Gartner’s IT Automation Predictions for 2019

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[Read our latest version of Gartner automation predictions for 2020.]

Towards the end of each year, Gartner releases its predictions for the upcoming year. These predictions start a lot of conversations, get lots of coverage in the tech press, and are always useful.

There’s also a lot of them. Besides Garter’s two Top 10 Trends lists (Strategic Technology and I&O), Gartner has recently released dozens of its “Predicts 2019” papers. To help prepare for the new year, we’ve scoured Gartner’s trend and predictions, explaining eight of their 2019 automation expectations below.

A few major themes stick out:

  • AI is being used to optimize IT Automation
  • IT Automation is being used to optimize Big Data
  • As processes are simplified, Big Data will become accessible to the broader workforce
  • The role of IT is changing

Overall/19 looks to be a busy year for IT Automation, AI, and Big Data.

What IT Automation Will Look Like in 2019

1.) “By 2023, 40% of I&O teams will use AI-augmented automation in large enterprises, resulting in higher IT productivity with greater agility and scalability.”

Traditionally, organizations have been using automation to lower operating costs on an ad hoc basis. This is an opportunistic automation strategy relies on scripts that cannot easily support the infrastructures needed for digitalization and digital transformation.

Starting earnestly in 2019, organizations will “move automation… to a systematic approach. This means leveraging automation beyond just lowering costs and improving quality to implementing agility and scaling digital implementations across the enterprise.”

AI-augmented automation is expected to play a large role in IT operations and application performance management. To better achieve the benefits of AI-augmented automation, Gartner suggests that organizations establish an automation Center of Excellence and train personnel on data science, among other things.

AI-augmented automation is expected to also make a large contribution to Big Data practices. In order to derive actionable insights from the data, organizations will increasingly use AI-augmented automation, driven in part by a dearth of data scientist who would otherwise process the “[overwhelming] volume, velocity, and variety of this data.”

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2.) “By 2025, 50% of data scientist activities will be automated by AI, easing the acute talent shortage.”

As AI technologies continue to develop, AI will become “democratized”, meaning AI will be applied to processes and tasks across the organization, leveraged and implemented by line-of-business personnel.

As AI becomes democratized, business personnel will use AI to automate big data processes. Digitalization and digital transformation are causing a growing surge in data, leaving business personnel scrambling to understand how best to leverage all of this data. Because of this, 50% of activities today conducted by data scientists are expected to be automated by AI by 2025.

This data surge is also placing a strain on data science resources because there are not enough data scientists to keep up with the surging demand for resources.

In response to this, organizations have been leveraging augmented analytics —software that combines AI and machine learning to automate data science processes— and will continue to do so at a higher rate in 2019.

3.) “By 2023, AI-enabled automation in data management will reduce the need for IT specialists by 20%.”

Organizations require flexible access to data. To meet this need, business personnel are placing fewer demands on day-to-day IT resources when completing data management tasks. Furthermore, cloud resources are reducing the need for permanent, on-site data management teams within IT.

Data management practices are also expected to change as vendors and organizations apply AI to simplify and automate data management. This includes automating tasks such as performance tuning, optimization, upgrades, patches, backups, and disaster recovery.

This will further democratize Big Data, enabling more users from across the organization to independently leverage these tools and processes. Data literacy will become a bigger issue because of this, which will in turn drive demand for better metadata.

Ultimately, organizations will train business personnel in data literacy so that they can independently procure actionable insights from AI-powered, automated data management systems.

4.) “By 2020, more than 40% of data science tasks will be automated.”

Augmented analytics, which identify patterns in data to provide actionable insights, will be embedded into enterprise applications. Augmented analytics will go hand-in-hand with the democratization of data to create a class of citizen data scientists.

Through 2020, the number of citizen data scientists will grow five times faster than professional data scientists because AI-powered augmented analytics —which can automatically identify data sets, recognize patterns, and develop hypotheses— will allow personnel across the organization to successfully leverage Big Data with little training.

5.) “By 2022, organizations utilizing active metadata to dynamically connect, optimize, and automate data integration processes will reduce time to data delivery by 30%.”

The demands of data management have out-paced the capabilities of most organizations. Because of this, IT leaders will “rapidly innovate” by leveraging important new technologies and trends —this includes cloud computing and automating routine data management tasks and tasks that are “beyond the capabilities of human specialists.”

To deliver effective data management solutions, organizations will apply automation to data management, allowing expert personnel to complete other valuable projects.

6.) “Automation continues to deliver on the promises of improved cost and higher effectiveness for the clients of infrastructure service providers.”

Infrastructure outsourcing, in general, is on the rise because organizations are demanding dynamic, scalable, state-of-the-art infrastructures that can support digital goals that include IoT, AI, and edge computing. The catch is that, under constant pressure to keep costs down, IT leaders need to accomplish more with less.

Establishing infrastructure that can meet the demands of digital growth, while lowering costs, “can only be executed by a massive injection of intelligent automation….”

Which is why Gartner expects that,

7.) “By 2020, more than 50% of current manual operational tasks in infrastructure managed services will be replaced by intelligent automation services.”

Application services providers are also offering intelligent automation solutions, bringing “artificial intelligence, machine learning, cognitive solutions, robotics, bots, and natural language processing (NLP)” to the manual tasks of application testing.

8.) “By 2021, intelligent automation will generate an additional 20% savings over what is achievable today, in application testing services for end users.”

Application testing services will also be impacted by automation over the next few years as automation is leveraged to improve efficiency and to reduce the time and resources needed to test applications. Service providers will also increase investments in automation tools that facilitate migrations from on-prem to cloud.

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Brian is a staff writer for the IT Automation Without Boundaries blog, where he covers IT news, events, and thought leadership. He has written for several publications around the New York City-metro area, both in print and online, and received his B.A. in journalism from Rowan University. When he’s not writing about IT orchestration and modernization, he’s nose-deep in a good book or building Lego spaceships with his kids.

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