This is a guest blogpost by Gal Horvitz, CEO, PNMsoft
Artificial Intelligence (AI) refers to a wide variety of algorithms and methodologies that enable software to improve its performance over time as it obtains more data. This technology is currently the hottest thing in the business process management (BPM) industry and it is continuing to heat up. In fact, it’s on fire!
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But, the funny thing is that although we see a lot of innovation with AI algorithms and methods, the concept of AI isn’t new. What is really new is how businesses are using AI. We like to call it the new generation of AI — one example is deep reasoning —which breaks AI’s traditional dependency on known datasets. Deep reasoning performs unsupervised learning from large unlabeled datasets to reason in a way that can be applied much more broadly. In other words, AI can “learn to learn” for itself.
Forrester Analyst Rob Koplowitz’s February 2017 report, “Artificial Intelligence Revitalizes BPM,” – where PNMsoft was one of the companies interviewed – explains that:
“The primary driver for BPM investments just two years ago was cost reduction through process optimization. Today it is customer experience, with enterprises expecting to put top priority on digital automation in two years.”
AI has the ability to take human cost and latency out of processes, as well as provide new interfaces that customers enjoy. With faster and more user-friendly operations, customers are happier, stay loyal to the business and are more favorable to buy more products and/or services from their current provider.
That’s why it’s no surprise that customer experience (CX) and business transformation are expected to skyrocket to the top two primary focuses of businesses looking to improve their processes.
But, in order to progress the practice of AI, companies must first feed the system initial data for the AI algorithms to analyze and suggest data-driven improvements from deep reasoning. Forrester reports 74% of firms say they want to be “data-driven,” but only 29% are actually successful at connecting analytics to action. If these companies want to drive positive business outcomes from their data, they must have actionable insights. Enterprises are realizing this is the missing link and have begun to invest in and grow large sandboxes of data sets that will ultimately help build the AI algorithms that can inspire significant digital transformation for their businesses.
We agree with Brent Dykes’, director of data strategy at Domo, theory that there are many key attributes of actionable insight. We will break them down for you so that you can put them into motion and begin to build the infrastructure needed to put AI and BPM to work.
- Alignment – Make sure the data you’re gathering directly feeds into the success metrics and key performance indicators (KPIs) you desire.
- Context – Determine why you need the data in the first place. Do you need it to make comparisons or benchmark your success? Context will enable your AI to make more accurate predictions.
- Relevance – Pulling the right data at the right time will help narrow down which actions need to occur and when.
- Specificity – The more specific the data is, the better sense of the next action to take will be.
- Novelty – When analyzing customer behavior, it is easier to spot a one-time occurrence over something that has repeatedly happened. Novelty occurrences are areas to pay close attention to and AI will be able to pinpoint them quickly.
- Clarity – How the data is communicated can say whether it can be acted on or not. If the data is not communicated well, it can be lost in translation.
AI or machine-learning technology can bring huge benefits to any industry. Here’s an example of a healthcare organization that has adopted data-driven AI and has connected analytics to action.
- By analyzing large amounts of medical data, the healthcare organization’s AI is helping clinicians give faster and more accurate treatment to their patients, and has the ability to learn to make better decisions going forward. For patients, the AI-driven healthcare system alleviates some of the burdens on a system struggling to keep up with ever-growing demand. By implementing these technologies, the organization can make better health decisions, diagnose disease and other health risks earlier, avoid expensive procedures, and help their patients live longer- which are all actionable insights driven from data and analytics.
Moving forward, we will continue to see companies adopt new technologies, like AI, as a means to improve their bottom lines and their efficiencies.