Automation is moving from a routine IT task to a race to cross an ill-defined finish line. AI tends to be the bug smearing the windshield and making it hard to see where you’re headed. Road hazards are further complicating the drive to increased efficiency.
“For some, automation is a buzzword and an uphill battle, but for most technical folks out there, it’s as simple as ABC. However, many technical leads and CIOs find themselves in trouble at the starting line,” says Muhammad Nabeel, chief technology officer at Begin, an entertainment streaming service in Pakistan.
At issue from the start are the usual company politics and AI — which can be more difficult to negotiate than bean counters and C-suite heavyweights combined.
“Nowadays, AI has a drastic influence on every walk of life, especially technology. Therefore, any CIO or head of technology must incorporate the AI factor,” Nabeel adds.
Although AI is a dominant force, it isn’t the only play in automation. Some established tools and rules still apply. Unfortunately, so do the previous pitfalls and challenges. Heaped on top of that are all the AI problems, too.
“This year, hidden costs and regulatory curveballs will bite if ignored. Beyond licensing fees, watch for integration spaghetti — systems that don’t “talk” smoothly — and training gaps that stall adoption. New data privacy regulations, like evolving GDPR [the European Union’s General Data Privacy Regulation] and AI transparency laws, mean CIOs must vet tools for compliance and ethical design,” says Dawson Whitfield, CEO and co-founder of Looka, an AI platform for designing logos.
All told, there’s a lot for IT to manage all at once. For the sake of sanity and strategy, perhaps it’s best to first consider the pitfalls and challenges before trying to map out a strategy.
Pitfall 1: Running into obstacles you can’t see
In the process of implementing automation and getting all the moving parts right, sometimes people forget to first evaluate the process they are automating.
“You don’t know what you don’t know and can’t improve what you can’t see. Without process visibility, automation efforts may lead to automating flawed processes. In effect, accelerating problems while wasting both time and resources and leading to diminished goodwill by skeptics,” says Kerry Brown, transformation evangelist at Celonis, a process mining and process intelligence provider.
The aim of automating processes is to improve how the business performs. That means drawing a direct line from the automation effort to a well-defined ROI.
“When evaluating AI and automation opportunities for the organization, there are often gaps in understanding the business implications beyond just the technology. CIOs need to ensure that they can translate AI capabilities into concrete business strategies to demonstrate strong ROI potential for stakeholders,” says Eric Johnson, CIO at PagerDuty, an AI-first operations platform.
Pitfall 2: Underestimating data quality issues
Data is arguably the most boring issue on IT’s plate. That’s because it requires a ton of effort to update, label, manage and store massive amounts of data and the job is never quite done. It may be boring work, but it is essential and can be fatal if left for later.
“One of the most significant mistakes CIOs make when approaching automation is underestimating the importance of data quality. Automation tools are designed to process and analyze data at scale, but they rely entirely on the quality of the input data,” says Shuai Guan, co-founder and CEO at Thunderbit, an AI web scraper tool.
“If the data is incomplete, inconsistent, or inaccurate, automation will not only fail to deliver meaningful results but may also exacerbate existing issues. For example, flawed customer data fed into an automated marketing system could lead to incorrect targeting, wasted resources, and even reputational damage,” Guan adds.
Pitfall 3: Mistaking the task for the purpose
A typical approach is to automate the easy, repetitive processes without giving thought to a problem that lurks beneath. Ignoring or overlooking the cause now may prove highly damaging in the end.
“CIOs often fall into the trap of thinking automation is just about suppressing noise and reducing ticket volumes. While that’s one fairly common use case, automation can offer much more value when done strategically,” says Erik Gaston, CIO of Tanium, an autonomous endpoint management and security platform.
“If CIOs focus solely on suppressing low-level tickets without addressing the root causes or understanding the broader patterns, they risk allowing those issues to snowball into more severe problems that can eventually lead to bigger risks down the road. It is often the suppressed Severity 3-4 issue that when left unattended, becomes the S1 or 2 overtime!” Gaston says.
Remember also that business goals and technologies change over time and so too must processes.
“Focus on high-impact areas, leverage the power of open-source tools initially, and monitor the outcome. Change when and where necessary. Do not adopt the ‘fire and forget” principle,’” says Nabeel.
Pitfall 4: Failing to plan for integration
Integration becomes a necessity at some point. With AI, integrating with human overseers is an immediate need. Often it must be integrated with other software as well.
“One mistake is assuming AI-driven automation can run without human oversight. AI is a powerful tool, but it still requires human checks to catch errors, bias, or security risks,” says Mason Goshorn, senior security solutions engineer at Blink Ops, an AI-powered cybersecurity automation platform.
However, even traditional automation tools require integration. Most in IT are aware of this but it doesn’t mean that planning for it made it into the final strategy.
“Another challenge is failing to plan for integration, which can lead to vendor lock-in and disconnected systems. CIOs should choose automation tools that work with existing infrastructure and support open standards to avoid being trapped in a single provider’s ecosystem,” says Goshorn.
Pitfall 5: Not allowing the data to drive decisions in what to automate
Often the plan isn’t really a plan but rather a rush to automate the low-hanging fruit to show a fast win. Unfortunately, a fast win isn’t necessarily the same as a big win. A cost-benefit analysis will steer you true whereas a quick pick might lead you astray.
“For pipelines that occur less frequently or require little time, automation provides lesser value. Like most business processes, a cost can be associated with automation, and the cost savings should exceed the cost of implementation and maintenance,” says David Brauchler, technical director & head of AI and ML security at cybersecurity consultancy, NCC Group, a cybersecurity company.
Identifying what processes should not be automated early on is another way to save effort, time and wasted cost.
“Any process that requires complex human reasoning, emotion, or interaction, or does not follow established rules and structures, are not suitable for automation. Of course, AI is blurring that distinction and getting better at simulating complex human behaviors and establishing structures where none seem to exist. However, considering the current state of development and possible legal and moral ramifications, such processes should be deprioritized for automation,” says Sourya Biswas, technical director, risk management and governance, NCC Group.
“Also, considering the lead time to analyze, implement and integrate automation, any process subject to major changes in operating conditions in the near future should not be considered for automation as it is likely that the ROI won’t be positive before the process itself becomes obsolete,” Biswas adds.
Pitfall 6: Focusing solely on cost
Given that economies are uncertain around the world from inflation, political upheaval, and other factors, it’s understandable that cost concerns are elevated now. But that narrow focus can leave you blind to other budget impacts.
“CIOs risk choosing the wrong technology, leading to integration challenges, unnecessary complexity, or vendor lock-in. A common pitfall is focusing solely on cost savings rather than broader benefits like agility, innovation, and customer experience, which can limit the actual value of automation,” says Derek Ashmore, application transformation principal at Asperitas, an IT consultancy.
Rising New Challenges
2025 is ushering in a lot of new challenges for IT to surmount in automation implementations. Although changes in regulation and associated compliance costs are ongoing issues, they are even more so now.
“This year, CIOs should be particularly vigilant about emerging regulatory requirements that could impact their automation strategies. Staying informed about industry-specific regulations and compliance standards is essential, especially regarding how automated systems handle data,” says Chris Drumgoole, EVP, Global Infrastructure Services at DXC Technology, a global technology services provider.
It isn’t just federal regulations you must watch closely, but regional and state regulations too.
“The integration of AI into IT automation is accelerating, with technologies like generative AI and agentic AI playing pivotal roles. State legislatures in the US are actively introducing AI-related bills, with hundreds proposed in 2025,” says Ashmore.
Ashmore warns that these legislative efforts include comprehensive consumer protection, sector-specific regulations on automated decision-making, chatbot oversight, generative AI transparency, data center energy usage, and public safety concerning advanced AI models.
“This surge in state-level regulation adds complexity to compliance for organizations implementing IT automation,” Ashmore adds.
Some of the rising challenges are more directly attached to automation implementations.
Unexpected expenses in operationalizing AI, increasing complexity in multi-cloud integration, and integration requirements across growing ecosystems are all putting pressure on IT, according to Deepak Singh, president and chief technology officer at Adeptia, an AI and self-service platform.
Also lurking in the background, but soon to raise its ugly head, is the problem of a growing shadow AI. Business users are routinely turning to free and low-cost AI subscription models to get their work done without corporate oversight or interference. On top of that is the growing number of AI models integrated or embedded in enterprise software and hardware, as well as in private devices like smartphones. That is a lot of unattended and potentially unsecured AI wandering around in the organization. For example, that is a lot of AI that can be gathering data to train future AI models on, and some of that data may be proprietary.
Last, but certainly not least, is the dearth of talent necessary to remake business processes in AI’s image and fit for automation tools of all kinds.
“Leaders should focus on upskilling the talent they already have and investing in communities to build strong talent pipelines. This way, as automation increases, the workforce can take an oversight role and enjoy more capacity to focus on innovation that can improve the bottom line,” said Tim Gaus, Smart Manufacturing business leader at Deloitte, a consulting firm.
The key to success lies in training domain experts to use AI and other technologies, and to accurately evaluate what processes can and can’t be successfully automated.
“Key to this for manufacturers is ensuring talent can span the manufacturing and IT disciplines. This can take the form of educating production staff in IT but must also focus on ensuring that IT staff and partners understand the real challenges and data environment on the production floor. IT and OT (Operational Technology) can no longer have walls between them and must operate toward common goals with sufficient understanding of each other’s domains,” Gaus says.