With mass cloud adoption and advances in artificial intelligence and machine learning, we have entered a new stage of automation sometimes called hyper automation. While many IT leaders have traditionally thought of automation primarily in terms of rules-driven, deterministic robotic process automation – think end-user monitoring, log monitoring, and systems and app monitoring – it’s time for organizations to expand their definition of automation.
Today, automation needs to encompass the entire enterprise and be deployed at scale. It should cover:
- Automated process discovery, which uses data mining to visually model processes, analyze process variances, and automatically identify opportunities for improvement. This often entails leveraging business transaction data from enterprise systems to map processes such as order to cash, procure to pay, or automated ticket data analysis using AI and natural language processing. This helps correlate incidents and root causes for quick identification of automation opportunities in the service-management process.
- Business process automation, which can now be done in an end-to-end manner with technologies to support both rules-based process steps as well as data-driven decision analytics for next-best-action for assisted automation.
- IT process automation, which can include simple and automated system health checks, automated provisioning of infrastructure for cloud-based applications, proactive monitoring of jobs, and taking process automation further to self-healing that is orchestrated through the service management processes.
- AI-enabled end-user automation, where human cognitive skills like voice-based automation such as voice recognition to sentiment analysis, probabilistic nature of unstructured form recognition, and unstructured email and chat analysis are added to software bots to enhance the ability for end-to-end process automation.
Whatever type of automation strategy an organization chooses, in today’s business environment, it needs to be comprehensive, domain-focused, and business-oriented, with clear objectives aligned to business KPIs.
When we think about a modern hyper-automation strategy, we think about automation that is “intelligent” across applications, IT operations, and business functions. This means that it has:
- A cloud foundation, with a flexible, cloud-based architecture for accelerated deployment and rapid time to market
- AI infusion, with AI technologies to leverage self-learning capabilities and proactive decisioning
- Observability, with the ability to move beyond classic monitoring to embedded monitoring to make applications observable
- Central orchestration, with platform-enabled optimization and utilization
- Digital dashboards, with the real-time availability of operational, service analytics, and transaction data
- Flexible tooling to eliminate vendor lock-in and enable freedom of choice
- Low-code/no-code to ensure business user-driven development for greater speed
- DevSecOps processes that gather together agile process and automation tools and enable security every step of the way.
With the possibilities of automation today, IT leaders can no longer take a one-size-fits-all approach to automation. They need to be strategic and look at their entire portfolio to establish a vision and a roadmap that spans domains. The consumer goods value chain is a great example of how automation can be leveraged differently across various domains. For example, automation for the consumer products planning and procurement function may consist of AI for stock replenishment, pricing decisions, and sales forecasting. On the other hand, the sales and marketing function may be focused more on sales support chatbots, voice and customer authentication, and personalization. This doesn’t mean that everything needs to be implemented all at once. Organizations can start with a few quick wins as they work on implementing use cases with greater complexity.
Business outcome-focused automation
As IT leaders embrace new forms of automation, they need to reconsider how they measure success. Gone are the days of task-focused KPIs. Rather, leaders need to establish and track business KPIs that measure the success of their initiatives. For example, a global life-sciences organization leveraged automation to achieve 75 percent improvement in the process accuracy and eliminated sales order creation failure, 70 percent improvement in order creation turnaround time, and a 90 percent reduction in delayed dispatch.
Before getting started on a hyper-automation initiative, organizations need to ensure they’re embarking on a strategy that can scale. Trying to build from scratch without leveraging predefined tools, processes, and templates will make it difficult to deliver a hyper-automation strategy that drives results.
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