you stand still. The key to any AI strategy is the data. You need complete and accurate data in order to minimize drift and hallucination in your results. The typical rule of thumb with any AI forecast is to have a minimum of 2X the time periods you are trying to forecast. (i.e. for a typical 12-month horizon, you would want to have a minimum of 24 months history for trend and seasonality purposes) Obviously the more data you can correlate the better. While considering a solution that can provide scalability for the data and time periods you need to generate accurate AI forecasts, you want to choose a solution that has the capability to ignore specific time periods and use data either from the whole database or limited to just the specific view as well as machine learning based capability to adjust the outliers.
Next, implement a planning solution that has the flexibility to adapt to your organization’s changing needs, the scalability to grow with a new level of data that integrates all of the siloed data into a collaborative, secure and governed environment. Leveraging a multi-dimensional analytics solution that stores your data in-memory will add speed and scale to your forecasting process. These solutions rely on the processor power and RAM of your infrastructure to deliver on these much-needed capabilities. Best in class solutions have embedded AI algorithmic forecasting capability built-in to its analytics engine thus providing your financial and operational plans to incorporate AI natively to increase forecast accuracy.
Why is it important to have AI algorithmic forecasting capability embedded into your solution?
It is all about letting the solution do the heavy lifting to streamline your process and removing the human bias from your forecast. Having this capability natively enabled in your process allows your cross functional team to schedule predictive baseline forecasts. Baseline forecasts help to streamline your forecast starting point by allowing for standardized key forecast settings such as version, forecast time horizon, and even advanced capabilities such as outlier / anomaly detection and handling to be applied. Baseline forecasts incorporate these inputs in advance and then allow for scheduling the process to be completed overnight so that your team can be immediately productive when they come into the office in the morning. No longer are they wasting time during the day waiting for forecast processes to complete and compile.
Another key benefit of having embedded AI algorithmic forecasting is the flexibility of allowing the planner to choose their AI forecasting methodology. The planner can choose to leverage the traditional time series forecasting (univariate) methodology where the algorithm correlates historical trends and seasonality to then project your future time periods. A second option for the planning is the ability to incorporate external drivers that influence your business model but are not found in your existing financial and operational data. Examples of these external drivers are weather, consumer spending indices, demographics, market constraints, etc. When your AI algorithmic forecast correlates these drivers against your historical performance, your forecast accuracy will significantly increase.
Whether you are a large, multi-national corporation or you are a local retailer, incorporating these recommendations into your planning process will empower your workforce to achieve more with less, drive transformation from spreadsheet analysis paralysis to data-driven actionable insights, streamline your processes allowing decision makers to respond quickly to changes in market conditions and best of all increase your forecast accuracy.
Introducing IBM Planning Analytics
IBM Planning Analytics is an integrated solution with built in modeling and AI capability that supports all your planning, analysis and reporting needs. Built on the powerful TM1 engine, IBM provides a truly modern AI-powered solution that unifies and streamlines all processes in a secure environment that allows enterprise-wide collaboration and transparency.
Deliver faster, more agile plans that pivot in real time to address changing demands and create more accurate and reliable forecasts with predictive forecasting.
Use IBM Planning Analytics to accelerate and enhance decision-making, enable dynamic process automation through AI and predictive capabilities to deliver more reliable and timely budgets, forecasts, plans, reports and insights to transform your organization from responsive to anticipatory with data you can trust.
IBM Planning Analytics offers everything you need out-of-the-box with a focus on ease-of-use, including flexible modeling, automated workflow, visualization, management, automated alerts and process reporting to help automate the distribution, collection and aggregation of the data in our powerful engine from all the right participants in your enterprise. It has the flexibility and scalability for the data and time periods you need to generate accurate AI forecasts, IBM Planning Analytics also has the capability to ignore specific time periods and use data either from the whole database or limited to just the specific view plus can integrate with Watson Machine Learning based capability to adjust the outliers.
IBM Planning Analytics is a complete and comprehensive solution that is owned by the business can solve even your most complex business requirements. Many of the other tools on the market lack many of the features and functionality we offer and can support only limited data volumes.
Key Links
Planning Analytics Product Page
Planning Analytics Free Trial
Bill Primerano YouTube Channel