6 Steps to Make Your Contact Center Data AI-Ready
Written by Lada Kozachok, Marketing Specialist at Wilmac Technologies
Customer conversations are a goldmine of insights IF you can unlock them…
Every day, contact centers capture thousands of interactions, 3–5k calls, chats, and emails, each filled with valuable customer signals. Yet much of this data goes untapped, sitting in recordings, transcripts, and disparate systems, underutilized and out of reach for meaningful analysis.
As AI becomes central to customer experience strategies, the challenge isn’t whether to adopt AI; it’s how to make underused contact center data ready for AI-driven insights.
Turning these raw interactions into AI-ready intelligence takes more than technology. It demands clear intent, strong data discipline, and an appreciation for the operational complexity of contact centers.
The payoff is significant: when data is cleaned, connected, and actively used rather than simply stored, AI can surface hidden patterns, guide smarter decisions, and transform customer experiences in ways that were once impossible.
This blog explores how to build that critical foundation and finally unlock the full, often underutilized, value of your contact center data in the age of AI.
Step 1. Define your AI objectives
Before starting technical implementation, clarify why you want your data to be AI-ready. Whether it’s to improve customer sentiment analysis, optimize staffing, or enhance compliance monitoring, clear objectives guide how data should be organized, enriched, and prioritized. Even if your organization doesn’t yet have specific AI use cases, establishing these goals early ensures that your data strategy aligns with your long-term business vision.
Step 2. Consolidate your data sources
Customer interactions often span multiple systems and modalities, from voice, chat, email, and screen recordings to platforms like CRM, WFO, and recording tools. Bringing all of this together is the first step toward making it usable for AI. Consolidating data into a single environment helps remove duplication, ensures consistency, and makes it easier to identify patterns across different channels.
Step 3. Ensure accessibility and flexibility
Your data should remain easily retrievable, protected, and stored in open, industry-standard formats. Too often, organizations depend on vendor-controlled systems that make data extraction difficult or use proprietary encryption methods that limit transparency. Building a secure, flexible, standards-based data environment reduces the risk of vendor lock-in and ensures you can deploy new AI or analytics tools without compromising data protection or undergoing complex migrations.
Step 4. Standardize formats and metadata
AI models perform best when data follows consistent structures. Ensuring that all data is standardized and structured for AI systems to use effectively is essential. By creating a common metadata schema across systems, covering elements like timestamps, agent identifiers, and call outcomes, you make data discoverable, interoperable, and ready for AI-driven insights without needing custom integrations. This consistency ensures every piece of data speaks the same language, simplifying downstream AI and analytics efforts.
Step 5. Get your audio transcribed and searchable
AI thrives on structured text. Converting voice interactions into transcripts is an essential step to unlocking the value within them. Once transcribed, data can be searched, indexed, and analyzed using techniques like natural language processing, topic detection, and trend tracking.
Step 6. Choose the right solution
Preparing your data for AI can be complex, especially when dealing with multiple legacy systems, proprietary formats, and strict retention requirements. A solution such as Wilmac Continuity Replay can help centralize and standardize your historical recordings in an open, searchable format, providing the strong data foundation needed for future AI initiatives.
Common Roadblocks in Preparing Data for AI Analysis
Even companies with advanced contact center operations can face hurdles when preparing their data for AI analysis. Some of the most common challenges include:
- Data stored in legacy systems such as NiCE, Verint, and other end-of-life solutions is difficult to access or migrate.
- Proprietary or encrypted file formats that require expensive conversion tools.
- Siloed data across different recording platforms that prevents unified analysis.
- Long-term retention and compliance regulations that complicate storage strategies.
- Cold storage environments in CCaaS platforms where retrieving older interactions is slow or costly.
These issues can delay progress, increase costs, and reduce the overall quality of AI insights if not addressed early. Building a clean, consistent, and accessible data foundation is one of the most important steps before any organization can see real value from AI.
How Wilmac Continuity Replay Can Help Improve Contact Center Data Quality
Simplifying Access to Interaction Data
Wilmac Continuity Replay centralizes and modernizes contact center data by extracting and converting recordings from various systems into a vendor-neutral, accessible format.
This ensures your organization can easily locate, use, and manage valuable interaction data across all environments.
Enabling Platform Flexibility and Control
By moving recordings out of legacy and live CCaaS environments into standard, open file types, Wilmac Continuity Replay gives organizations full control over their data.
This flexibility reduces vendor lock-in and supports seamless transitions to new technologies as business needs evolve.
Creating a Unified, Searchable Archive
Instead of managing fragmented datasets, Wilmac Continuity Replay consolidates recordings and transcripts into a single, searchable archive.
This unified structure makes it easier for teams and AI tools to analyze interactions, uncover insights, and improve overall data quality.
Strengthening Compliance and Governance
Automated archiving and policy-based retention ensure data is stored securely and meets regulatory requirements for industries such as finance, healthcare, and insurance.
Wilmac Continuity Replay simplifies audit readiness and long-term governance without manual intervention.
Enhancing Data Readiness for AI
With interaction data standardized, indexed, and accessible, organizations can feed higher-quality inputs into AI and analytics systems.
Wilmac Continuity Replay transforms raw, scattered information into structured, AI-ready intelligence that drives more accurate insights and faster decision-making.
