Automation is becoming a strategic trend and goal for most successful organizations around the globe. But what kind of automation are they zeroing in on, and are they applying any particular methodology? The field of automation is undoubtedly a vast arena with different methods and acronyms. Finding the right fit for your business can therefore be challenging.
Here are four facts about AI-powered decision-making you might not know.
1. What is AI-powered decision making?
AI-powered decision automation consists of three different tools or enablers: digital process automation, decision automation, and machine learning. Together, they form a powerful trident of automation that spans from start to finish.
Digital process automation (DPA) provides the foundation and maps out entire processes. Through its low-code functionalities, software development can be 10 times faster than traditional methods. On top of that, integrations enable more cohesion among systems that communicate with each other and compile all data, giving employees a holistic overview of progress.
Adding decision automation coupled with machine learning provides up-to-date and actionable recommendations that can be leveraged to create and automate better decisions. Suppose you’re handling an insurance matter. Specific parameters will define the outcome, whether it’s managed manually or automated. The difference is that automated recommendations and decisions are transparent to alert against harmful bias.
To illustrate the combination further, consider the choices you make in the morning before leaving your home. For example, is it going to rain? Should you apply Ai-powered automation in this context, different parts of the decision process can resemble different automation techniques.
First, you look at the weather forecast. You gather the available data to make accurate decisions or predictions, and that’s machine learning. Based on that data, you might have rules that define whether to bring an umbrella or a raincoat. That’s decision automation. Lastly, this is part of an entire process that you’re executing on, and that’s the whole journey from leaving your home and going to the office. In this case, without being drenched by the time you get there.
2. Respond to change – fast
Today, the ability to respond to disruptive events is vital if you want to stay competitive within your field. Therefore, the systems at your disposal must correlate to that assertion. Large systems are great for many things, but it often comes with a caveat, just like most things. Larger systems are usually very robust, and changes take time because they’re built for off-the-shelf applications. To summarize, the systems dictate the ways of working, not the other way around.
AI-powered automation, in combination with low code, puts the end-users in the driver’s seat. The systems must abide by their ways of working, and they should be included when developing them. After all, they are the ones who know what needs to be done and how they can streamline the process. Empowering the workforce to become citizen developers will significantly increase development speed.
3. Reduce development workload and cost
AI-powered automation empowers the business to deploy new solutions, either with or without the help of IT. AI-powered automation puts an end to inefficient processes and endless feedback loops between departments that waste valuable time for everyone included.
Integrated and cross-functional ways of working are catalysts for innovation within the organization. This will also help mitigate bottlenecks and silos.
Don’t spend months or years developing digital solutions for all the needs in your business. With AI-powered automation, you can launch innovative applications quickly and together with different functions. Save resources and increase automation without adding to more Shadow IT.
4. Add further automation into the mix
Some might believe AI-powered automation is competing with other technologies. In some cases, that’s undoubtedly true. In others, not. There’s often a choice of which path you want to take when automating core functions. Robotic Process Automation (RPA) is a common choice when first dipping toes into the automation pool. AI-powered automation can be enhanced by implementing other technologies such as RPA.
Machine learning and decision automation are implemented in different stages of the process. They are what you can call activities within the process, automation of specific steps that previously demanded human interaction. The same enforcement can also be applied to RPA. A software robot can relieve a human from tedious tasks such as moving data from one system to another in specific steps. Similarly, Data Fabric can also play a role by automating data discovery, governance, and consumption. Data fabric allows organizations to provide the right data, at the right time, regardless of where it resides.
So circling back, it’s essential first to figure out what kind of automation you want to zero in on that can provide your business with the right tools to succeed. Once you’ve made that choice, the next challenge is to choose the right vendor that fits your need and gives you the right gut feeling.
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