04: Blockers to IT automation
Technology isn’t the problem; change is the problem
Isometric illustration of a person working on a laptop with multiple projected screens floating behind the laptop
Potential blockers

One of the biggest potential blockers to incorporating AI and automation can be summed up in one word: fear. The C-suite, IT leaders and employees all fear—or are at least concerned about—several aspects of using AI, including:

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Job change/loss Cost to implement Regulation and governance Where to start Fear of change
Straightforward solutions

These are big topics, and rightly so. Some may seem like giant hurdles to overcome. But there are ways to address these concerns, minimize anxiety and even use them to build a culture more open to change and innovation:

Job change/loss

Our IBM leaders were again unanimous when asked how AI will affect employees: AI and automation are not designed to replace jobs, but rather to augment workers and provide them with more time to upskill and work on the types of innovation that only human beings can provide. IBM research backs them up: on average, 87% of executives surveyed expect job roles to be augmented rather than replaced by gen AI. This is a good thing, given that organizations that deliver top employee experiences outperform on revenue growth 31% more than others.1 Job changes, however, are coming. Surveyed executives estimate that 40% of their workforce will need to reskill as a result of implementing AI and automation over the next three years.2

“I hear, ‘If I enable too much automation, I'll work myself out of a job.’ But that’s simply not true. The fact is that technology is moving so quickly, as it always has, that there's always that next thing. There are always more mature things to focus on. There are always higher-value opportunities for humans by allowing automation in.”2 —Keri Olson

“Senior executives are trying to save money through optimization. They don't want to cut headcount; they want more productivity from what they already have. That's what digital transformation is like. If I want to be more efficient, I just work harder. But if I have technology and tools to change how that work is done, and I let computers offload some of it and machine learning make recommendations ... that's being more productive.” —William Lobig

Cost to implement

Adding any new technology does come with a cost. There are the initial outlays, such as new licenses or SaaS services. However, as these new tools support greater productivity and efficiency, you can begin to recoup your investment, especially if you prioritize projects that make you more competitive. Another key to cost control: view spending through a wide-angle lens, assessing the entirety of the IT costs required to implement AI. This holistic view—across IT, cloud, and people—can help you deliver enhanced business value while still keeping costs under control.

Advice for CIOs: Extend FinOps capabilities across the enterprise to gain visibility into costs and spending across all AI, hybrid cloud and application modernization investments. Understand what your people are working on and how much they cost, and map that back to specific projects, applications, and initiatives to optimize spend.—The CEO’s guide to generative AI

“Organizations really need to look at their application disruption costs. If they're seeing disruption, downtime, or lagging performance, they need to understand how much that’s costing them in terms of lost customers, lost revenue, or lost time. You have to measure both sides of the equation.” —Keri Olson

Regulation and governance

When it comes to industry and government regulations, adherence is paramount. Equally critical is protecting the privacy of your organization’s two most vital assets: employees and intellectual property. The right implementation partner can help you negotiate these concerns. IBM, for example, believes smart regulation should be based on three core tenets:

  1. Regulate AI risk, not AI algorithms
  2. Make AI creators and deployers accountable, not immune to liability
  3. Support open AI innovation, not an AI licensing regime.
Choosing where to start

As discussed in the previous chapter, if you start small and then scale from there, you can quickly use automation to rack up small successes that add up to big wins. Low-risk, high-visibility opportunities can lead to quick wins, while tracking and measuring tangible and intangible KPIs can demonstrate the value proposition of AI. In addition, you can help smooth the process by seeding new teams with experienced talent to serve as cross-functional guides and advocates.


There are always higher value opportunities for humans by allowing automation in. Keri Olson

“High, manual effort is so embedded in the day-to-day of DevOps teams, app engineers and central ITOps teams that it’s almost incomprehensible for them to understand how they would do their day-to-day process without toil. But if you can pinpoint which subsections of your IT that you know are underwater because of that toil, you can look for ways to automate or implement some type of AI-powered automation and immediately see hours back to your team.” —Tim Cronin



Change in general

When it comes to the larger fear of change, though, the solution is more nuanced. Often, technology isn’t the problem; change itself is the problem. To embrace the possibilities of AI and IT automation, an organization will need a culture shift to get everyone—not just IT teams—thinking differently and even rethinking entire parts of the organization. Because culture starts at the top, these changes must be a C-suite effort, not something left solely to a CIO.

AI won’t replace people—but people who use AI will replace people who don’t.—Augmented work for an automated, AI-driven world

Business success is often driven by a shift in perspective. As IBM Institute for Business Value research found, “organizations that prioritize their operating model as an enabler of transformation outperform their skills-centric peers in multiple dimensions; for example, 55% higher performance in profitability and efficiency.”

Driving AI-powered change can benefit both employees and the bottom line. For example, IT automation projects can help break down organizational silos. That in turn brings new opportunities to innovate, collaborate, and identify operations ripe for simplifying or streamlining, especially in large enterprise-level organizations where different teams often use different practices, tools, and applications.

Augmented work 
for an automated, 
AI-driven world
Chapter 05 →
The ethics of AI and automation
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1 Experience is everything, IBM Institute for Business Value, 2023
2 Augmented work for an automated, AI-driven world, IBM Institute for Business Value, 2023.