We’re hearing a lot about the “outcome economy”—how businesses must be results-oriented to meet evolving customer demand and gain greater market share. Today’s marketplace is connected, always on and increasingly competitive. Companies are adopting what we call an “as-a-service” approach to achieve better outcomes fast, consuming and leveraging leading-edge technologies such as cloud and automation. As IT systems grow exponentially, and cloud solutions proliferate, non-automated, manual systems increasingly are becoming a major business liability. Today’s systems are simply becoming too big and complex to run completely manually, and working without automation is largely unsustainable for many enterprises across all industries.

Automation involves a set of tools, processes and insights that allows IT environments to self-modify and adjust, and some enterprises have started using intelligent automation to drive a new, more productive relationship between people and machines. For example, IT automation is often used to auto-scale and load-balance large fleets of servers, manage global content distribution based on geographic demand, enable self-healing of IT systems and manage security mostly with limited ongoing manual intervention.

Moreover, automation enables the ability to adapt and improve the service experience without manual intervention. However, while these tools offer new strengths and capabilities, they are meant to complement and enhance human skills.

Effective automation depends on adequate insights collected from all the systems relevant to the service experience and business outcome you’re trying to augment. Insights from data is necessary to create opens paths to automated predictions and ultimately using machine learning, or artificial intelligence, as part of a full scope the as-a-service construct.

Specific insights known as telemetry allows signals to be harvested and interpreted so automation can better adjust production systems to maintain a healthy business. The insight gathered from such analytics allows automation to validate and compose modification rules. For example, sensors that detect a supply chain issue could automatically reroute or fine-tune related functions, such as dispatch or logistics, to solve or generate a workaround for the issue. The business flow can adapt and realign automatically with the ultimate goal of improving the customer experience.

Automation Creates High Resiliency

Two common business outcomes that depend on efficient automation are highly resilient systems and experimentation platforms.

Highly resilient systems include automation that can detect, avoid, heal and remediate any deviations from normal, healthy business function. To detect deviations, automation capabilities need to understand what the “steady state” of the system is and what constitutes the “health” of the system under varying conditions. For each detected deviation from an established steady state, a specific automation is triggered that attempts to return the system back to the steady state.

The best way to determine if resiliency automation works effectively is through a process known as “fault injection.” Highly resilient systems run under constant fire drills in which operations insert faults into the system while developers continuously build responding resiliency automation. Automation also can provide a higher degree of experimentation and increase agility, two key attributes of as-a-service economy. Automatically provisioning a component such as a virtual machine, for example, is only a piece of the puzzle since automation is most valuable when it contributes to improving a customer experience or delivering a business outcome.

A platform that’s constantly testing, experimenting and developing allows companies to try new ideas in production quickly without fear of failure or outage. When confidence in system resiliency is high, it allows businesses to test new things directly in production (A/B testing). If an experiment fails, there is no harm done as automation returns the system to steady state. If an experiment succeeds, it is quickly absorbed into the production itself.

A fast, efficient experimentation platform enables businesses to react faster to failures and successes—and pivot accordingly without excess wasted resources. For example, a retail company might change a shopping basket feature for 1 percent of its customers. With constant measurement and instrumentation, the company can automatically derive insights, determine if the change is effective and create a chain of automated reactions. If, say, the demand spikes for a new offering based on a limited customer pilot, the system can reset stocking levels ahead of geographic or further customer segment rollout. This ability increases a company’s agility and adaptability, improving the customer experience and delivering on the most important factors determining success in today’s as-a-service business environment.

Robotics Process Automation

More companies are turning to robotic process automation to eliminate tedious tasks, freeing corporate workers to focus on higher value work. But RPA requires proper design, planning and governance if it's to bolster the business.

What is robotic process automation?

RPA is an application of technology, governed by business logic and structured inputs, aimed at automating business processes. Using RPA tools, a company can configure software, or a “robot,” to capture and interpret applications for processing a transaction, manipulating data, triggering responses and communicating with other digital systems. RPA scenarios range from something as simple as generating an automatic response to an email to deploying thousands of bots, each programmed to automate jobs in an ERP system.

What are the benefits of RPA?

RPA provides organizations with the ability to reduce staffing costs and human error. Bots are typically low-cost and easy to implement, requiring no custom software or deep systems integration. Such characteristics are crucial as organizations pursue growth without adding significant expenditures or friction among workers.

Enterprises can also supercharge their automation efforts by injecting RPA with cognitive technologies such as ML, speech recognition, and natural language processing, automating higher-order tasks that in the past required the perceptual and judgment capabilities of humans Such RPA implementations, in which upwards of 15 to 20 steps may be automated, are part of a value chain known as intelligent automation (IA).

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