Our experience of a minute, an hour, or any duration of time is significantly impacted by our attention—and how that attention impacts our emotional state. It’s not simply a matter of perception; it’s a complex interplay of psychological and physiological mechanisms. Think about the difference between spending a road trip sitting in the car, counting mile markers, or queueing up your favorite podcasts and playlists with a tote bag full of snacks and good company.
Focusing on time itself can increase the speed of the internal "pacemaker." This can lead to a feeling that time is passing more quickly than it is and potentially overestimating durations. Conversely, if your attention is engaged with what you’re doing rather than tracking time, time may be underestimated. This occurs because fewer "pulses" from the internal clock are accumulated. It’s the difference between being in a flow state and being bored. When someone is bored, time tends to drag, as attention is drawn to the passage of time itself instead of other stimuli. That’s why, when you’re a business relying on your employees’ and customers’ engagement, time becomes your most valuable asset.
But as the need for more apps to organize more data and perform more tasks to accommodate more commerce becomes the norm, it can make it seem like there’s a race against the clock to get to where you need to be. The finite reality of time that a business has on the daily—let alone during a busy holiday sale season or during a top-of-funnel ad interaction with a potential new customer—is deeply felt. It’s estimated that over half of digital impressions of internet users before they scroll is under three seconds. Loading a product landing page on a mobile device takes around eight seconds. If you don’t have the right technology to help you stay ahead, it’s easy to fall behind.
We can’t make more time, but it is possible to make the time you do have count more. Technologies like agentic AI are making new levels of optimization possible, even as businesses are facing increased data loads and application use. It’s like having the perfect scenic byway to take when traffic is backed up on the interstate. And that’s why IBM is now using agentic AI in their IT automation tools, with the goal to take intelligent automation to the next level and to make the most of the time that teams have.
The Road Ahead
It’s estimated that by the end of the year, 50% of IT organizations will be able to use AI-powered automation to reduce manual effort by 30%. Those stats highlight just how necessary it is to meet the demands of being fast in today’s digital economy. With internet users’ attention spans averaging just 2.5 seconds and customer expectations for instant service growing, AI-powered automation ensures businesses can keep pace, making it indispensable for scaling IT operations effectively.
How agentic AI plays a role in IT workflows is like the role a fast car, a map, and a full tank of gas play when you hit the road, metaphorically speaking, according to Vishal Chahal, Vice President of the IBM India Software Lab. “The fast car represents AI’s speed in processing data and executing tasks, the map symbolizes its ability to navigate complex IT environments through observability and trend analysis, and the full tank of gas reflects its capacity to sustain operations autonomously.” All together, this combination allows IT teams to optimize workflows efficiently, “much like a well-equipped road trip ensures a smooth journey, reducing delays and enhancing resilience against unexpected challenges,” says Chahal.
According to experts like Chahal, agentic AI is a critical component in IT automation because it empowers systems to handle the increasing complexity and volume of modern IT operations that human teams alone cannot manage efficiently. Enhancing IBM's IT automation and observability solutions with intelligent automation allows teams to gain visibility, improve cost efficiency and increase resiliency across complex operations and hybrid environments. “This capability is vital as businesses now rely on an average of 1000+ applications, creating a data deluge that requires rapid processing and decision-making—tasks at which agentic AI excels,” says Chahal. By analyzing vast datasets in real-time, the agentic AI tools can identify patterns and anomalies, enabling automation tools to optimize workflows and maintain system resilience, ultimately saving time and reducing operational costs.
But it takes having multiple tools and solutions in place to achieve that optimization and efficiency. “This synergy tackles critical IT pain points by enabling proactive automation, enhancing reliability, and supporting scalability for IT leaders,” says Chahal. That’s especially important in a time when 76% of IT decision-makers struggle with hybrid cloud complexity.
Automation solutions equipped with agentic AI keep business on track, which is critical, when unplanned downtime now averages $14,056 per minute, with figures soaring to $23,750 for large enterprises. “By providing actionable insights and automation, these solutions simplify complex IT environments, making them indispensable for leaders aiming to scale operations while maintaining reliability and cost-effectiveness,” says Chahal.
Keeping It Between the Lines
AI—let alone the more recent innovation of agentic AI—is a nascent technology. There’s an underlying fear that new applications of the tech in software programming can produce unwanted or inconsistent results. But achieving reliable IT automation with agentic AI can be achieved right now, with active governance coupled with transparency, especially for large language models (LLMs). High-quality training data is foundational—80% of AI reliability hinges on data integrity—ensuring models accurately reflect IT environments. This is where agentic AI has an advantage: it can maintain an auditable trail of training data, ensuring compliance and quality. To build more safeguards for reliability, IBM takes it a step further, employing rigorous testing and validation, simulating scenarios like peak loads to refine AI behavior, and giving IT teams the ability to fine-tune the LLM or small-language models, using data from watsonx.data. IBM data shows that by using watsonx.data, a company can build agents on a robust data foundation and improve the accuracy of its AI by up to 40%.
“Together, intelligent automation tools create a controlled, transparent system, minimizing risks like inconsistent outputs,” says Chahal. “Human oversight complements this, refining decisions and ensuring trust in evolving IT landscapes.”
One case study from IBM found that a financial firm using AI-powered automation cut operational costs by 30%, indirectly increasing profitability by streamlining customer-facing processes. Currently, 70% of IT leaders cite cost reduction as a priority. “These outcomes highlight agentic AI’s tangible benefits, making it a compelling example of its value in driving business success through enhanced IT operations,” says Chahal.
So why not wait? Why not let others widen the path and report back on any roadblocks? Well, because there’s a strong chance that late adopters won’t have a chance to catch up.
“Over the next 12-18 months, agentic AI is poised to evolve significantly, driven by advancements in multi-model large language models,” says Chahal. “AI Agents will become more sophisticated in their reasoning capabilities, with improved chain-of-thought processing and better integration across multiple tools and APIs. We're likely to see agents that can handle increasingly complex, multi-step workflows with greater autonomy and reliability. The integration of multimodal capabilities will allow agents to work seamlessly across text, code, images, and other types of data.”
His advice? IT leaders should prioritize integrating agentic AI into their automation strategies while addressing governance and skill gaps. “Implementing tools like IBM Instana for unified management and maintaining auditable data trails will be critical to avoid inconsistent outcomes, as seen in 60% of preventable IT outages.”
Additionally, Chahal advises, IT leaders must prepare their teams for an AI-augmented workforce. With agentic AI handling routine tasks, upskilling staff in AI oversight and strategic roles is essential. “Leaders should foster collaboration with agentic AI centers like IBM’s, to leverage expertise from partners and startups, ensuring tailored solutions,” he adds. In fact, IBM is currently innovating lead agents that create and optimize other agents. By balancing that kind of leading innovation with strong governance, IT leaders can use agentic AI to drive efficiency—and save some money—while mitigating risks in an increasingly automated
Learn more about IBM Intelligent IT Automation here.
