Organizations today are navigating an unprecedented degree of change. From evolving customer demands, complex legal and regulatory environments, increasing amounts of data, and an unexpected global health crisis, organizations of all sizes and across all industries and geographical markets are being challenged in ways they have never seen before. It is especially during times of change when processes can fluctuate greatly, and organizations need to be agile and timely in effectively responding, adapting, and overcoming these changes to best serve their customers. Increasing efficiencies, monitoring risk, enhancing compliance, and maximizing resources are more critical than ever. While traditional methods of process optimization have their uses, a new generation of solutions is needed to help meet new demands, better serve clients, accelerate digital transformation, and thrive during economic challenges.
How do processes impact a company’s bottom line?
Enterprises have hundreds, if not thousands, of processes that they depend on. Across every operational function, from customer service, accounts payable, new business development, claims processing, to onboarding, document workflows, risk management, and other mission-critical areas, processes are what move a business forward and drive growth. Processes that are ineffective are costly and can have significant business impacts, including reducing efficiencies and negatively impacting the customer journey. According to a Forrester study, for example, three out of every 10 customers who had a bad claims experience switched insurance carriers within a year of the incident.Increasing efficiencies, monitoring risk, enhancing compliance, and maximizing resources are more critical than ever. Click To Tweet
Inefficient workflows and poor customer experiences caused by bad processes can impact a company’s customer retention rate and, by extension, their bottom line. Traditional methods of trying to understand and get to the root cause as well as ongoing, real-time monitoring of these issues are fundamentally flawed and add minimal analytics for process-specific data. This has demonstrated the need for advanced process analysis methods to provide modern organizations with complete and accurate visibility into how their processes are truly performing.
Traditional methods of analyzing processes have limitations
Manual Process Measurement Methods: Some organizations execute their process measurement initiatives in an entirely manual style. These programs often expend valuable internal personnel resources or require engaging outside consultants, which can be costly. Additionally, because manual methods require significant time and resources, the number of processes that can be analyzed via this method is often very limited, and the resulting analysis typically only provides partial visibility into actual operational workflows.
Manual process discovery methods are cumbersome and time-consuming, requiring personnel to manually observe and time each individual process. This would include conducting interviews, manually logging, merging and uploading data into spreadsheets, and creating a map of the process flow; and then identifying patterns based on that data. This is commonly performed by using sticky notes to “walk the wall” for process discovery. This method can take several months, sometimes longer, and by the time the data is collected, sorted, and analyzed, the processes that were being analyzed will likely have changed. The extensive amount of time spent to discover and analyze the processes ends up not providing the expected value. Furthermore, the data this method yields is usually incomplete and subjective and is a snapshot of what someone observed in a particular time and at a single location or potentially biased by the opinions or assumptions of the personnel executing the process.
Business Intelligence: Business intelligence (BI) platforms are highly valuable for providing insight into specific systems and functions they govern but are generally limited to the analysis of a single step of the process. However, BI tools provide no mechanism for the analysis of how those discrete process steps (or tasks) relate to or impact the other steps of the process. This limitation results in these tools’ inability to identify the root cause of why a process is underperforming and where corrective action may be required. Additionally, BI systems can provide traditional KPI-type metrics, but lack the most crucial dimension of a process execution: time.
Process Mining: Standard process mining software technologies can be a good fit for basic, well-behaved process types, but these platforms are often not comprehensive, agile, cost-effective, or complete. They do not provide a 360-degree view of how business processes are functioning in real-time with numerous methods of visualization and analysis. These limits become apparent when processes fail to conform to a rigidly defined process schema map. This results in an inability to provide insights for complex, ad-hoc processes not able to be represented by a trivial process diagram. Traditional process mining tools also often involve extensive personnel resources, with some platforms requiring anywhere between 10 and 20 employees to properly implement, manage, and maintain, resulting in a deployment process that can last for months. For all the effort required, traditional process mining tools further disappoint by failing to deliver the breadth of process analytics necessary to fully investigate the myriad variations from one process type to the next.
In the end, many of the traditional process mining tools are just that – tools, and not complete solutions. Think of it this way: it is useful to have a hammer when you have a nail, but good luck trying to sink a screw without a screwdriver. The same can be said when enterprises try to utilize broken methods as solutions to complex business problems – it doesn’t work.
What is process intelligence? And why is it needed?
Process intelligence refers to solutions that provide accurate end-to-end visibility into how business processes are functioning in real-time, across different silos, multiple systems of record, departments, systems, locations, and data input sources. Process intelligence utilizes a timeline-based discovery methodology to provide a dynamic and detailed view of how processes are performing over time – regardless of the process complexity or variability. These solutions are powered by artificial intelligence (AI) technologies, which enable comprehensive analyses and advanced capabilities, such as predictive analytics. process intelligence is a new approach that is needed now more than ever as organizations have unprecedented amounts of valuable data circulating within the enterprise via increasingly complex and inter-connected business-critical processes.
Process intelligence provides capabilities that are more advanced than traditional methods of analyzing organizational processes. Key differentiators and benefits include:
Timeline Visualization: Process intelligence provides visibility into every operational process, even those that are ad hoc or complex. These solutions connect with any back-end system, including databases, legacy systems, trading partners, and vital systems such as ERP or CRM. The platform then extracts data from these diverse systems to provide a dynamic timeline visualization of processes. Forgot to add a system? No worries, users can add new data sources at any time and further develop their analysis. No need to rebuild the source data; events fall in place as they happened.
Process Analysis: In order to meet the unique requirements for a diverse set of process types, a process intelligence tool must provide a variety of best practices-based analysis tools that are immediately available to the process analyst. This includes comprehensive tools for analyzing queue-based workflow environments through a pre-built collection of analyses to detect and measure all touchpoints and work queues, identifying misroutings, redundant or missed steps, process aging and consistency of performance.
Dynamic Process Query: The ability to provide a comprehensive yet simple graphical interface that enables users to define specific process pattern search conditions (task sequence, timing of event transitions, etc.) and find the exact processes that match the criteria. Something that would traditionally be nearly impossible, or require pages of SQL code can now be completed with a few clicks of the mouse and get results with subsecond response time.
Process Monitoring: Process intelligence platforms both monitor and analyze processes to ensure every step of the process is followed and identify any potential deviations from the ideal path. When they don’t, tasks can be alerted with soft nudges to staff, or the explicit triggering of an action in any system the enterprise is using.
Operational workflows can be challenging to identify in their entirety, let alone analyze and optimize when done using traditional methods. Process intelligence platforms can connect with disparate systems to expose 100 percent of an organization’s critical processes. Additionally, they use the information that already exists within an enterprise to create a visual model of workflows, analyze them in real-time, generate real-time alerts to process deviations, evaluate potential fraud, identify bottlenecks, monitor for risk and compliance, and predict future business outcomes.
Process intelligence is transforming today’s leading enterprises
Process intelligence should be leveraged in any organization that is very process-oriented and document-intensive, such as financial services, insurance, and healthcare. A leading Fortune 100 financial services firm with more than 5 million clients used process intelligence to strengthen its compliance measures, resulting in reduced exposure risk, both in terms of PR and from a regulatory compliance perspective. The solution enabled the organization to monitor 100 percent of transactions with only three full-time personnel. With traditional methods, the initiative required close to 16 full-time staff just to perform a 15% statistical sampling and would take longer to yield insights. Leveraging process intelligence, the firm was able to enhance compliance while experiencing a direct savings of $2 million annually.
In the healthcare sector, process intelligence is being used by a major U.S. health insurance provider serving 8 million members to ensure that customer service inquiries were handled as efficiently as possible. Customer calls, emails, and website inquiries were routed to over 300 process queries and were classified based on the type of request. process intelligence was able to centralize data from disparate systems onto a single view of each customer’s journey, allowing the processes to be reconstructed in full. As a result, the organization realized its operations were not working as initially envisioned. The total number of customer touches was 2.6 times higher on average than expected. Using advanced methods of process optimization, they were able to accurately identify deviations in its workflows, including errors in routing software and pinpoint areas to be improved with modest investments in training. This resulted in significant cost savings and improved customer experience.
Do more in the enterprise with digital intelligence
With all the operational benefits process intelligence provides, it is prudent to recognize that digital intelligence is a holistic view of an organization’s business processes, people and the information that drives them from a variety of critical perspectives. So having an understanding of not just your processes but also all the critical information locked within unstructured content is critical to a digital transformation strategy. It is dependent on having real-time access to all your business-critical data no matter which business process platform it lies within. This includes the vast amount of data that exists in various business documents including claims, invoices, proof of delivery, loan agreements, contracts, orders, identity documents, tax forms, pay stubs, utility bills and more.
By applying OCR, machine learning, and NLP to content, data from within these document processes is transformed into actionable data and instantly made accessible to the decisions being made within a process. With digital intelligence, organizations gain the valuable, yet often hard to attain, insight into their operations that enables true business transformation. With access to real-time data about exactly how processes are currently working and the content that fuels them, digital intelligence empowers enterprises to make a tremendous impact where it matters most: customer experience, competitive advantage, visibility, and compliance.
Scott Opitz is Chief Marketing Officer driving the development and execution of global marketing strategies. He joined ABBYY with the acquisition of TimelinePI, for which he was co-founder, President, and CEO from its inception. In this role, he oversaw the integration of TimelinePI’s process intelligence products (now ABBYY Timeline) into ABBYY’s worldwide sales and distribution channels.