Artificial intelligence (AI) is transforming work as we know it, but how do executives embrace idea of an autonomous enterprise? In this article, we’ll explore the characteristics of a self-driving company, where to start with your vision and how to lay the groundwork for an autonomous future.
The autonomous enterprise is a self-driving business that has applied artificial intelligence and automation to the problems of operations and management. Stages of self-sufficiency are marked by the ability to operate with less human intervention, and a fully autonomous enterprise is one that is able to configure, monitor and maintain itself independently. Correspondingly, it’s capable of learning, adapting to changes and intelligently automating.
For example, AI is helping hotels implement hyperdynamic pricing that factors in news events, weather predictions and trending Google searches to generate minute-by-minute pricing adjustments. Add the Samsung Neon artificial human or Alexa at the front desk, autonomous security drones, and selfcleaning rooms powered by Roomba and you have an autonomous enterprise. Think autonomous applications supporting autonomous devices and automated services.
While the autonomous enterprise might sound like a futuristic ideology, the technologies are widely available today, and new generations are suited for enterprise use. It’s only a matter of making everything work together and then coaching systems until you can trust them to perform unaided. The key is to align AI to your top business objectives and know which data streams coincide. Playbooks can then be used to tune the system to your best practices.
With continued training, the benefits can lead to total autonomy. Self-optimized performance metrics have the potential to accelerate responsiveness and time to market and can create more predictive customer service experiences.
Ingredients for autonomy
What makes up an autonomous enterprise? The first ingredient is AI-based analytics with actionable insights. AI should be applied to the business so it can start to understand what’s happening. Machine learning and behavioral analytics define normal activity on a dynamic basis and recognize events, patterns, anomalies, relationships as well as root causes. These baselines help AI advance from analytical tools into virtual assistants that can proactively advise and prevent problem recurrence.
For instance, insights can show that 90% of production stops occur on Tuesdays at 3 p.m., and the issue is IT network connectivity or bandwidth challenges. The system can also generate recommendations based on the most effective solutions used in the past. Moreover, it can act on its own suggestions using automation.
Reaching the milestone of true autonomy
The milestone of true autonomy is reached only when you add the second ingredient: automation. The autonomous enterprise must have a means for taking corrective action in response to the intelligence gleaned.
The latest tools are far better at designing complex workflows and acting on behalf of staff more flexibly and reliably than previous ones. Today, they have the ability to act widely enough to actually ease the burdens of management. So, how do you know where to start?
Avoiding mistakes and building foundations for autonomy
Don’t make the mistake of developing a self-driving product for consumers before you have mastered a sense of autonomy for those within your own walls. It’s far easier (and faster) to build a self-driving vehicle using a self-driving enterprise.
One good place to start is with your IT department or with any functional area that is plagued by manual administrative processes, yet has reached a digital maturity where the underlying systems and infrastructure are programmable using software-based controls. IT networks are undergoing a transformative phase in which legacy systems are being displaced by software-defined infrastructures or SD-wide area networks (SD-WAN).
By starting at the digital core of the enterprise, executives are establishing foundational environments built entirely on software-defined IT infrastructures. First, these environments are ripe for experimentation with AI-based automation, known as AIOps (AI for IT operations). Use cases are plentiful and benefits are known to accelerate the pace of IT.
According to Gartner, “Organizations that automate more than 70% of their network change activities will reduce the number of outages by at least 50% and deliver services to their business constituents 50% faster.” Second, the IT team will be instrumental in helping champion autonomy as executives gain confidence, applying AI to wider production and operations.
Furthermore, these environments are flexible, allowing entirely separate pilot infrastructures to be created for security segmentation purposes. This precaution cannot be overstated, particularly when experimenting with large streams of customer data and sensitive information.
AI is breaking new boundaries, from black boxes that predict patient death to computers that can understand emotions and even read your mind. We may be mesmerized by the way forward-leading products can change the world around us, but it’s particularly important for executives to think rationally and strategically about where to start with autonomy and to consider that IT environments are ultimately the bedrock for the digital transformation that autonomy represents.