Part 5 in the “RPA and AI in Finance” series, which examines the role that robotic process automation and artificial intelligence can play in finance operations
Everyone wants their own robot these days, to increase efficiency and free up time for work that matters. However, to many, “robot” remains a buzzword. While more people are starting to grasp the benefits of robotic process automation (RPA), it becomes even fuzzier when we start to talk about artificial intelligence (AI).
That’s why I’ve teamed up with Thomas Schultz and Enversion to break down how you can make AI work for you in practical terms, as opposed to just having it on your “I gotta have that, too” list. Last week we bridged the gap between RPA and AI, so hopefully you’re ready to start jumping into AI—but if not, read our article from last week, “A Tale of Robots: From Assembly Lines to Knowledge Workers.” The key takeaway is that we introduced to you a five-step model to tackle AI projects. Here’s where we start.
Step 1: Anchor the projects to true business pains
It seems like an obvious step, but if you’re not purposeful about why you need AI and what to use it for, then you’ll run into trouble. Here’s how we see it.
Challenge: AI is currently on the top of all hype curves. That makes it tempting to join the pack: “If they have one – we want one!” Even hard-core business experts are not immune to that psychology. Since most AI projects are not just a walk in the park – data collection and data preparation alone are expensive and resource-consuming tasks – you need to exhibit stamina and allocate the needed resources and executive attention.
That’s why you must choose a business pain that’s important to alleviate for everyone to have the guts to see it through and not just ditch it midway when the going gets tough.
Fix: Ask what true gains you will achieve by solving a problem with AI. Alternatively, ask what will happen if you do nothing. It’s simple: Even if it is “exciting AI stuff,” do your business-case homework. In short, you must treat an AI project like any other important project set up to solve real business pains. It’s fair that you might want to do some experimentation first, but if you’re serious about doing this, you must put your money where your mouth is.
Don’t let yours become another failed AI project
The reality is that many AI projects fail to deliver the envisioned benefits. That’s not just because you didn’t follow Step 1 above, but also simply because it’s still an immature technology where successful real-life application is still somewhat far away. That’s why there’s more to it than just Step 1, which we’ll uncover in future articles.
How is this relevant to finance, you might ask? Well, AI can do wonders for finance, as for any other function. Think about business intelligence as a self-service. It’s great that you have reports available at a click of a button, but what if you could simply ask a bot to figure out what happened, where it happened, and why it happened? That would save you a ton of time doing long-needed analyses and instead jump straight to what-if scenario-modeling and how to make it happen. Powerful, right? That’s just one of the potential uses of AI.
Have you already had thoughts about jumping into AI, or are you at an earlier stage where RPA or simple macros are the only “robots” working for you? If you have any examples of successful AI application, we’d love to hear about it. Please email us:
Anders Liu-Lindberg and Thomas Schultz
The next blog in this series will discuss how to succeed with AI.
This article originally appeared on LinkedIn and is republished by permission.
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