As more enterprises and service providers adopt cognitive automation to improve their manual processes, reading the tea leaves or better yet, examining case studies suggests a new job landscape with some fairly drastic improvements in efficiency.
Harvard Business Review (HBR) provides a useful summary article explaining how to deconstruct work into tasks that can be automated. Here three characteristics are used to assess our work:
- Repetitive vs. variable work;
- Dependent vs. interdependent work; and
- Physical work vs. mental work.
Any automation assessment model should also take into consideration the nature and complexity of both the inputs and outputs onto which we can overlay these three characteristics to assess the impact of automation based upon the nature of work itself.
Basic automation has arguably had the highest level of impact so far. It is applied to rote, highly repeatable and low variance tasks. For example, basic automation supports the IT back office such as regular back-ups of data or automated provisioning of software resources (such as email accounts and CRM access).
These activities can be highly automated due to the nature of the work and low probability of exceptions to workflows. The inputs can be highly structured with very little variability while outputs are often binary. The result is either a successful completion or an exception. These tasks are very independent with interactions typically only with application interfaces. There is very little mental effort required.
Dealing with Complexity
“Cognitive automation” deals with complex tasks where more advanced machine learning is employed to supplement or replace the rules-based approach used to automate rote processes. Take, for example, customer service. Customer service involves interactions with customers and other staff internal to the organization. Chatbots are often presented as an example of complex activities where machine learning and natural language processing are put together to enable automation of certain areas of customer engagement whether it is to answer a question about a product, provide updates on account information or to handle a support issue.
The level of variance of input and output associated with these activities can be enormous so machine learning—with its ability to analyze massive data sets—is employed to solve the problem. However, can a chatbot replace a customer service or technical support staff? The answer, at least today, is decidedly “no.” The true effectiveness of a chatbot—to determine the customer’s mood, adjust the interaction and understand when to escalate to a higher authority—is limited. As a result, chatbots are often used as an assistive technology to enable support or service representatives to manage a larger workload queue and to focus on high-value activities.
Instead of using cognitive automation to completely replace human activities, it is often most effective when it is used selectively. A recent Everest Group blog has identified examples of “one robot per employee” where the emphasis is on automating discrete tasks of knowledge workers instead of attempting to apply automation to an entire workflow. For example, the task of meeting-setting can be automated where a virtual agent coordinates identification of different attendee availability.
Cognitive Automation Trends
Even though there are foreboding articles and studies on the looming massive job loss due to automation, we are still far away from wholesale replacement of most jobs due to their complexity. Ultimately, the impact of cognitive automation to jobs—either positive or negative—is based upon key factors regarding the nature of the work. The characteristics of the inputs and outputs of most processes along with the level of complexity of the discrete tasks necessary in a majority of work done today by humans makes replacement unlikely in the foreseeable future.
For a more in-depth look on this topic, check out Greg's article in Future of Sourcing, "Cognitive Automation: The Next Panacea or Evil Job Bandit?"