If there's one thing that successful companies know, it's the importance of delivering on customer needs today, while building the infrastructure and systems to be agile to meet tomorrow's evolving needs. Increasingly, leading organisations recognise that automating core processes - and eliminating manual work, reducing errors, and freeing up employee resources in the process - plays a critical role in meeting these goals.

Companies are turning to automation like never before, but the key question remains: where do I start, and how do I choose the right solution for my firm? With rapid advances in the areas of Artificial Intelligence (AI) and Machine Learning (ML), companies are reevaluating the usefulness of their legacy systems and dated approaches to document processing, as they look for better ways to unlock and parse through data, gain efficiencies, improve customer and employee satisfaction, and ultimately drive business growth.

One U.S.-based Fortune 500 insurance company, for example, was looking to embrace the latest intelligent automation to streamline processes, improve data quality and increase overall market competitiveness. When the insurer measured existing processes, many of which hadn't been updated in decades, it discovered that employees responsible for Accounts Payable processes were spending the majority of their time on manual document sorting and data keying.

Over the years, this 200-year old company had acquired multiple systems that couldn't talk to each other, despite the need for them to share data. They also couldn't handle the diversity of invoices and supporting documents from vendors, customers and others, resulting in processing backlogs and missed service-level agreements (SLAs).

With about 550 billion invoices sent annually across the globe, this problem is all too common. Despite the focus on digital transformation and data insights, the first mile of data processing - getting it out of disparate forms and documents into a useable, machine-readable format - remains overly complex, manual and expensive. The variability, poor readability (with handwriting, fax marks, low-quality images) and lack of standardisation of both paper and machine-generated forms make it almost impossible for outdated rule-based systems to reliably and efficiently read and process these pages for downstream decision-making.

The result? Bottlenecks, tedious data entry, clerical errors, and frustrated customers who demand a quality customer experience, but instead are left waiting for answers.

And this problem pervades nearly every data-centric industry, from banking and financial services to insurance and healthcare as well as the public sector, where medical or insurance claims, loans, bank statements, mortgage applications, policy certificates, tax and other forms must be processed day-in and day-out.

Fortunately, there are solutions available to streamline these processes. To find the right fit, companies should carefully consider solutions that deliver on the potential of automation today and can scale moving forward.

CONSIDER THE FOLLOWING OPTIONS:

• Patchwork legacy systems. Some companies attempt to fix the inadequacies of legacy technology by patching together various systems with custom code and applications. It is estimated that companies spend 70-80% of IT budgets on system maintenance, and even with investment, these systems struggle to accommodate unexpected surges in business, new use cases, or changing regulatory or customer conditions. This drives organisations deeper into technical debt, adding more cumbersome manual workarounds that fail to address - or re-imagine - the underlying business process. And, since these systems lack flexibility, as processes or needs change, organisations have to tack on more solutions.


• Rules-based automation. With the ability to automate well-defined processes and alleviate some of the burden of processing repetitive tasks, Robotic Process Automation (RPA) has gained popularity in recent years. However, RPA requires structured data inputs, meaning it doesn't have the flexibility and intelligence to extract data from diverse document sources and formats. Because of this, RPA is a partial enterprise solution that needs to be supplemented with intelligent technology.


• Machine Learning-based technology. Machine Learning, a form of AI, has the intelligence to handle increasingly diverse data inputs and complex tasks. Its ability to learn based on the real-world data that it's exposed to enables ML solutions to reliably extract and classify data from different types of forms, drive lower error rates and greater automation, and do it in a more scalable way that can adapt to dynamic inputs and changing business processes or needs.

The global insurance company referenced above chose the dual-solution route. After kick-starting their automation initiatives, they realised that a large portion of their workflow involves document processing, and they needed a Machine Learning solution to lift, unlock and structure the data from invoices for faster, more reliable downstream processing. What's more, employees that were previously spending their time manually entering data are able to focus on more strategic work.

True digital transformation through process automation begins with understanding your business goals and customer needs, and having the vision - and courage - to re-imagine existing approaches. Companies that automate outdated document processing practices with advanced technology unlock higher quality data - the critical first step toward faster, more informed business decisions and customer outcomes.

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