The need for effective automation is real. Did you know, for example, that the average information worker spends only 2.8 hours a day engaging in high-value work? Much of the rest of their time is spent completing manual, mundane tasks, such as transferring data from system-to-system, or pestering colleagues for signatures and follow ups. Being forced to dedicate such an immense amount of time to manual work lengthens workdays, equates in the minds of employees to poor leadership, which has been found to be a leading cause of burnout and is horrible for bottom lines.
Over the last decade or so, automation software has arisen to help reduce manual work. But what we’ve seen is that automation alone doesn’t solve the problem. Most automation tools on their own are ineffective at automating the complex processes that employees follow, which depend on connecting to many different combinations of systems and vary tactically from user to user. Most out-of-the-box task automation software is not easily customizable, modular, nor composable enough to fit the needs of unique users and teams, and so it doesn’t empower companies to optimize processes end-to-end.
The best way to avoid these shortcomings is to strive to build, implement, and manage automation software in a way that puts people first.
What Is The Need for People-First Automation Software?
Our strategies for using automation should be guided by a goal of empowering people with the kind of dexterity, enablement, and agility that they need in order to work smarter. Such automation technology must itself accommodate and adapt to users’ unique working needs and preferences. Functionally rigid or inaccessible tools won’t do. Users should benefit from an organization’s use of automation software without having to learn how to navigate a new UI or how to write code. Ideally, they should be able to compose their own workflow solutions combining elements of automation with that of other advanced technologies to extend their capacity in whichever way works best for them.
In practice, composability, made possible in part by a people-first approach to automation, increases the speed and scale with which you’re able to build and deliver solutions, as well as adapt to new and shifting business needs. It reduces the burden on your developers to write custom code for every business need. And it does this by effectively expanding the pie of who internally can design and deploy software from solely developers to essentially every business user with a laptop. Users are able to devise their own means of eliminating manual work in their specific contexts.
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That’s the kind of business enablement we all want to provide our people when we buy them things like task-automation tools. But it’s the kind of enablement we can only actually deliver when we think people-first. And the benefits are immense. As Amit Zavery, VP and head of platform for Google Cloud, wroteOpens a new window , “When the ability to create business applications is extended beyond IT to the people closest to the challenges…the speed at which a business can move and the number of people working on solutions can both increase dramatically.â€
At Tonkean, we’ve seen this first hand. Last year, at the onset of the COVID-19 pandemic, an enterprise client of ours was asked to turn around a new technology solution for a client of theirs in the healthcare space in just 3 weeks. Their IT team informed them that the solution would normally take 6-9 months to build custom. Smart use of composable, people-first automation allowed their Solutions team to build the solution in just 9 days.
People-First Software = Customized, Componentized, Adaptable
One important component of a people-first approach to software is the ability of the software to learn and internalize user preferences, such as where notifications for them should be sent, or what systems individuals prefer working in. One way this is facilitated is through the use of NLP technology, or natural language processing, which refers to the ability on the part of computers to understand and act upon written text and spoken words.
This is key in order for an automation solution to act upon data that only lives in people’s heads—a key roadblock in the ability of automation to truly increase operational efficiency to this point. It’s also integral to scale.
We recently worked with a travel-booking site that transitioned from form-based to chat-based support as part of an initiative to improve customer support. With the improved customer experience of real-time support also came the pressure of handling higher volumes of requests in a timely manner. To ensure that each support request was receiving the right level of attention, the site needed a way to properly analyze each incoming chat in real-time, tag each conversation, and then route the conversation through the proper handling process.
Analyzing and tagging conversations manually required a significant amount of effort from support agents, and in turn, took time and attention away from serving customers. Additionally, each support agent needed to work across multiple systems and interface with many different teams such as customer service teams from other airlines to ensure each request was properly handled. Doing that manually also added complexity and time to resolution for each request, which significantly limited the amount of requests each support agent could handle.
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The only way they were able to conquer this complexity was by taking a people-first approach to automation. One way they did this was by using NLP to auto-handle the task of analyzing and tagging conversations. They used Tonkean’s process orchestration tools to automate additional routine requests, along with complex ones, such as the initial information-gathering processes of more involved tickets.
Likewise integral to people-first automation is a capacity for adaptivity. This was likewise exemplified by the travel booking site. Before, when a support ticket came in from a traveler whose flight had been canceled, for example, the travel booking site would analyze the text of the conversation, tag the ticket appropriately, and send it to the person best situated to solve the problem in question. When COVID-19 hit, however, the number of support tickets they were receiving due to canceled flights, fickle governments, confused customers, etc. quadrupled. The processes they had in place, suddenly overburdened, were liable to break down.
But because they had designed not just this specific automated process, but all of its automated support processes to be adaptive conducive to amelioration, welcoming of quick fixes its support team was able to update the workflow themselves so it could identify which kinds of incoming support conversations were related to the coronavirus, and respond back automatically to gather more information, thus deflecting call volume from support agents. Upon completion, they then automatically routed those tickets to the correct personnel.
Simply put, in order to use something like automation to actually increase efficiency or effectiveness, it must operate foremost with people in mind. It must be so dexterous as to accommodate peoples’ unique needs.
That’s key for ensuring our technology works for us, rather than forcing us to work for our technology.
The future of automation inside organizations will be bright if we work to achieve that goal.