Everyone wants to be AI-first, but few realize it’s not just about the best AI tools but about rewiring teams, skillsets, and the culture to take an AI-first approach to working. Read on to learn what it really takes to build AI-ready IT teams.

The rise of agentic AI has brought both excitement and uncertainty. While organizations are in awe of its potential to drive growth, there’s also concern about how it will change workplace dynamics. Every organization wants to make the most of this potential, but few have the teams or processes in place to do it effectively.

This is because becoming AI-first isn’t about just adding smart tools to existing systems. It’s about reshaping how people think, how workflows operate, and how humans and AI learn to work side by side. It calls for a switch in mindset. 

Workflows, processes, and roles all need rethinking. Since AI can now take on repetitive, low-risk work, human technicians can move higher up the value chain towards proactive problem-solving. The IT workforce of the future will have a combination of AI agents and human agents. 

IT leaders have to now set the tone for this new way of working. Organizations must build teams that can adapt fast, reskill employees for the AI era, and cultivate an AI-first culture where people and AI learn, experiment, and grow together. This involves reassessing what skills matter, how teams collaborate, and how to build systems that let humans and AI work side by side without friction or fear.  A key piece of this is educating teams early, setting clear expectations, dispelling fears about AI replacing humans, and showing how AI amplifies their expertise. 

The task ahead for IT leaders is clear: set the foundation, prepare teams, and build the culture that makes this new way of working sustainable.

Inculcating an AI-first mindset 


AI adoption is happening faster than our understanding can catch up. Most employees are still unsure of what AI means for their roles and how it fits into daily work. There’s concern, fear, and resistance to using agentic AI in day-to-day tasks.

The first step to building AI-first teams is education and exposure. IT leaders must take the time and initiative to educate every employee on the value that agentic AI brings, and help them see how AI is not a replacement but a collaborator. It removes friction and frees technicians from repetitive work so they can focus on higher-value, more satisfying tasks.

It begins with encouraging every employee to use AI tools for work, even if for simple and generic tasks such as writing emails. Some organizations even mandate the use of basic AI tools so as to help employees get over the initial resistance that a new, transformative technology brings.

Leaders can then encourage technicians to gradually start using AI for work, such as summarizing tickets or writing scripts. 

And once employees start trusting AI and gaining confidence that their jobs aren’t at risk, they’ll be able to customize and configure AI for their requirements and come up with new ways of working that bring out the best of both humans and AI agents. 

Teams will also begin experimenting with AI, pushing the boundaries, and looking at how best they can tap into the potential of AI to make their lives easier. Building an AI-first culture means normalizing experimentation and making iteration a part of daily work. 


Upskilling and AI-coaching teams

Once the initial fears are dispelled and teams are comfortable with AI and the new way of working, it is time to actively coach employees on the agentic AI way of working. This involves training them on not just using AI-powered products but take the AI-first approach to working. 

Leaders need to redesign processes so AI becomes part of execution. Walk teams through the new processes, show them where AI fits, and redefine what accountability looks like when humans and AI share tasks.

The next step is strengthening the foundational elements of an AI-first IT team. 

Building data literacy

AI is only as powerful as the data it runs on. For IT teams to use AI effectively, data across systems and workflows must be unified, clean, and contextual.

Every technician should know how data flows, where it lives, and how to maintain its integrity. Teams should be trained on practices like documentation, consistent naming, accurate tagging, and elimination of duplication. They should know what data matters, how to protect it, and how to interpret AI-generated insights responsibly.

Data literacy becomes core to every role. Teams that understand their data help improve AI accuracy in their workflows and drive ROI from AI investments. 

Driving proactive action and automation 

Agentic AI shifts IT from reactive support to proactive, predictive operations. Teams must learn to move from responding to events to anticipating them. 

For example, IT leaders must encourage technicians to start using AI for repeatable tasks like patch management, script generation, endpoint updates, etc., and gradually turn them into semi-autonomous workflows supervised by technicians.

Employees have to be trained to use AI insights to detect early warning signs and prevent escalations before they happen. Similarly, all technicians must be taught to track and measure AI-driven improvements. 

Gamifying the agentic AI way of working and incentivising employees to use AI also helps switch employees’ mindset. For example, highlighting work done by employees using AI, offering bonuses tied to AI efficiency KPIs, and helping employees quantify the time saved or time reallocated to certifications or stretch projects can help drive adoption and proactive use of AI. 

Embedding security and governance 

As AI integrates deeper into IT operations, governance becomes critical to daily operations. Data governance norms will evolve, and teams must work within clear boundaries around what data can be shared with AI tools, what remains restricted, and how privacy guardrails operate.

The clearer the rules, the more confidently teams can use AI. 

Responsible AI adoption also means keeping humans in the loop. Every workflow should have clear ownership, with someone accountable for validating outcomes, refining models, and ensuring AI decisions stay aligned with business and ethical standards.

Enabling continuous iteration 

As AI begins handling L1 deflection or automated patching, feedback loops are critical. Teams must routinely review AI outputs, document outcomes, and refine workflows. 

Processes must be established to help technicians review the results from the AI, document any issues they notice, and suggest changes to workflows so as to avoid these issues. Teams should be trained on how to review what the AI did, flag what didn’t work, and feed this learning back into the system. That’s how AI matures safely and how teams build trust in its decisions.

Equipping teams with the right tools

Adopting agentic AI is a process that includes multiple moving pieces. The right tools make the shift smooth and scalable, and businesses need endpoint management and IT service management tools that help IT teams adopt AI responsibly and strategically.

An ideal tool brings all critical IT functions into a single, connected system, unifies core IT functions, and fits seamlessly into existing IT workflows. It is also one that is purpose-built for IT, rather than something built for other tasks like customer support. An AI-ready platform is one that has security embedded into every layer of the platform. 

SuperOps is a unified endpoint and service management platform built specifically for internal IT. SuperOps also offers free onboarding, data migration assistance, and 24/5 live human support, making it easy for IT teams to adapt to the new way of work.  Schedule a demo with our team today to learn more!