Summary
Artificial intelligence is transforming industries, but many companies struggle with how to implement it effectively. In this episode, Kal Romer discusses what an AI strategy should include, the importance of identifying use cases and business pain points, and how organizations can move up the AI maturity curve. Learn about targeted AI applications, anomaly detection, and the role of high-quality data in training AI models. Infoverity helps businesses assess their readiness, structure data, and implement scalable AI solutions to drive real business value. Listen now to gain insights on how to successfully integrate AI into your enterprise.
Transcript
Jocelyn 00:01:
You’re listening to Infoverity’s Trust Us podcast, where you can gear up for your data management journey with bite-sized discussions on industry trends and thought leadership. On each episode, we feature industry experts to help you navigate your path to mastering your enterprise data.
Welcome back to the Trust Us podcast. Today, we’ll be talking to Kal Romer about what an AI strategy is. We’ll explore what to consider when building an AI strategy, industry trends, use cases, how to get started, and how Infoverity can help.
Kal, welcome to the podcast. Would you like to take a moment to introduce yourself to the audience?
Kal Romer 00:47:
Yeah, of course. And thank you so much for having me, Jocelyn and Taylor. I’ve been a fan of the podcast for a while and am excited to be on.
A little bit about myself—I’ve been with Infoverity for going on seven years now. I joined the organization as a data scientist and have been focused on helping the industry at large move up the maturity curve in the broader analytics space. I’ve spent a lot of time in enterprise data management, largely due to where the industry was when I started my career. It hasn’t always been easy. There weren’t always good product offerings that enabled the average enterprise to move toward advanced analytics and AI applications. But now, we’re at a unique intersection of decades of AI research and the growth of enterprise software platforms that support businesses in driving value with AI.
Jocelyn 01:51:
We’re so glad that you’re here. Couldn’t ask for a better subject matter expert on today’s topic. Thank you again for joining us.
Let’s dive right in. Can we start by discussing what is important for organizations to consider when building their AI strategy?
Kal Romer 02:11:
For sure. AI has been on every board’s radar for the past couple of years, especially since the explosion of ChatGPT and other large language model applications. Over this time, we’ve observed three different types of companies: those that are over-invested, those that are under-invested, and those stuck in analysis paralysis due to how fast things are moving.
When considering an AI strategy, it needs to start with your organization’s DNA. What are your use cases? Where are your pain points? Identifying these factors first helps in targeting AI solutions effectively. Another critical aspect is ensuring that your AI roadmap delivers incremental ROI. AI maturity doesn’t happen overnight, so it’s essential to show progress along the way toward your ideal AI future.
Taylor Beckt 03:23:
That’s a great response, Kal. You mentioned the AI maturity curve—what groundwork needs to be laid before an organization is truly prepared for AI implementation?
Kal Romer 03:45:
For sure. It starts with basic data maturity. Every company today relies on data, whether for operational decision-making or strategic growth. The first step is ensuring that data is properly managed, accessible to the right users, and structured for business intelligence.
Many companies begin with basic analytics, such as reporting on sales performance and supply chain logistics. As they move up the maturity curve, they start expanding their use of data. A strong foundation is necessary, including modern technology platforms and enterprise data management best practices.
Organizations need well-governed data, continuous data quality improvements, and trust in data products. This involves mastering core domains of data and ensuring reference data is structured and available for downstream AI applications.
Jocelyn 05:33:
That’s so helpful for our listeners. Now that we understand how to prepare for AI, can you share a practical use case that illustrates AI strategy in action?
Kal Romer 06:15:
Absolutely. Let’s talk about anomaly detection, a broad AI application relevant across industries.
For example, healthcare and insurance companies use anomaly detection to ensure claims are processed correctly and not denied for improper reasons. In finance, AI can help identify fraudulent activity. In manufacturing, AI-powered anomaly detection can track IoT sensor data to detect deviations in equipment performance, helping prevent costly failures.
Traditionally, identifying and addressing anomalies has been a human-driven process. Now, AI can assist by recognizing patterns and highlighting anomalies that need attention, allowing teams to focus on remediation rather than manual detection.
Taylor Beckt 08:24:
That’s great insight. AI is one of the biggest buzzwords in the industry, but how do market trends intersect with AI strategy? What should organizations understand?
Kal Romer 08:53:
Great question. Given that we’re recording this on January 31, 2025, one of the biggest news stories this week has been about DeepSeek, a new AI model in the large language model space. It triggered a trillion-dollar market shift, highlighting key AI trends.
One major trend is targeted AI applications rather than massive general-purpose models. A study called Textbooks Are All You Need showed that AI models trained on high-quality, curated data (rather than massive, unstructured internet data) can perform just as well as leading models. This proves that companies don’t need billion-dollar AI investments to succeed—well-structured, domain-specific data can be just as powerful.
We expect AI adoption to shift toward industry-specific, highly targeted applications, where businesses fine-tune foundational models with their own data rather than relying solely on massive general models.
Taylor Beckt 11:14:
This is a lot of information. How can Infoverity help organizations navigate their AI journey?
Kal Romer 11:26:
It all goes back to the AI maturity curve. The first step is assessing where an organization currently stands and planning the next steps. Infoverity helps companies:
- Improve data management and governance.
- Master and structure data for AI applications.
- Develop targeted AI solutions that align with business needs.
- Implement AI-enhanced tools like text-to-SQL and AI-powered business intelligence.
- Conduct proof-of-concept initiatives to validate AI’s value before large-scale investment.
We work with organizations at every stage, ensuring they have the right data foundation to succeed with AI.
Jocelyn 15:58:
That’s really encouraging! Kal, thank you so much for joining us today.
Kal Romer 16:11:
Absolutely. Thanks for having me!
Jocelyn 16:34:
For over a decade, Infoverity has been a trusted leader in enterprise data management consulting, with experts worldwide and headquarters in Columbus, Ohio, and Valencia, Spain.
To learn more, visit infoverity.com. Additional contact details are in the show notes.
