Throughout the Ready. Or Not. series, we’ve explored topics like agentic AI, digital trust, the human factor, and vibe coding. In this fifth and final episode of season one, the conversation shifts to the one constant behind every AI conversation: data.
Nathan Macintosh sits down with Ben Lorica, former chief data scientist at O’Reilly Media and founder of Gradient Flow, to discuss what AI readiness really looks like. Their conversation moves beyond algorithms and applications to the work organizations need to do before AI can succeed.
They explore why AI is changing the way we think about governance, why collecting more data isn’t always the answer, and why preparation matters just as much as adoption.
Watch the full episode on Readiverse.
Key Takeaways
- AI readiness starts with understanding and organizing the data you already have.
- Governance now applies to AI systems, not just people.
- More data isn’t always better. Strive for better data.
- AI introduces new risks that require new processes, not just new technology.
- The organizations best prepared for AI are building strong data foundations today.
Organizations are generating and managing more data than ever before. It’s easy to assume the next step is simply collecting more data. Ben explains why preparing and governing the data you already have may be a much stronger foundation for AI.
One thing I appreciated about Ben’s perspective is that he never presents AI readiness as a technology problem alone. It’s an organizational challenge that starts long before teams begin putting AI to work.
Here are a few ideas that stayed with me.
AI Is Only as Good as the Data Behind It
“Focus on the data you have … and get that ready for AI.”
– Ben Lorica
One of Ben’s first points challenged a common assumption. When organizations talk about becoming “AI ready,” the instinct is often to collect more data. Ben sees it differently. Instead of prioritizing quantity, he encourages organizations to focus on quality and prepare their existing data for AI.
That starts with understanding what data you have, organizing it, and making sure it’s accurate and well governed. As AI becomes part of more business processes, organizations will rely on many different types of information, from spreadsheets to text, images, audio, and video. If that data isn’t reliable, AI won’t fix the problem – and it can make it even more difficult to spot.
There’s understandable pressure to move quickly with AI. This conversation reminded me that taking the time to build a solid data foundation may be one of the smartest investments we can make. Clean, well-governed data helps organizations make better decisions today while preparing them for whatever comes next.
AI Changes the Role of Governance
Ben points out that governance has a broader job to do. It’s no longer just about managing how people access and use information. Organizations also need to think about how AI interacts with that information and the actions it takes.
As AI becomes part of everyday work, it can access, analyze, and act on information at a scale and speed that’s difficult for people to match. That means organizations need to understand what AI can access, how it’s using that information, and what safeguards should be in place to protect sensitive data.
What’s interesting is that the fundamentals of governance haven’t changed. Clear policies around access, security, and accountability are just as important as they’ve always been. What is changing is the number of systems interacting with organizational data and the pace at which information moves across the business.
To me, that’s one of the most important takeaways from this episode. AI doesn’t replace good governance. It makes it even more important.
Sneak Peek: When Data Starts to Multiply
What happens when AI allows five people to do the work of 100? Ben explains why the real challenge isn’t productivity. It’s the explosion of data that comes with it.
Responsible AI Starts With Responsible People
One thing Ben emphasizes throughout the conversation is that organizations can’t rely on technology alone to make AI responsible. The people using AI play an important role, too.
Whether employees are entering prompts, uploading documents, or fine-tuning models, they need to understand what information they’re sharing and how it could be used. Guardrails aren’t just about restricting access. They’re also about helping people make informed decisions when working with AI.
Ben points out that organizations should think beyond what goes into an AI system. They should also pay attention to what comes out. AI can unintentionally generate sensitive information, making review and oversight of the outputs just as important as the prompts that started the interaction.
It’s another reminder that responsible AI isn’t just a technology challenge. It’s a shared responsibility between the people using AI and the policies that guide them.
Plan for the Unknown
There’s understandable pressure to adopt AI quickly. New tools are emerging almost daily, and organizations don’t want to fall behind. But Ben makes the case that readiness isn’t just about moving fast. It’s about having the right processes in place before they’re needed.
Toward the end of the conversation, Ben points out that many AI teams haven’t fully considered what they’ll do when things go wrong. I love Nathan’s response because it was exactly what I was thinking:
“Why wouldn’t they think of that? That’s all I think about.”
– Nathan Macintosh
In cybersecurity, resilient organizations don’t wait for an incident before deciding how they’ll respond. They establish roles, define processes, and prepare for different scenarios long before they’re needed. Ben argues that AI deserves the same level of preparation.
That means asking questions many organizations haven’t fully considered yet, like:
- What data should AI have access to?
- Who should be involved if an AI-generated output creates a problem?
- How will decisions be made if something unexpected happens?
These conversations may not be as exciting as launching an AI initiative, but they’re just as important.
One Final Takeaway
As this season of Ready. Or Not. comes to a close, one thing has become clear to me. Every episode explored a different AI concept or trend, yet they all reinforced the same idea: successful AI adoption isn’t just about the technology. It’s about the people, processes, and preparation that make it possible.
Organizations don’t have to have every answer before embracing AI. But the more intentional they are about building strong foundations today, the more prepared they’ll be for whatever comes next.
Watch the full episode on Readiverse.
FAQs
Q: What does AI readiness mean?
A: AI readiness begins with understanding, organizing, governing, and protecting the data your organization already has. Strong data practices create the foundation AI depends on.
Q: Should organizations collect more data for AI?
A: Not necessarily. Ben recommends focusing first on improving the quality and organization of existing data before expanding data collection efforts.
Q: What’s the role of employees in responsible AI use?
A: Employees play an important role in AI governance. They need to understand what information is appropriate to share with AI, review AI-generated outputs carefully, and follow organizational policies for using AI responsibly.
Q: Why does AI change data governance?
A: AI systems increasingly access, analyze, and act on organizational data. That means governance policies need to apply to machines as well as people.
Q: Why should organizations prepare for unexpected AI issues?
A: AI can introduce new risks, from exposing sensitive information to producing unintended results. Preparing in advance by defining responsibilities and response processes helps organizations address those situations with greater confidence.
Q: What’s the biggest takeaway from this episode?
A: AI readiness isn’t just about adopting new technology. It’s about building solid governance, good data practices, and resilient organizational processes that help allow AI to be used responsibly and effectively.
Katherine Demacopoulos is Senior Director of Global Content Strategy and Programs at Commvault.
