On Thursday at an internal town hall, BNY CEO Robin Vince unveiled the next-generation version of Eliza, its AI platform that allows employees to create bespoke agents and carry out tasks such as accelerated check processing built on reams of company data. "We think of AI as generating capacity for us," Vince said at the town hall. According to a BNY spokesperson, 98% of the company's employees are trained on generative AI, with the majority using Eliza daily.
Employees will soon have access to a single integrated platform where enterprise knowledge, data, and actions come together. The new capabilities include searching across all of an organization's key data sources, including Workday's own data cloud, as well as Google Drive, SharePoint, and Office365. AI agents can act proactively by anticipating needs, summarizing insights, and providing support for projects. In addition, these agents are enabled to create presentations, documents, dashboards, and even entire learning courses based on existing company data.
"What we had noticed was there was an underlying problem with our data," Ahuja said. When her team investigated what had happened, they found that Salesforce had published contradictory "knowledge articles" on its website."It wasn't actually the agent. It was the agent that helped us identify a problem that always existed," Ahuja said. "We turned it into an auditor agent that actually checked our content across our public site for anomalies. Once we'd cleaned up our underlying data, we pointed it back out, and it's been functional."
Many customers say, 'I don't really have an AI problem, I have a data problem.' They need to prepare their data. Files here, images there, videos elsewhere - they have these legacy platforms that don't support unified access. The challenge becomes quite complex because most enterprise data is unstructured: contracts, invoices, videos, presentations, and it's scattered across different systems. The real value comes from bridging unstructured and structured data.
Varshney comes from IBM, where he was the head of data and artificial intelligence for the technology pioneer's consulting business. At Citi, he will report to Anand Selva, the bank's chief operating officer; and will work closely with the firm's chief technology officer, David Griffiths, to scale AI across the company, according to a memo sent to Citi employees on Tuesday morning seen by Business Insider.
When it comes to evaluating the return on investment for cloud-based artificial intelligence projects, the discussion tends to swing between two extreme viewpoints-either enterprises are raking in big gains or they're stuck in a never-ending quagmire of false starts and expensive lessons. Google Cloud's latest study, "The ROI of AI 2025" paints a hopeful picture, claiming that early adopters of AI agents are seeing returns within the first year.
For the past five years, much of the enterprise conversation around artificial intelligence (AI) has revolved around access - with access to application programming interfaces (APIs) from hyperscalers, pre-trained models, and plug-and-play integrations promising productivity gains. This phase made sense. Leaders wanted to move quickly, experimenting with AI without the cost of building models from scratch. " AI-as-a-service " lowered barriers and accelerated adoption.
Despite the hype surrounding artificial intelligence, C3.ai seems stuck in neutral, failing to capitalize on the sector's explosive growth. The old Wall Street adage, "buy the rumor, sell the news," doesn't quite fit here. With C3.ai, the strategy appears to be sell the rumor, sell the news, and, frankly, just sell the stock. The company's inability to turn AI enthusiasm into sales or profits, coupled with leadership turmoil, paints a grim picture for its future.
Anthropic has closed a deal to raise $13 billion from investors in a new funding round that nearly triples its valuation to $183 billion, including dollars raised a larger-than-expected haul that makes the artificial intelligence company one of the most valuable startups in the world. The financing, one of the largest to date for an AI company, was led by investment firm Iconiq Capital alongside co-leads Fidelity Management and Research Co. and Lightspeed Venture Partners.
Constant growth and scaling are probably the dream of any modern entrepreneur. This stimulates an increase in the customer base and orders, and as a result, allows the company to generate more revenue. However, achieving these goals requires a lot of effort using relevant tactics. Don't focus on what worked in the past. Instead, use only tested strategies that have proven their effectiveness nowadays. As a small spoiler, we would like to advise you to focus on promoting online sales channels, implementing enterprise AI solutions, and expanding your product range as your capabilities grow. Where to look for new opportunities? How to expand your company? We will try to answer these questions in detail in this article, sharing with you the five best strategies for company growth in 2025!
In the race to stay ahead, organizations have thrown open the doors to every AI tool under the sun. The result? AI overload. According to the Wharton School, AI spending has skyrocketed by 130% in just the past year, and 72% of companies are planning to invest even more in 2025. Yet, here's the kicker: 80% of organizations report no tangible enterprise-wide impact from their generative AI investments.
However, what's interesting about the way Apple's integration with ChatGPT for Enterprise has been structured is that it's not hard-coded to only restrict or allow ChatGPT itself. Instead, Apple's support documents indicate that IT administrators will be able to restrict or allow any "external" artificial intelligence provider, not just OpenAI's technology. That leaves the door open for Apple to forge other deals with large AI players used in the enterprise environment, without having to recode things at the protocol level.
Large Language Models aren't the issue. They just do what they were designed to do, which is to generate plausible responses based on statistical patterns. They can write fluent paragraphs, summarize documents, and even mimic strategic thinking. But they don't reason, and they don't verify. They only guess. If a model says that Company A acquired Company B, it's not referencing logic. It's assembling words based on probability.
Organizations are moving past simple automation tools and chatbots in their race to deploy artificial intelligence at enterprise scale, aiming for the implementation of autonomous AI agents.
Today, we announced xAI has selected Oracle to offer xAI's Grok models via OCI Generative AI service for a wide range of use cases and will use OCI's leading AI infrastructure to train and run inferencing for its next-generation Grok models.
Chinese firms like RedNote are deploying open-source LLMs not just as models but as instruments of ecosystem control and geopolitical leverage. Meanwhile, Western firms remain committed to proprietary architectures.
