According to a recent LinkedIn post from Seekr Technologies Inc, enterprises are estimated to have spent $37 billion on AI in 2025, yet only about 20% report revenue growth attributed to these investments, citing Deloitte data. The post frames this shortfall less as a model performance issue and more as a problem of explainability in AI systems.
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The company’s LinkedIn post highlights that when AI outputs cannot be clearly traced back to underlying data, organizations may face stalled compliance reviews, difficulties obtaining auditor certification, and reluctance from business owners to act on AI-generated insights. The post suggests that firms embedding explainability from the outset are better positioned to capture financial returns, while those attempting to retrofit explainability risk regulatory pushback.
As shared in the LinkedIn post, Seekr points readers to its enterprise guide to explainable AI for 2026, which it says addresses requirements for XAI, common implementation pitfalls, and criteria for evaluating platforms. For investors, this emphasis on explainability aligns Seekr with regulatory and governance trends in AI, and may position the company to benefit from enterprise demand for compliant, auditable AI solutions as spending continues to grow.