Latest news with #enterpriseAI
Globe and Mail
a day ago
- Business
- Globe and Mail
C3.ai: Could the Stock Really 10x by 2027?
(NYSE: AI) and its recent developments, including a substantial contract with the Air Force and renewed partnerships, have positioned it as a notable player in the enterprise AI sector. Despite past financial challenges, these strategic moves may signal a potential turnaround. Investors should consider the implications of these developments on future trajectory. Where to invest $1,000 right now? Our analyst team just revealed what they believe are the 10 best stocks to buy right now. Continue » *Stock prices used were the market prices of June 17, 2025. The video was published on June 18, 2025. Should you invest $1,000 in right now? Before you buy stock in consider this: The Motley Fool Stock Advisor analyst team just identified what they believe are the 10 best stocks for investors to buy now… and wasn't one of them. The 10 stocks that made the cut could produce monster returns in the coming years. Consider when Netflix made this list on December 17, 2004... if you invested $1,000 at the time of our recommendation, you'd have $658,297!* Or when Nvidia made this list on April 15, 2005... if you invested $1,000 at the time of our recommendation, you'd have $883,386!* Now, it's worth noting Stock Advisor 's total average return is992% — a market-crushing outperformance compared to172%for the S&P 500. Don't miss out on the latest top 10 list, available when you join Stock Advisor. See the 10 stocks » *Stock Advisor returns as of June 9, 2025 Rick Orford has no position in any of the stocks mentioned. The Motley Fool recommends The Motley Fool has a disclosure policy. Rick Orford is an affiliate of The Motley Fool and may be compensated for promoting its services. If you choose to subscribe through their link, they will earn some extra money that supports their channel. Their opinions remain their own and are unaffected by The Motley Fool.
Forbes
2 days ago
- Business
- Forbes
From Chatbots To Workbots: Why NiCE's AI Strategy Focuses On Execution
NiCE is betting that AI agents that complete tasks—not just conversations—will separate winners from pretenders in the enterprise AI race The artificial intelligence hype cycle has reached peak saturation, with technology vendors scrambling to slap "AI-powered" labels on everything from refrigerator recipe suggestions to chatbots that rely on simple keyword matching. Yet, for all the breathless marketing rhetoric, most business leaders are still waiting for AI that simplifies operations and improves data analysis. NiCE, a customer experience platform provider, is making a calculated bet that the next phase of enterprise AI won't be about making chatbots sound more human. The next wave of business outcomes will leverage AI agents that can navigate complex business processes from start to finish with minimal human intervention. NiCE's CEO, Scott Russell said, 'Optimizing knowledge is not just critical for AI to truly thrive in your environment, but it's also the key for transforming service from reactive to proactive, identifying opportunities to solve issues and predict future needs.' NiCE's recent product launches and strategic moves reveal a move toward enhanced automation and agentic AI. The first wave of enterprise AI focused primarily on making data more accessible through conversational interfaces—essentially putting a chat layer on top of existing applications and knowledge repositories. While this represented significant progress in democratizing data access, it only scratched the surface of AI's potential business value. Organizations could ask questions and get answers, but the burden of reasoning through complex decisions and taking action remained entirely on human operators. Going forward, technology vendors, such as NiCE, will use AI to deliver solutions that can reason through multifaceted problems and take semi or fully autonomous action. This evolution from conversational AI to agentic AI represents the difference between AI that informs and AI that performs. Agentic AI enhances a company's ability to analyze context, weigh multiple variables, make informed decisions based on key business performance indicators, and execute actions across interconnected systems. Traditional conversational AI helps a customer service representative find relevant information. Still, agentic AI can help a representative evaluate a customer's complete history more easily, assess risk factors, determine appropriate responses based on business rules, and automatically trigger the necessary workflows to resolve issues end-to-end. "There's a big difference between AI that talks and AI that gets things done," explains Barry Cooper, President of NiCE's CX Division. "While others are building agents that mimic conversations, we're building agents that fulfill customer needs—end to end." This distinction becomes crucial when examining NiCE's CXone Mpower Agents. Traditional AI chatbots had limited access to data, offered scripted responses, and were confined to specific areas of the business, such as front-office or back-office operations. NiCE's AI agent platform aim to break through these constraints by operating across the entire enterprise ecosystem—from initial customer contact through mid-office approvals to back-end fulfillment systems. Admittedly, Agentic AI is the AI buzzword of 2025, but early, well-scoped use cases show promise. Technology companies are increasingly working to streamline AI deployment as traditional approaches require extensive technical resources, custom development, and lengthy implementation cycles. NiCE's model simplifies AI agent creation while maintaining enterprise-grade sophistication through what they call vibe coding, allowing business users to tailor each agent's personality and communication style without requiring technical expertise. While the concept of vibe coding remains ill-defined, and its merits are hotly debated within the enterprise software community, there is a broad consensus around the underlying goal of making AI agents easier to code and deploy. The specific term matters less than the fundamental shift toward empowering business users to create and customize AI functionality without requiring deep technical expertise. In a rapidly evolving tech landscape, no single vendor can deliver everything an enterprise needs to succeed with AI, cloud, data, and digital transformation. Today, companies are no longer looking for isolated solutions—they need interconnected ecosystems. That's why strategic partnerships are essential. By working together, enterprise technology vendors can bridge data and function silos, improving workflows and accelerating innovation. Just as importantly, these alliances help enterprises extract greater value from existing technology investments by ensuring that new capabilities work in concert with the tools already in place. Over the past several months, NiCE has expanded its partnership with Amazon Web Services (AWS) and added ServiceNow and Snowflake to the mix. At Interactions 2025, NiCE announced an expanded collaboration with AWS, bringing together NiCE's domain expertise and rich interaction data with AWS's cloud infrastructure and generative AI services, including Amazon Bedrock, Amazon Q, and the Amazon Nova family of large language models. The partnership addresses some of the most pressing challenges facing enterprise AI deployments: fragmented workflows, disconnected data, and inconsistent global performance. The partnership focuses on three core pillars. First, content-aware automation ensures that AI-generated responses are highly relevant and context-specific. Using the Amazon Q Index, Mpower Agents are equipped with up-to-date business content—from product documentation to policy details and case histories—enabling them to respond accurately and confidently in real time. Second, the integration delivers enterprise-wide orchestration by bridging front, middle, and back-office operations. NiCE's CXone Mpower Orchestrator automates workflows across functional teams, while Amazon Q Business extends this reach into a broader set of enterprise applications—eliminating silos and streamlining complex processes. Additionally, global scalability is made possible through AWS's robust cloud infrastructure. With low-latency performance and high availability across regions, multinational organizations can deploy and scale AI-driven customer service experiences quickly and consistently around the world. NiCE's partnership strategy also extends beyond AWS to include other critical enterprise platforms, such as ServiceNow and Snowflake. NiCE's latest partnership with ServiceNow aims to eliminate long-standing service gaps by tightly integrating real-time customer engagement with enterprise workflow automation. Announced at ServiceNow's Knowledge 2025 event, the collaboration integrates NiCE's customer service platform with ServiceNow's AI and Customer Service Management (CSM) tools to streamline operations across the entire organization, from the front office to the back. The goal: fully automated customer service fulfillment. The combined solution routes inquiries based on sentiment, intent, and service-level agreements (SLAs)—bridging siloed departments to accelerate resolution times and enhance both customer and employee experiences. Role-based AI copilots assist agents and back-office teams with real-time insights and next-best actions, while continuous optimization tools flag issues and launch workflows automatically. These relationships provide access to complementary technologies and customer bases, allowing NiCE to integrate with the broader enterprise software ecosystem that companies rely on for operations, data management, and workflow automation. NiCE's strategic collaboration with Snowflake aims to unlock the full value of customer interaction data by making it accessible, secure, and actionable across the enterprise. By integrating Snowflake's AI Data Cloud with CXone Mpower, NICE can improve data sharing, breaking down silos that have traditionally limited the impact of customer insights. Snowflake serves as the backbone of the CXone Mpower data lake, centralizing interaction data and enriching it with information from other enterprise systems. This unified data foundation allows organizations to automate key processes—from billing to claims handling—while powering AI-driven analytics, dashboards, and decision-making. The result: faster fulfillment, greater accuracy, and a deeper, organization-wide understanding of the customer experience. Apparently, 2025 is the year of the brand refresh. The technology industry has witnessed updates from Five9, Google's G, Hitachi HPE, and Qualcomm's introduction of Dragonwing, alongside NiCE's own transformation. Every brand refresh has its own story to tell, but NiCE's new logo and marketing campaign represent more than a desire for fresh typography and color schemes. The rebrand indicates the company's strategic desire to expand its AI vision beyond the contact center to encompass its broader portfolio of finance and security solutions. The company describes the rebrand as positioning "NICE to empower brands to deliver AI-powered experiences that are proactive, human-centered and intuitive—whether connecting with customers, protecting communities or combatting financial crime." NiCE's solution involves partnering with actress Kristen Bell, who serves as the face of the company's "NiCE World" brand campaign. The initiative positions Bell as the "NiCEst Person in the World," NiCE said the campaign "builds on NiCE's reimagined brand, championing a future where AI isn't just intelligent – it's connected, intuitive and working behind the scenes to make life better. The enterprise AI market remains in flux, with new entrants and existing players continually repositioning themselves. NiCE's focus on domain expertise, integration depth, strategic partnerships, and automation suggests a company that understands both the technical and implementation requirements necessary for large-scale AI adoption. As enterprises increasingly demand AI that delivers results, NiCE's bet on fulfillment-focused automation may prove prescient. Of course, there's still the matter of cost and return on investment. Most companies struggle to understand and plan for the true product and operational costs of AI. Organizations need to work with their technology vendors to deploy well-scoped use case that deliver measurable return on investment, fast. The question isn't whether AI will transform customer experience—it's which companies will build AI that completes the transformation rather than just talking about it.
Globe and Mail
11-06-2025
- Business
- Globe and Mail
NVIDIA Partners With Europe Model Builders and Cloud Providers to Accelerate Region's Leap Into AI
Model Builders Across Europe — Including France, Italy, Poland, Spain and Sweden — to Deliver Sovereign Models With NVIDIA Nemotron AI Models Tailored to Local Languages and Culture Coming to Perplexity, Delivered as NVIDIA NIM Microservices and Hosted on Regional AI Infrastructure From NVIDIA Cloud Partners PARIS, June 11, 2025 (GLOBE NEWSWIRE) -- NVIDIA GTC Paris at VivaTech -- NVIDIA today announced that it is teaming with model builders and cloud providers across Europe and the Middle East to optimize sovereign large language models (LLMs), providing a springboard to accelerate enterprise AI adoption for the region's industries. Model builders and AI consortiums Barcelona Supercomputing Center (BSC), Dicta, H Company, Domyn, LightOn, the National Academic Infrastructure for Supercomputing in Sweden (NAISS) together with KBLab at the National Library of Sweden, the Slovak Republic, the Technology Innovation Institute (TII), the University College of London, the University of Ljubljana and UTTER are teaming with NVIDIA to optimize their models with NVIDIA Nemotron™ techniques to maximize cost efficiency and accuracy for enterprise AI workloads, including agentic AI. Model post-training and inference will run on AI infrastructure in Europe from NVIDIA Cloud Partners (NCPs) participating in the NVIDIA DGX Cloud Lepton ™ marketplace. The open, sovereign models will provide a foundation for an integrated regional AI ecosystem that reflects local languages and culture. Europe's enterprises will be able to run the models on Perplexity, an AI-powered answer engine used to answer over 150 million questions per week. Companies will also be able to fine-tune the sovereign models on local NCP infrastructure through a new Hugging Face integration with DGX Cloud Lepton. 'Europe's diversity is its superpower — an engine of creativity and innovation,' said Jensen Huang, founder and CEO of NVIDIA. 'Together with Europe's model builders and cloud providers, we're building an AI ecosystem where intelligence is developed and served locally to provide a foundation for Europe to thrive in the age of AI — transforming every industry across the region.' Optimizing Model Accuracy and Inference Savings With NVIDIA Nemotron Europe — the world's third largest economic region — is home to industries spanning manufacturing, robotics, healthcare and pharmaceuticals, finance, energy and creative. To accelerate the region's AI-driven transformation, NVIDIA partners are delivering their open LLMs with support for Europe's 24 official languages. Several models also specialize in national language and culture, such as those from H Company and LightOn in France, Dicta in Israel, Domyn in Italy, in Poland, the University of Ljubljana and the Slovak Republic models, BSC in Spain, NAISS and KBLab in Sweden, TII in the United Arab Emirates and the University College London in the U.K. The LLMs will be distilled with NVIDIA Nemotron model-building techniques — including neural architecture search — as well as reinforcement learning and post-training with NVIDIA-curated synthetic data. These optimizations will reduce operational costs and boost user experiences by generating tokens faster during inference. The Nemotron post-training workloads will run on DGX Cloud Lepton hosted by European NCPs including Nebius, Nscale and Fluidstack. Developers will be able to deploy the sovereign models as NVIDIA NIM ™ microservices running on AI factories — on premises and across cloud service provider platforms — using a new NIM microservice that supports more than 100,000 public, private and domain-specialized LLMs hosted on Hugging Face. Adding Europe's Sovereign AI Insights to Perplexity Supporting AI diversity for enterprises across the region, Perplexity will integrate the sovereign AI models into its answer engine, which is used by European enterprises, publishers and organizations, including telecommunications and media giants. Perplexity uses LLMs to improve accuracy in search queries and AI outputs. The answer engine draws from credible sources in real time to accurately answer questions with in-line citations, perform deep research and complete assistive tasks. 'Perplexity's goal is to provide accurate, trustworthy answers to any question from any person, wherever they are,' said Aravind Srinivas, cofounder and CEO of Perplexity. 'Bringing NVIDIA-optimized sovereign AI models to Perplexity empowers innovation in Europe with AI built and running in the region.' Availability The first distilled models from Europe's model builders are expected to be available later this year. Watch the NVIDIA GTC Paris keynote from Huang at VivaTech and explore GTC Paris sessions. About NVIDIA NVIDIA (NASDAQ: NVDA) is the world leader in accelerated computing. For further information, contact: Allie Courtney NVIDIA Corporation +1-408-706-8995 acourtney@ Certain statements in this press release including, but not limited to, statements as to: together with Europe's model builders and cloud providers, NVIDIA building an AI ecosystem where intelligence is developed and served locally to provide a foundation for Europe to thrive in the age of AI — transforming every industry across the region; the benefits, impact, performance, and availability of NVIDIA's products, services, and technologies; expectations with respect to NVIDIA's third party arrangements, including with its collaborators and partners; expectations with respect to technology developments; and other statements that are not historical facts are forward-looking statements within the meaning of Section 27A of the Securities Act of 1933, as amended, and Section 21E of the Securities Exchange Act of 1934, as amended, which are subject to the 'safe harbor' created by those sections based on management's beliefs and assumptions and on information currently available to management and are subject to risks and uncertainties that could cause results to be materially different than expectations. Important factors that could cause actual results to differ materially include: global economic and political conditions; NVIDIA's reliance on third parties to manufacture, assemble, package and test NVIDIA's products; the impact of technological development and competition; development of new products and technologies or enhancements to NVIDIA's existing product and technologies; market acceptance of NVIDIA's products or NVIDIA's partners' products; design, manufacturing or software defects; changes in consumer preferences or demands; changes in industry standards and interfaces; unexpected loss of performance of NVIDIA's products or technologies when integrated into systems; and changes in applicable laws and regulations, as well as other factors detailed from time to time in the most recent reports NVIDIA files with the Securities and Exchange Commission, or SEC, including, but not limited to, its annual report on Form 10-K and quarterly reports on Form 10-Q. Copies of reports filed with the SEC are posted on the company's website and are available from NVIDIA without charge. These forward-looking statements are not guarantees of future performance and speak only as of the date hereof, and, except as required by law, NVIDIA disclaims any obligation to update these forward-looking statements to reflect future events or circumstances. Many of the products and features described herein remain in various stages and will be offered on a when-and-if-available basis. The statements above are not intended to be, and should not be interpreted as a commitment, promise, or legal obligation, and the development, release, and timing of any features or functionalities described for our products is subject to change and remains at the sole discretion of NVIDIA. NVIDIA will have no liability for failure to deliver or delay in the delivery of any of the products, features or functions set forth herein. © 2025 NVIDIA Corporation. All rights reserved. NVIDIA, the NVIDIA logo, DGX Cloud Lepton, NVIDIA Nemotron and NVIDIA NIM are trademarks and/or registered trademarks of NVIDIA Corporation in the U.S. and other countries. Other company and product names may be trademarks of the respective companies with which they are associated. Features, pricing, availability and specifications are subject to change without notice. A photo accompanying this announcement is available at .
Reuters
10-06-2025
- Business
- Reuters
AI company Glean hits $7.2 billion in valuation in latest funding round
June 10 (Reuters) - U.S. enterprise AI search startup Glean said on Tuesday it had notched a $7.2 billion valuation in its latest funding round, led by asset manager Wellington Management. The company raised $150 million in a Series F funding round, which also included participation from new investors including Khosla Ventures. The latest investment will help Glean to enhance its product offerings and bolster international presence, the company said.
Forbes
10-06-2025
- Business
- Forbes
Workforce Reskilling Is The Competitive Edge In The Agentic AI Era
Agentic AI AdobeStock_1157006091 From Generative to Agentic — A New Chapter in Enterprise AI Generative AI brought along tools like chatbots and auto-generated content, but the next frontier is Agentic AI – systems that can plan, decide, and act across processes. Agentic AI is designed to handle ambiguity and equipped to make autonomous decisions, and companies are excited about its potential, with the Agentic AI market expected to be worth $196B by 2034, reaching an impressive CAGR of nearly 44%. However, scaling agentic solutions requires a multi-disciplinary delivery model—combining deep industry, data, technology, and process expertise with specialized talent. Furthermore, agent orchestration is the emerging backbone of enterprise architecture: a way to coordinate multiple specialized AI agents as an intelligent network. In practice, this means bridging data, processes, and customer engagement through AI-driven workflows instead of point-to-point integrations. Enterprises will increasingly run AI-infused processes with agents handling data flows and humans supervising outcomes. 'Agentic AI demands more than smart algorithms—it demands smart organizations,' says Sanjeev Vohra, Chief Technology and Innovation Officer at Genpact. 'Enterprises need to reimagine how their people and processes interact with technology. That starts with deeply reskilling the workforce for a future where AI is embedded in every decision.' The Workforce Wake-Up Call — Why Talent, Not Tech, Will Decide Agentic AI Success Despite the hype, the real bottleneck is talent. According to a recent Prosper Insights & Analytics survey, 43.5% of executives already use Gen AI tools, but only 26.5% of employees say the same. This data highlights an 'unforeseen talent gap' between executives and employees – suggesting that there is not necessarily a lack of interest in AI, but a shortage of skilled people to implement it. Prosper - Heard of Generative AI Prosper Insights & Analytics Globally, employees are eager to learn. In Genpact's research, nearly 80% of workers said they want new AI-related skills and 59% said they'd be more comfortable with AI if they understood it better. Yet few companies have scaled training. The research also revealed that only around one in three employees are offered AI training, and just 21% have participated. Enterprises are already taking action by segmenting their workforce into AI builders and consumers. Builders (data scientists, engineers, domain experts) create and refine AI tools, while the broader workforce is made 'AI-fluent' – trained to use AI outputs and embed them in decision-making. This dual investment in specialized talent and broad AI literacy is now viewed as essential for thriving in the agentic era. Beyond Automation — Rethinking Roles, Skills, and Human-Machine Collaboration AI will transform legacy roles, with some fading, but many new ones are emerging. We're already seeing this shift in action, with 'prompt engineers' who craft inputs for AI models, 'AI translators' who turn machine outputs into strategic advice, and 'agent overseers' who manage fleets of AI tools. The survivors will have blended skillsets combining domain expertise, technical savvy, and human judgment. Even as AI handles more tasks, human collaboration remains crucial as humans will be responsible for oversight, creative decisions, and ethical judgment while offloading repetitive or data-intensive steps to agents. As a result, organizations must encourage 'learning by doing' and engage experts to create micro-projects so employees can practice new skills on real problems. Embedding Responsible Innovation — Ethics, Upskilling, and Culture at the Core As Agentic AI grows, so does the need for controls and conscience. According to a recent Prosper Insights & Analytics survey, employees have several concerns with the use of AI, specifically in terms of them agreeing it requires human oversight (33.6), more transparency on the data it uses (29.6%), and that can cause job loss (27.9%). Prosper-Concerns About Recent Developments in AI Prosper Insights & Analytics Every AI deployment must include robust oversight, including operating within controlled parameters and following responsible AI guidelines. This means using role-based access controls, audit logs, and clear human-in-the-loop checkpoints from day one. Establishing guardrails early builds trust and avoids the mistakes of 'blind' automation. 'The future belongs to companies that scale curiosity, not just code—by building human-centered upskilling programs and by fostering a culture of shared learning and responsible innovation, where experts share knowledge across networks. In this way, organizations instill a sense of collective responsibility. And the result is a virtuous cycle: a more skilled and diverse workforce that innovates both rapidly and responsibly,' Vohra says. A Playbook for Change — How to Start Building an Agentic-Ready Workforce Today To build a workforce that can thrive in the Agentic AI era, organizations must first map their future organization and reimagine roles by identifying which jobs will evolve, such as prompt engineers or AI ethicists, as well as which may no longer be needed. Determining which employees are AI builders or users will enable an organization to launch targeted training that reflects any needed shifts. With a clear view of the future roles it will need, organizations must then pair investments in core AI talent with efforts to achieve widespread AI fluency. This can be done by incorporating continuous learning into daily workflows and encouraging hands-on learning through micro-projects, hackathons, internal training, and peer mentorship. Celebrating these efforts during regular reviews and building cross-functional pods that combine business, IT, and data talent can reinforce a collaborative culture where learning, experimentation, and AI adoption compound to address all facets of a problem. Lastly, organizations cannot build an agentic-ready workforce unless they are prioritizing responsible AI. To address concerns, organizations must ensure clear oversight, offer human-in-the-loop systems, and implement guardrails to manage risks from day one of their AI implementation journey. Innovation can never outpace oversight, and that may require organizations to start with small proofs-of-concept to ensure oversight and gauge talent and infrastructure readiness. Reskilling is no longer optional—it's a survival imperative. Enterprises that embrace these shifts and build blended skillsets (domain + tech + human judgment) will gain the decisive edge in the Agentic AI era.
