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Trust Issues Keep Firms Cautious About Agentic AI Rollouts

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For today’s firms and their labor force, the future of work is being spelled out in two words: artificial intelligence (AI). But the vision of AI that firms are keen on embracing is not just as a tool, but as a colleague, a software system capable of thinking, doing, and even acting on its own.

That, after all, is the promise of agentic AI: systems that can not only generate content or parse data, but go beyond passive tasks to autonomously make decisions, initiate workflows, and even interact with other software to complete tasks end to end. It’s AI not just with brains, but with agency.

These systems are being tested across industries such as customer service, software development, finance, logistics and healthcare. Think: booking meetings, launching marketing campaigns, processing invoices, or managing entire workflows autonomously.

But while some corporate leaders may hold lofty views for autonomous AI, the latest PYMNTS Intelligence in the June 2025 CAIO Report, “AI at the Crossroads: Agentic Ambitions Meet Operational Realities,” reveals, there is a trust gap among executives when it comes to agentic AI that hints at deep concerns about accountability and compliance.

However, full-scale enterprise adoption remains limited. Despite growing capabilities, agentic AI is being deployed in experimental or limited pilot settings, with the majority of systems operating under human supervision.

Trust Gap

But why are mid-market companies hesitating to unleash the full power of autonomous AI? The answer is both strategic and psychological. While the technological potential is enormous, the readiness of systems (and humans) is far less clear.

For AI to take action autonomously, executives must trust not just the output, but the entire decision-making process behind it. That trust is hard to earn — and easy to lose.

The PYMNTS Intelligence report data found that 80% of high-automation enterprises cite data security and privacy as their top concern with agentic AI.

Agentic AI platforms typically require access to a wide range of internal systems and data repositories to operate effectively. This raises risks related to unauthorized access, unintentional data exposure, and noncompliance with privacy regulations such as the General Data Protection Regulation (GDPR) in the European Union or the California Consumer Privacy Act (CCPA) in the U.S.

Other concerns were integration issues (62%) and the accuracy of AI-generated outputs (57%).

These figures reflect a general trend: agentic AI is being treated as a high-potential, high-risk technology that has yet to meet the compliance, reliability and trust thresholds required for enterprise-scale deployment.

Read the report: AI at the Crossroads: Agentic Ambitions Meet Operational Realities

Unlike traditional automation tools, agentic systems may involve dynamic decision-making across multiple systems, requiring permissions and credentials that can span departments or functions. Misconfigurations, insufficient access controls, or opaque decision paths can elevate the likelihood of a data breach or policy violation.

Many implementations are restricted to sandbox environments or read-only modes, preventing the AI from executing critical actions without human intervention.

Additionally, agentic systems frequently involve goal-directed behavior — such as booking meetings, managing tickets, or escalating issues — that requires not only linguistic fluency, but also context retention, task memory and policy adherence. Testing and validating these capabilities in production environments has proven complex, and organizations have expressed reluctance to approve unsupervised execution without further assurance of reliability.

The combination of integration friction and limited predictability has led many companies to limit agentic AI systems to internal pilots or assistive roles, where outputs are reviewed and approved by human operators before action is taken.

Across sectors, agentic AI is being adopted in phased or modular deployments, usually in configurations that include: human-in-the-loop systems, where AI agents perform tasks or suggest actions, but humans retain final decision authority; task-specific agents, where AI tools are limited to defined domains such as scheduling, content summarization, or customer service triage; sandbox testing, where systems are deployed in isolated environments for performance evaluation, without access to live systems or sensitive data; and proof-of-concept pilots, or short-term experiments used to evaluate ROI, reliability and integration complexity.

 



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2 Artificial Intelligence (AI) Stocks That Could Help Make You a Millionaire

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The cat is out of the bag with artificial intelligence (AI). Trillions of dollars in value have been added to stock portfolios on the backs of the AI revolution in just a few years. Nvidia is knocking on the door of a $4 trillion market capitalization. It is difficult to find undervalued AI stocks right now.

But it is not impossible. Here are two AI stocks — ASML (ASML -0.73%) and Alphabet (GOOG 0.51%) — that look undervalued and can help investors become millionaires if they buy and hold for the long term.

Image source: Getty Images.

Helping build advanced computer chips

ASML is the leading seller of lithography equipment for making advanced semiconductors. In some cases, it is the only provider on the market. Lithography in this case is the use of lights and lasers to print tiny patterns on objects such as semiconductors. Advanced semiconductors require intricate designs over microscopic areas, which helps them generate more efficient computing power for AI applications.

With its advanced extreme ultraviolet lithography systems (EUV), ASML is the only provider of machines that help make advanced semiconductors for the likes of Nvidia. This makes it a vital point in the semiconductor supply chain and a monopoly seller of its equipment today. Not a bad place to be in when semiconductor demand is soaring because of the insatiable need for more AI computer chips.

Over the past 12 months, ASML generated $33 billion in revenue, which has grown a cumulative 353% in the last 10 years. Operating income has grown 551% to $11 billion. The company’s growth is not linear because of lumpy equipment sales to large factories and the cyclicality of the semiconductor industry, but over the long term, demand prospects look fantastic. Manufacturers are planning hundreds of billions of dollars in capital expenditures to build new semiconductor factories. These factories will be stuffed with ASML lithography equipment.

ASML has a trailing price-to-earnings (P/E) ratio of 33. This is not dirt cheap in a vacuum, but I believe it makes the stock undervalued because of its future growth prospects, which will bring this P/E ratio down to a much more reasonable level. Buy ASML stock today and hold on tight for the long term.

ASML PE Ratio Chart

ASML PE Ratio data by YCharts

AI for consumers and enterprises

One of the reasons for the increased demand for computer chips and ASML equipment — perhaps the largest reason — is Alphabet. The owner of Google, Google Cloud, YouTube, Waymo, and Gemini keeps doubling down on AI.

The big technology company can win in AI by playing two fronts: consumer and enterprise applications. With everyday users it is adding new AI tools to Google Search while building out advanced conversational AI with the Gemini application. Gemini now has an estimated 350 million active users and is growing rapidly, although it is still smaller than OpenAI’s ChatGPT.

With immense scale and resources, Alphabet will be able to deploy AI tools across its applications that are used by billions of people around the globe.

On the enterprise side, Google Cloud is one of the leading AI cloud companies due to its advanced computing infrastructure. Google Cloud revenue grew 28% year over year last quarter to $12.3 billion, making it the fastest-growing segment for Alphabet. The division has invested heavily in its own computer chips called Tensor Processing Units (TPUs), which make it more efficient to build AI software applications on Google Cloud.

There is expected to be hundreds of billions of dollars spent on AI cloud workloads in the coming years, which will help Google Cloud keep growing as a bigger piece of the Alphabet pie.

Overall, Alphabet generated a whopping $360 billion in revenue over the past 12 months and $117.5 billion in operating income. Investors were previously worried about saturation of usage at Google Search, which has now proliferated around the globe. However, with the rise of AI applications, Alphabet looks to have increased its addressable market in organizing the world’s information, the company’s famous slogan. This will help revenue and earnings keep growing over the next decade.

Today, you can buy Alphabet stock at a measly P/E ratio of 20. This makes the stock undervalued if you plan on holding for many years into the future.

Suzanne Frey, an executive at Alphabet, is a member of The Motley Fool’s board of directors. Brett Schafer has positions in Alphabet. The Motley Fool has positions in and recommends ASML, Alphabet, and Nvidia. The Motley Fool has a disclosure policy.



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Russia allegedly field-testing deadly next-gen AI drone powered by Nvidia Jetson Orin — Ukrainian military official says Shahed MS001 is a ‘digital predator’ that identifies targets on its own

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Ukrainian Major General Vladyslav (Владислав Клочков) Klochkov says Russia is field-testing a deadly new drone that can use AI and thermal vision to think on its own, identifying targets without coordinates and bypassing most air defense systems. According to the senior military figure, inside you will find the Nvidia Jetson Orin, which has enabled the MS001 to become “an autonomous combat platform that sees, analyzes, decides, and strikes without external commands.”

Digital predator dynamically weighs targets

With the Jetson Orin as its brain, the upgraded MS001 drone doesn’t just follow prescribed coordinates, like some hyper-accurate doodle bug. It actually thinks. “It identifies targets, selects the highest-value one, adjusts its trajectory, and adapts to changes — even in the face of GPS jamming or target maneuvers,” says Klochkov. “This is not a loitering munition. It is a digital predator.”



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Artificial Intelligence Predicts the Packers’ 2025 Season!!!

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On today’s show, Andy simulates the Packers 2025 season utilizing artificial intelligence. Find out the results on today’s all-new Pack-A-Day Podcast! #Packers #GreenBayPackers #ai To become a member of the Pack-A-Day Podcast, click here: https://www.youtube.com/channel/UCSGx5Pq0zA_7O726M3JEptA/join Don’t forget to subscribe!!! Twitter/BlueSky: @andyhermannfl If you’d like to support my channel, please donate to: PayPal: https://paypal.me/andyhermannfl Venmo: @Andrew_Herman Email: [email protected] Discord: https://t.co/iVVltoB2Hg





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