Note: This is the second of two guest blogs from Everest Group analysts on aware automation.
In a previous blog post, we explored the evolution of enterprise IT infrastructures from a cost-center positioning to one that enables digital transformation through a concept known as aware automation — a combination of intelligent automation and cognitive/Artificial Intelligence (AI)-driven automation. In this post, we’ll explore some potential use cases and best practices for aware automation within the enterprise.
Exploring Aware Automation for Enterprises
Leveraging automation, analytics, and AI to drive an aware automation initiative enables the introduction of a broader and more complex set of operational use cases into IT infrastructure services automation. As adoption levels scale and processes become orchestrated, the benefits potentially expand beyond cost savings to exponential value around user experience enrichment, services agility and availability, and operations resilience. Put another way, it’s an expansion from cost reduction-focused use cases to ones centered on driving business value with aware automation (see Figure 1).
Given the potential benefits on offer with aware automation, nearly 73% of large enterprises have included intelligent automation as part of their broader IT services adoption strategy1. Of these, 32% have already moved beyond the pilot stage and have achieved meaningful/scaled adoption across their organizations1. Enterprises are beginning to realize that traditional automation has limited value to offer and the transition to aware automation is necessary to drive superior business performance and brand equity.
The chart below shows how cognitive automation can address more complex use cases in an enterprise’s IT infrastructure landscape.
Establishing a Business Value Measurement Framework for Aware Automation
Aware automation helps enterprises focus on the holistic business value across three broad dimensions: cost impact, productivity impact, and user experience and innovation impact (see the chart below for some examples and connected metrics). To know whether aware automation is proving its worth, enterprises need to create a framework, based on an organization’s tactical needs and strategic plans, for identifying success metrics and measuring ongoing progress.
Exploring Challenges and Best Practices
As enterprises look to scale an aware automation initiative, they must redesign their IT operations and processes. As a best practice, before an enterprise decides to implement aware automation, it must prepare/identify datasets, tools, people and use cases that will have a significant impact. The figure below shows some of the challenges that enterprises typically face while adopting aware automation, which can limit efforts to realize benefits.
To overcome these challenges, enterprises need to devise a holistic cognitive automation adoption strategy, which should include several elements:
- Continuous governance to track the progress of cognitive automation initiatives
- Drive hiring and re-skilling/cross-functional training initiatives for skill development
- Drive necessary change management through gamification and change champions
- Dedicated data management team to train the cognitive model
- Avoid technology and knowledge silos to reap full benefits of cognitive automation
- Adopt a lean operating model for better outcomes
The dynamic requirements and complexities associated with today’s IT infrastructure services warrant adaptive, self-learning and self-correcting systems. Re-imagining the IT infrastructure services layer by embedding aware automation holds the key to realize the true value of digital initiatives. However, to achieve breakthrough value, it is critical that enterprises embed aware automation within a tightly-integrated, lean operating model cutting across the IT infrastructure services stack. Additionally, enterprises need to have a well-defined implementation roadmap for aware automation, where they measure the effectiveness of aware automation initiatives on an ongoing basis and perform necessary course corrections.
To read more on the topic, please refer to Everest Group’s published report: Everest Group — AI Stands to Make IT Infrastructure Services “Invisible”
1 Everest Group survey with 200 CIOs / IT heads of large enterprises (>US$ 1 billion revenue)