AI Looks Different Inside Private Companies
Cabinetworks and others are adopting AI cautiously, one function at a time
Private companies are beginning to adopt AI, but the public record shows that early deployments are narrow and limited in scope. When these companies talk about AI at all, they tend to describe small systems designed to fix specific internal problems rather than broad transformations of how the business operates.
That pattern is visible this week as Cabinetworks Group, a privately held U.S. kitchen cabinet manufacturer, disclosed its first AI initiative.
The company said it selected eGain’s AI Knowledge Hub and AI Agent for Contact Center to modernize customer support and knowledge management. The deployment focuses on replacing fragmented documents, chat logs, and informal job aids with a centralized system.
Cabinetworks called the decision a response to operational complexity. “As we continue to grow and serve more customers, we recognized the need to modernize our approach to knowledge management and customer service,” said Matt Conant, vice president of customer experience and care at Cabinetworks Group.
AI, at the Edges of the Business
Across industries, private companies that publicly discuss AI tend to focus on customer support, internal knowledge systems, and employee-facing tools. These deployments are typically described through vendor press releases and case studies, not company-authored strategy updates.
A-dec, a privately held dental equipment manufacturer, is one example. The company has been featured in an eGain manufacturing case study describing its use of AI-driven knowledge management to improve contact center performance and self-service. The case study emphasizes faster access to information and improved answer consistency, not automation of manufacturing operations.
SELCO Community Credit Union, a member-owned financial institution, publicly described its AI deployment in similar terms. In a vendor announcement, the organization said it selected an AI knowledge hub to centralize internal information and improve employee onboarding and consistency. “Our commitment to service excellence starts with empowering our employees with accurate, accessible knowledge,” said Dan Bilderback, director of talent development at SELCO.
Similar patterns appear in other private firms’ disclosures. Privately held companies such as IKEA and Mars have publicly discussed AI in contexts like customer engagement, internal productivity, and workflows. Public disclosures from these companies tend to avoid detailed descriptions of AI use in core production systems or margin-sensitive operations.
Across these examples, several common elements appear. The deployments are limited to specific functions. They are framed as tools to support employees or customers rather than replace them. They are described using vendor language such as “knowledge hub,” “agent assist,” and “self-service.” Financial impact, headcount implications, and timelines for expansion are usually absent.
This pattern is backed up by broader survey data showing that while AI use is widespread, organization-wide scaling remains limited.
The Limits of Early AI Adoption
Research from McKinsey shows that AI use is common but scaling remains limited. The 2025 McKinsey State of AI survey found that about 88 percent of organizations report regular AI use in at least one business function, but most are still in experimentation or pilot phases rather than scaling AI broadly across the enterprise. Only 23 percent reported scaling an “agentic AI” system somewhere in the organization.
McKinsey also reported that while AI tools are becoming commonplace, most organizations have not yet embedded them deeply enough into workflows and processes to realize material enterprise-level benefits. Enterprise-wide financial impact remains elusive for many respondents.
Independent risk surveys further document the challenges organizations face when deploying AI. An EY survey reported that nearly all large companies surveyed have incurred some financial losses linked to AI risk factors such as compliance issues, flawed outputs, or sustainability disruptions. Total estimated losses across respondents were in the millions.
Broader executive polling also suggests mixed ROI from AI initiatives. A recent PwC survey highlighted that a significant share of CEOs report seeing little to no financial benefit from AI adoption to date, with only a minority reporting gains on both revenue and cost metrics.
These findings suggest that many organizations are still testing AI in constrained environments where mistakes are easier to manage and outcomes are more measurable. For private companies, which do not face pressure to demonstrate AI leadership to public investors, there is little incentive to frame early deployments as transformational. Instead, public disclosures tend to focus on contained use cases that address specific operational problems.
Cabinetworks fits squarely within that. Its AI deployment is limited to customer support and knowledge management. It was announced through a vendor-led release. It emphasizes modernization and efficiency rather than transformation. And it leaves open how, or whether, AI will extend beyond those boundaries.