AI for CX Summit – Day 4 Session 2 Recap
As AI adoption accelerates across contact centers and enterprises, one challenge continues to hold organizations back: their data.
In the final session of the AI for CX Summit, “AI Ready Data: Unlock Business Value with Intelligent Data Strategies,” two leaders from Cloud Communications Group tackled the question that often comes before the tech, tools, or training models: Is your data actually ready for AI?
Moderated by Scott Logan, CMO of AmplifAI, the session featured expert insights from:
Key topics included:
Whether you’re leading AI strategy, working in data operations, or managing CX transformation, this session offered valuable takeaways for anyone tasked with getting enterprise data AI-ready. You’ll benefit from this discussion if you’re:
Even the most sophisticated AI won’t deliver value if it’s trained on poor-quality inputs. Stu explained that AI isn’t magic—it reflects the data it’s given. That means inconsistencies in format, missing values, duplicate records, or mislabeled fields can directly undermine your AI models.
While many assume that unstructured data is “good enough,” the real difference lies in data integrity and completeness. Whether you’re building AI for coaching recommendations, fraud detection, or customer churn, your results will only be as trustworthy as the information powering them.
Data silos aren’t just technical—they’re often political and operational. Stu broke this down into three types of silos:
These silos restrict the full view AI needs to understand patterns across the organization. To break them down, organizations need to map out their data ownership, governance rules, and integration plans—then align cross-functional teams to share and standardize inputs.
Rather than overhauling your entire data infrastructure, Stu encouraged leaders to focus on what’s already working. That might mean targeting standardized CRM exports, common QA scorecards, or reliable survey results.
Once these “clean” data sources are identified, companies can pilot AI in lower-risk, higher-readiness areas—gaining proof points and executive buy-in before expanding to more complex or disjointed datasets. It’s about building a phased approach, using wins to build credibility and organizational momentum.
Most AI failures aren’t because of bad tools—they stem from poor alignment around the data itself. Without clear governance—who owns what, how it should be labeled, and where it should reside—teams spin their wheels chasing disconnected datasets.
Stu highlighted the need for even basic governance practices:
This structure allows AI teams to move faster while ensuring compliance, consistency, and context for every dataset they use.
The allure of feeding all enterprise data into a single model is strong—but highly unrealistic. Stu cautioned against launching AI projects with undefined scope and unclear data quality, as this often leads to delays, frustration, or outright failure.
Instead, he recommended letting your data maturity shape your roadmap. For example:
By working from the bottom up—data first, outcomes second—leaders ensure that AI has a stable foundation to succeed.
Yes, AI can help you prep your data for AI. Stu explained how generative AI can support:
This meta-application of AI—using it to analyze, categorize, and enrich existing data—can help teams move faster and more confidently toward readiness.
“Start at the bottom of the triangle—with your data—and work your way up. You wouldn’t hire a super-talented employee without knowing what job you need them to do. Don’t treat AI any differently.”
— Mike Stu, Chief Strategy Officer, Cloud Communications Group
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