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Enterprises Confront the Real Price Tag of AI Deployment

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The rush to integrate artificial intelligence (AI) into enterprise operations is colliding with a complex and sometimes underestimated reality: Deploying AI at scale can be pricey, and the true cost can extend far beyond the per-million-token rates on vendor websites.

According to recent PYMNTS Intelligence data, the cost of deploying AI is the second biggest drawback of generative AI adoption, with 46.7% citing it as a concern, following only integration complexity.

On paper, the cost of using today’s generative models is falling based on what AI companies are charging.

For example, OpenAI’s GPT-4 with an 8K context window had cost $30 per million input tokens and $60 per million output tokens as of early 2023. This year, GPT-4 Turbo, which is more powerful, nonetheless costs 50% to 67% less: $10 per million input tokens and $30 for the output.

According to Stanford’s 2025 Artificial Intelligence Index report, as AI models become more capable and smaller, the costs for applying them in use cases — inference — “have fallen anywhere from nine to 900 times per year,” the report said.

When it comes to infrastructure, costs have declined by 30% annually, while energy efficiency has improved by 40% each year, according to the Stanford report. Moreover, open-weight models that are free to use are closing the gap with closed models in performance.

But these headline numbers tell only part of the story.

Although the cost of the models has dropped since 2022, the overall cost of ownership “has been resistant to declines,” said Muath Juady, founder of SearchQ.AI. “The real expenses lie in the hidden infrastructure, including data engineering teams, security compliance, constant model monitoring, and integration architects necessary to connect AI with existing systems.”

For every dollar spent on AI models, businesses are spending five to $10 to make the models “production-ready and enterprise-compliant,” Juady told PYMNTS. “The integration challenges tend to be more expensive than the technology itself and require substantial investment in change management and process redesign, which many organizations underestimate.”

Moreover, the cost of AI deployment “is not a one-time expense but an ongoing operational commitment,” Juady added.

So why is AI adoption soaring? Juady said, “businesses that are successfully adopting AI are not waiting for costs to drop further; they are identifying specific use cases where even current costs can provide a measurable ROI.”

Read also: High Impact, Big Reward: Meet the GenAI-Focused CFO

Self-Hosting Can Lower Costs

For many enterprises, early decisions, such as whether to self-host, use the cloud or use third-party infrastructure, can dictate as much as 40% of AI expenses, said Pavel Bantsevich, project manager and solutions advisor at Pynest. Cloud-based hosting may be ideal for prototypes, but costs can spike as workloads scale.

Bantsevich said he worked with a U.S. construction company that’s been in business for a century to develop an AI predictive analytics tool and hosted it in the cloud. Infrastructure costs came to under $200 a month. But once it went live and people started using it, costs soared to around $10,000 a month. Switching to self-hosting using Meta’s open-source Llama model instead of the cloud lowered the cost to about $7,000 a month and has remained under control.

In another case, a European retailer client of Bantsevich’s with more than 50,000 employees wanted to implement a computer vision module for self-checkout machines. But the company didn’t want to use the cloud. It self-hosted instead using a small Llama AI model that performed well. Costs came to less than $10 a month per machine. “If a cloud solution had been selected, the numbers would have gone sky high,” he said.

Bantsevich believes that costs will continue to decline because datasets are more readily available today and cloud providers also have cut rates to retain customers. “It is likely we shall see AI costs be similar to electricity bills in the near future,” he predicted.

Meanwhile, Bill Chief Financial Officer Rohini Jain advised businesses to take advantage of AI that is already embedded in the platforms they use, such as those for invoicing, payments or forecasting, rather than adding standalone tools with “uncertain” pricing. “Integrated solutions typically offer better ROI and more predictable costs, such as subscription pricing,” she said.

Fergal Glynn, CMO and AI security advocate of Mindgard, said deploying AI can cost as little as $10,000 for basic projects, while large-scale enterprise systems can run into millions of dollars. Most companies spend between $50,000 and $500,000 for practical use cases like analytics tools or chatbots; smaller firms often pay less by using off-the-shelf AI.

Nicole DiNicola, global vice president of marketing at Smartcat, told PYMNTS that adopting AI doesn’t have to be “all or nothing.”

“Many platforms, including free or low-cost options, make it easy for organizations to start small and scale their adoption over time,” DiNicola said. “Unlike legacy SaaS, which often requires lengthy onboarding, upfront costs, and full-scale deployment to show value, AI can deliver meaningful impact without being fully integrated organization-wide.”

DiNicola pointed to teams embedding AI into workflows and already gaining efficiencies and cost savings. “AI tends to compound in value, but even small-scale adoption can drive clear and measurable improvements.”

A worse outcome would be letting the cost and complexity of AI scare a business into avoiding AI deployment in the first place.

“Inaction is often the more expensive path, even if it’s less obvious upfront,” DiNicola added. “While that delay might feel safe, early adopters are already building momentum, improving processes, learning faster, and expanding their competitive advantage.”

Read more:

How to Choose Between Deploying an AI Chatbot or Agent

Small Business, Big AI: How SMBs Are Leveling the Playing Field With Enterprise Giants

AI in Accounting Services May Level Playing Field for Small Businesses



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Physicians Lose Cancer Detection Skills After Using Artificial Intelligence

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Artificial intelligence shows great promise in helping physicians improve both their diagnostic accuracy of important patient conditions. In the realm of gastroenterology, AI has been shown to help human physicians better detect small polyps (adenomas) during colonoscopy. Although adenomas are not yet cancerous, they are at risk for turning into cancer. Thus, early detection and removal of adenomas during routine colonoscopy can reduce patient risk of developing future colon cancers.

But as physicians become more accustomed to AI assistance, what happens when they no longer have access to AI support? A recent European study has shown that physicians’ skills in detecting adenomas can deteriorate significantly after they become reliant on AI.

The European researchers tracked the results of over 1400 colonoscopies performed in four different medical centers. They measured the adenoma detection rate (ADR) for physicians working normally without AI vs. those who used AI to help them detect adenomas during the procedure. In addition, they also tracked the ADR of the physicians who had used AI regularly for three months, then resumed performing colonoscopies without AI assistance.

The researchers found that the ADR before AI assistance was 28% and with AI assistance was 28.4%. (This was a slight increase, but not statistically significant.) However, when physicians accustomed to AI assistance ceased using AI, their ADR fell significantly to 22.4%. Assuming the patients in the various study groups were medically similar, that suggests that physicians accustomed to AI support might miss over a fifth of adenomas without computer assistance!

This is the first published example of so-called medical “deskilling” caused by routine use of AI. The study authors summarized their findings as follows: “We assume that continuous exposure to decision support systems such as AI might lead to the natural human tendency to over-rely on their recommendations, leading to clinicians becoming less motivated, less focused, and less responsible when making cognitive decisions without AI assistance.”

Consider the following non-medical analogy: Suppose self-driving car technology advanced to the point that cars could safely decide when to accelerate, brake, turn, change lanes, and avoid sudden unexpected obstacles. If you relied on self-driving technology for several months, then suddenly had to drive without AI assistance, would you lose some of your driving skills?

Although this particular study took place in the field of gastroenterology, I would not be surprised if we eventually learn of similar AI-related deskilling in other branches of medicine, such as radiology. At present, radiologists do not routinely use AI while reading mammograms to detect early breast cancers. But when AI becomes approved for routine use, I can imagine that human radiologists could succumb to a similar performance loss if they were suddenly required to work without AI support.

I anticipate more studies will be performed to investigate the issue of deskilling across multiple medical specialties. Physicians, policymakers, and the general public will want to ask the following questions:

1) As AI becomes more routinely adopted, how are we tracking patient outcomes (and physician error rates) before AI, after routine AI use, and whenever AI is discontinued?

2) How long does the deskilling effect last? What methods can help physicians minimize deskilling, and/or recover lost skills most quickly?

3) Can AI be implemented in medical practice in a way that augments physician capabilities without deskilling?

Deskilling is not always bad. My 6th grade schoolteacher kept telling us that we needed to learn long division because we wouldn’t always have a calculator with us. But because of the ubiquity of smartphones and spreadsheets, I haven’t done long division with pencil and paper in decades!

I do not see AI completely replacing human physicians, at least not for several years. Thus, it will be incumbent on the technology and medical communities to discover and develop best practices that optimize patient outcomes without endangering patients through deskilling. This will be one of the many interesting and important challenges facing physicians in the era of AI.



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AI exposes 1,000+ fake science journals

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A team of computer scientists led by the University of Colorado Boulder has developed a new artificial intelligence platform that automatically seeks out “questionable” scientific journals.

The study, published Aug. 27 in the journal “Science Advances,” tackles an alarming trend in the world of research.

Daniel Acuña, lead author of the study and associate professor in the Department of Computer Science, gets a reminder of that several times a week in his email inbox: These spam messages come from people who purport to be editors at scientific journals, usually ones Acuña has never heard of, and offer to publish his papers — for a hefty fee.

Such publications are sometimes referred to as “predatory” journals. They target scientists, convincing them to pay hundreds or even thousands of dollars to publish their research without proper vetting.

“There has been a growing effort among scientists and organizations to vet these journals,” Acuña said. “But it’s like whack-a-mole. You catch one, and then another appears, usually from the same company. They just create a new website and come up with a new name.”

His group’s new AI tool automatically screens scientific journals, evaluating their websites and other online data for certain criteria: Do the journals have an editorial board featuring established researchers? Do their websites contain a lot of grammatical errors?

Acuña emphasizes that the tool isn’t perfect. Ultimately, he thinks human experts, not machines, should make the final call on whether a journal is reputable.

But in an era when prominent figures are questioning the legitimacy of science, stopping the spread of questionable publications has become more important than ever before, he said.

“In science, you don’t start from scratch. You build on top of the research of others,” Acuña said. “So if the foundation of that tower crumbles, then the entire thing collapses.”

The shake down

When scientists submit a new study to a reputable publication, that study usually undergoes a practice called peer review. Outside experts read the study and evaluate it for quality — or, at least, that’s the goal.

A growing number of companies have sought to circumvent that process to turn a profit. In 2009, Jeffrey Beall, a librarian at CU Denver, coined the phrase “predatory” journals to describe these publications.

Often, they target researchers outside of the United States and Europe, such as in China, India and Iran — countries where scientific institutions may be young, and the pressure and incentives for researchers to publish are high.

“They will say, ‘If you pay $500 or $1,000, we will review your paper,'” Acuña said. “In reality, they don’t provide any service. They just take the PDF and post it on their website.”

A few different groups have sought to curb the practice. Among them is a nonprofit organization called the Directory of Open Access Journals (DOAJ). Since 2003, volunteers at the DOAJ have flagged thousands of journals as suspicious based on six criteria. (Reputable publications, for example, tend to include a detailed description of their peer review policies on their websites.)

But keeping pace with the spread of those publications has been daunting for humans.

To speed up the process, Acuña and his colleagues turned to AI. The team trained its system using the DOAJ’s data, then asked the AI to sift through a list of nearly 15,200 open-access journals on the internet.

Among those journals, the AI initially flagged more than 1,400 as potentially problematic.

Acuña and his colleagues asked human experts to review a subset of the suspicious journals. The AI made mistakes, according to the humans, flagging an estimated 350 publications as questionable when they were likely legitimate. That still left more than 1,000 journals that the researchers identified as questionable.

“I think this should be used as a helper to prescreen large numbers of journals,” he said. “But human professionals should do the final analysis.”

A firewall for science

Acuña added that the researchers didn’t want their system to be a “black box” like some other AI platforms.

“With ChatGPT, for example, you often don’t understand why it’s suggesting something,” Acuña said. “We tried to make ours as interpretable as possible.”

The team discovered, for example, that questionable journals published an unusually high number of articles. They also included authors with a larger number of affiliations than more legitimate journals, and authors who cited their own research, rather than the research of other scientists, to an unusually high level.

The new AI system isn’t publicly accessible, but the researchers hope to make it available to universities and publishing companies soon. Acuña sees the tool as one way that researchers can protect their fields from bad data — what he calls a “firewall for science.”

“As a computer scientist, I often give the example of when a new smartphone comes out,” he said. “We know the phone’s software will have flaws, and we expect bug fixes to come in the future. We should probably do the same with science.”

Co-authors on the study included Han Zhuang at the Eastern Institute of Technology in China and Lizheng Liang at Syracuse University in the United States.



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The Artificial Intelligence Is In Your Home, Office And The IRS Edition

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