Michigan Election Ad Prompts Questions About Use of GenAI

Companies are investing in AI at record levels, yet most are still struggling to translate it into measurable business value. The well-known MIT study, State of AI in Business 2025, concludes that 95% of enterprise GenAI pilots have failed to […] The post The Two Mistakes Slowing Down AI Adoption (and How to Overcome Them) appeared first on TechSpective.

Companies are investing in AI at record levels, yet most are still struggling to translate it into measurable business value. The well-known MIT study, State of AI in Business 2025, concludes that 95% of enterprise GenAI pilots have failed to […] The post The Two Mistakes Slowing Down AI Adoption (and How to Overcome Them) appeared first on TechSpective.

Initially launched in November 2023, Microsoft 365 Copilot brings a range of generative AI (genAI) features to Microsoft Office productivity apps, such as Word, Outlook, Teams, and Excel. With capabilities ranging from quick meeting summaries to in-depth data analysis, it’s available via a paid add-on license for Microsoft 365 enterprise and small-business customers. Initially hampered by underwhelming capabilities and a hefty price tag for businesses of all sizes, M365 Copilot has slowly gained traction in business as its abilities have increased and the integrations between Copilot and various M365 apps and services have improved. With numerous feature rollouts over the past three years, Microsoft has gradually repositioned M365 Copilot from a simple chatbot to a collection of autonomous agents that can carry out tasks across the M365 ecosystem. The company has also goosed adoption by introducing a more affordable pricing tier for small businesses and (temporarily, as it turns out) a

Ripple seeks GenAI staff as XRPL adds x402 payment, letting AI agents use XRP and RLUSD for automated on-chain transactions across services.

Enterprises are moving aggressively into generative AI. On the surface, that seems like the right call. The technology is powerful, accessible, and increasingly embedded in how businesses build applications, automate processes, and support decision-making. A development team can connect an application to a large language model in days. A product team can add AI features in weeks. Business leaders see quick wins, faster innovation, and a path to modernizing nearly every part of the company. These are the upsides everyone is talking about. The part we don’t discuss enough is the economic trap forming underneath all this convenience. Most enterprises think of tokens as a technical billing detail. They are not. Tokens are the unit of economic dependency in generative AI. Every prompt, response, summarization, retrieval step, workflow action, and agent decision is measured and monetized through tokens. Tokens are not just part of the plumbing. They are the tollbooth between your enterprise

Marketers have made enormous strides with generative AI (GenAI) over the last year, moving from experimentation to large-scale deployment. But the next shift in AI maturity – agentic AI – is already underway. This shift will push organizations beyond prompt-based productivity and into a world where AI can act, learn [...] The post Are you an agentic AI Observer, Planner or Adopter? appeared first on SAS Blogs.

Enterprise GenAI (generative AI) deployments succeed when teams run them with the same discipline they apply to other user-facing services. The model sits in the middle of a pipeline that handles identity, policy, retrieval, inference, and logging. Each stage affects quality, latency, cost, and risk. A pilot can hide these dependencies. Production traffic exposes them. Familiar sequences are seen across large organizations. A small group proves a use case in days. Leadership asks for broad rollout. Usage climbs and the system behaves differently. Response times vary across the day. The assistant answers confidently with incomplete context. Cloud spend drifts upward without a clear owner. Teams respond by stacking more controls and more prompt variants. Progress slows. Scale becomes manageable when GenAI is treated as a service with explicit constraints and measurable outcomes. It’s best to rely on a set of production disciplines to get there. UST Define the production contract Wri

The rise of generative AI (genAI) technology has prompted a growing debate about the future of software-as-a-service (SaaS) business models.  Some of the fears are overblown: enterprises are unlikely to vibe-code their own applications to replace their SaaS suppliers anytime soon, while software vendors have yet to see per-seat sales fall off due to mass automation of white-collar jobs. (In fact, some now predict the opposite will happen.) At the same time, AI has the potential to change the way work is carried out, with AI agents empowered to interact with software applications on behalf of users. For software vendors, that could mean a future where applications are accessed less through traditional user interfaces as AI agents connect via APIs.  It’s an inevitable shift, says Box CEO Aaron Levie, and one that requires software vendors to adapt their existing products and business models to prepare for agent workflows.  Computerworld recently spoke with Levie about how Box — and other