Recent surveys from McKinsey and Deloitte show that over 60% of companies are using AI in at least one business unit outside of IT or analytics. From automated policy review in legal teams to AI-driven reporting in finance, the technology is increasingly embedded in frontline and operational processes. However, the rate of adoption in non-technical …
Enterprise investments in AI are accelerating, but proving return on investment remains a common challenge. Many deployments begin as pilots with unclear success metrics and diffuse outcomes. As AI matures into a core component of business operations, leaders must transition from experimentation to value demonstration. This article explores frameworks and best practices for tracking, measuring, …
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AI agents—autonomous or semi-autonomous systems that perform tasks on behalf of users—are no longer theoretical. They’re being piloted and deployed across industries for scheduling, data integration, document processing, and more. However, moving from prototype to production requires thoughtful architecture, oversight, and alignment with business goals. This article explores what it takes to operationalize AI agents …
In the early phases of AI adoption, speed and accessibility led many enterprises to rely on public large language models (LLMs). While these tools unlocked experimentation and early use cases, long-term AI maturity demands greater control. This article explores why organizations are re-evaluating reliance on external AI providers, and how private model deployments are enabling …
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