If you’re a CFO, operating partner, or finance leader at a PE-backed portfolio company, you’re probably under pressure to do more with AI. The challenge isn’t access to tools. It’s AI data governance: getting your data, controls, and security in shape so AI actually delivers value instead of amplifying risk.
The signal from the broader market is clear: 33% of CFOs named data readiness gaps in quality, accessibility, and completeness as the #1 blocker to AI ROI. The leaders who get AI data governance right first are the ones who’ll see returns.
In a recent Consero Global webinar, Brock Kahanyshyn, CISO and EVP of IT at Consero, Sandy Serkes, CEO of Valora Technologies, and Jeff Bradbury, VP of IT and Security at BV Investment Partners, unpack what AI data governance and security really look like in regulated, real-world environments. They cut through the AI hype to focus on the unglamorous foundations — clean data, clear ownership, security frameworks, and the human judgment AI can’t replace.
What is AI Data Governance?
AI data governance is preparing your data, your people, and your decision-making frameworks to operate alongside AI safely and productively.
AI readiness has three layers:
- Being ready for tools to take on human tasks
- Training AI on the right content
- Giving AI clear decision guidance to follow.
“Most organizations have no idea what data they have, why they have it, where it came from, whether they should have it, how old it is, whether it’s sensitive or not. Everybody’s just kind of lost.” — Sandy Serkes
Readiness also means accepting uncertainty — being prepared to retrain, retool, and course-correct when AI gets it wrong.
Evaluating AI Readiness Across a Portfolio
Private equity firms are evaluating AI readiness through two lenses: how AI creates value, and how it gets adopted safely. The real test is if a portfolio company can scale a pilot.
Key takeaways:
- AI value creation can come from productivity gains, stronger reporting, better customer experience, or more efficient operations.
- Safe adoption depends on the foundation underneath. Siloed data makes everything harder — 28% of investor-backed CFOs name siloed systems as a top AI ROI blocker.
- AI readiness is operational maturity, not a technology issue. The question isn’t “can they use AI?” — it’s “can they use it safely and create measurable value?”
“Most companies can run an AI pilot. It’s whether or not they can scale that AI solution in a way that’s repeatable, controlled, and tied to some type of business outcome.” — Jeff Bradbury
Data Management vs. Information Governance
These two terms get used interchangeably, but they’re not the same — and both need to be in place before AI enters the picture.
Data management is about storage and accessibility — typically structured data like databases, rows, and columns.
Information governance is the layer on top: records management, eDiscovery, privacy, AI readiness, and lifecycle management for the full data estate.
AI is only as strong as the data it’s trained on. Clean, curated, lifecycle-managed data produces strong results. Messy data full of duplicates and orphaned sensitive information produces poor results and potential liability.
“AI is going to be trained on your enterprise data, and it will only be as strong or as weak as the data that’s available to it.” — Sandy Serkes
The Risks of Adopting AI Before Your Organization is Ready
When organizations rush AI adoption without a strong data foundation, the technology doesn’t fix the underlying problems — it amplifies them at scale.
Key takeaways:
- AI amplifies whatever exists. Strong data foundations create leverage; weak ones create exposure.
- Poor permissions get exposed faster. Unapproved tools create privacy and compliance risks. Bad data produces bad decisions — at speed.
- Polished, authoritative output doesn’t equal accurate output, which makes human review more important, not less.
“AI can make a bad answer very convincing. It can produce something that sounds polished and authoritative even when it’s wrong.” — Jeff Bradbury
Cleaning Up ROT and the Path to Autoclassification
Before AI can produce reliable results, the data it’s trained on has to be cleaned up. That means identifying and removing the ROT: redundant, obsolete, and trivial data clogging most enterprise environments.
ROT is the industry’s polite term for junk data, and, for most organizations, about 50% of their data qualifies.
Key takeaways:
- Removing duplicates is the easiest, fastest, lowest-cost, highest-impact first step.
- Autoclassification is the next level: software determines what data you have, where it lives, what should stay, and what should go.
- The gold standard is rules-based scoring that determines AI acceptability for each data asset in the organization.
“ROT is probably the easiest, fastest, low-cost, high-impact thing that you can surface and get rid of. But that’s literally step one — that’s just scraping the surface.” — Sandy Serkes
Security Frameworks That Accelerate AI Adoption
Frameworks like SOC 2 and CIS controls are the structure that lets organizations adopt AI faster and with more confidence.
These frameworks answer the questions that always come up during AI adoption:
- Where the data lives
- Who can access it
- Who approves its use
- Whether vendors are monitored
- What happens when something goes wrong
Without that structure, every AI initiative becomes a one-off debate that slows the business down.
Key takeaway: The discipline of becoming SOC 2 compliant tightens up processes across the entire organization — not just AI initiatives.
“When properly implemented, security frameworks help clear those obstacles and allow your organization to accelerate a lot faster.” — Jeff Bradbury
Handling Sensitive Data Before You Introduce AI
For organizations handling PII, PHI, or PCI, the requirements for AI are non-negotiable and go beyond what most people think of as sensitive data.
Must-have #1: a clean, accurate understanding of where all sensitive data lives — not just the obvious folders, but the wayward documents, file shares, and structured data that contain salary histories or other regulated information.
Sensitivity isn’t just regulated categories. Trade secrets, M&A plans, and executive compensation all qualify — and they’re harder to detect with simple pattern matching.
Must-have #2: clear, legally vetted policies for handling sensitive data. AI must obey them the same way employees do.
Strongly recommended: define internal ownership for AI inputs and outputs, and use autoclassification to identify what you have and where it should sit.
“AI will certainly find it. So you have to know where that stuff lives.” — Sandy Serkes
What Good AI Readiness Looks Like in Practice
The best companies aren’t treating every AI initiative the same way. They’re matching the level of review to the level of risk and using a tiered approach to keep low-risk experimentation moving while protecting high-risk data.
“Good” starts with clarity: who owns AI strategy, which tools are approved, what the rules around data are. A two-lane (or multi-lane) approach works well:
- Lane one: low-risk data for drafting, summarization, and brainstorming.
- Lane two: sensitive or regulated data — structured review, deeper due diligence.
Tiered systems set implicit priorities, so organizations don’t have to “boil the ocean.” They know which systems to remediate first and what the game plan is over the next two to three years.
The right frameworks let employees experiment with confidence because they know what they can and can’t do.
How AI Fits Into Existing Compliance Frameworks
AI doesn’t require organizations to throw out the compliance frameworks they already have. It’s a new way of using data that’s already governed.
Key takeaways:
- The legal consensus is that AI falls under the same control mechanisms as any other data handling — SOC 2, HIPAA, ISO. The obligations are the same; the use cases are new.
- AI-specific frameworks exist for organizations that need a starting point: the EU AI Act, NIST’s AI control matrix, and CIS guidance.
- CIS offers tiered implementation, which lets organizations match their controls to their size and maturity.
No need to reinvent the wheel. Pick a framework and use it as the foundation for AI operations.
Balancing Innovation with Security and Compliance
The best organizations don’t treat innovation and compliance as opposites. They bring the right people together early to define safe paths before AI adoption fragments across the business.
Key takeaways:
- Governance should remove uncertainty. Employees shouldn’t have to guess which tools are approved, what data can be uploaded, or whether a use case needs review.
- A risk-based approach lets low-risk cases move quickly while concentrating attention on high-risk ones.
- Accuracy matters as much as security. AI doesn’t know what’s right and wrong — it finds statistically relevant matches, even if the data is outdated or wrong.
- You can’t “unbake the cake.” Once an LLM is trained on bad or uncurated data, there’s no real way to pull it back without starting over.
- Agentic AI raises the stakes. When agents create their own data without human oversight, accuracy and lineage problems compound quickly.
What Separates AI Winners From Strugglers Over the Next 12-24 Months
Looking ahead, the difference between organizations that succeed with AI and those that struggle will be disciplined execution and the foundation underneath it.
Consero’s 2026 report found 97% of investor-backed firms are now active in the AI space, but only 42% have AI broadly deployed or fully embedded. Being active isn’t the same as having impact.
Key takeaways:
- Winners turn AI into a managed business capability — not just a chatbox that does cool things. That requires trusted data, clear ownership, practical governance, and use cases tied to measurable value.
- Companies with siloed data, fragmented tools, and unclear ownership will struggle to scale.
- Institutional knowledge is shifting from people’s heads into the data itself. Autoclassification codifies that knowledge into the systems where it lives.
- Sharing skills and AI knowledge across the organization protects against turnover and builds a collaborative AI culture.
- Human-in-the-loop is still essential. Judgment, experience, and wisdom are uniquely human — and AI’s role is to free people up for higher-order work, not replace it.
Build the Finance Foundation to Unlock AI ROI
Strong AI data governance starts with one thing: a clean, well-controlled data foundation the rest of the business can build on. That’s exactly what Consero delivers. As an AI-enabled finance partner for PE-backed and growing companies, we combine best-in-class systems, AI-driven automation, and senior finance talent through SIMPL®, our proprietary reporting and engagement platform.
Our clients close the books in 5-10 business days, see 20-40% cost savings vs. building in-house, and gain the audit-ready, AI-ready financial operations that make scaling possible. Whether you’re standing up a new platform, integrating add-ons, or preparing for exit, we’ll help you build a finance function that’s ready for what’s next.
In a 30-minute conversation, we’ll benchmark your finance operations, identify where data, processes, or visibility might be holding you back, and show you what’s possible.
Talk to a Consero finance expert about what a modern, AI-enabled F&A function looks like for your business. We’ll map it out together — it’s 30 minutes, zero pressure.
No sales pitch. Just a roadmap tailored to you.
