Takeaways from Certinia LIVE: AI doesn’t fix bad data

Event Recap May 01, 2026

BOTTOM LINE UPFRONT

What we heard at Certinia LIVE: AI in services operations is here, but most firms aren’t ready to capture its value because their data foundation isn’t. Tools like Veda don’t solve staffing and margin challenges on their own; they expose poor data quality and lack of ownership. Until firms fix data governance and align how resource decisions are actually made, AI will scale the problem instead of solve it.

“Are we ready for this?” It’s the question we heard time and time again at Certinia LIVE, eight days after Certinia launched Veda – its new AI operations engine for professional services – on April 15th.

For most firms, the answer is not yet. Because while it’s tempting to blame the staffing gap in PE-backed services firms on tech, data’s the culprit. We see it time and time again: CRM implementations that fail due to poor data ownership, ERP rollouts that produce numbers nobody trusts, and now AI generating recommendations nobody acts on.

And staffing only highlights this dynamic, where the gap between utilization targets and actuals lives in the lag between identifying a need and filling it. The delay is measured in days, but the impact shows up in margin. By the time it appears in reporting, the damage is already done. Projects get staffed with whoever is available, not whoever is right. More process doesn’t fix it, but better data does.

That’s exactly what we heard at Certinia LIVE:

1. Bad data is still the bottleneck

Your PSA probably looks like it has what it needs to support intelligent staffing decisions, but in reality, it probably doesn’t. Skills profiles sit untouched since go-live. Availability data lags reality. Role definitions don’t reflect how engagements are actually priced and staffed.

Which means decisions don’t come from the system but from experience. There’s almost always someone who just knows: who’s actually available versus technically available, which teams work, what the system says versus what’s true.

That’s not a resource management system. It’s a single point of failure.

“The first question I ask before any Veda conversation is: what is the agent going to reason on? Because in most firms I walk into, the answer is data that hasn’t been meaningfully maintained since the system went live. The inputs, not the technology, are the issue.” – Adam Rosenfield, Director of Certinia Implementations

And this distinction matters even more with AI than with any prior system. Layer AI on top of poor data, and you get wrong answers (even more troubling – those wrong answers will be delivered with confidence).

2. Governance is the foundational step

The instinct is to start with technology. The answer is to start with governance.

Most firms never assign clear ownership of resource data health, so what was built at implementation quickly degrades, even as reports continue to run. Once that ownership is in place, the system starts to function as intended: skills reflect how the business actually differentiates its people, resource profiles stay current, and project data becomes usable for more than just billing.

None of this is technically difficult. But it does require ownership – and accountability for making the data something the business can actually rely on.

3. Certinia Veda is a powerful tool, but only as effective as the data it’s built on

Every board deck has an AI slide. Few have a data readiness slide. That gap explains most of the disappointment from firms that moved fast.

Veda is powerful because it operates inside the system that already governs your business, using your actual role structures, pricing logic, and financial rules. But that also means it’s only as effective as the data it’s built on.

The conversation is shifting. This is no longer about whether to invest in a platform, but about whether you’re ready to get value from what it can now do.

“The conversation at Chicago was telling. Everyone wanted to talk about what Veda does. The harder and more important question is whether the data environment is ready for it. That’s the work that should ideally be in place ahead of adoption to ensure maximum ROI.” – Seamus Egan, Managing Director

In other words: turning Veda on is straightforward. Getting it to deliver is not. The work happens before go-live: aligning on how staffing decisions are made, where the data breaks down, and who owns fixing it.

FAQ

What is Certinia Veda, and how does it support staffing decisions at PE-backed services firms?

Certinia Veda is an AI-powered operations engine built natively within the Certinia PSA platform that uses your firm’s existing role structures, pricing logic, and financial rules to support intelligent resource management and staffing decisions. For PE-backed professional services firms, Veda offers the potential to close the gap between utilization targets and actuals — a gap that directly impacts margin. However, Veda is only as effective as the underlying data it reasons on, which means firms with degraded skills profiles, lagging availability data, or poorly maintained role definitions will see limited ROI at best, and confidently wrong recommendations at worst. Accordion helps services firms assess data readiness and governance posture before Veda adoption to ensure maximum value from day one.

Why do AI-powered resource management tools fail to deliver ROI in professional services firms?

AI tools like Certinia Veda fail to deliver ROI when the underlying PSA data hasn’t been meaningfully maintained since go-live. Skills profiles go untouched, availability data lags reality, and role definitions drift from how engagements are actually priced and staffed — leaving the system unable to support the decisions it was built to inform. The result is that staffing continues to rely on tribal knowledge rather than system data, creating a single point of failure and scaling the problem AI was supposed to solve. For PE-backed firms where margin erosion shows up in reporting only after the damage is done, poor data governance is a value creation risk, not just an operational inconvenience.

What data governance steps should professional services firms take before implementing AI staffing tools?

Before implementing AI-driven staffing tools like Certinia Veda, professional services firms should establish clear ownership of resource data health, update skills profiles to reflect how the business actually differentiates its talent, align role definitions with current engagement pricing models, and close the lag between real-world availability and what the system reflects. Most firms skip this work and move straight to technology — which is why most AI implementations underdeliver. Accordion’s pre-adoption readiness assessments help PE-backed services firms identify where data breaks down, assign accountability for maintaining it, and build the governance foundation that turns AI investment into measurable margin improvement.

How does poor resource data quality affect margin and valuation at PE-backed services firms?

At PE-backed professional services firms, poor resource data quality directly compresses margin by forcing staffing decisions based on who’s available rather than who’s right for the engagement. The lag between identifying a need and filling it — measured in days — is invisible in real-time but shows up as margin deterioration in reporting, often after the damage is already done. When this problem persists into an AI environment, it doesn’t just perpetuate bad decisions; it accelerates them with greater confidence, making course correction harder. For sponsors focused on EBITDA performance and exit readiness, data governance in the PSA system is a financial issue, not an IT one. Accordion works with PE-backed services firms to address resource data quality as a core component of value creation planning.

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