AI adoption is accelerating fast. A McKinsey study found that 88% of organizations now use AI in at least one business function. But as AI becomes embedded in everyday tools—ERPs, CRMs, EPMs, and analytics platforms—many organizations are discovering a hard truth:
AI is only as good (and as safe) as the data and systems behind it.
In a recent webinar hosted by goVirtualOffice, industry leaders explored why AI governance, compliance, and ERP readiness must come before large-scale AI adoption—and how organizations can build a strong foundation without slowing innovation.
AI Is Already in Your Systems (Whether You Realize It or Not)
AI is no longer a standalone experiment. It is increasingly embedded into core business platforms:
- ERP systems
- CRM platforms
- Financial planning and analysis (FP&A) tools
- Reporting and analytics solutions
Many organizations are already “using AI” simply by running modern enterprise software. The real question is not whether AI is in use—but whether it’s being used intentionally, securely, and compliantly.
Why AI Requires Structure and Oversight
One of the biggest risks with AI adoption is unintentional misuse.
During the webinar, a real-world example highlighted the issue:
A finance team member uploaded quarterly financials into an AI tool to generate insights—without realizing the data was being sent to a public cloud. The result? Financial information was unintentionally exposed six days before earnings were scheduled to be released.
No harm was ultimately done, but the root problem was clear:
There were no governance guardrails in place.
AI governance isn’t about blocking innovation—it’s about defining:
- What data can be used
- Where that data can go
- Who can access AI tools
- How outputs can be shared
Without these guardrails, even well-intentioned teams can create serious compliance risks.
What Is AI Governance?
At its core, AI governance means putting clear boundaries around how AI is used inside the organization.
This includes:
- Policies defining acceptable AI use
- Controls over sensitive data inputs
- Permission-based access to AI tools
- Oversight of AI-generated outputs
- Ethical and regulatory considerations
For highly regulated industries—such as healthcare, finance, or government—AI governance is not optional. Some organizations may even choose to delay AI adoption entirely until regulations and risks are better understood.
Assess Your AI Readiness
ERP: The Backbone of Responsible AI
ERP systems play a critical role in AI readiness.
An ERP acts as the single source of truth, centralizing:
- Financial data
- Operational data
- Inventory and supply chain data
- HR and payroll data
When data lives across disconnected systems, AI tools struggle to deliver accurate insights. Even worse, fragmented data increases the risk of errors, misinterpretation, and compliance violations.
A well-structured ERP enables:
- Standardized processes
- Clean, consistent data
- Defined roles and permissions
- Audit trails and traceability
This foundation allows AI to be layered on safely and effectively.
The Hidden Complexity of Too Many Systems
One webinar poll revealed that nearly half of organizations operate seven or more systems to run their business.
Beyond ERP and CRM, this often includes:
- Payroll systems
- Time and expense tools
- Middleware integrations
- Reporting and analytics platforms
Each integration introduces risk—especially when data is moved manually. Many finance teams still rely on exporting data into spreadsheets, reconciling it manually, and re-uploading it into another system.
AI cannot fix broken processes. In fact, it often magnifies them.
Data Quality: The Non-Negotiable Requirement
A recurring theme throughout the webinar was simple but powerful:
Good data produces good AI outcomes. Bad data guarantees poor ones.
Before deploying AI:
- Clean and validate data
- Standardize naming conventions and structures
- Define ownership of key datasets
- Ensure historical data is reliable
Organizations that skip this step often struggle with trust—teams stop believing AI outputs, which defeats the entire purpose of adoption.
The AI Lifecycle: From Discovery to Governance
Successful AI initiatives follow a phased approach:
- Discover – Identify opportunities and define goals
- Design – Architect workflows, data sources, and controls
- Build & Deploy – Implement, test, and validate
- Govern & Monitor – Continuously oversee usage, outputs, and access
Governance does not stop at deployment. As roles change, data evolves, and systems scale, AI governance must evolve alongside the business.
Build AI on a Strong ERP Foundation
Practical AI Use Cases Inside ERP-Centric Organizations
When governance and ERP foundations are in place, AI can deliver real value across functions:
- Finance & Accounting: Faster closes, automated reporting, anomaly detection
- Operations & Supply Chain: Demand forecasting, inventory optimization
- Reporting & Decision Support: Real-time insights, multilingual reporting
- Risk & Compliance: Policy reviews, control monitoring, audit support
Some organizations have reduced month-end close cycles from 15 days to 5 days by leveraging AI responsibly within ERP systems.
Crawl, Walk, Run: A Smarter Way to Adopt AI
A key takeaway from the discussion was the importance of mindset.
Instead of trying to implement everything at once:
- Crawl: Stabilize data and core ERP processes
- Walk: Introduce targeted automation and AI use cases
- Run: Scale AI across departments with confidence
This approach reduces risk, improves adoption, and ensures long-term success.
The Big Takeaway
AI is not the enemy—and it’s not magic either.
Organizations that succeed with AI:
- Start with clean, centralized data
- Use ERP as the operational backbone
- Establish clear AI governance
- Validate outputs continuously
- Treat AI as an evolving capability, not a one-time project
When AI, compliance, and ERP strategy work together, organizations gain speed, insight, and control—without sacrificing trust or regulatory integrity.
Frequently Asked Questions (FAQs)
See full transcript
This transcript has been lightly edited for clarity and readability.
00:00 – 01:30 | Welcome & Webinar Introduction
Welcome to today’s webinar, brought to you by goVirtualOffice. We’ll be diving into AI, compliance, and ERP, with a focus on getting your data house in order— a critical foundation for any AI or ERP initiative.
My name is Liz Bergson, and I’m very pleased to welcome you today. We hope you’ll spend this hour focused on the topic, engage with the content, and walk away with practical insights you can apply.
01:30 – 03:30 | Audience Engagement & CPE Information
Please use the GoToWebinar questions box to interact with us today. Say hello, let us know where you’re joining from, and feel free to share questions or feedback throughout the session.
Today’s program qualifies for CPE credit. To receive your certificate, please respond to at least three of the four polling questions. Your certificate will be available in your Encorsa dashboard approximately one hour after the session.
03:30 – 05:00 | Sponsors & Speaker Introductions
Today’s program is brought to you by goVirtualOffice, a long-standing partner dedicated to helping organizations leverage technology effectively.
We’re joined today by Shaun Brown, Senior ERP Implementation Manager at goVirtualOffice, and Brian Boulger, Director of Transformation at the Eliassen Group. You’re in excellent hands with their combined experience across finance, ERP, and digital transformation.
05:00 – 07:30 | Poll #1 – Industry Representation
Our first poll asks which industry best represents how your organization conducts business—manufacturing, wholesale distribution, retail, business services, or other.
We’re seeing a wide mix of industries represented today, including healthcare, education, construction, government, banking, and software. Thank you for sharing—this diversity helps frame today’s discussion.
07:30 – 11:00 | AI Adoption & Compliance Foundations
A McKinsey study found that 88% of organizations now use AI in at least one function. The key question is not whether AI is being used, but how it’s being used—and whether appropriate governance and compliance controls are in place.
AI is increasingly embedded in everyday business systems such as ERPs, CRMs, and EPMs. Many organizations are already using AI, sometimes without fully realizing it.
11:00 – 16:00 | AI Governance, Risk & Real-World Examples
AI governance means putting guardrails around how AI is used—what data can be entered, what outputs are acceptable, and who has access. Without governance, even well-intentioned use of AI can create serious risk.
We worked with a client who unintentionally released quarterly financials early by entering sensitive data into an AI tool without proper controls. While no harm was done, it highlighted the need for governance, training, and clear policies.
16:00 – 22:00 | AI Governance Frameworks & Readiness
AI governance includes readiness policies, permission frameworks, workforce training, data privacy controls, and third-party risk assessment. Strong data governance is foundational—good data drives good AI outcomes.
22:00 – 30:00 | ERP as the Backbone of AI & Data Strategy
ERP systems serve as the central source of truth for organizational data. Centralizing and standardizing data within an ERP makes AI more effective, auditable, and scalable.
Many organizations operate with seven or more systems. Understanding how those systems connect—and eliminating manual handoffs—is critical for real-time data and reliable AI insights.
30:00 – 42:00 | AI Lifecycle, Use Cases & Governance in Practice
Successful AI adoption follows a lifecycle: discover opportunities, design workflows, build and deploy solutions, then continuously monitor and govern usage.
Common AI use cases include financial close acceleration, management reporting, demand forecasting, policy monitoring, and multilingual reporting—all enabled by clean, centralized ERP data.
42:00 – 55:00 | Q&A, Best Practices & Key Takeaways
A crawl-walk-run approach helps organizations avoid overloading implementations. Start with a strong foundation, then layer in automation, integrations, and AI over time.
Validate AI outputs continuously. Compare results against known benchmarks, ensure input data quality, and maintain clear ownership for governance and oversight.
55:00 – 60:00 | Closing Remarks & Resources
Thank you to our speakers and to everyone who joined today. You can access the recording and slides through your Encorsa dashboard, and CPE certificates will be available shortly.
AI governance defines how AI tools interact with ERP data, including access controls, data usage policies, compliance requirements, and oversight mechanisms.
ERP systems centralize and standardize data. Without a strong ERP foundation, AI tools struggle with incomplete, inconsistent, or unreliable data.
Yes—but only with strict governance. Industries like healthcare and finance require clear policies, legal review, and controlled access to AI tools.
By using control samples, parallel reporting, and reconciliation against trusted datasets before relying on AI outputs for decision-making.
In most cases, yes. Governance enables responsible experimentation and prevents costly compliance or data exposure issues later.
