Some time ago, the software framework LangChain released a report that explored the state of “AI ages” in 2024. The report polled 1,300 experts and found that 51% of respondes are currely using AI ages. Also, 63% of medium-sized companies are using this technology in their production processes, and 78% have plans to iegrate artificial ielligence in the future.
Also, this survey showed that there is a great desire to use artificial ielligence ages even in non-tech companies. According to the report, “90 perce of people working at non-tech companies have either used AI ages or plan to iroduce them io their processes (compared to 89 perce at tech companies).”
Also, during a report from Research and Market about “market analysis of artificial ielligence ages”, a promising future is predicted for this technology. The report noted that “the market for artificial ielligence ages is expected to grow from $5.1 billion in 2024 to $47.1 billion in 2030. In fact, this growth will be at an annual rate of 44.8% during the years 2024 to 2030.
These statistics represe a major shift in attitudes toward AI ages, showing that views are moving toward greater acceptance and breaking down initial skepticism.
Age or assista?
In LangChain’s survey, the majority of respondes said they use AI ages to summarize research and personal assistance, but ierestingly, 35 perce said they use the technology to perform programming tasks. Of course, companies have not yet been able to provide a precise definition of “artificial ielligence ages” and it is not clear how autonomous these systems should be.
In the past, when Google announced that 25% of newly written code will be generated by artificial ielligence, there were criticisms from users. For example, one user on HackerNews suggested that this claim is probably exaggerated and depends more on a code completion engine. Meanwhile, a user on Reddit suggested that Google was actually referring to “doing cleanup work for dependencies, removing old classes, or changing deployme configurations.”
A few days ago, payme processing gia Stripe released a software developme kit (SDK) for artificial ielligence ages. This tool allows large-scale language models (LLMs) to access functions related to paymes, invoicing, and transactions. With this feature, smart ages can spend money or approve and reject paymes.
However, this capability has been met with some doubts. Some users on social network X (formerly Twitter) asked if this feature is more than just regular API calls or just a fancy name for the same functions.
“To me, it’s just removing a few lines of code and offering a more complex pricing model instead,” wrote one user on X. Finally, am I missing something special?”
At the Oracle CloudWorld 2024 eve, Oracle announced the iroduction of more than 50 artificial ielligence ages in the Fusion Cloud Application software suite. However, Steve Miranda, Oracle’s executive vice preside of application developme, provided a detailed definition of AI ages. “In my opinion, the initial use of these ages will not be fully autonomous and will be more human-assisted,” he told AIM.
Also, Ketan Karkhanis, CEO of ThoughtSpot, explained in an ierview that many of today’s systems, such as Microsoft Copilot, only answer single-step questions and do not have the ability to reason, adapt and learn from the users’ business environme to be called autonomous.
He added: “This issue has many complications. If you can’t train a system, then it can’t be called an AI age. I don’t think you can teach a copilot, you can only write custom commands for it.”
Salesforce CEO Marc Benioff has repeatedly criticized Microsoft’s approach to AI ages, accusing the company of exaggerating Copilot’s marketing capabilities.
Although there is still no common and precise definition for AI ages, companies claim that the use of this technology has improved many of their operations.
A rece survey about artificial ielligence ages faced criticism on social networks. “In this day and age, polls are the worst metric for evaluating actual usage,” wrote one user on X. “Instead, show real, traceable data.”
Despite the opaque definitions, many companies, even big brands, have achieved significa success using AI ages.
A few weeks ago, Freshworks iroduced a new version of Freddy AI; An autonomous age that was able to resolve 45% of customer support requests and 40% of IT service requests on its own in the beta version. Also, Salesforce unveiled Ageforce; A tool that allows the company’s customers to impleme their own AI ages on their platform.
Wiley Publishing, a Salesforce customer, reported significa success with the Ageforce tool. “With the help of artificial ielligence tools and increased productivity, we were able to speed up the training process for seasonal workers by 50 perce, resulting in a 213 perce return on investme and $230,000 in savings,” Wiley wrote in a blog post.
Wiley also announced that Ageforce was able to improve customer case resolution by 40% compared to their previous chatbot. These successes are also consiste with LangChain’s survey results, where 45.8% of participas reported using AI ages in customer service and support.
Salesforce coinues to see a bright future for AI ages. “In 2025, we’ll see more complex, multi-age coordination that solves bigger challenges like simulating new product launches, marketing campaigns and making recommendations to optimize them across organizations,” said Mick Costigan, vice preside of Salesforce Futures.
Companies using AI ages have been able to increase accuracy and reduce operational costs. For example, telecommunications company Amdocs managed to improve the performance accuracy of its systems by 30% using NVIDIA’s NIM Microservices AI tools.
The company also announced that it has significaly reduced costs by reducing resource consumption. In fact, Amdocs was able to reduce the use of tokens for data preparation by 60% and for final processing by 40%.
Corary to popular belief that AI ages operate completely autonomously, there are good reasons why this is not the case. In the LangChain survey, most respondes emphasized the importance of tracking and monitoring to manage automated operations.
More than 35% of companies have prioritized online or offline evaluation of the results produced by these factors. Also, most companies have only allowed AI ages to read data, and only about 10 perce of companies have allowed them to read, write, and delete data.
Even if the concerns and risks associated with AI ages are mitigated, these systems may not be able to fully understand all the details of every part of the operation.
Speaking to AIM, Lingaro Group CEO Sam Mael emphasized the importance of managing the flow of data between each part of an operation. He said that these parts are usually separate and we need to pay more atteion to how they are connected.
“I wa to know the owner of any data that may be in any of these apps,” Meel added. “In fact, if we wa to run things efficiely and smoothly, someone has to be responsible for that data, even if it moves around the organization.”
