Artificial Intelligence (AI) is a must when it comes to Accounts Payable automation! But not all AP automation partners are created equal.
“Artificial Intelligence” is broadly defined as “the theory and development of computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages.” By that definition, even calculators qualify as Artificial Intelligence.
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Many organizations have needed or have been leveraging AI before it was a commonly recognized acronym. In fact, any SaaS automation is, at its core, artificial intelligence, so it is one of the top tools for creating value for a finance and accounting team.
There have been some amazing developments in AI, some of which have been game-changing for SaaS automation. But AI has its limits. The example of a fatality involving Uber’s self-driving car reminds us to be cautious on the approach to artificial intelligence. It’s a game-changer for routine tasks, but it must recognize exceptions to the norm and leave those to the human in the driver’s seat.
AI can and should be used in the automation of financial processes. While some tasks need to be optimized for the best possible solution, which may take the judgment of a human, many tasks need to be maximized for the best fast solution, which is where AI shines. A computer cannot make good decisions about everything, but the more decisions you can safely delegate to AI, the more energy and focus you can give to things that have to be “perfect.”
Of course, you must be the one to decide what can be optimized and what must be perfect. That is why you should be in the driver’s seat when it comes to managing AP exceptions, to define your tolerances and triggers so the software knows what should be flagged as an exception. There are a few items to consider when you are comparing AP automation partners, their philosophy, their oversight, and their trust.
- Because philosophy drives process, before you ask about any prospective partner’s technical expertise with AI, it is important to understand their philosophy about delegating decisions to machines. For instance, we have two basic principles that form the foundation of our philosophy and approach to using AI.
- One is “don’t get ahead of the technology.” Just because the machine can be taught to do it, does not mean the technology is dependable enough (yet) to put it in charge of the decision. Make sure the partner puts a focus on “baking in” best practices. During implementation they should work with you to set up checks and balances, define exceptions and tolerances, and document process and workflow. Make sure the tasks that are part of your automation have been vetted by humans before they are delegated to even the most advanced machine.
- The second is “delegate, but don’t abdicate.” Self-driving cars do have a safety driver, a human whose job it is to take over if technology fails. If that human doesn’t intervene when necessary, that’s abdication, not delegation.
- Both your team and the AI need to have crystal clear instruction on when to stop the process until a human takes over. Meaning you must have control over approved tolerances and discrepancies that trigger an exception flag. The software may make recommendations, but when an exception is detected, a human is required to review and approve before the process moves forward. By baking in best practices, you can prevent “accidents” due to machine error and greatly reduce accidents due to human error as well. That is delegation at its best.
- Oversight is the ease with which you can review the decisions made by the software and the decisions that are pending human judgment. How efficiently and effectively does your computer interface with the human “driver?” Just as the dashboard of a car is designed to allow the driver to assess all systems at a glance, the dashboard of your AP automation should allow your team to quickly assess the status of work in progress and urgent items needing their attention.
- The dashboard should be intuitive and informative. You should be able to set your own thresholds and triggers and highlight areas that need attention according to individual priorities. This enables users to structure their time appropriately and provides managers instant and easy access to the information they need to balance workloads and accountability.
- And ultimately, a SaaS platform with mobile functionality, allowing users and managers to perform that oversight no matter where in the world they might be.
- Ultimately the success of any automation project comes down to the trust you and your teams can put in the technology. Which has less to do with the dependability of the AI than it does with how well the people involved understand the decisions the AI is making. Make sure your AP automation partner has an emphasis on training and change management. It is during this phase of your implementation that trust is cemented between your team, your partner, and the automated process that is AI.
- When you know, understand, and have input into how the software is designed to make decisions, when you are comfortable with what the software did or recommended, and when you have ready access to the data and details involved, then you are more likely to work synergistically with AI. If you do not have this information and this level of trust, then you will not feel confident letting go of those tasks that can be handled by the software which defeats the purpose of automation.
The goal with AI is not to put software in charge of all your decisions. Ultimately the goal is to minimize stress and maximize productivity by making it easier for you to adhere to best practices, reduce human error, have more real-time access to your data; allowing your team to have the freedom to focus on the decisions only they can make. An AP automation solution should be designed to make driving and navigating a whole lot easier, while still leaving you firmly in the driver’s seat.