By Keith Andes, Head of Product Marketing at EasyVista. EasyVista were finalists in the ‘Best SaaS Product for Workflow Automation‘ category at The 2025 SaaS Awards.

 

Every IT and operations team I talk to is hearing the same message from the top: “We need to be AI-first.”

It shows up in board slides, strategy decks, and executive calls. The mandate is clear, but the path isn’t.

Teams are left trying to turn a buzzword into a plan. They’re already stretched thin keeping up with service requests, assets, and security noise, and now they’re expected to layer AI on top. For some industries like finance and healthcare, the pressure is even sharper because boards are demanding proof of innovation. For mid-sized orgs, it can feel like just one more initiative piled onto an already overloaded backlog.

The result is hesitation. Not because people don’t see the potential, but because the mandate feels abstract, expensive, and disconnected from reality. Without clarity or extra capacity, most teams wait it out.

Progress starts when the work is broken down. Automation proves itself in high-volume, repetitive tasks that follow consistent patterns. AI proves itself in areas where older tools couldn’t help, like interpreting natural language, predicting incidents, or surfacing knowledge in context. With the right foundation, these become manageable steps forward instead of a moonshot.

The real source of hesitation

Hesitation isn’t lack of ambition. It comes from two things: vague direction and limited bandwidth. Leaders say “AI-first,” but don’t define what that means for incident management, change, or service delivery. At the same time, teams don’t have extra resources to chase hype. They’re cautious because they’ve seen past programs fail under poor data quality, missing governance, or shifting priorities.

That caution is rational. It’s a signal to start smaller and clearer, not bigger and louder.

What organizations are actually doing

Survey data confirms what we see in practice. Companies are cautious with AI, but practical with automation. Most start with automation where the patterns are obvious, like password resets, ticket routing, asset updates, and reporting accuracy.

AI adoption shows up more selectively, in places that traditional tools couldn’t solve. Chatbots that can actually understand real language, predictive models that flag incidents before they escalate, contextual knowledge search that reduces handling time. These are incremental, not massive transformations, but they create visible wins and free up time for teams already short on capacity.

Why some programs stall

Most stalled AI and automation efforts fail for predictable reasons. The first is data. If asset records are incomplete or tickets are riddled with inconsistent fields, layering AI on top just amplifies the noise. Leaders often underestimate how much poor data hygiene undermines results, and teams are right to be cautious when they know the inputs cannot be trusted.

The second is process clarity. Automating a broken process does not fix it. For example, if ticket categorization varies wildly between teams, an attempt to build an automation on top of that inconsistency won’t go well, and only makes the problem more visible and harder to untangle later.

The third is overreach. Leaders aim for sweeping transformations before building credibility with smaller, high-volume wins. The temptation is to promise something revolutionary, but most organizations see better results when they prove value in places where automation is obvious and repeatable.

These pitfalls do not mean people are resistant to AI. They mean they have lived through failed programs before and are careful not to repeat the same mistakes. That caution is a strength when it is used to guide smaller, more grounded steps forward.

A practical playbook for progress

Turning an AI-first mandate into tangible results requires a simpler frame. Leaders who succeed usually start by mapping workflows to see where time really goes. The goal is not to chase buzzwords but to identify the repetitive, high-volume work that slows people down every day.

Once those candidates are clear, the next step is to choose one pilot. It should be small enough to implement quickly but visible enough that people notice the impact. Password resets, ticket routing, or onboarding tasks often make good first choices because they are common pain points that affect a wide range of users.

From there, teams can layer AI where automation alone cannot solve the problem. Free text interpretation, predictive incident models, or contextual knowledge surfacing all become natural extensions once the foundation of workflow automation is stable.

The last step is communication. Sharing simple outcome metrics builds trust and momentum far faster than broad slogans. A metric like “30% faster password resets” or “15% fewer escalations” is concrete, believable, and directly connected to people’s work. These visible wins turn an abstract mandate into something teams can feel and trust, making the next step forward easier to take.

How adoption gains momentum

The organizations making progress don’t try to “AI-ify everything,” they focus on workflows that matter, automating the repetitive and standardized work that bogs people down, applying AI where intelligence is required and older tools fell short, measuring outcomes, and expanding step by step. Momentum builds this way because leaders get proof points, teams build confidence, and adoption feels less like a leap into the unknown and more like a continuation of what’s already working.

Building trust along the way

AI-first mandates collapse without trust. Teams need to know what AI is expected to do and what it isn’t. If people suspect it’s a cover for headcount cuts, adoption dies before it starts. Transparency about intent matters as much as the technology itself.

Trust also depends on stability. If the process is broken or the data is a mess, AI just creates faster chaos. Leaders who acknowledge this and invest in process and data first earn more credibility than those who push slogans.

AI that works

AI and automation don’t need to feel intimidating to create value. The best results often come from behind-the-scenes improvements that reduce friction, speed up work, and give teams back capacity.

The pressure to be AI-first will only grow. The way forward is to translate that pressure into practical, work-first results. That’s where real progress happens.