There’s a palpable tension in IT today. Teams are stretched to their limits with a growing backlog of initiatives, while executives expect IT to lead the charge on transforming an organization into an AI-driven one.

And the numbers paint a somber picture. IT teams are drowning in work as digital transformation projects have not slowed down but rather accelerated. In fact, IT project requests in 2025 jumped 18% compared to the year prior, and nearly one in three IT projects (29%) missed their deadlines, creating tension with business stakeholders.

But here’s the part few IT leaders say out loud: the answer isn’t about putting in more hours to catch up, it’s about supercharging the teams you already have with existing tools at your disposal. When the work itself has outpaced traditional capacity, the solution becomes about enabling existing staff to be more productive and tackle previously backlogged work.

So what should you do when the gap between what’s needed and what’s possible keeps widening? You start rethinking who gets to build, who gets to automate, and how work actually gets done.

The answer to the skills crisis lies in unlocking the talent you already have—and that is precisely the shift happening right now with vibe coding.

The rise of vibe coding

It starts with a simple idea. You, the domain expert, describe what you want in natural language and an AI agent (or agents) take it from there, planning, reasoning, and executing end to end. It’s the moment when domain experts—be it in IT or other domains—finally get to build the systems they’ve been waiting for.

Now, domain experts no longer have to master complex coding syntax to turn their ideas into workflows, processes, or service steps. They can finally build the systems they know best. And the IT service organizations that understand this first will deliver experiences their competitors can’t match.

Want an onboarding sequence with provisioning, equipment, training, and approvals? Just describe it. The AI agent maps the flow, identifies the dependencies, pulls in the right steps, and assembles the workflow. Want to assess incidents faster? Simply tell the AI agent the conditions. The agent reads employee messages, extracts context, spots patterns, matches related incidents, and sets up the next steps automatically.

I know what you’re thinking: “Oh, look, another tech exec showing how AI is going to replace jobs.” Let me be clear: Developers don’t disappear in this world. They just stop getting pulled into repetitive maintenance work and shift their focus to higher impact areas like architecture, design and solving real problems—which is the work that actually needs a human developer and further innovation.

IT service before and after agentic AI

If you’ve worked inside a traditional IT service environment, you already know the pain points by heart: the static forms, the rigid workflows, the dependence on specialists, the manual handoffs and the endless context switching. None of this is news to you.

Agentic AI changes the service cycle at every layer, beginning right at the point of contact. An employee reaches out from wherever they already work—maybe Slack or a web portal. The AI agent immediately reads the intent, extracts the details, checks for related issues, and fills in the fields that a human used to handle. This means no portals, forms, or back-and-forth just to understand what’s going on.

As the case develops, the agent analyzes if something should be classified as an incident. It looks at the configuration items involved, detect similar open issues, and even recommend likely root causes. And all of this pulls from a dynamic configuration management database (CMDB) that maps systems and assets in real time, giving IT analysts the context they’re usually scrambling to piece together.

Escalations feel different too. The AI agent hands the human specialist a complete, ready-to-use summary of what’s happening. And the technical support engineer finally gets to focus on solving the problem rather than chasing down information. Teams can even swarm incidents directly in Slack with full links to the underlying records.

All of this adds up to results you can feel immediately: faster responses and lower mean time to repair (MTTR). The best part? You get it with the team you already have.

Who gets to build

The most transformative part of vibe coding is access. Suddenly, the people who actually understand the work can help build it, from IT service specialists to HR partners to operations managers—really, anyone who knows what needs to happen and can describe it, then passing it on to AI agents to handle execution.

This is how organizations reclaim capacity. In fact, 67% of organizations report that AI is reshaping technical work, requiring upskilling of the existing workforce. Developers get the breathing room to focus on infrastructure and modernization. Business teams get the freedom to build and iterate in real time. And leaders get an operating model that’s more adaptable and resilient, one that doesn’t fall apart the moment the talent market tightens.

Nobody’s perfect

It should go without saying that vibe coding is no panacea. It’s a powerful start, but don’t treat it as a finished product.

As industry analysts like Vernon Keenan have noted, a large language model (LLM) is like a power plant in that it provides the raw energy, but requires a robust orchestration grid and shared enterprise context to be truly usable in an enterprise. Without deterministic control layers, rigorous observability, and context into your business, natural language prompts can still lead to hallucinations that could end up creating more manual cleanup for your teams.

The key is to adopt a vibe-but-check mindset, where AI handles the creative heavy lifting while humans provide the essential governance. Ensure your orchestration platform has a trust layer and auditable execution traces so that every agentic workflow remains grounded in actual business logic.

The question leaders need to answer now

Do we wait until this becomes the standard? Or do we treat the talent crisis as the moment to proactively rethink how work gets done?

Organizations that act early will greatly reduce operational friction, improve employee experience, protect their teams from burnout, and create an enterprise where domain experts become creators, not just requesters.

The shift has already begun. The organizations that lean into it will feel the difference first.

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