Digital transformation can be the difference between becoming the next Netflix and becoming the next Blockbuster Video. With corporate survival on the line, "digital transformation" is no longer merely an impressive buzzword to throw around in boardrooms. It's the ticket for entry into the digital era, a fundamental business strategy for every modern company.

Most companies are making the digital pivot, which shows in the growth of technologies and services supporting digital transformation. The global digital transformation market will reach an estimated $2.3 trillion next year with a five-year (2019-2023) compound annual growth rate of roughly 17%, according to the International Data Corporation (IDC).

While the global business community is spending heavily on digital transformation initiatives, many enterprises fail to adopt one of its critical components: automation. A mere 15% of organizations identify themselves as adopters or leaders in automated processes, finds a Dell Technologies report. For enterprises chasing digital transformation, ignoring automation is a problem.

The Importance of Automation

Organizations digitizing without automating impose severe limitations on their own transformation efforts. And it all starts with data. Data drives digital transformation, uncovering the valuable insights that lead to more informed decision making. But human brains and manual processes can't keep pace with the seemingly endless volumes of data society produces. They can't tame data's increasing complexity either. Only automation can handle complex data at scale.

Let's say a company's digital transformation goal is providing first-rate online experiences for its customers and employees. That means not just rolling out sophisticated apps and services but also keeping them working seamlessly.

Lacking automated tools, enterprises would be hard-pressed to deliver continuous availability to their users. If performance issues surface, DevOps and SRE teams would have to piece together siloed data from various monitoring tools. While the rest of the world is zooming by, these teams would comb through data looking for anomalies and trying to determine the issue's root cause — before even starting on a fix. That's an inefficient and slow process, and it shows in the digital experience.

Now, let's look at the same company with automated tools, like artificial intelligence for IT Operations (AIOps). Instead of human teams scanning an entire IT ecosystem for fluctuations in performance, the AIOps platform does it. The tool spots data anomalies, diagnoses the problem in real-time and hands all of that information over to the humans responsible for mitigating the problem. In the meantime, the tool's machine learning capabilities track these destructive incidents and prevent them from repeating. This automated incident lifecycle puts the company's ultimate goal — a seamless digital experience — within reach.

Perhaps that's why businesses using autonomous operations and AIOps solutions expect more growth than less automated organizations. According to the referenced Dell Technologies report, 36% of organizations with high levels of automated operations expect revenue to rise by 15% or more. Only 10% of organizations with low levels of automation project the same growth.

Why, then, do 90% of organizations report they are still struggling with IT staff wasting time on mundane, manual tasks that could be automated?

The Benefits of Partial Automation

IT leaders point to various barriers to adopting and implementing automated technologies — lack of in-house expertise, security concerns and budget restrictions, just to name a few. There's another highly human element: people tend to fear full automation. Even DevOps practitioners, known for eliminating process overhead by embracing automation, sometimes prefer a level of tech oversight.

Those eager to digitally transform but cautious to fully automate can take a human-in-the-middle approach to automated technologies. This partial automation often engenders trust in the technology while allowing an organization to reap many of the technology's transformative benefits.

Here are partial automation techniques that allow enterprises to dip their toes into the world of automation:

1. Transparent processes

Skeptical IT leaders typically benefit from understanding automated tools' back-end models. Luckily, some vendors give users a peek behind the curtain. "There are indicators of the so-called ‘black box' opening up and end-users maturing to a point where they are ready to ask questions about what's happening in the analytics layer, how the process is working and how the outcomes can be better," explained Lead Gartner Analyst Pankaj Prasad about IT Operations teams better understanding AIOps technology.

2. Confidence ratings

IT teams can orchestrate automated technology for human collaboration depending on the technology's confidence level. If the system analyzes a decision, determining that the variables decisively point to one decision over another, then the technology likely won't ask for a human's helping hand. In the case of uncertainty, however, the system can treat the human operator like a teacher, essentially saying, "I made this decision based on these variables. Do you think I made the right decision?"

3. Reward training systems

Another tactic for instilling trust in automated systems is having humans train the technology, much like they would train a pet or teach a child. When parents see a toddler sharing, they say "good job," encouraging repetition of that positive behavior. Similarly, IT teams can tag an automated system's decision as "good." That invites it to do more of the same. Now suppose the opposite. If a child grabs toys from a playmate's hand, the negative behavior will likely be met with a stern "no, don't do that." A person at the wheel can similarly instruct an automated system not to repeat an undesirable action and instead steer it toward a more ideal outcome.

4. Semi-supervised automation

Semi-supervised automation also puts human operators in the driver's seat. Similar to the reward system, this technique allows people to take incremental steps to the technology's full automation. Humans can collaborate with technology on ideal outcomes. In an AIOps tool, for example, that could be customizing clustering algorithms that the machine learning (ML) then executes.

Adopting digital-first business practices without automated technologies will only keep companies out of the dust heap for so long. To achieve true market leadership, enterprises must optimize their digital transformation investments by incorporating some level of automation. Even partially automated technologies can tame unwieldy data, turning it into the data-driven insights that are vital to corporate survival and success.