Latest news with #enterpriseAI
Geek Wire
2 days ago
- Business
- Geek Wire
The realities of enterprise AI deployments, with Amazon Web Services VP Francessca Vasquez
On this episode of the GeekWire Podcast, we dive into enterprise AI adoption with Francessca Vasquez, a vice president at Amazon Web Services who leads its work with enterprise customers through the AWS Professional Services Group and the AWS Generative AI Innovation Center. Vasquez shares insights from more than 1,000 customer engagements across industries, explaining how companies are moving from AI experiments to deployments. We discuss the rise of AI agents, the challenges businesses face in scaling AI initiatives, and why some industries are surprising everyone with their pace of innovation. We also discuss specific examples including Yahoo Finance's multi-agent news analysis system, the PGA Tour's AI-powered real-time golf commentary, Formula 1's root cause analyzer for troubleshooting race-day issues, and Jabil's shop floor assistant for manufacturing employees. Listen below, or subscribe in any podcast app, and keep reading for highlights. On the rapid pace of AI evolution: 'The pace of innovation right now for generative and agentic AI is just a lot faster than what I've ever seen in my career. I couldn't tell you what are things going to look like in 12 to 18 months. That's how fast things are moving.' Industries adopting AI faster than expected: 'If you had asked me 24 months ago, did I think one industry would really just be leading in adoption, I probably would have said, just look at all of your digital native companies… I would have been wrong. … We've seen a lot of innovation happening in financial services. We've seen a lot happening in manufacturing and healthcare.' On moving from proof of concept to production: 'The only way you get to value is by actually putting things into production. … Of these same companies that were doing experiments or proof of concepts a year ago, only about 30% of them actually got into production. With the work that we do, we've been able to increase that well over 50%.' What makes for successful enterprise AI deployments: 'These things still require having very good leadership conviction. The companies that may be progressing further, they've got a leadership team who believes in the pace of technology, or they've got senior technical thought leaders on their board. That makes a very big difference.' Beyond the technology: 'How [companies] think about their culture and their people, that becomes a huge differentiator for both talent development and attraction. … It's more than just the technology of models and latency and tokens. It's also about the people and the culture and what experiences you want.' On AI agents and the future of work: 'I expect that any organization that's offering consulting services, they will all have to have some level of generative AI and agentic AI in their workflows — all of them, every last one of them.' Related Stories and Links: Audio editing by Curt Milton.
Yahoo
2 days ago
- Business
- Yahoo
Palantir's Commercial Strategy Is Finally Paying Off
Key Points AIP powers real operational AI, not demos. Cloud partnerships are unlocking new opportunities. Palantir's commercial success isn't just theoretical, it's backed by real-world examples of customer adoption. 10 stocks we like better than Palantir Technologies › Palantir Technologies (NASDAQ: PLTR) has long been recognized for its work in the shadows -- helping government agencies, such as the Department of Defense and the CIA, make sense of vast amounts of data. But for years, investors questioned whether it could ever succeed in the commercial world. While the company often emphasized commercial expansion, the numbers have historically lagged behind those of its core government business. But that's changing -- and fast. Today, Palantir's commercial business is not just growing, it's accelerating. Thanks to the interest in artificial intelligence (AI) and the company's strategy, it may finally be building a sustainable and scalable engine outside public business. AIP is the inflection point Palantir's Artificial Intelligence Platform (AIP) is emerging as its breakout commercial product. Launched as the fourth platform after Gotham, Foundry, and Apollo, AIP is Palantir's answer to the rapid rise in demand for enterprise AI solutions. Commercial customers can use it to integrate large language models (LLMs) into their internal data workflows, all while preserving the security, governance, and compliance that Palantir is known for. AIP enables enterprises to build and scale AI agents with full access controls, audit trails, and encryption baked in. With AIP, customers can immediately see tangible benefits in areas such as automation, problem-solving, and workflow improvements without compromising on data and security requirements. Palantir has also rolled out AIP boot camps, five-day hands-on workshops that help companies go from zero to a working AI use case using their internal data. Customers don't just learn how to prompt -- they architect LLM-powered workflows, evaluate fine-tuning vs. prompting, and build real systems. For some customers, boot camps have helped them resolve problems they'd been wrestling with for years. Not surprisingly, these efforts have yielded tangible improvements in financial metrics. In the first quarter of 2025, U.S. commercial revenue increased 71% year over year to $255 million, and U.S. commercial total contract value (TCV) bookings rose 183% to $810 million. The U.S. commercial customer count also grew 65% to 432. Strategic cloud partnerships could be an important growth lever Beyond the launch of AIP and AIP boot camp, Palantir has also expanded its partnerships with cloud giants to integrate its AI platform within the ecosystems that customers already use. With Google Cloud, Palantir launched Foundry and AIP integrations for commercial clients in retail, healthcare, and logistics. With that, companies leverage BigQuery and other Google Cloud services while tapping into Palantir's data modeling and orchestration tools. On Amazon Web Services (AWS), Palantir is working with enterprise clients to run AIP workloads using Claude and other models. A major U.S. insurer, for example, used AIP on AWS to reduce underwriting times from two weeks to just three hours. These integrations remove key roadblocks to adoption. Besides, these partnerships are a win-win-win. Customers don't have to rip and replace their existing infrastructure. Cloud providers retain clients within their ecosystem. And Palantir gains distribution and scale. Real-world examples of customer adoption Palantir's commercial success isn't theoretical. It's happening in the field. For instance, Heineken transformed its supply chain using AI agents to optimize delivery and shipping. With the help of AIP, the team was able to build what had previously taken them three years in just three months. AIG, an insurance giant, expects the adoption of AIP to aid in AI-powered underwriting, aiming to double its five-year revenue growth rate from 10% to 20%. This example suggests that AIP not only helps improve efficiency, but also acts as an enabler in increasing the top line. Another example is Rio Tinto, which leverages the Foundry and AIP platforms to orchestrate and optimize train routes and maintenance needs for dozens of unmanned trains running 24/7. These examples show that AIP isn't just a shiny front end for AI experimentation -- it's delivering real outcomes across industries. What it means for investors Palantir's long-awaited commercial pivot is finally delivering tangible results. With AIP gaining traction and strategic partnerships expanding its reach, Palantir is transforming from a government-first software vendor into a scalable enterprise AI company. But while the growth is promising, investors should approach with measured optimism. Palantir trades at a rich valuation, reflecting high expectations for future expansion. At the time of this writing, the stock has a price-to-sales (P/S) ratio of 121. So, the key question is whether Palantir can sustain this momentum into the future. If the company continues to deliver strong commercial wins, and the valuation becomes more attractive, this could be a compelling long-term investment opportunity. All said, investors should keep the stock on their watchlist. Should you buy stock in Palantir Technologies right now? Before you buy stock in Palantir Technologies, consider this: The Motley Fool Stock Advisor analyst team just identified what they believe are the for investors to buy now… and Palantir Technologies 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 $687,149!* Or when Nvidia made this list on April 15, 2005... if you invested $1,000 at the time of our recommendation, you'd have $1,060,406!* Now, it's worth noting Stock Advisor's total average return is 1,069% — a market-crushing outperformance compared to 180% 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 July 15, 2025 John Mackey, former CEO of Whole Foods Market, an Amazon subsidiary, is a member of The Motley Fool's board of directors. Lawrence Nga has no position in any of the stocks mentioned. The Motley Fool has positions in and recommends Amazon and Palantir Technologies. The Motley Fool has a disclosure policy. Palantir's Commercial Strategy Is Finally Paying Off was originally published by The Motley Fool
Forbes
3 days ago
- Business
- Forbes
AWS Targets Enterprise AI Agent Production Gap With AgentCore Platform
Agents Amazon Web Services has introduced AgentCore, a managed platform specifically designed to bridge the challenging transition from AI agent prototypes to production-ready enterprise applications. The platform addresses infrastructure complexities that frequently stall enterprise AI initiatives, offering seven integrated services that handle runtime management, memory systems, and security controls. The announcement signals AWS's recognition of a critical market need. While organizations increasingly experiment with AI agents, many struggle to deploy them at scale due to infrastructure limitations, security concerns, and operational complexity. AgentCore positions AWS to capture enterprise spending as companies move beyond pilot projects toward production deployment. A Look at the Platform Components AgentCore consists of seven core services that work independently or together. The runtime component provides serverless execution environments with complete session isolation and support for workloads lasting up to eight hours—currently the longest in the industry. This addresses a fundamental challenge where traditional serverless platforms struggle with the unpredictable execution patterns of AI agents. The memory service manages both short-term conversational context and long-term knowledge retention across sessions. Unlike basic chatbot implementations, AgentCore Memory maintains persistent learning capabilities, enabling agents to improve performance over time. This persistent memory capability differentiates the platform from simpler AI assistant tools that reset context between interactions. Security integration happens through AgentCore Identity, which connects with existing enterprise identity providers including Amazon Cognito, Microsoft Entra ID, and Okta. The service enables agents to access internal systems while maintaining proper authentication and authorization controls. This enterprise-grade security model addresses compliance requirements that often delay AI agent deployments. Additional services include AgentCore Gateway for API integration, a browser tool for web automation, a code interpreter for secure code execution, and observability features powered by Amazon CloudWatch. The modular design allows organizations to adopt components incrementally rather than requiring complete platform migration. Competitive Landscape and Market Positioning AgentCore enters a competitive enterprise AI agent market currently dominated by platform-specific solutions. Google's Vertex AI Agent Builder offers similar capabilities but requires organizations to operate within Google Cloud's ecosystem. Microsoft's Azure AI Foundry Agent Services provide deep integration with Microsoft products but lack the framework-agnostic approach that AgentCore emphasizes. The platform's support for open-source frameworks including Strands Agents, LangChain, CrewAI, and LlamaIndex differentiates it from vendor-locked alternatives. Organizations can use any foundation model, including those hosted outside Amazon Bedrock, providing flexibility that appeals to enterprises with diverse AI strategies. This approach contrasts with Google's Vertex AI Agent Builder, which primarily integrates with Google's model ecosystem. AWS has also introduced a marketplace for pre-built AI agents and tools, creating a distribution channel that could accelerate enterprise adoption. The marketplace approach mirrors successful software distribution models and may provide AWS with additional revenue streams beyond core platform services. Implementation Challenges and Enterprise Considerations Despite its comprehensive feature set, AgentCore faces implementation hurdles common to enterprise AI deployments. The platform requires organizations to restructure workflows around agent-based automation, which can encounter resistance from teams accustomed to traditional software development practices. Technical skills gaps remain a significant barrier, with many organizations lacking the expertise to effectively deploy and manage AI agents at scale. Security concerns persist despite AgentCore's built-in controls. AI agents can accumulate system permissions that create expanded attack surfaces, and their autonomous decision-making capabilities introduce unpredictable behavior patterns that conventional security tools struggle to monitor. Organizations must implement additional governance frameworks to ensure agents operate within acceptable risk parameters. The platform's consumption-based pricing model, while offering cost flexibility, can create budget uncertainty for organizations with variable AI workloads. Runtime costs depend on CPU utilization and memory consumption, making it difficult to predict expenses for complex agent deployments. This pricing structure may favor organizations with predictable agent usage patterns over those with sporadic or experimental implementations. Strategic Implications for Enterprises AgentCore represents AWS' strategic response to enterprise AI maturation. As organizations move beyond generative AI experiments toward production automation, managed agent platforms become critical infrastructure. The platform's emphasis on security, scalability, and observability addresses key enterprise requirements that have limited AI agent adoption. However, success depends on AWS's ability to reduce operational complexity while maintaining enterprise security standards. Organizations that successfully deploy AI agents report significant productivity gains and cost reductions, but implementation requires careful planning and skilled technical teams. AgentCore's effectiveness will ultimately be measured by its ability to democratize AI agent deployment beyond technically sophisticated early adopters implementing proof of concepts. AgentCore's framework-agnostic approach positions AWS to capture enterprise spending regardless of specific AI implementation choices. This strategy may prove more sustainable than vendor-locked alternatives as the AI agent market matures and organizations seek to avoid technology dependencies that could limit future flexibility.
Forbes
4 days ago
- Business
- Forbes
Are Agentic AI Systems Quietly Taking Over Enterprises? 3 Ways To Keep Humans In The Loop
Are Agentic AI Systems Quietly Taking Over Enterprises? Imagine a future where AI agents run the majority of your company's daily operations by handling complex tasks, managing workflows, and resolving customer issues around the clock, all while reporting to another AI agent manager who then reports to you. Picture reaching out to McKinsey and instead of a human consultant, being connected with a customized AI agent that provides expert insights instantly. That future is nearly here. Agentic AI is rapidly reshaping how enterprises operate. At Salesforce, these AI agents now manage 30 to 50 percent of internal workflows, and more than 85 percent of customer service inquiries are resolved by AI, dramatically easing the burden on human staff. CEO Marc Benioff, known for his bold branding, has even called himself the 'Taylor Swift of Tech,' comparing Salesforce's AI transformation to the sweeping impact of Swift's multi-era world tours. Salesforce isn't alone. McKinsey & Company has introduced its own "Lilli" agents, AI tools capable of conducting deep research, generating data-driven insights, and producing presentation-ready charts and slides. As these systems evolve, they are poised to take over tasks traditionally assigned to junior consultants, potentially reshaping the firm's hiring needs and operational structure. The broader implication? We are moving toward a future where firms like McKinsey, BCG, Bain, or Deloitte might offer AI agents as the first point of contact—consultants that never sleep, scale instantly, and continually improve. The rise of enterprise AI agents is no longer speculative; it's unfolding now, and fast. But how far will it go? Could AI agents eventually displace 80-90% of today's workforce within these firms? Will humans have a meaningful role in workflows as automation scales? These are not just hypothetical questions—they are strategic imperatives. As agentic AI begins to power everything from back-office functions to client-facing operations, the challenge is clear: how do we keep humans meaningfully in the loop? Here are three strategies to ensure that, even in an era of hyper-automation, the human touch remains essential to enterprise success. 1. Design Human-In-The-Loop (HITL) Agentic Ai Systems with Unique Human Roles As Agentic AI systems increasingly take on core operational functions, it is imperative for enterprises to reimagine organizational roles and workflows to ensure continued and meaningful human involvement. Rather than assigning humans to tasks that AI can readily perform, the focus should shift toward areas where human expertise remains indispensable, such as strategic decision-making, ethical governance, nuanced client engagement, and cross-functional leadership. To enable this transition, organizations must design and implement robust human-in-the-loop (HITL) frameworks. These systems embed human oversight into AI-driven processes, particularly in high-impact areas like talent acquisition, financial decision-making, legal analysis, and healthcare. For instance, in a consulting environment, an AI agent might generate an initial draft of a client strategy or market report. However, it is the responsibility of the consultant to interpret the findings, tailor the insights to the client's specific context, and ensure overall quality and relevance. Supporting these evolving workflows are a new wave of hybrid roles such as AI strategy leads, human-AI collaboration specialists, and HITL analysts. These roles serve as essential interfaces between AI systems and business outcomes, safeguarding against errors while optimizing the value AI delivers. By embedding human judgment, accountability, and strategic alignment into AI-enabled operations, organizations can unlock the full promise of Agentic AI while maintaining human agency at the core of enterprise decision-making. 2. Build an AI-Ready Workforce for Human-AI Collaboration As Agentic AI becomes increasingly integrated into enterprise operations, it is essential to invest in up skilling the workforce in both AI literacy and systems thinking. Employees need a clear understanding of how AI systems function, where they create value, and what their limitations are. This knowledge allows them to interpret AI outputs thoughtfully, identify potential risks or biases, and collaborate with these systems effectively. When AI is approached as a collaborative partner rather than a mysterious or autonomous tool, organizations can foster greater adoption, trust, and alignment with business goals. For example, in financial services, portfolio managers who are trained in AI concepts can use algorithmic tools to enhance investment strategies while still applying their own market expertise for final decisions. In marketing, teams can combine AI-powered customer segmentation with human creativity to develop more tailored and impactful campaigns. By cultivating these skills across functions, companies create a workforce that is not only technically capable but also strategically positioned to guide and govern the responsible use of AI throughout the organization. 3. Establish AI Governance and Escalation Frameworks to Ensure Accountability As AI systems are increasingly deployed in critical business functions, it is essential to establish strong governance and escalation frameworks to maintain oversight and accountability. These protocols ensure that when AI-generated recommendations conflict with legal standards, ethical principles, or stakeholder expectations, human experts can intervene. For example, in financial services, if an AI system produces a credit decision that appears biased, compliance officers should have the authority to pause and review the process before action is taken. To support this oversight, organizations should form dedicated structures such as AI ethics boards or enterprise-level agent councils. These groups evaluate high-impact use cases, assess risk, and define clear escalation paths for teams interacting with AI systems. By embedding governance into the AI lifecycle, enterprises can scale intelligent automation responsibly while preserving human judgment and organizational integrity. Leading Through the Age of AI Agents Agentic AI is no longer a vision of the future; it is an active force reshaping the enterprise landscape. As organizations embrace these powerful systems, the challenge is not simply technological but deeply human. Success will depend on how well companies design for collaboration between intelligent agents and the people who guide them. By embedding thoughtful human oversight, investing in AI literacy, and governing automation with intention, enterprises can unlock the full potential of agentic AI while ensuring that people remain at the heart of innovation and decision-making.
Yahoo
4 days ago
- Business
- Yahoo
Jefferies Sticks With Buy on Microsoft After Survey
A fresh Jefferies survey of 40 enterprises finds M365 Copilot usage jumped to 82%, up from 77% last fall, and forecasts Copilot could drive roughly $11 billion in revenue for Microsoft (NASDAQ:MSFT) by 2026. Respondents told Jefferies they plan to boost their Microsoft spending by about 4% over the next two years. That confidence helped Jefferies reaffirm its buy rating on MSFT and set a $600 target, calling the company a top AI winner. Brent Thill's team points out that at 31-one times 2026 EPS, Microsoft still looks attractively valued given Copilot's growth runway. Warning! GuruFocus has detected 7 Warning Sign with MSFT. As businesses weave AI deeper into their workflows, Copilot's revenue potential could become a major pillar of Microsoft's cloud story. Even with conservative assumptions, Jefferies pegs Copilot at just over $5 billion in 2025 and $11 billion in 2026. A separate Morgan Stanley (NYSE:MS) CIO survey shows tech leaders planning year?over?year spending increases of more than 6% on Microsoft tools. If those plans hold, Copilot's boom may prove to be the next big chapter in enterprise AI. This article first appeared on GuruFocus. Sign in to access your portfolio
