The Intelligent Automation Wave: Catch It or Be Swept Away by It

    While automation is an established practice in IT, we’ve only scratched the surface in terms of what is possible. As complexity and workloads continue to grow, and machine learning algorithms continue to proliferate, we’re entering a time that’s ripe for massive expansion of intelligent automation. This post examines the factors that are setting the stage for explosive automation growth, and it looks at the implications of these trends for IT organizations.

    Introduction: The Coming Automation Boom

    In his keynote presentation at the Automation Live Virtual Summit 2020, Glenn O’Donnell, a VP and research director with Forrester Research, examined some of the broader forces shaping automation trends. He outlined how industries like manufacturing have seen blue-collar jobs grow increasingly automated over the past few decades. He also showed how, within the enterprise, automation will increasingly be applied to white-collar roles.

    In today’s enterprises, teams continue to contend with a consistent, exponential rise in the pace of change and in technological complexity. Teams saddled with manual tasks are reaching a point where they can’t keep pace, and many have long since passed that point. Further, the complexity and rate of change will only continue to grow. As O’Donnell put it, “If you’re struggling now, it will only get worse.”

    While many of the trends pushing the move to automation have been extant for many years, I’d argue stars are aligning, setting the stage for a massive boom in automation demand and innovation in the coming months.

    First, while the pandemic has monumentally shifted virtually everything, including enterprise plans and strategies, it seems clear the overall impact will be to intensify the pressure to employ automation as a key means for adaptation. While increased use of automation was often already in the plans, the advent of the pandemic has forced the issue for many organizations. Now, the business’ needs to scale, reduce costs, and speed transformation are all serving to generate accelerated, urgent demand for automation.

    In addition, there are also a couple fundamental economic principles at play:

    Economic Principle #1: Downward Sloping Demand Curves

    This principle states that when something gets cheaper, we use way more of it. At first glance, you might look at how this principle would apply to a commodity. However, this principle can have transformative effects when it comes to technology. Think about how the drop in computing prices ushered a broad range of advances. It isn’t just that people bought more computers, it’s that people reimagined problems as ones that could be solved through computing. For example, in decades past, photography was treated as a chemical process, one that required the application of film, paper, and chemicals to develop and print photos. Ultimately, cheap computing power enabled people to transform this into a computing problem, ushering in an era of digital photography.

    I’d argue artificial intelligence (AI) and machine learning are poised to reach a similar tipping point, with dropping prices and reduced barriers to entry fueling accelerated growth. For example, an increasing number of offerings are making prediction algorithms more easily accessible to non-data scientists. As with computing in years past, reduced cost and enhanced simplicity won’t just drive increased usage of prediction algorithms; they will stimulate a rapid proliferation of entirely new use cases to which prediction algorithms will be applied.

    Economic Principle #2: Cost-Price Elasticity

    This principle explains how, when the cost of one item drops, usage of complementary or associated items can rise. For example, when the price of coffee drops and consumption increases, the demand for ancillary products, like cream and sugar, also rises.

    What does this principle have to do with automation? In recent months, AI and machine learning have seen significant advancements in terms of cost and ease of use. As prediction algorithms grow cheaper and more accessible, automation will be one of the ancillary technologies that will see a spike in demand. It’s safe to say that these and other trends will usher in a massive boom in automation usage and use cases. Together, automation and AI will enable not just “know engines,” that is, applications that offer enhanced analytics and decision making, but “go engines” that fuel operational execution of AI-powered insights.

    The IT Imperative: Leading Change or Being the Victim of It

    Depending on who you are, what you do, and where you work, the prospect of a coming automation boom can elicit dread, uncertainty, or glee.

    Any time you’re talking about automation, the topic can be fraught. The reality is that automation has changed jobs, and in some cases, eliminated them. In the manufacturing sector, many roles and tasks have been automated over decades, fundamentally altering the nature of many workplaces, industries, and regions.

    On an individual level, it’s essential to me to approach this topic with a healthy dose of empathy, humility, and realism.

    In his session, O’Donnell offered an interesting look at the different types of roles in enterprises and society more generally, and how they’ll fare as we move into a future that will continue to grow increasingly automated. In the category of white-collar workers referred to as “paper pushers,” substantive declines can be expected. On the other hand, enterprise roles requiring creativity and empathy will fare much better.

    As O’Donnell said, “It’s better to be the automator than the automated.” He even spoke about how he’s looked to automate himself out of a job over the years, and, perhaps counter-intuitively, explained it can be a recipe for success. However, it makes sense that by leading the charge on automation, you’ll be delivering significant value to the business and, more often than not, will be rewarded for those efforts.

    In many ways, a similar choice is presented to IT leadership and their organizations as a whole. IT executives can choose to lead change or be the victims of that change. Automation represents an increasingly vital competitive imperative for organizations, and that’s even more true now than it was in the pre-pandemic past.

    By aggressively investing in automation, IT organizations can begin to significantly enhance the value they deliver to the business. One vital way to set the stage for success is by establishing an automation center of enablement. Compared to a center of excellence, a center of enablement is more focused on supporting broader adoption and promoting best practices that minimize risk. It’s also focused on advancing a more extensive service orchestration and automation strategy and promoting an automation-first culture.

    Service Orchestration and Automation: Key to Capitalizing

    When looking at the demands, trends, and plans around automation, it is vital to approach this topic in a holistic manner. Service orchestration and automation are about much more than approaches like robotic process automation, which tends to be focused on repetitive, lower-level tasks.

    Within an enterprise, it will take service orchestration and automation approaches to fully capitalize on the opportunities that are emerging. Through service orchestration and automation, enterprises will be able to establish a range of capabilities:

    • Event-driven automation. Leveraging conditional logic, heuristics, and machine learning workflows, teams will be able to automate responses to a wide range of factors.
    • Self-service automation. By provisioning self-service capabilities, organizations will enable business users to leverage AI, machine learning, and orchestrated workflows in a way that’s aligned with best practices and corporate policies, without having to know what’s happening under the hood.
    • Scheduling, monitoring, and alerting. Through the combination of automation, AI, and machine learning, teams will be able to gain much more sophistication in tracking and managing their increasingly complex, dynamic environments. Real-time service monitoring and scheduling will be employed against dynamically fluctuating services and infrastructures, and staff will be able to harness intelligent, real-time alerts and intuitive process visibility.
    • Resource provisioning. Automation, AI, and machine learning will power dynamic, intelligent provisioning of resources, for example, equipping teams with the ability to do on-demand provisioning of cloud resources based on complex, dynamic environment variables.
    • Data and workflow management. These technology enhancements will enable not just better analytics but also better processes for harnessing data. Teams will establish the automation of data pipelines and workflows that span hybrid environments, enabling improved capture and enhancement of data.
    • Workload orchestration. Teams will be able to establish capabilities that power central, unified management of workloads across environments and domains.

    Conclusion

    IT teams are under enormous pressure to support key initiatives in today's enterprises, while adapting to large-scale disruption. The IT teams that lead the way in harnessing automation will be the ones that realize the greatest success, within the organization and for the business as a whole.

    If you’re looking for insights for putting intelligent automation to work for your organization, be sure to visit the Automation Live Virtual Summit 2020 registration page. Now available on-demand, this online event brings together presentations of automation experts from a range of disciplines. These sessions deliver a wealth of strategies and insights for capitalizing on the advantages of intelligent automation in the near and long term.