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Gen AI Moves From Experiment to Imperative

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A year ago, most chief product officers (CPOs) were still getting a feel for gen AI’s ability to create text, images, video and audio from human inputs underpinning large language models (LLMs). Product leaders had high expectations for the software’s ability to partially automate and streamline labor-intensive tasks ranging from production innovation and production costs to data security and compliance. But few had firmed up plans to fold the technology into their operations. That’s all changed.

Today, nearly all product leaders say they believe gen AI will reshape how they work. Nearly all expect it to streamline workflows within three years. Ninety-five percent say it will sharpen decision making. More than 8 in 10 expect improvements in data security. These figures are up from last year, when 70% expected workflow improvements, 67% anticipated decision-making accuracy gains and half foresaw data security and privacy advantages.

The shift over the past year among CPOs reflects a deeper change in institutional mindset. Gen AI is no longer experimental—it’s strategic. The pressure to deliver more with fewer resources has pushed firms to scale automation of routine, labor-intensive tasks, not just explore how that can be done. Product leaders are now responding with greater optimism, but their sanguine outlook remains tempered by their sense of responsibility for ensuring the technology, when used, is accurate, fair and without biases that skew its outputs.

However, gen AI optimism isn’t evenly distributed.

Tech firms lead in terms of belief and pace of adoption. Forty-four percent of tech product executives now say gen AI’s benefits outweigh its risks, compared to 31% across all other sectors. Tech firms are also more likely to act on their beliefs by testing vendor solutions and pursuing early implementations.

Still, the broader consensus is clear: Gen AI’s role in enterprise product development is effectively now baked in. What remains unsettled is who will shape that future. Half of tech firms identify OpenAI as the leader. However, only one-third of goods firms agree, with another 30% naming Google instead. Services firms prefer Microsoft, but at only 24%, followed by Nvidia and Google, each with 19%. No single provider commands a majority, meaning the larger enterprise AI landscape is still up for grabs.

These are just some of the findings detailed in “From Experiment to Imperative: US Product Leaders Bet on Gen AI,” a PYMNTS Intelligence exclusive report. This edition examines the shifting expectations for gen AI use in enterprise settings. It draws on insights from a survey of 60 chief product officers and heads of product at firms with at least $1 billion in revenue last year. Focusing on projected outcomes, perceived risks and evolving vendor preferences, the survey was conducted in June 2025.

Gen AI Expectations Surge Across Enterprise Product Teams

Product leaders now expect real returns, not just pilot results.

Over the past year, expectations for gen AI’s positive impact on enterprise operations have risen. This rise signals a fundamental shift from cautious experimentation to strategic commitment. In March 2024, enterprises anticipated that the technology would positively affect decision-making accuracy, internal workflows or data security to the tune of 67%, 70% and 50%, respectively. By June 2025, those projections had surged across the board, with many now approaching near-unanimous confidence levels.

Workflow optimization stands out as the area of most agreement. Some 98% of product leaders now expect gen AI to improve internal processes—an increase from 70% last year. Similarly, expectations for enhanced decision making have nearly doubled to 95% from 67% in March 2024. These gains reflect growing belief in gen AI’s role as a core operational lever, not just a peripheral tool.

Security and compliance saw some of the steepest gains. The share of enterprises expecting improvements in data security spiked to 83% from 50%. Expectations for better regulatory compliance jumped to 85% from 47%. Gen AI is now framed less as a risk vector and more as an engine for improving efficiency, control and visibility in business operations.

The heightened assessments were broad-based across other operational categories. For instance, expectations for improvements in customer experience spiked to 93% in June 2025 from 70% in March 2024. Market adaptability surged 97% from 55%. Perceived benefits in business expansion soared to 93% from 67%.

Across areas like staffing, production costs and fraud detection, confidence levels increased by 25 percentage points, to 87% in June 2025 from 62% 15 months earlier. Product leaders now see gen AI not only as a pathway to innovation, but as a pressure-tested lever for efficiency, accuracy and growth.

Tech Sector Dominates on Gen AI Risk-Reward Confidence

Tech CPOs see more upside—and are moving faster to seize the opportunity.

Confidence in gen AI’s value proposition is rising across industries, but no group is more bullish than tech-sector product leaders. As of June 2025, 44% of tech CPOs say the technology’s benefits outweigh its ethical concerns. This is more than double the share in the services sector (20%) and well ahead of the goods sector (33%) and full sample average (31%).

Still, most executives remain measured in their outlook. Across the full sample, 69% say gen AI’s benefits and ethical concerns are roughly equal. Among services firms, that share climbs to 80%. This rise suggests a more reserved posture by a sector focused on protecting its reputation and credibility before consumers. Even in the goods segment—where optimism runs higher—two-thirds still frame the benefits and risks as evenly balanced.

Tech stands apart. Its leaders aren’t just more confident—they’re increasingly moving with intent. Their 44% benefit-first stance likely reflects stronger familiarity with AI tools, shorter iteration cycles and the organizational models needed for agile deployment. That posture is translating into faster pilots, broader vendor engagement and earlier deployment.

This divergence matters. Tech firms moving to operationalize gen AI are better positioned to shape internal standards, influence vendor priorities and define early benchmarks for success—all outcomes that can fuel widespread adoption across other industries. By contrast, sectors still weighing trade-offs may forgo speed in exchange for certainty, but they risk falling behind as gen AI tools evolve at lightning speed.

What starts as a difference in risk appetite may evolve into a structural divide—one that determines which companies will lead as enterprise gen AI adoption accelerates.

Vendor Preferences Split by Industry and Use Case

There’s no clear leader yet as firms back different gen AI providers for different goals.

The gen AI vendor landscape remains fragmented, with no single provider earning a majority market share across sectors. Instead, the preferences of corporate users vary widely by industry, reflecting differing priorities around deployment style, institutional readiness to change, data needs and technical integration requirements.

OpenAI, the maker of ChatGPT, leads in overall visibility, driven almost entirely by its dominance in the tech sector. Half of tech firms name the company as their preferred gen AI provider, compared to one-third of goods firms and just 4.8% of services firms. OpenAI’s early lead in prompt-based LLMs, the beating heart of gen AI, and developer-first tools likely explain its traction with product-led teams.

Conversely, services firms gravitate toward giants known for their ability to integrate a variety of tools across enterprises. Microsoft has captured 1 in 4 services firms. Rival Amazon AWS (Amazon Web Services) comes in 10 percentage points behind, at 14%. Nvidia registered 19% among services firms—well above its share among tech and goods firms—likely due to its infrastructure capabilities and long-standing position in data-rich environments.

Google shows cross-sector strength, capturing nearly 30% of goods companies, 25% of tech firms and 19% in services. It remains one of the only vendors with traction across all three sectors, underscoring its hybrid value proposition of cloud flexibility and AI tooling.

The rest of the vendor market remains highly fragmented. Vendors like Meta, Apple and Mistral each earn low, single-digit support. Apple, in particular, appears to be playing catch-up. The iPhone behemoth has failed to reach double-digit market penetration in any segment.

This fragmentation signals a market in motion. As companies using gen AI prioritize features like fine-tuning, security, vertical specialization and model transparency, provider allegiances will likely remain dynamic—creating room for both incumbents and challengers to shape what “enterprise-ready” gen AI really means.

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PYMNTS Intelligence is the leading provider of information on the trends driving AI adoption patterns. To stay up to date, subscribe to our newsletters and read our in-depth reports.

Methodology

From Experiment to Imperative: US Product Leaders Bet on Gen AI” is based on a survey of 60 chief product officers and heads of product at U.S.-based technology, goods and services firms with $250 million or more in annual revenue conducted in June 2025. The report examines how product leaders are evaluating gen AI’s long-term role in enterprise strategy, including expected outcomes, perceived risks and vendor preferences. The sample includes executives with varying levels of strategic authority, enabling segmented analysis by sector, confidence level and risk posture. PYMNTS Intelligence designed the survey to assess expectations for gen AI performance across use cases, as well as how those expectations have evolved year over year.



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Deepfake and AI Technology | Criminal

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At present, Artificial Intelligence (AI) has become an important part of our lives. This technology makes our work easier, helps in new discoveries and makes everyday life convenient. But every coin has two sides. Along with the advantages of AI, it also has some serious disadvantages and dangers, especially when misused. Today we will discuss the misuse of AI, especially deep fake technology, and its negative effects.

ALSO READ: Misuse of AI Technology And The Growing Threat Of Deepfakes.

🔍 What is Misuse of AI?

Misuse of AI means using artificial intelligence technology in the wrong way. This includes actions that are morally wrong, violate the law, or harm society and individuals. There are many forms of misuse of AI, such as blackmailing people by creating deep fake videos, committing cyber crimes, or spreading false news. According to research, misuse of AI falls mainly into two categories: exploitation of AI capabilities and compromise of AI systems through hacking or jailbreaking 1.

ALSO READ: Mano KTK Leaked Video Viral, Misuse Of AI In Pakistan

❌ 5 Disadvantages of AI Technology

Job Losses

AI and automation are threatening millions of jobs worldwide. Experts estimate that by 2030, 3 to 14% of employees will have to learn new skills or change jobs. Low-skilled jobs, such as administrative work and construction, are most at risk 2.

Bias and Discrimination

AI algorithms are often human-made and may contain biases from developers. For example, an AI recruitment tool from Amazon discriminated against female candidates because it was trained on historical data that was male-dominated 2. Similarly, facial recognition systems are more likely to make errors in recognizing dark-skinned women 2.

Privacy Violations

AI systems can predict the behaviour of individuals by collecting data about them. Using data from location history, social contacts and online activities, AI can accurately track your movements, posing a serious threat to privacy 2.

Deepfakes and Misinformation

Deepfake videos or audio created with the help of AI can be used to spread misinformation, blackmail people or commit financial scams. For example, an employee of a company in Hong Kong was scammed of 25 million USD through an AI-generated video call 1. According to a study, 98% of deepfake videos are related to adult content, and 99% of these target women 1.

Cybersecurity Threats

AI enables hackers to carry out even more sophisticated cyber attacks. It can automatically generate and personalize phishing emails, viruses, and malware, thereby bypassing traditional security systems 3.

📚 How can AI be misused by students?

Concerns about misuse of AI in education are growing. According to a survey, 48% of students admitted they have used ChatGPT in homework or tests, and 53% have had essays written by it 10. This is increasing the problems of plagiarism and cheating, and affecting students’ ability to learn. However, plagiarism detection companies such as Turnitin say that the use of AI-generated content is not as widespread as thought—about 10% of assignments have been found to contain some AI content, and only 3% of assignments are mostly generated by AI 5. Still, many teachers are becoming more distrustful of students, and false positives from AI detection tools can harm students, especially non-native English speakers 58.

⚠️ Negative Effects of AI

Social Impact

AI can lead to increased social polarization. Social media platforms’ AI algorithms show users content that matches their existing opinions, creating echo chambers and deepening divisions in society 6.

Ethical Concerns

AI systems lack transparency, and many decisions are “black boxes” that are difficult to understand. For example, AI risk assessment tools (such as COMPAS) used in US courts may show racial or gender biases, but their decision-making process is not transparent 2.

Environmental Impact

Large AI models require an enormous amount of energy to train. According to one estimate, training a single AI model can produce 300,000 kg of CO2 emissions, which is equivalent to 125 round-trip flights between New York and Beijing 2.

Impact on Human Connections

The overuse of AI-powered chatbots and virtual assistants can reduce human relationships and genuine communication 6. Some experts worry that AI can undermine the emotional and social abilities of humans.

Domination by Big Tech

AI technology and research are dominated by big companies such as Google, Apple, Microsoft, Amazon, and Meta. These companies are setting the direction of AI, which can have an impact on innovation and their business interests 6.



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Talk on ethical challenges of AI

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The Dr. Pritam Singh Foundation, in collaboration with IILM University, hosted a discussion on “Human at Core: AI, Ethics, and the Future” at Tech Mahindra, Cyberabad, on Saturday, in memory of the late Dr. Pritam Singh, a noted academic.

After launching the discussion, Assembly Speaker Gaddam Prasad Kumar highlighted the ethical challenges of Artificial Intelligence (AI), warning against algorithmic bias, threats to data privacy, and job displacement. He called for large-scale reskilling and emphasised that India must shape AI technologies to reflect its values of fairness, transparency, and inclusivity. He urged corporate leaders to establish strong governance frameworks, audit algorithms for bias, and ensure responsible adoption of AI.

Delivering the keynote address, Chairman of Administrative Staff College of India (ASCI) K. Padmanabhaiah stressed India’s opportunity to leverage AI for inclusive growth across healthcare, agriculture, education, and fintech — while ensuring technology remains human-centric and trustworthy.

One of the founders of the Dr. Pritam Singh Foundation P. Dwarakanath, Director at IILM University Chaturvedi, Director at the Institute for Development & Research in Banking Technology (IDRBT) Deepak Kumar, Managing Director of Signode Asia Pacific Gaurav Maheshwari, Pritam Singh’s son Vipul Singh, and author and economist Vikas Singh spoke.



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Walmart’s latest AI innovations represent a shift for big retail

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With fears about the strength of consumer spending running high due to tariffs, inflation and other economic pressures, retailers are working hard to sustain revenue growth. While some retailers are leaning into worker-led personalized experiences for shoppers, other retailers are focusing more on leveraging artificial intelligence to optimize the shopping experience.

Walmart is one of those retailers, adding new “super agents” that aims to save time and effort for both workers and shoppers. At its recent Retail Rewired innovation event, Walmart highlighted the launch of four “super agents,” which include Marty for sellers and suppliers, Sparky for shoppers, the Associate Agent and the Developer Agent.

With agents performing capabilities in the realm of payroll, paid time off, merchandising and finding the right products for any event, Walmart is consolidating its powerful, time-saving tools for the sake of a streamlined experience for multiple points of interaction with the company.

“Having a plethora of different agents can very quickly become confusing,” Suresh Kumar, chief technology officer for Walmart Global, said at the event.

The Associate Agent, for example, is “a single point of entry where any associate can find access to all of the agents we’ve built on the back end,” explained David Glick, senior vice president for Enterprise Business Solutions at Walmart. “As you speak to it more, as you work with it more, it’ll know more about you.”

The evolution comes alongside a broader shift for retail, an industry actively seeking to counteract cost concerns from consumers and the government, and Walmart isn’t alone in its push toward all things AI. Amazon’s Prime Day event over four days in July saw generative AI use jump 3,300% year over year, according to TechCrunch. Meanwhile, Google Cloud AI partnered with body care retailer Lush to visually identify projects without packaging, ultimately reducing the expense of training new hires.

Making digital twins of Walmart stores

Walmart is also all-in on physical and spatial AI, specifically digital twins (a virtual copy of any physical object or space — in Walmart’s case, their stores and clubs). Using digital twin technology powered by spatial AI, Walmart can “detect, diagnose and remediate issues up to two weeks in advance,” Brandon Ballard, group director for real estate at Walmart US, said at Retail Rewired. Using this technology comes with big savings, according to Ballard. “Last year, we cut all of our emergency alerts by 30% and we reduced our maintenance spend in refrigeration by 19% across Walmart US,” he added.

“At its core, retail is a physical business,” said Alex de Vigan, CEO and founder of Nfinite, which generates large-scale visual data for training spatial and physical AI models. “We’ve seen retailers use digital twins to reduce setup time for new promotions, reallocate labor more efficiently, and improve robotic picking accuracy, small gains that add up quickly when margins are under stress,” he said.

While the impact of digital twins may not be outwardly visible to consumers in the same way, say, Walmart’s Sparky agent is, its effects will be real. “Better stock accuracy, faster site updates and fewer order issues mean a smoother retail experience, even in a tighter economy,” said de Vigan.

Another innovation on the back end is Walmart’s use of machine learning to better understand how long it will take to get a delivery order on a customer’s doorsteps, effectively managing expectations while increasing efficiency.

As for what consumers can see, Sparky is already helping shoppers generate baskets built on an intuitive understanding of their needs. Walmart is currently working on enabling the agent to take action on reordering products, ultimately reducing the mental load that shoppers deal with.

For retailers, AI is one way to combat any slowdown in consumer spending, but we’ve yet to see how a fully integrated AI shopping experience — both in person and online — will shape our relationship with retail moving forward.



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