Automation is a key enabler of digital transformation. It is in virtually every organization’s best interest to plan, build, and leverage strategic implementation of automated processes to significantly reduce the costs of routine operations and increase the speed and quality of service delivery. However, as innovation and applications of Artificial Intelligence (AI) continue to develop, cognitive automation has become one of the fastest growing technologies in the emergent enterprise landscape. According to the IDC, worldwide corporate spending on AI and cognitive automated systems will amount to roughly $98 billion worldwide by 2023.
What is Cognitive Automation?
Cognitive automation is an intelligence-driven technology where automation rules are defined by the machine’s ability to process and learn from unstructured data sets. Its defining trait is its ability to mimic human behavior; making use of AI-based machine learning (ML), contextual reasoning, and iterative self-correction via data training models. In other words, cognitive automation has the uncanny ability to learn, adapt, and get smarter over time.
How Does It Compare with Other Automation Types?
Cognitive automation is often compared to Robotic Process Automation (RPA), which is a form of business process automation that uses “software robots” to record and replicate the actions of human interactions as opposed to IT process automation which is based on programming. While slightly different in practice, both RPA and IT process automation offer similar benefits in being able to increase efficiency by automating repetitive tasks.
However, cognitive automation goes a step beyond both standard RPA and IT process automation in that it incorporates a combination of AI-based technologies such as machine learning, natural-language processing (NLP), predictive analytics, virtual chat agents (or Chatbots), big data processing, contextual analysis, and more. The result is a more flexible automation framework that can learn from and process data to make more complex decisions based on intelligence and reasoning.
Exploring the Differences
Now that we’ve explored the high-level definitions of cognitive automation versus IT process automation, let’s take a look at a few of the key differences between the two:
- Rules and Implementation – At a basic level, both IT process automation and cognitive automation require a certain level of programming to implement. However, the key difference is that IT process automation is governed by the configuration of a basic framework or logic workflow where pre-defined criteria will trigger a static action or set of actions. On the other hand, cognitive automation is an intelligence-based technology where the machine is able to define its own rules based on the data it collects and learns from.
- Use and Function – IT process automation is best applied when automating simple, repetitive tasks that don’t require human intervention. For instance, you might consider using IT process automation to automate the creation of purchase orders, spawn tasks, send notifications, authorize approvals, or create reports. For organizations that manage workflows that require many steps, multiple stakeholders, and precise data entry, IT process automation is crucial in making those processes more responsive and efficient.
However, for more complex tasks that require human involvement, cognitive automation has emerged to fill those gaps. With its ability to learn and dynamically process data, identify patterns, and recommend solutions; cognitive automation can augment staff decision-making, speed up training for newly onboarded employees, and provide proactive solutions. These all work together to reduce or even eliminate staff involvement for certain tasks.
With the use of AI/Machine Learning in the enterprise on the rise, IT organizations are now leveraging cognitive automation to help drive faster decision-making and reduce operational costs. For example, ChangeGear, an IT Service Management (ITSM) platform, uses cognitive automation to give IT staff real-time assistance with quick resolution recommendations, knowledge augmentation, and predictive insights. The platform also uses sentiment analysis to analyze user satisfaction in real-time, so that IT staff can prioritize and escalate an unhappy user to an appropriate team. Check out some of these additional use cases of how AI and cognitive automation can be applied to the ITSM.
When comparing cognitive automation to IT process automation, it’s important to consider the different technologies, use cases, and goals you want to accomplish as each offers unique value. The best way to plan for implementing automation is to partner with a vendor that understands both the technology and your business; and has the experience to align the two to help you net a positive ROI.