Why IT leaders need a new approach to AI

For IT leaders, AI is both a leadership challenge and a technological one. Many companies are racing to adopt AI while still struggling to demonstrate where it delivers business value. At the same time, IT leaders are beginning to question what increasingly capable AI means for the future of their own role.

I believe both challenges come from the same problem: focusing on deploying AI rather than the work it needs to improve. Because the companies I see creating the greatest value aren't treating AI as a bolt-on tool. They're enabling specialized AI agents to operate within governed workflows, embedding AI into the way work gets done.

The paradox faced by IT leaders

Most CIOs (94%) are pursuing AI initiatives, but of these initiatives, only 5% show measurable business results within six months. What's going wrong?

They're treating AI as a bolt-on tool, rather than something to embed. Because while AI has become a strategic priority, many organizations still struggle to point to meaningful operational impact.

Part of the difficulty is that there isn't yet a well-established blueprint for creating or measuring value with AI. Companies may see some isolated productivity gains, but it's harder to connect them to wider business outcomes. At the same time, IT leaders are expected to show that those investments are delivering measurable results as soon as possible.

Progress is also slowed by ongoing concerns around security, privacy, and risk, with 45% of IT decision-makers identifying them as barriers to broader AI adoption. The result is a growing gap between the pace of AI adoption and confidence in where it delivers measurable value.

Moving from tasks to workflows

Much of the first wave of enterprise AI focused on making individuals more productive. AI can summarize documents, answer questions, generate content, and retrieve information in seconds. Those capabilities save time, but individual productivity gains don't automatically change business outcomes.

Businesses, however, don't create value through isolated tasks. They create value through workflows that connect people, systems, and decisions.

That means the question isn't simply where AI can be added. It’s asking: How would we redesign this process if AI were part of it from the beginning? The focus shifts away from individual tasks, and towards the operational workflows the business already depends on.

So, instead of becoming another application employees choose to use, AI plays an active role in how work gets done. And when it's part of operational workflows, teams can begin measuring its contribution by the business results it drives, not just time saved on a single task. That's where productivity gains start becoming operational value.

The power of governed workflows

AI only creates business value when it can participate in the work an organization already depends on. That requires more than a standalone chatbot or assistant. It requires AI to operate within the systems, processes, and governance that shape how work gets done.

Operating within that environment gives AI something bolt-on tools never can: context.

Instead of relying on prompts alone, it understands the systems it's working across, the information available, and the role it's expected to play. That's what allows specialized AI agents to contribute consistently rather than starting every interaction from scratch.

Embedding AI into existing workflows also changes how it's governed. A tool that sits outside an organization's systems can only be governed through what it reports back. An agent operating within those systems, however, fits into the organization's existing controls. Giving IT teams visibility into how it behaves and the ability to set clear operating boundaries from the start. Meaning governance isn't layered afterwards; it's built into how AI operates.

That visibility becomes even more important as AI agents gain greater autonomy. IT teams need to understand where AI is being used and what it can do. The more visibility they have, the better equipped they are to assess risk and put the right guardrails in place.  

Together, this combination gives companies the confidence to move beyond experimentation and embed AI into everyday operations.

How embedded AI can deliver value

For IT teams, that means AI becomes part of the work they're already doing rather than another tool they have to use. Instead of requiring them to gather information, switch between systems, and write detailed prompts, AI already has the right context, which saves time and eliminates manual work.

That's the role specialized agents like the TeamViewer Intelligent Agent (Tia) are designed to play. Working within TeamViewer, Tia draws on real-time device data and historical support cases to help IT teams understand issues faster. It brings together the information that's already available, empowering IT to make better-informed decisions without replacing their judgment. It can also carry out certain actions, but only ever with human approval.

Because Tia operates within an existing workflow, it can improve over time. As teams resolve recurring issues, it becomes better equipped to support future work without requiring people to fundamentally change how they work.  

That doesn't mean embedded AI is making decisions instead of IT teams. In fact, 43% of IT professionals say they want AI to provide recommendations while retaining the final decision themselves. Embedded AI is designed to bring together the information that's already available across the workflow, so IT teams can understand the context behind its recommendations and respond faster.

The next role of IT leaders

As AI takes on more routine operational work, the role of IT leaders evolves with it. Increasingly, that role will shift from managing technology to orchestrating outcomes. As AI agents take on more operational tasks, IT leaders will need to decide how they fit into the wider organization and where people should remain involved. This means:

• IT leaders will play a more active role in implementation: Successfully introducing AI is no longer just about deploying the technology. IT leaders need to be clear about where AI creates value and what successful adoption looks like. They also need to create space for employees to experiment, without making AI feel like a threat to their role.
• IT leaders will set the boundaries for how AI operates: They need to decide where AI can act independently and where people need to stay involved. Those decisions will shape how AI is used across the business and how confidently employees adopt it.
• IT leaders will become responsible for building trust in AI: Trust depends on more than technical performance. IT leaders need to establish clear accountability for AI-driven outcomes and make sure decisions can be reviewed and corrected when needed.
• IT leaders will strengthen governance around AI: Governance has stopped being a supporting activity. It becomes an ongoing responsibility that ensures AI is used safely and in line with the business.

Summary

Adoption is no longer what distinguishes organizations. Nearly all of them are investing in AI, and most are still working out what that investment changes.

The companies seeing the greatest value from AI aren't treating it as a standalone tool. They're embedding specialized AI agents into governed workflows, where they can support the work the business already depends on.

With this shift, IT leaders are now responsible for creating the right environment for AI to operate in. They need to build governance into existing workflows while ensuring AI aligns with and furthers business priorities.