Session Overview
In the breakout session “From Insight to Impact: Building an AI-Ready Enterprise” at Verint Engage 2025, we heard about the journey into enterprise AI adoption. The world’s second-largest pest control company with over 22,000 employees across 800 locations, faces the dual challenge of rapid organic growth and integrating dozens of acquisitions each year. Against this backdrop, the organization is pursuing a strategic AI roadmap grounded in four categories of “empowerment”.
The session emphasized that high-quality data is the bedrock of successful AI deployment, with transcription accuracy emerging as a critical dependency. A key case study showcased the use of the AI tool Genie, which identified both a costly website bug and systemic gaps in agent performance during customer cancellations. Beyond tactical fixes, Genie also enabled new use cases for sales directors and managers by streamlining coaching insights and preparing team meeting materials. LIn this use case, next steps are to integrate workforce, CRM, and call data into a centralized warehouse, unlocking more holistic insights to fuel transformation.
Industry Perspective
This journey mirrors broader market dynamics where organizations are eager to leverage AI across customer-facing and back-office functions, but data quality and executive alignment remain the primary barriers. theCUBE Research has found that over 70% of AI projects fail to scale due to challenges in governance, executive sponsorship, and poor data foundations; a trend reflected in this session with transcription errors and skeptical stakeholders.
The empowerment framework provides a practical model for structuring AI initiatives across the enterprise:
- Customer Empowerment: Tools like password reset automation and natural language processing for call routing can significantly improve CX while reducing costs.
- Employee Empowerment: AI-powered post-call summaries and agent assist features boost productivity and reduce burnout.
- Manager Empowerment: Automated quality management and coaching insights free leaders from manual analysis and improve retention strategies.
- Business Transformation: Enterprise-wide use cases, from threat detection to trend analysis, reshape competitive advantage.
The Genie case study demonstrates how quickly AI can uncover high-value insights when applied to real business data. Identifying a website bug affecting 800 customers monthly highlights how AI can directly prevent revenue leakage. Similarly, exposing gaps in agent performance around cancellations shows AI’s role in ensuring process consistency and protecting customer lifetime value.
Yet the challenges highlighted make it clear why AI readiness requires more than technology. “Shadow IT” remains a persistent risk when business units implement their own tools in the absence of sanctioned solutions, raising both governance and security concerns. Even further, without clean transcription data, insights risk being misleading as shown when Orkin was mis-transcribed as “Oregon Pest Control” or “American Pest Control.”
Moving Forward
For enterprises seeking to become truly AI-ready, the example here offers several lessons:
- Data Quality is Non-Negotiable: Transcription errors can undermine trust in AI insights. Organizations should invest in automated data cleansing and ongoing tuning to ensure accurate analysis.
- Executive Buy-In is Critical: AI adoption succeeds when skeptics are engaged directly in the process. Allowing executives to query AI systems themselves can transform doubt into advocacy.
- Empowerment as a Framework: Organizing AI initiatives under customer, employee, manager, and business empowerment categories helps align investments with strategic outcomes.
- Governance Over Shadow IT: Clear guardrails and timely delivery of enterprise AI solutions are essential to prevent departments from adopting unsanctioned tools.
- Centralized Data Warehousing: Integrating CRM, workforce, and call data into a single source of truth enables deeper insights, predictive capabilities, and sustainable transformation.
As organizations move beyond experimentation, the imperative is to balance innovation with governance. Adoption of tools like Genie and Quality Bot demonstrate the transformative potential of AI when paired with the right frameworks. AI readiness is not about adding tools in isolation but about building the data, governance, and cultural foundation to turn insight into sustained business impact.
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