According to a recent LinkedIn post from Seekr Technologies Inc, global enterprise spending on AI is referenced at $37 billion in 2025, with a cited Deloitte estimate that only about 20% of adopters are seeing revenue growth. The post attributes much of this gap not to model quality, but to shortcomings in explainability that impede traceability from AI outputs back to underlying data.
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The company’s LinkedIn post highlights that weak explainability may slow compliance reviews, hinder auditor certification, and reduce business users’ willingness to rely on AI-generated recommendations. It further suggests that organizations embedding explainability from the outset are better positioned to secure regulatory acceptance, while those adding it late could face regulatory pushback or deployment delays.
As shared in the LinkedIn content, Seekr is promoting an enterprise guide to explainable AI for 2026 that outlines core XAI requirements, common implementation pitfalls, and criteria for evaluating platforms. For investors, the emphasis on explainability aligns with rising regulatory scrutiny around AI, implying potential demand for solutions that can help enterprises convert AI spending into measurable, auditable business outcomes.
If Seekr’s offerings are closely linked to explainable AI tooling or platforms, this thematic focus could signal a strategic attempt to address a high-friction adoption barrier in the enterprise market. Positioning around compliance, auditability, and ROI may support pricing power and stickier customer relationships, which could be relevant for assessing the company’s growth prospects within the enterprise AI and governance segment.