Whether an organization reaps the benefits of AI depends on the state of its data and how effectively the organization aligns information, priorities, and a definition of success across teams.
When AI is grounded in accurate, complete, and governed data from a trusted system of record, it can deliver meaningful outcomes. But when data, processes, and objectives are not connected across functions, AI’s impact is limited, and teams risk relying on insights or actions that lack the context and trustworthiness needed to drive business outcomes.
Recent Ivanti research on scaling AI in IT operations reinforces what many leaders are seeing firsthand. Although organizations are investing in AI, many struggle to move beyond early use cases because they lack foundational alignment and governance. However, the AI payoff is showing up at companies that are doing both pieces of work in parallel—the technology investment and the alignment work underneath it.
Fragmented data, inconsistent processes, and competing priorities across teams are often framed as a technical challenge, but it’s not. It’s an organizational challenge rooted in governance.
Our latest research shows that governance is where many organizations are most exposed to blind spots that undermine organizational oversight. Deployment is moving faster than the controls, processes, and data foundations needed to support AI. Most IT teams say they’ve assigned ownership for AI initiatives, but far fewer have real clarity on accountability, consistent governance practices, or confidence that policies are being applied uniformly across the business.
The gap between nominal and actual accountability matters. Effective AI requires more than access to data. It depends on trusted, governed data, clear ownership, and shared understanding on how decisions are made. When these foundations are in place, organizations can use AI with greater confidence, transparency, and accountability. When they are not, teams risk inconsistent outcomes, reduced trust in AI-driven insights, and difficulty scaling AI responsibly across the organization.
The impact shows up quickly. A significant portion of IT professionals report seeing AI outputs that could have real operational consequences, and governance is now one of the most cited barriers to scaling AI—ahead of skills gaps or technology limitations.
Handled well, governance doesn’t slow AI down. It does the opposite. It creates alignment and defines when AI can act independently, when humans should step in, and how decisions get made across teams.
And that’s the real point: governance is alignment, operationalized.
When companies are in alignment, the results are measurable.
At organizations where AI is treated as business-critical—not just experimental—54% of IT professionals say it makes their work both faster and better, according to our research. That’s more than double the 24% at companies still stuck in pilot mode.
That gap reveals something important. AI isn’t inherently transformative. It becomes transformative when the organization around it is ready to support it.
You see the same pattern in how people use AI. Advanced users report saving about six hours a week, compared to just three hours among those at the earliest stages of use. Over time, that difference compounds. Our research finds that IT professionals are reclaiming more than 312 hours a year—nearly eight full work weeks—because AI is taking repetitive, manual work off their plates.
And those hours don’t just disappear. They get reinvested into higher-value work, strategic projects, and solving problems that don’t fit neatly into a workflow or a ticket queue.
That’s the real return on AI—not just efficiency, but capacity.
But here’s the catch: those outcomes aren’t evenly distributed. They show up in organizations where the underlying work of alignment has already started. Where data is shared, priorities are clear, and teams are operating from the same definition of success.
In other words, AI doesn’t create alignment. It rewards it.
Data is the most measurable layer of alignment, but there are others. Here are three other areas where alignment shows up and interacts with AI.
1. Strategy helps define priorities and desired outcomes. To achieve this, teams need to be pulling toward shared outcomes rather than a collection of departmental ones.
**2. **Governance provides the trusted data, context, and controls AI needs to operate responsibly. This requires that rules about data ownership, access, and decision rights be consistent across the org chart.
**3. **Culture influences adoption, trust, and whether insights ultimately translate into action. People need to trust the information available to them and be empowered to act on it.
Invest in AI tools without investing in those three layers, and you end up with a faster version of whatever you were already doing. Organizations that establish alignment across strategy, governance, and culture create a foundation for AI to drive more informed decisions, greater operational efficiency, and meaningful business outcomes.
The onus is on leadership to get things moving on alignment. AI won’t wait for companies to get their POVs aligned.
The work of aligning strategy, data, and policy across functions was always worth doing. AI is just making it harder to put off that work much longer.
For organizations willing to act, that pressure isn’t a constraint—it’s a catalyst. Melissa Puls is CMO and senior vice president of customer success and renewals at Ivanti.