What’s next in IT automation: 6 trends to watch

Automation experts offer a peek into the not-so-distant future. Keep these items on the radar screen
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We’ve recently covered the factors fueling IT automation, the current trends to watch as adoption grows, and helpful tips for those organizations just beginning to automate certain processes.

Oh, and we also shared expert advice on how to make the case for automation in your company, as well as keys for long-term success.

Now, there’s just one question: What’s next? We asked a range of experts to share a peek into the not-so-distant future of automation. Here are six trends they advise IT leaders to monitor closely.

[ Want more lessons learned from your peers and automation experts? Get our free resource, Automation: The IT leader's guide. ]

1. Machine learning matures

For all of the buzz around machine learning (and the overlapping phrase “self-learning systems”), it’s still very early days for most organizations in terms of actual implementations. Expect that to change, and for machine learning to play a significant role in the next waves of IT automation.

Mehul Amin, director of engineering for Advanced Systems Concepts, Inc., points to machine learning as one of the next key growth areas for IT automation.

“With the data that is developed, automation software can make decisions that otherwise might be the responsibility of the developer,” Amin says. “For example, the developer builds what needs to be executed, but identifying the best system to execute the processes might be [done] by software using analytics from within the system.”

That extends elsewhere in this same hypothetical system; Amin notes that machine learning can enable automated systems to provision additional resources when necessary to meet timelines or SLAs, as well as retire those resources when they’re no longer needed, and other possibilities.

Amin is certainly not alone.

“IT automation is moving towards self-learning,” says Kiran Chitturi, CTO architect at Sungard Availability Services. “Systems will be able to test and monitor themselves, enhancing business processes and software delivery.”

Chitturi points to automated testing as an example; test scripts are already in widespread adoption, but soon those automated testing processes may be more likely to learn as they go, developing, for example, wider recognition of how new code or code changes will impact production environments.

2. Artificial intelligence spawns automation opportunities

The same principles above hold true for the related (but separate) field of artificial intelligence. Depending on your definition of AI, it seems likely that machine learning will have the more significant IT impact in the near term (and we’re likely to see a lot of overlapping definitions and understandings of the two fields). Assume that emerging AI technologies will spawn new automation opportunities, too.

“The integration of artificial intelligence (AI) and machine learning capabilities is widely perceived as critical for business success in the coming years,” says Patrick Hubbard, head geek at SolarWinds.

3. That doesn’t mean people are obsolete

Let’s try to calm those among us who are now hyperventilating into a paper bag: The first two trends don’t necessarily mean we’re all going to be out of a job.

It is likely to mean changes to various roles – and the creation of new roles altogether.

But in the foreseeable future, at least, you don’t need to practice bowing to your robot overlords.

“A machine can only consider the environment variables that it is given – it can’t choose to include new variables, only a human can do this today,” Hubbard explains. “However, for IT professionals this will necessitate the cultivation of AI- and automation-era skills such as programming, coding, a basic understanding of the algorithms that govern AI and machine learning functionality, and a strong security posture in the face of more sophisticated cyberattacks.”

Hubbard shares the example of new tools or capabilities such as AI-enabled security software or machine-learning applications that remotely spot maintenance needs in an oil pipeline. Both might improve efficiency and effectiveness; neither automatically replaces the people necessary for information security or pipeline maintenance.

“Many new functionalities still require human oversight,” Hubbard says. “In order for a machine to determine if something ‘predictive’ could become ‘prescriptive,’ for example, human management is needed.”

The same principle holds true even if you set machine learning and AI aside for a moment and look at IT automation more generally, especially in the software development lifecycle.

Matthew Oswalt, lead architect for automation at Juniper Networks, points out that the fundamental reason IT automation is growing is that it is creating immediate value by reducing the amount of manual effort required to operate infrastructure.

Rather than responding to an infrastructure issue at 3 a.m. themselves, operations engineers can use event-driven automation to define their workflows ahead of time, as code.

“It also sets the stage for treating their operations workflows as code rather than easily outdated documentation or tribal knowledge,” Oswalt explains. “Operations staff are still required to play an active role in how [automation] tooling responds to events. The next phase of adopting automation is to put in place a system that is able to recognize interesting events that take place across the IT spectrum and respond in an autonomous fashion. Rather than responding to an infrastructure issue at 3 a.m. themselves, operations engineers can use event-driven automation to define their workflows ahead of time, as code. They can rely on this system to respond in the same way they would, at any time.”

4. Automation anxiety will decrease

Hubbard of SolarWinds notes that the term “automation” itself tends to spawn a lot of uncertainty and concern, not just in IT but across professional disciplines, and he says that concern is legitimate. But some of the attendant fears may be overblown, and even perpetuated by the tech industry itself. Reality might actually be the calming force on this front: When the actual implementation and practice of automation helps people realize #3 on this list, then we’ll see #4 occur.

“This year we’ll likely see a decrease in automation anxiety and more organizations begin to embrace AI and machine learning as a way to augment their existing human resources,” Hubbard says. “Automation has historically created room for more jobs by lowering the cost and time required to accomplish smaller tasks and refocusing the workforce on things that cannot be automated and require human labor. The same will be true of AI and machine learning.”

Automation will also decrease some anxiety around the topic most likely to increase an IT leader’s blood pressure: Security. As Matt Smith, chief architect, Red Hat, recently noted, automation will increasingly help IT groups reduce the security risks associated with maintenance tasks.

His advice: “Start by documenting and automating the interactions between IT assets during maintenance activities. By relying on automation, not only will you eliminate tasks that historically required much manual effort and surgical skill, you will also be reducing the risks of human error and demonstrating what’s possible when your IT organization embraces change and new methods of work. Ultimately, this will reduce resistance to promptly applying security patches. And it could also help keep your business out of the headlines during the next major security event.”

[ Read the full article: 12 bad enterprise security habits to break. ] 

5. Continued evolution of scripting and automation tools

Many organizations see the first steps toward increasing automation – usually in the form of scripting or automation tools (sometimes referred to as configuration management tools) – as "early days" work.

But views of those tools are evolving as the use of various automation technologies grows.

“There are many processes in the data center environment that are repetitive and subject to human error, and technologies such as Ansible help to ameliorate those issues,” says Mark Abolafia, chief operating officer at DataVision. “With Ansible, one can write a specific playbook for a set of actions and input different variables such as addresses, etc., to automate long chains of process that were previously subject to human touch and longer lead times.”

[ Want to learn more about this aspect of Ansible? Read the related article: Tips for success when getting started with Ansible. ]

Another factor: The tools themselves will continue to become more advanced.

“With advanced IT automation tools, developers will be able to build and automate workflows in less time, reducing error-prone coding,” says Amin of ASCI. “These tools include pre-built, pre-tested drag-and-drop integrations, API jobs, the rich use of variables, reference functionality, and object revision history.”

6. Automation opens new metrics opportunities

As we’ve said previously in this space, automation isn’t IT snake oil. It won’t fix busted processes or otherwise serve as some catch-all elixir for what ails your organization. That’s true on an ongoing basis, too: Automation doesn’t eliminate the need to measure performance.

[ See our related article DevOps metrics: Are you measuring what matters? ]

In fact, automation should open up new opportunities here.

“As more and more development activities – source control, DevOps pipelines, work item tracking – move to the API-driven platforms – the opportunity and temptation to stitch these pieces of raw data together to paint the picture of your organization's efficiency increases,” says Josh Collins, VP of architecture at Janeiro Digital.

Collins thinks of this as a possible new “development organization metrics-in-a-box.” But don’t mistake that to mean machines and algorithms can suddenly measure everything IT does.    

“Whether measuring individual resources or the team in aggregate, these metrics can be powerful – but should be balanced with a heavy dose of context,” Collins says. “Use this data for high-level trends and to affirm qualitative observations – not to clinically grade your team.” 

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Kevin Casey writes about technology and business for a variety of publications. He won an Azbee Award, given by the American Society of Business Publication Editors, for his InformationWeek.com story, "Are You Too Old For IT?" He's a former community choice honoree in the Small Business Influencer Awards.