Understanding AI/Machine Learning + Process Automation


Creating the Data Ecosystem for Your Transition to an AI World

When Alan Turing was developing the Turing Test for determining machine intelligence — what we today refer to as Artificial Intelligence (AI) — he first posed the question, “Can machines think?” At the time, this seemed preposterous, but technology advances in recent years now have us asking a similar question: “Can machines learn?” The answer is “yes,” as long as we supply them with enough of the right kind of data.

This ability to learn is a specific, data-oriented form of AI that has the power to dramatically increase productivity and streamline business processes. Companies are increasingly looking to AI and specifically machine learning (ML) as a way to improve their businesses. While many of these applications are still in the early developmental phases, companies must prepare now in order to successfully implement AI and ML in the future and take full advantage of the power of this technology. Specifically, organizations need to establish the data ecosystems today that will become the foundation of ML applications, training models and business process development in the future.

This white paper is the second in a two-part series on the impact of AI on process automation. In the first, we examined this impact at a high level. In this paper, we take a closer look at ML specifically, and the actions organizations need to take today to prepare for a high-velocity AI-driven world.

Understanding AI/ML and BPM

AI is a broad term that has been applied to everything from robots to self-driving cars to personal digital assistants such as Alexa. AI also promises great productivity gains for companies by integrating natural language processing and ML into business applications. Application developers are using the natural language processing features of AI to streamline human interaction with the machine through natural language interfaces and language translation.

With AI, organizations are able to streamline their business processes by automating routine tasks, improving user interfaces and quickly analyzing large amounts of data. Adding these capabilities to process automation helps reduce the amount of time spent on certain tasks, improving customer experience and reducing costs. Once fully implemented, the technology ultimately will enhance the K2 low-code approach to solving process automation issues.

Businesses are also implementing AI to automate tasks, specific decisions or even higher-level workflows, such as typical team assignments for various projects. These automated applications rely on machine learning to focus more on predictive data analysis and use algorithms that learn from data without relying on discrete rulesbased programming. This allows the machine to use the inference results from data patterns to direct an action that fits the true nature of the business as its been executing over time, instead of relying on discrete, hard-coded and possibly outdated rules.

While AI is gaining acceptance in some industries, many companies remain reluctant to adopt it as part of their business plans. Data science experts remain a rare — and expensive — commodity and most companies lack the financial resources to hire them. In many cases, the value and return on investment of these technologies remain untested and corporate executives hesitate to invest in an unproven technology with so many competing initiatives (e.g. cloud computing, cybersecurity). As a result, the leading adopters tend to be from the hi-tech, telecom and financial services sectors where fierce competition drives organizations to continuously seek new innovations that give them a competitive advantage.

Even so, no one doubts that AI and ML are the future of enterprise computing. This future, however, is coming fast and competitive advantage could depend on being ready sooner than your peers. Companies must begin preparing now so they will be ready to take full advantage of the increased productivity, improved decision-making processes and new ways to serve customers.

Laying the Foundation

Machine learning analyzes data flow in the enterprise. Anticipating this eventual capability, the most critical issue is how companies prepare the data that will be used as the basis for machine learning. Data scrubbing and formatting is one of the greatest challenges in implementing AI so companies should adopt clean data format methods now.

To address this challenge, IT and the lines of business need to organize around a specific business problem they are trying to solve. Defining that specific problem creates a direct correlation between the input, its format and shape, and ultimately the decision output. Therefore, understanding the outcome and the kinds of decisions that need to be made will determine which data sets you use and how you need to scrub, transform or clean them.

While you need clean data to populate an AI application, it should not be too perfect. This might seem counterintuitive, but at the core, AI is just algorithms that adjust their own inputs through weights as they learn over time. So, actual machine “learning” results from understanding and processing big sets of realworld data. This is typically achieved through what is called supervised learning. Explained on a high level, you take a large, clean, real-world data set and feed 80 percent of the data through the ML network, and then use the remaining 20 percent to gauge the accuracy of the predictions.

Finding the Low-Hanging Fruit

Companies need to look for opportunities where AI can make a difference and identify the most valuable use cases for AI. This could be considered the “low-hanging fruit” in an organization—processes that would benefit the most from automation and where good, clean data might be readily accessible.

The next step is building a solid technical infrastructure and investing in developing the digital assets for collecting and organizing the data needed for AI and machine learning. Businesses also need to integrate technology into workplace processes and develop a workforce culture that optimizes the interaction between humans and machines.

Finally, organizations need a common platform that allows IT and the lines of business to optimize the data that will be used to build the training models for ML. For example, companies using K2 are able to make process decisions more effectively (because they are based on historical data). The process metrics and reporting that the K2 platform provides can also be used as inputs to help make the ML algorithms learn even faster, often resulting in quick wins.

Companies that currently use K2 are in the best position to take advantage of emerging ML technologies.

AI and machine learning are poised to transform process automation. More decisions can be automated based on real business execution trends, freeing staff to focus on strategic efforts or direct customer service. Enterprises need to take steps now to ensure they have the systems in place to take full advantage of the technology’s ability to improve productivity and streamline processes.

Is your data ready for the future?

For more information or to request a demo, visit K2.com.