In a world characterized by consumer experience as the primary competitive advantage, digital transformation is a strategic imperative for organizations across industries. Businesses have shifted their view of digital technology from a tool to an essential element across operations. This mindset is crucial to their ability to drive customer excellence, redesign how they deliver services, and discover new areas of growth.
While efficiency is the obvious benefit of digital transformation, value creation must be the holy grail. With the capability for change at speed and a high standard of quality, AI-led automation is fast becoming a critical factor in this transition. From facilitating the redirection of budgets to new products and services to freeing up employee time for value creation, automation can create progressively substantial value for companies. Unfortunately, the proliferation of automation has outpaced the quality of its execution at scale. There are many reasons for this.
An accomplished automation program requires several aspects of a business to come together simultaneously. Companies looking to incorporate automation into their digital transformation roadmaps must organize their enterprise for success from a stakeholder standpoint. Security, governance, and compliance management must complement comprehensive infrastructure. Most importantly, it is critical to identify the right processes to automate, those that can generate the most value. An in-depth understanding of those selected with all scenarios mapped out based on real execution data can provide the baseline for automation program design. Artificial Intelligence based analytics can deliver measurement and optimization, which also helps forecast accurate RoI. These areas are also most frequently the stumbling blocks in automation initiatives.
Consider, for instance, a large bank where automation initiatives didn’t reduce the AHT (average handle time) because exceptions were omitted in the roadmap. In another case, a logistics company might not see the RoI of automation because of the wrong process choice, one with many wasted steps. Each of these omissions and information leakages can have a substantial impact on the outcome of the automation process. So, why do the best-laid automation plans often go awry?
The primary concern is incorrect process identification and, in the case of the right selection, a skewed understanding of functioning. Conversations with subject matter experts and process leaders yield anecdotal inputs. With process definitions based on best-case scenarios, a rare occurrence in real execution, these inputs are frequently contaminated by human bias. Furthermore, this incorrect process mapping also omits day-to-day exceptions. Documented SOPs almost never reflect the reality of execution since they don’t account for challenges, exceptions, and real-world scenarios. In the case of outsourcing, partners are usually reluctant to reveal in-depth automation process and reports, if any, tend to be at a surface level.
Second, is the ineffective change management process, again based on hypothesis and conjecture. These issues can lead to a breakdown in the delivery process, inefficient goal setting, and inaccurate metrics for progress. The fact of the matter is that a manual approach to automation is inherently flawed.
Empirical data can address each of these challenges. Gathering and leveraging empirical data is possible through process discovery. Here’s how it works. Process discovery bots monitor an organization’s business process for a specific period to collect data on how individuals use various applications to perform tasks. AI-based analytics are then deployed to analyze the data and recommend the best candidates for automation through an evaluation of time, effort, and financial expenditure. Combining real execution data with best-in-class AI analytics, process discovery enables leaders to have a nuanced view of actual process functioning, allowing them to make informed decisions about their automation roadmap.
AssistEdge Discover is a process discovery product designed to serve this need. Its smooth yet straightforward user interface and design allow the tool to deliver powerful insights through a non-intrusive process of tracking keystrokes and clicks. AssistEdge Discover creates a comprehensive bank of observational data based on the user keystrokes using a single point of truth to help organizations identify processes that are tangible and material to the business, and most conducive for automation. Additionally, by gathering information at the execution layer, the tool tallies all possible variations using proprietary machine learning algorithms, accounting for exceptions and workarounds.
AssistEdge Discover combines this rich empirical data with sophisticated neural network algorithms to provide organizations with valuable and detailed automation roadmaps. These maps and the insights generated by the analytics engine create a robust foundation by not just distinguishing processes for automation but by creating automation workflows, making the design, development, and implementation of these programs quicker and more efficient. Thee workflows can then be moved directly to an RPA tool for a seamless automation process.
Automation can be an incredibly powerful tool across functions for businesses across industries. It is vital, however, for the quality of execution to take precedence over adoption so that organizations can unlock the real power of automation. Process discovery is integral to this journey. Powered by the ability to derive and analyze exhaustive and specific execution data, AssistEdge Discover takes the speculation out of the planning process to accelerate and amplify the speed and impact of automation.
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