Despite how prominent the rise of artificial intelligence in business has been, CIOs have been understandably cautious about AI in the workplace experimentation. According to a BMC survey, about 94% of respondents say that AI in IT is now part of their IT strategy. Yet, only 17% have moved beyond the experimental phase, and just 5% have reportedly seen tangible results from mature implementations.

Other C-suite leaders are looking at CIOs as data experts to help steer AI for work initiatives. Meanwhile, CIOs are already juggling budget constraints, evolving leadership demands, incapable legacy systems, and a shortage of skills. Employees are exploring AI at work in IT without much direction, while leaders aren’t sure how to communicate realistic AI business trends and expectations to upper management.

Rigid IT infrastructures make transformation even harder. According to a PwC survey, 58% of CIOs say they are investing heavily in artificial intelligence in information technology, but 82% report that actually achieving value from these technologies is a major challenge.

There’s still a lot CIOs don’t realize about AI and IT. The urgent need to “adopt or be left behind” raises a real dilemma—if not now, then when? The struggle to get significant value from AI in the workforce is not just about unpredictability, but also about the flaws in innovation strategies.

By the end of 2026, over 90% of organizations are predicted to face an IT skills crisis in the AI in IT industry, with not enough talent to fill key roles. This threatens to add even more pressure to already overworked departments, raising an important question—should artificial intelligence for IT really be the top priority for CIOs right now?

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Anticipate a change in your leadership style

CIOs are under mounting pressure to change how they work and lead in the age of AI in IT. But a change in leadership isn’t just about a new strategy—it requires a new mindset. Many leaders in IT today have become industry veterans, with decades of experience navigating shifts like artificial intelligence in the workplace. While these leaders have witnessed technology’s golden era, some are naturally resistant to the rapid changes that ai in the workplace now demands.

This resistance is understandable. Senior executives who have led ai in IT transformation initiatives have often experienced at least one major failure along the way. Employees affected by these failed ai for work projects often report negative emotions, as setbacks can lead to layoffs or stalled career growth. While not every company can afford these failures, true evolution in ai in the workforce is rarely possible without risk.

The speed of digitization—and the growing impact of ai in information technology—in just the last five years has been almost unbelievable. Ignoring the need for a shift in leadership style, especially as ai for IT becomes more central, is more dangerous than the alternative. Traditional leadership can be limiting. Employees now need more freedom to try new ideas and experiment with ai at work. These ideas can be challenged, but not punished. Work will have to become more collaborative, spreading across networks rather than being trapped in hierarchies. Leaders must enable connections and step aside to let innovation in artificial intelligence in IT services happen.

This sounds simple, but with a technology as new as ai in tech, leaders often feel the urge to control every detail. While understandable, this approach can create silos, encourage micromanagement, and keep leaders from seeing the bigger picture of ai business trends and long-term growth.

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AI adoption is a marathon, not a sprint

The most important thing at the moment is to set realistic goals and expectations from AI projects in IT. A general misconception seems to be floating around about ai in IT or ai in tech being able to solve problems of all magnitudes, especially among non-technical leaders who don’t understand certain intricacies. At best, ai for IT can be described as more of a purpose-driven technology that eliminates specific redundancies leading to productivity gains. But despite being a self-learning model, ai in IT industry is far from perfect. If anything, ai and work needs more human surveillance and oversight now more than ever. Controlled implementation and quality assurance will lay the groundwork for whatever comes next.

As a leader, you must think of how to use ai at work as an augmentation tool that enhances human capabilities. Some roles and responsibilities will become vestigial as ai for IT proliferates and that’s unavoidable, but in the long run, organizations will be able to scale beyond existing benchmarks. The truth is, agentic AI, AGI, and Gen AI have had a bittersweet effect so far with some people favoring it while others not so much. There’s no right or wrong here, just perspectives. In IT, where employees are already knee-deep in resolving current concerns, abruptly introducing a new technology like ai in IT might be more devastating than beneficial if we consider the following scenarios:

• Employees face more workload with changes in infrastructure, processes, and tech stack; the workforce isn’t happy about the change and resent management; transformation fails, and all invested resources don’t translate into improved ROI for the business.
• Employees face more workload with changes in infrastructure, processes, and tech stack; the workforce isn’t happy about the change and resent management; transformation, however, is successful, and the business recognizes more ROI from said changes.
• Employees welcome change and are happy about it; not a lot of infrastructural changes are required and eventually, there’s less workload on the workforce; transformation, however, isn’t effective, and business objectives don’t align with expected ROI.
• Employees welcome change and are happy about it; not a lot of infrastructural changes are required and eventually, there’s less workload on the workforce; transformation is successful, and the business recognizes more ROI from said changes.

3 of the 4 scenarios above (75%) result in a disruption that’s either not beneficial for the team, the organization, or both. This is why an abrupt change to accommodate AI in IT almost certainly won’t work for you. Hence, taking things slow is the best bet.

• First, test the waters, have discussions with the C-suite and with your team to understand how they think of AI and how you can bridge the gap between what’s expected and what’s possible.
• Don’t force change but rather spend time understanding how you can enable your team to embrace it.
• Communicate realistic outcomes from these changes so everyone in the organization is aligned on the specifics.
• Experiment on a smaller scale, implement when it feels ready, and scale things that work.

AI in IT won’t be much without data. Many CIOs recognize this—about 47% say data transformation is their focus, ranking it above customer relationship management, support, and marketing. Around 32% believe data management is the top challenge for CIOs and IT teams, tied with handling legacy systems. Most organizations have more data than they can use, leading to silos, unorganized structures, and missed opportunities. With ai in IT industry, it becomes easier to train LLMs on untapped data, supporting both data transformation and ai for IT initiatives. Still, there’s the matter of trustworthy and secure ai in IT—not all organizations are comfortable sharing sensitive data, so selective and regulated ai in tech implementation may be necessary.

Cybersecurity will remain critical. The past decade’s ransomware attacks and breaches highlight the ongoing need for strong protocols. The unpredictability of these threats weighs on IT leaders, who must invest in better tools and train staff in safety. Preempting security breaches is key. Half-measures to accommodate ai for IT will leave vulnerabilities, so maintaining a secure ai in IT environment is essential.

Technical debt adds another hurdle. Organizations still allocate over 70% of their IT budget to managing technical debt, making innovation with ai in IT even harder. As legacy systems continue to drain resources, opportunities for transformation are compromised.
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The current state of AI in IT

A trillion-dollar solution for a problem that doesn’t exist – Goldman Sachs has explored what leaders think about ai in IT and a common consensus seems to be against that of AI. AI in tech isn’t designed to solve the complex problems that would justify the costs and what we’re seeing right now is a myriad of resources being thrown for the development of ai in IT industry. Another study, in partnership with the Upwork institute of research, found that about 77% of employees view ai and work as an extension to their workload instead of a technology that eases it. What’s worse, most employees don’t know how to use ai at work to meet the productivity expectations of their employers while they struggle to keep up with new tools and workflows.

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The state of ai in IT adoption across industries as reported by Goldman Sachs. These views, however, are contradictory as a lot of companies have reported productivity gains as well. Honeywell – a conglomerate that primarily deals in aerospace, industrial, and energy automation – has spent the better part of the last few years developing its data-driven enterprise and is now ready to leverage a layer of ai for IT atop this data goldmine across every department of its business.

The Fortune 500 giant has implemented ai in IT copilots to automate IT help desk requests, reducing manual ticket resolutions by 80%. An internal chatbot has been trained on hundreds and thousands of manuals and internal articles to answer questions in real-time, saving hours of work for their employees. A workforce as strong as 95,000 now has access to more than a dozen ai in IT use cases already in production. They moved way beyond the experimentation phase and have implemented an organization-wide AI initiative with IT being at the core of it.

The data and IT leaders at Honeywell expressed that the reason why they were able to get ai-ready and scale things this quickly is because they figured out their data strategy first; there is no ai for IT strategy without a data strategy. They’re focused immensely on their data enterprise warehouse (EDW) and with that in place, it’s easier to source the training materials necessary for dependable and clean ai in IT.

There’s also a strong argument for the development of cleaner, safer ai in IT for which the supporting infrastructure needs to be built in-house. This does require strong engineering overhead for 6-12 months along with cost-intensive initiatives, but the payoff is worth it. It might be a tight rope to walk but companies like ServiceNow have been able to develop scalable ai in IT industry solutions not just for IT but for functions across the board. Smaller companies that cannot afford such initiatives are relying on smaller ai in tech startups that have emerged uncontrollably in the past couple of years. Moderne.ai - a product that eliminates technical debt – is an example of such tools that couldn’t have existed before the ai in IT boom. Everyone else has adopted one of the major players in the ai in IT space – Microsoft AI copilot, Google, or OpenAI – in some form or another.

AI in IT seems to be a transitional state across a spectrum of organizations from those who’re seeing no ROI to those who’re already seeing tremendous impact on revenue. According to a correlations report, larger enterprise organizations (2001 – 5000 employees) are more likely to see negative ROI from ai in IT investments as compared to SMBs. But when it comes to IT budget allocation, smaller organizations are far more skeptical of their ai in IT spends since they are least likely to spend more than 20% of their IT budget on AI while larger enterprises are most likely to (and have already) allocated more than 20% to ai in IT.

AI in IT spend and ROI also factors in the location – apparently, European companies are lagging far behind their US counterparts by 45-70% in terms of ai in IT adoption across various sectors of different sizes. For sectors that are significantly larger in the US than Western Europe (software, pharma, healthcare, etc.), there seems to be an even more pronounced disparity upwards of 70%.

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What are the most probable reasons for this?

• Shortage of skill: A general lack of skilled professionals has created a pressure system that requires upskilling and reskilling efforts of about 12 million employees to improve productivity through AI-led projects.
• Compensation and career growth: Europe has more AI professionals than the US but of the 22% who study in Europe, only 14% work there because US companies offer better pay and growth.
• Why is US offering better compensation than Europe? Because of significantly more VCs and incubators that are ramping up AI investments in the country.
• Complex regulations: The EU AI Act isn’t easily comprehensible — about 70% of the companies find them to be too complex.

European companies also seem to be investing less willingly in their IT teams, especially when it comes to ai in IT and ai for IT. According to another report, larger firms in Europe spend 10% less on IT than their US counterparts, and not enough IT leaders see this as a concern. With strict budget allocations to IT in general, it’s not surprising that a much smaller share is dedicated to digital transformation initiatives like ai in tech and ai in IT industry projects.

Smaller to medium-sized companies have started to lose trust in ai in IT, while larger enterprises with over 5,000 employees have reportedly gained more trust in ai for IT over the past year. Interestingly, trust was higher in companies where ai in IT projects were initiated by IT teams rather than driven by the C-suite. This may be because IT leaders are more responsible for delivering business outcomes like ROI from ai in IT than anyone else. These statistics suggest that larger organizations have a greater appetite for risk with ai in IT industry initiatives than smaller ones, and rightfully so. It shows that ai in IT needs more space and patience to grow—instant returns are unrealistic for any new initiative, and nothing substantial happens without taking risks.

This begs the question – what do you think about AI in IT? Do you think that:

• AI is still in its nascent stages and there’s not much value in it anyway. Europe isn’t missing out at all.
• Companies have reportedly seen more workload with AI than productivity—IT teams have to learn new technology on top of an existing tech stack.
• AI isn’t the messiah everyone thinks it is—most use cases are surface-level and don’t translate to any tangible productivity or ROI. T1 support will likely get completely automated at best but nothing more.
• AI is still a ‘good to have’ and not a 'must have'. Most probably, it will continue being that way for a while.
• Tools like Moderne.ai — a product that helps eliminate tech debt — are simply extensions of a problem that can be solved without AI.
• AI is a bubble that will eventually pop, only the top corporates will survive, and most AI startups will go under or squander more resources in an attempt to go back to their pre-AI roots.
• IT budget should be spent on more important problems like addressing legacy systems, upskilling employees, strengthening IT infrastructure, etc.

Or do you think that:

• AI is a revolutionary technology that will completely change the way IT teams work with data.
• Yes, AI is in the nascent stages but tools that exist now couldn’t have been possible without it.
• About 70% of IT teams have already implemented AI chatbots and helpdesk. AI use cases are prolific and will continue to become more effective.
• Developing cleaner AI in-house like ServiceNow is the safest bet. AI that develops as a result will not only be clean but dependable.
• Important aspects of IT like cybersecurity, infrastructure, and analytics will become safer, more agile, and more robust with AI.
• With the right upskilling and reskilling, IT teams should become more productive than ever with AI.
• Europe is missing out, but restrictive AI laws might push for development of cleaner AI.

What might IT look like in a few years?

Most likely, entry-level IT support will be replaced entirely by automation and ai in IT chatbots for internal troubleshooting. About 70% of IT leaders have already started using Gen AI for chatbots and ai for IT helpdesks. Employees might soon be able to troubleshoot basic operations themselves, such as onboarding and remote actions, thanks to ai in IT industry solutions. Many organizations have already implemented helpdesk ai in tech chatbots for internal IT support that can pull previous answers, respond to user queries directly, resolve tickets automatically, and handle system alerts. T1 support functions might be fully automated, leading to significant cost savings for IT leaders. Although controversial, this could be a realistic scenario that aligns closely with ai in IT transformation initiatives and business goals.

Positions like cybersecurity professionals, solution architects, and implementation analysts will still be in demand but may also see more outsourcing or staff augmentation as ai in IT grows. We'll see more AI bots reporting to cybersecurity experts, while IT leaders recognize the costs of maintaining full-time SOCs. The rise of ai in IT may even impact high-paying jobs, creating a domino effect that increases competition in the vendor market—more outsourcing, more innovative vendors, and more competitive pricing.

No matter what, IT will remain a department that needs a hands-on approach, and any disruptive ai in IT technology will require ongoing monitoring. While some jobs may become obsolete, there’s also a strong chance that new, in-demand careers will emerge from these shifts—roles like prompt engineers and AI & ML engineers have already started to surface.

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Brace yourself; pace yourself

Things have never been this unprecedented for ai in IT and ai in tech. Everyone seems more intrigued than excited about what’s next. AI chatbots have quickly become synonymous with support, even though they were almost unheard of in the ai in IT industry just a couple of years ago. It’s remarkable how quickly we’ve adapted—some are more critical of these changes, while others want to move fast out of fear of being left behind. You don’t want to move too fast or too slow, but you need to find your own pace as ai in IT evolves.

Success with ai for IT depends on people—you, your team, and your ability to communicate critical factors to other decision-makers. Break down data silos, improve transparency, and empower your team to voice concerns, especially as ai in IT becomes more central. The way you define your IT budget directly impacts your ability to support ai in IT transformation initiatives. But even before that, you need to look at practical use cases inspired from how other IT leaders in the tech space have implemented AI.

IT leaders are torn between AI being a revolution and AI being an exaggerated new toy for companies to play with. There is, however, a leaning towards AI being more than just a bubble waiting to pop and the whole work forgetting about it. Some things will irrevocably change because of it and resisting will only make it worse. More than anything, it demands a mindset change over a perspective change. Thinking of AI as an augmentation tool is easier than thinking of it as a replacement tool. Yes, AI isn’t solving the big problems right now, but it might eventually will. As an IT leader, you need to upskill and prepare your team for gradual changes. AI is not worth going head over heels crazy for but it’s not something you should ignore either. Its ubiquitousness definitely calls for having an open mind at least.