I wore the world's first HDR10 smart glasses TCL's new E Ink tablet beats the Remarkable and Kindle Anker's new charger is one of the most unique I've ever seen I wore the world's first HDR10 smart glasses TCL's new E Ink tablet beats the Remarkable and Kindle Anker's new charger is one of the most unique I've ever seen Scaling AI in business is less about technology and more about accountability, governance, and healthy relationships between humans and agents. Scaling the AI agents in business is now a focus on accountability and governance. Half of working hours may be reshaped by the use of AI agents. Business accountability for AI agents will require humans in the lead' versus in the loop. In 2025, agentic AI was still mostly a promise.

I wore the world's first HDR10 smart glasses TCL's new E Ink tablet beats the Remarkable and Kindle Anker's new charger is one of the most unique I've ever seen I wore the world's first HDR10 smart glasses TCL's new E Ink tablet beats the Remarkable and Kindle Anker's new charger is one of the most unique I've ever seen Only 15% of US-based organizations have reached scaled, orchestrated, multi-agent adoption, according to the latest Deloitte research. J. David Ake/Getty Images Add us as a preferred source Only 15% of organizations have reached scaled multi-agentic orchestration. Most business leaders are reevaluating their business models in light of advances in agentic AI. Scaling agentic AI must begin with sufficient resources to transform the workforce. Most US-based companies are under pressure and working hard to shift from experimenting with AI agents to deploying them in production, according to the latest research from Deloitte .

I wore the world's first HDR10 smart glasses TCL's new E Ink tablet beats the Remarkable and Kindle Anker's new charger is one of the most unique I've ever seen I wore the world's first HDR10 smart glasses TCL's new E Ink tablet beats the Remarkable and Kindle Anker's new charger is one of the most unique I've ever seen Industries are finding the strategies that work best for their business needs, according to Salesforce's latest Agentic Enterprise Index. The number of active AI agents in organizations has tripled in the last year. AI agents have improved their capabilities by 350% and can now handle complex tasks. Employee use of AI agents has increased 3X as trust deepens. The average number of AI agents activated per organization increased nearly threefold since 2025, according to the 2026 Agentic Enterprise Index, new research from Salesforce that analyzes aggregate AI usage data from the company's Agentforce platform.

To optimize AI's potential within a hybrid workforce, leaders need to adapt workplace strategies--re-evaluating roles, skills, and culture. As adoption of AI agents looks set to surge by as much as 300% in the next two years, leadership teams are carefully considering the implications of a hybrid human-AI workforce. Unlike existing enterprise-level automation that relies on manual input, AI agents are capable of autonomously coordinating complex tasks, interacting with multiple tools and environments across an organization. In early applications that center on customer service, HR, and sales, adoption of agentic AI has led to productivity gains of 30-50% . Their autonomy positions agents more as collaborators than tools, working side-by-side with human employees in blended teams that look poised to upend traditional workplace dynamics. More than three-quarters of HR leaders believe that the deployment of AI agents will transform existing workplace norms, driving a complete reappraisal of how roles and responsibilities are distributed, how skills are prioritized, and how workplace culture is shaped.

Data in the report show 4% of workers believe they have already lost a job because of AI. Data in the report show 4% of workers believe they have already lost a job because of AI. Exclusive: IPPR thinktank calls for new measures to boost employees' influence at'pivotal moment' in history Workers urgently need more bargaining power over the way AI is adopted in the workplace to ensure the benefits are fairly shared, according to a TUC-backed report from a leading thinktank. The Institute for Public Policy Research (IPPR) is calling for a package of measures to boost employees' influence at what it calls a "pivotal moment in the history of work". Its report cites survey data showing that while 20% of workers say AI is making their working life better, 21% say it has made it worse - and 4% believe they have already lost a job because of the technology.

Sam Altman Says AI'Jobs Apocalypse' He Once Predicted Probably Won't Happen. OpenAI CEO Sam Altman speaks during the BlackRock Infrastructure Summit on March 11, 2026 in Washington, DC. OpenAI CEO Sam Altman speaks during the BlackRock Infrastructure Summit on March 11, 2026 in Washington, DC. Throughout his rise to becoming one of the most influential CEOs in artificial intelligence, OpenAI's Sam Altman made repeated bold assertions about the impact that the new technology would have on jobs. He has said that AI will "probably replace most of the jobs people do today," that entire job categories will be "totally, totally gone," and that those impacted by the dramatic shifts will "find all sorts of new things to do. Now, however, Altman appears to have changed his tune, saying he is "delighted to be wrong" about the impact AI would have on employment. I don't think we're going to have the kind of jobs apocalypse that some of the companies in our space advocate or talk about, he said during a virtual interview at a Commonwealth Bank of Australia (CBA) conference in Sydney on Tuesday. "I thought there would have been more impact on entry-level white-collar jobs being eliminated by now than has actually happened, Altman said.

Successful AI implementation requires shifts in workplace culture as well as use cases that can scale across the enterprise. In finance departments that have long been defined by precision and control, AI has arrived less as a neatly managed upgrade than as a quiet insurgency. Employees are already using it while leadership races to impose structure, governance, and strategy after the fact. The result is a paradox: one of the most tightly regulated functions in the enterprise is now among the most experimentally transformed. What's emerging is a layered shift in how work gets done. From variance commentary and fraud detection to contract review and close narrative drafting, AI is embedding itself across workflows, particularly where unstructured data once slowed down everything.

In networks, effective dynamic treatment allocation requires deciding both whom to treat and also when, so as to amplify policy impact through spillovers. An early intervention at a well-connected node can trigger cascades that change which nodes are worth targeting in the next period. Existing treatment strategies under network interference are largely static while dynamic treatment frameworks typically ignore network structure altogether. We integrate these perspectives and propose Q-Ising, a three-stage pipeline that (i) estimates network adoption dynamics via a Bayesian dynamic Ising model from a single observed panel, (ii) augments treatment adoption histories with continuous posterior latent states, and (iii) learns a dynamic policy via offline reinforcement learning. The Bayesian mechanism enables uncertainty quantification over dynamic decisions, yielding posterior ensemble policies with interpretable spillover estimates. We provide a finite-sample regret upper bound that decomposes into standard offline-RL uncertainty, network abstraction error, and first stage error in Ising state estimation. We apply our method to data from Indian village microfinance networks and synthetic stochastic block models under simulated heterogeneous susceptible-infected-susceptible (SIS) dynamics and demonstrate that adaptive targeting outperforms static centrality benchmarks.

Apple CEO Tim Cook told analysts that AI adoption has happened faster than expected. Apple CEO Tim Cook said on the company's earnings call on Thursday that it could take "several months" to meet skyrocketing demand for the Mac Mini, the company's compact but mighty, screen-free desktop computer. Cook's remarks come after coders determined in recent months that the Mac Mini was the perfect machine for agentic AI tasks. "On the Mac Mini and Mac Studio, both of these are amazing platforms for AI and agentic tools," Cook said on the earnings call, in response to analyst questions. "And customer adoption of that is happening faster than we expected." The news comes amid another record-setting quarter for the company.

Secure multi-party computation (MPC) allows parties to perform computations on data while keeping that data private. This capability has great potential for machine-learning applications: it facilitates training of machine-learning models on private data sets owned by different parties, evaluation of one party's private model using another party's private data, etc. Although a range of studies implement machine-learning models via secure MPC, such implementations are not yet mainstream. Adoption of secure MPC is hampered by the absence of flexible software frameworks that "speak the language" of machine-learning researchers and engineers. To foster adoption of secure MPC in machine learning, we present CRYPTEN: a software framework that exposes popular secure MPC primitives via abstractions that are common in modern machine-learning frameworks, such as tensor computations, automatic differentiation, and modular neural networks. This paper describes the design of CRYPTEN and measure its performance on state-ofthe-art models for text classification, speech recognition, and image classification. Our benchmarks show that CRYPTEN's GPU support and high-performance communication between (an arbitrary number of) parties allows it to perform efficient private evaluation of modern machine-learning models under a semi-honest threat model. For example, two parties using CRYPTEN can securely predict phonemes in speech recordings using Wav2Letter [17] faster than real-time. We hope that CRYPTEN will spur adoption of secure MPC in the machine-learning community.