Across industries, a version of the same conversation is playing out in technology leadership meetings. Enterprises have deployed AI broadly, and foundation models keep getting more capable. Yet the outputs still feel generic, shaped by industry patterns rather than by the organization producing them. McKinsey’s AI Trust Maturity Survey found that while overall AI maturity…
Your CFO has a simple question. “We’re spending more on AI. What are we getting for it?” Most CIOs cannot answer it — not because AI isn’t creating value, but because the accounting systems we inherited were built before AI existed as a category of labor. This June, the conversation shifted from token maxing to…
At Cisco, VP of engineering Jason Andrews deals with all the same technical issues as every other company deploying AI, including ensuring it’s governed, secure, and integrating multiple data sources, legacy systems, and AI models. But these issues are relatively straightforward compared to the bigger challenges relating to the fast pace of change, specifically how…
For many engineering teams, AI adoption means individual engineers write code faster while overall team velocity remains stagnant. Individual speed and team speed are produced by different things, and AI has mostly accelerated the first but not the second. The step from individual AI adoption to team advantage is one many organizations haven’t taken yet,…
AI agents can turn a clear description into working software, the engineer’s judgement is what makes the difference: deciding what to build, catching the tradeoff the agent didn’t know to weigh, and owning the call on whether the result is right. That judgement has always been the hard part of engineering. It just used to…
Outsourcing worked – until it didn’t. After Akirolabs achieved early market validation and onboarded its first enterprise customers, outsourcing began to create strategic limitations around scalability, intellectual property (IP) ownership, security and delivery execution. The challenges started after the first enterprise customers confirmed product-market fit. At that point, delivery speed became directly tied to business…
In the client-server era, the penalty for inefficient programming, such as unoptimized database calls, was largely confined to application responsiveness. Today, in the AI era, code, architectural, and platform inefficiencies are no longer just a performance issue, they’re a financial and environmental liability. Left unchecked, poor code cascades into soaring token costs and spikes data…
Microsoft and Mistral are betting that the future of enterprise AI is in sovereign infrastructure and model choice, rather than with one locked-in system. The companies have announced a “significant expansion” of their strategic partnership, which includes a multibillion dollar commitment from Microsoft. Mistral will add to its GPU infrastructure in Europe and extend access…
Ever since Chinese AI startup DeepSeek launched three years ago, enterprise executives have been nervous about relying on Chinese AI models. But now that the latest Chinese AI offerings, Alibaba’s 2.4-trillion-parameter model Qwen3.8 Max and Moonshot’s 2.8-trillion-parameter model Kimi K3, are promising even more powerful performance, those IT executives are being forced to again ask…
HireQuotient, an AI-native recruiting platform, today announced its integration with Paylocity (Nasdaq: PCTY), bringing AI-powered candidate sourcing and screening capabilities to Paylocity customers in manufacturing, building services, construction, healthcare and insurance, which are industries where deskless and frontline hiring has historically been underserved by AI recruiting tools. Employers in these sectors who already use HireQuotient…