One key challenge for leaders today is how to reimagine business processes so that the enterprise is better prepared to compete and deliver on their growth targets. Developments in techniques such as machine learning, artificial intelligence and process automation bring the promise of transforming operational practices in ways that produce new levels of proficiency, savings and service. These technologies can be instrumental in meeting corporate strategic objectives for an improved customer experience and greater efficiency. As a result, a growing number of companies are transforming workflows in innovative ways and achieving significant, measurable performance improvements.

Artificial intelligence and machine learning are two important innovations that can drive business process transformation. The terms are often used interchangeably, but they are not quite the same thing. Both surface when the topic is big data, analytics or workflow automation, and understanding the differences is important. Start with artificial intelligence as the broader concept: machines being able to carry out tasks in ways that we would consider “smart.” These tasks, however, are performed with instructions that humans provide. Think of machine learning, on the other hand, as an application of AI: when given access to data, smart machines learn for themselves without being directed.

Trends Toward Transformation

According to IDC, worldwide spending on cognitive and artificial intelligence (AI) systems will reach $19.1 billion in 2018, an increase of 54.2% over the amount spent in 2017.1 Another study from The Economist Intelligence Unit found that 75% of executives say that AI will be “actively implemented” in their companies within the next three years, and 85% believe that AI is their path to sustain competitive advantage.2 Clearly, we are at the beginning of the next wave of an industrial revolution driven by AI.

While digitizing business processes may seem like it’s all about technology, the transformation that matters most is strategic. Research conducted this year by AIIM (Digitalizing Core Business Processes) found that 75% of the organizations surveyed view process automation as “important” or “very important” to the survival of their organization. Respondents indicate that reduced IT and administration costs are prompting workflow automation initiatives initially, while extended strategic benefits in customer-facing activities such as improving customer correspondence with increased personalization, onboarding, and case management are also driving transformation plans.

Where do organizations find immediate benefit? Organizations are adopting techniques to digitize their core services and processes in these common areas:

  • Application reviews and approvals (48%)
  • HR processes (36%)
  • Customer correspondence (32%)
  • Finance (29%)

Applying Artificial Intelligence and Machine Learning To Your Business

How can you use artificial intelligence and machine learning to automate core processes and innovate digitally? Below are a few areas to consider. These may or may not be relevant to your organization. The key point is that there’s a good chance your enterprise has routine business processes — many of which are manual and paper-based — that can be automated leveraging artificial and machine learning systems. Such initiatives can enable your employees to devote more time to serving customers and other higher-value work.

Invoice Processing — Accounts Payable is a fundamental activity for consideration. But as other areas of business have become increasingly paperless, the accounts payable process in many organizations continues to be plagued by the burden of paper and manual workflow. AI and ML can be applied to automatically capture critical data, eliminate manual intervention and automate the workflow to increase the speed and accuracy of the process.

Claims Processing — The activity of processing claims has a number of challenges. Capturing data accurately and efficiently is a must — as is managing that data with high precision and ensuring throughout the process that you are meeting constantly changing information governance and regulatory compliance requirements. To meet these challenges, organizations are applying business process automation using Artificial Intelligence and Machine Learning to more accurately and efficiently process claims with higher levels of security and governance.

eDiscovery — Machine learning has transformed electronic discovery, making it less costly and more efficient. Techniques like predictive coding have significantly reduced the amount of data that ultimately needs to be reviewed, saving a great deal of time and money in the process. It has also transformed machine translation, transcription, optical character recognition and other technologies employed in eDiscovery.

Digital Mail — A digital mail room is one where all incoming mail is processed using advanced document scanning and data capture technologies. By digitizing incoming mail, companies automate the classification and distribution of information within the organization. While the legal admissibility of scanned documents was once seen as a barrier to digitization, the reality today is that exceptions only apply to a small minority of documents.

Best Practices

With all the different approaches to artificial intelligence and machine learning, it can be difficult to know where to start. Highlighted below are three best practices to consider. To maximize these and other opportunities, you may want to consider engaging a managed services provider as a consulting and/or outsourcing partner in assessing and implementing these types of solutions.

Capture data beyond “scan and store” — For many people “digitization” tends to imply scanning a piece of paper and storing an image file. But what is needed today is a much more expansive approach. This begins by analyzing your documents beyond simply identifying rudimentary data elements like type, date and account number. With expanded data points identified you are in a better position to automate the process and apply machine learning in ways that matter.

Analyze your data for process improvement opportunity — This practice is about assessing your data with the goal of streamlining operational efficiency. It’s an approach that can help drive innovation and cost containment. What information do we have? What is its value? How can the information be used to make our enterprise more resourceful, profitable and future-ready? Gaining more timely and precise visibility into key business processes — for example, by using a near-real-time performance management dashboard— allows you to make better business decisions about how to continuously improve performance and more accurately predict results.

Put the “intelligent” into intelligent information management — Advanced technologies like machine learning, AI and robotic process automation all work to streamline the process of classifying, locating, extracting and verifying data. The next step is human: bringing intelligence and insight into the mix. For that to happen, the systems that can prepare data for analysis and run queries against the information need to be in place.

Artificial intelligence and machine learning are important tactics to help you meet your strategic objectives. Understanding their benefits and limitations as well as engaging in an initial assessment of your current state are the first steps toward identifying the areas where you can realize the greatest return. If done right, these two technologies can be game-changing for your organization.

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