Organizations are moving fast on AI. Budgets have been approved, platforms selected, pilots launched. And yet, a growing number of leaders are quietly asking the same question now that they’re a year or two into their journey: why isn’t this working the way we expected?
The answer, more often than not, isn’t the AI model, tool, or all-in-one platform.
The Foundation Comes Before the Intelligence
Across the enterprise organizations we work with, there’s a pattern emerging. Teams invest in AI capabilities on top of platforms like ServiceNow, expecting the tools to deliver measurable efficiency gains. What they get instead is inconsistent output, low adoption rates across the organization, workflow and process friction, and a growing sense that the investment isn’t paying off.
And it all boils down to the data that’s powering the platform – the data that’s not governed, cleaned, or ready for showtime.
This isn’t a new problem, but AI has made it more expensive. When workflows, automation rules, and AI recommendations all draw from the same underlying data, the quality and integrity of that data stops being an IT maintenance issue and becomes an operational one.
Read More: SalesTechStar Interview with Ilyas Kurklu, Co-founder and CEO of Replenit
What Poor Data Foundations Actually Cost
The costs show up in ways that don’t always get connected back to data quality, but they should because the impacts are significant:
-
Automation that doesn’t automate.
Take ServiceNow for example. The power of the platform is in its workflow engine, but that engine runs on configuration data. When an organization’s Configuration Management Database (CMDB) is incomplete, inaccurate, or out of date, the workflows built on top can’t run the way it was designed to and can produce unreliable results. Teams end up doing manually what the platform was supposed to do for them, and the ROI case for the investment starts to erode.
-
AI recommendations teams don’t trust.
Even more “AI-mature” companies are dealing with usage inconsistencies and process improvement gaps because employees stopped trusting the output. Once an employee is burned by a wrong suggestion or recommendation, trust becomes fragile and overall confidence in the technology begins to wane. When teams lose that confidence, they revert to manual processes. The AI keeps running but people work around it – all the while AI investments keep pouring in.
-
Decisions made without a shared source of truth.
When data is siloed, inconsistent, or accessible only to certain teams, leadership ends up making decisions based on different versions of operational reality. IT sees one picture, finance sees another, and alignment takes longer than it should. As a result, organizations move slower than technology.
-
Compliance and risk exposure that’s hard to see until it’s a problem.
In highly regulated industries, governance gaps, or incomplete or inaccessible configuration data, create audit exposure and slow down the response to security and vulnerability events. While the average cost of a breach is significant, what’s less visible, and equally costly, is the ongoing drain of managing risk in an environment where you can’t fully see what you have.
Why Governance Is the Missing Piece
Most organizations that have invested in a platform like ServiceNow have the technical capability to address these issues, but they often lack the governance structure that makes clean data a sustained operational discipline.
That distinction matters more than most leaders initially recognize. Without clear ownership of data standards or executive-level alignment on what trusted data actually means for the organization, the problem keeps coming back.
Governance, done well, creates the conditions for speed and confidence, where automation can be trusted, and AI outputs and recommendations can be acted on.
The organizations making the most of their AI investments are treating data foundations as a strategic priority from the start and maintaining that disciplined approach over time.
The Revenue Connection Is Real, Even If It’s Not Obvious
There’s a reason I look at this through a revenue lens. Operational efficiency and revenue gains are intimately connected.
When automation works as it should, teams can handle more volume without adding headcount. When IT operations can resolve incidents faster because the database is accurate, downtime is reduced and business continues as usual. When HR, IT, and operations teams are working from a shared source of truth, decisions move faster and initiatives get executed with less friction. All of these workflows have revenue consequences.
What organizations often overlook is that the investment in a solid data foundation is upstream of all of it. Organizations can’t automate their way to efficiency with bad data, and governance issues can’t be solved with AI. The organizations that are seeing real returns on their platform investments are the ones who understood that sequencing early.
A Practical Starting Point
For revenue leaders who are asking why their AI investments aren’t delivering, ask yourself these three diagnostic questions:
- Do you know what data your AI and automation are actually drawing from, and do you trust it?
- Can the right people access the right information without a manual process in the way?
- When someone challenges a number or a recommendation that came from the platform, is there a clear, governed answer?
If you’re uncertain about your response to any of these questions, it’s time to think about your data foundation. Every day without it, decisions get made on instinct instead of insight, opportunities get missed, and competitors pull ahead. The good news is that it’s a solvable problem, and the operational and financial upside of solving it is transformative.
About the Author of this Article
Jason Whitesides is Chief Revenue Officer at Ondaro
About Ondaro
Ondaro is a pure-play ServiceNow Elite Partner focused on designing, enabling and sustaining end-to-end business transformation.
Read More: You Have Cloned Your Voice. Now Your AI Is Making Cold Calls
