The IT help desk is evolving. Once defined by queues of tickets and reactive troubleshooting, support today is being reshaped by automation and artificial intelligence (AI). Intelligent systems now assist with everything from b

As AI shifts from experimental to essential, tech executives say that more than half of AI deployments will be functioning autonomously in their company in the next two years, according to a new survey by professional services firm Ernst & Young (EY). While generative AI (genAI) technology has captured the attention of business leaders for the past several years, agentic AI, a specific kind of AI system that acts autonomously or semi-autonomously to achieve goals, has largely flown under the radar. That changed in late 2024 when search traffic for agentic AI and AI agents began to surge, according to Google Trends data. Now, half of more than 500 tech executives surveyed by EY in its latest Technology Pulse Poll said AI agents will make up the majority of upcoming AI deployments. The survey revealed that 48% are already adopting or fully deploying AI agents, and half of those leaders say that more than 50% of AI deployments will be autonomous in their company in the next 24 months. The EY survey also showed rising AI investment, with 92% of tech leaders planning to boost AI spending and over half believing they’re ahead of competitors in AI investment. Additionally, 81% of tech executives surveyed said they feel optimistic about AI’s promises related to achieving their organization’s goals over the next 12 months. Tech companies are leading the charge in adopting agentic AI, according to James Brundage, a leader in EY’s Technology Sector group. “Despite economic uncertainty, executives remain confident in AI’s value, ramping up investment and shifting from pilots to full deployment. Still, they’re under pressure to show real ROI through measurable business results,” he said. Among respondents planning to increase their AI budgets, 43% say agentic AI will claim more than half of their total AI budget. Leading reasons for adopting agentic AI include staying competitive (69%), helping customers (59%), and for internal strategy purposes (59%). Tech companies are always early adopters, and many believe they’re ahead of the competition, but that confidence in AI often exceeds the reality, according to Ken Englund, a leader in EY Americas’ Technology Sector Growth. “It is still very early in the AI lifecycle, so it remains to be seen where these companies stand against the competition, and an outside-in view will be a critical measuring stick,” Englund said. Tapping into agentic AI requires structural change Investment in agentic AI is accelerating, reshaping enterprise architecture. While genAI gets most of the spotlight, advances in classical AI and machine learning are also key to enabling agentic AI, according to Englund, who sees the technology as a “flexible framework” for using the right tools to deliver outcomes across platforms. AI agents offer more than a productivity boost; they’re fundamentally reshaping customer interactions and business operations. And while there’s still work to do on trust and accuracy, the world is beginning a new tech era — one that might finally deliver on the promises seen in movies like Minority Report and Iron Man, according to Salesforce CEO Marc Benoiff. Salesforce has embedded AI into its CRM through the Einstein 1 Platform and tools like Agentforce, enabling businesses to deploy autonomous agents across sales, service, marketing, and commerce. Its generative AI tools, Einstein GPT and Einstein Copilot, act as intelligent assistants that draft communications, summarize case histories, auto-fill records, and answer questions using company data. To achieve competitive advantage in this new world, businesses must shift their focus from isolated genAI tools like chatbots to deep integration of advanced AI systems — especially agentic architectures, where autonomous AI agents collaborate to manage and optimize complex workflows, according to a recent report from services firm Accenture. The Accenture report was based on a survey of 2,000 C-suite and data-science executives across multiple countries and industries. Although many companies recognize AI’s potential, the report said, true enterprise reinvention requires structural change, strong leadership, and, crucially, a robust data foundation — an area where many still struggle, particularly with unstructured data. Additionally, outdated IT systems and inadequate employee training hinder progress. However, a small group of “front-runner” companies are succeeding by combining foundational AI investments with “bold, strategic initiatives that embed AI at the core of their operations,” the report said. Only 8% of companies — so-called “front-runners” — are scaling AI at an enterprise level, embedding the technology into core business strategy. But of those front-runners that scaled their AI implementations, many found a solid return on investment. According to Accenture: Front-runners with annual revenue exceeding $10 billion grew their revenue 7% faster than companies still experimenting with AI. Across all sizes, front-runners outperformed the other three company groups, delivering shareholder returns that were 6% higher. After deploying and scaling AI across their enterprise, companies expect to reduce their costs by 11% and increase their productivity by 13%, on average, within 18 months. Most tech leaders are still not AI savvy, CEOs say But earlier this month, Gartner Research issued the results of a study showing that just 44% of CIOs are deemed by their CEOs to be “AI-savvy.” The survey of 456 CEOs and other senior business executives worldwide also revealed that 77% of respondents believe AI is ushering in a new business era, making the lack of AI savviness amongst executive teams all the more meaningful. “We have never seen such a disproportionate gap in CEOs’ impressions about technological disruption,” said David Furlonger, a distinguished VP analyst and Gartner Fellow. “AI is not just an incremental change from digital business. AI is a step change in how business and society work,” he said. “A significant implication is that, if savviness across the C-suite is not rapidly improved, competitiveness will suffer, and corporate survival will be at stake.” CEOs perceived even the CIO, chief information security officer (CISO), and chief data officer (CDO) as lacking AI savviness. Respondents said the top two factors limiting AI’s deployment and use are the inability to hire adequate numbers of skilled people and an inability to calculate value or outcomes. “CEOs have shifted their view of AI from just a tool to a transformative way of working,” said Jennifer Carter, a principal analyst at Gartner. “This change has highlighted the importance of upskilling. As leaders recognize AI’s potential and its impact on their organizations, they understand that success isn’t just about hiring new talent. Instead, it’s about equipping their current employees with the skills needed to seamlessly incorporate AI into everyday tasks.” This focus on upskilling is a strategic response to AI’s evolving role in business, ensuring that the entire organization can adapt and thrive in this new paradigm. Sixty-six percent of CEOs said their business models are not fit for AI purposes, according to Gartner’s survey. Therefore, executives must build and improve AI savviness related to every mission-critical priority. Hiring workers with the right skills is also part of the effort, noted EY’s Englund. “According to our technology pulse poll, 84% of tech leaders say they anticipate hiring in the next six months as a result of AI adoption,” he said. “We continue to see strong overall demand for AI skills and an increase in those skills involved in the deployment of AI production solutions. In particular, we see increased recruiting of AI experienced Product Managers, Data Engineers, MLOps, and Forward Deployed Engineers (FDE’s),” Englund said. In the rush to implement AI, many companies are also turning to outside freelancers with the skills they need. New research from Fiverr, a global freelance worker marketplace, found an 18,000% surge in businesses seeking freelance help to implement agents and a 641% increase for freelancers who specialize in “humanizing AI content.” Last week, Fiverr published its Spring 2025 Business Trends Index, which uses data from tens of millions of searches on its platform over the last six months to provide a snapshot of today’s (and tomorrow’s) economy. [ Related: Freelancers now represent more than one in four US workers ] The demand for freelancers who have the skills to work with AI agents shows that businesses are eager — but often unsure about — how to deploy the “digital colleagues” who can independently manage tasks like reading emails, scheduling meetings, or answering customer questions. “At the same time, a spike in searches for freelancers who can rewrite chatbot scripts, marketing emails, and website copy to sound more natural highlights a clear takeaway: AI might be powerful, but it still needs a human touch,” Fiverr said in its report.

Enterprise IT leaders are becoming uncomfortably aware that generative AI (genAI) technology is still a work in progress and buying into it is like spending several billion dollars to participate in an alpha test— not even a beta test, but an early alpha, where coders can barely keep up with bug reports.  For people who remember the first three seasons of Saturday Night Live, genAI is the ultimate Not-Ready-for-Primetime algorithm.  One of the latest pieces of evidence for this comes from OpenAI, which had to sheepishly pull back a recent version of ChatGPT (GPT-4o) when it — among other things — delivered wildly inaccurate translations.  Lost in translation Why? In the words of a CTO who discovered the issue, “ChatGPT didn’t actually translate the document. It guessed what I wanted to hear, blending it with past conversations to make it feel legitimate. It didn’t just predict words. It predicted my expectations. That’s absolutely terrifying, as I truly believed it.” OpenAI said ChatGPT was just being too nice. “We have rolled back last week’s GPT‑4o update in ChatGPT so people are now using an earlier version with more balanced behavior. The update we removed was overly flattering or agreeable — often described as sycophantic,” OpenAI explained, adding that in that “GPT‑4o update, we made adjustments aimed at improving the model’s default personality to make it feel more intuitive and effective across a variety of tasks. We focused too much on short-term feedback and did not fully account for how users’ interactions with ChatGPT evolve over time. As a result, GPT‑4o skewed towards responses that were overly supportive but disingenuous. “…Each of these desirable qualities, like attempting to be useful or supportive, can have unintended side effects. And with 500 million people using ChatGPT each week, across every culture and context, a single default can’t capture every preference.” OpenAI was being deliberately obtuse. The problem was not that the app was being too polite and well-mannered. This wasn’t an issue of it emulating Miss Manners. I am not being nice if you ask me to translate a document and I tell you what I think you want to hear. This is akin to Excel taking your financial figures and making the net income much larger because it thinks that will make you happy. In the same way that IT decision-makers expect Excel to calculate numbers accurately regardless of how it may impact our mood, they expect that the translation of a Chinese document doesn’t make stuff up. OpenAI can’t paper over this mess by saying that “desirable qualities like attempting to be useful or supportive can have unintended side effects.” Let’s be clear: giving people wrong answers will have the precisely expected effect — bad decisions. Yale: LLMs need data labeled as wrong Alas, OpenAI’s happiness efforts weren’t the only bizarre genAI news of late. Researchers at Yale University explored a fascinating theory: If an LLM is only trained on information that is labeled as being correct — whether or not the data is actually correct is not material — it has no chance of identifying flawed or highly unreliable data because it doesn’t know what it looks like.  In short, if it’s never been trained on data labeled as false, how could it possibly recognize it? (The full study from Yale is here.)  Even the US government is finding genAI claims going too far. And when the feds say a lie is going too far, that is quite a statement. FTC: GenAI vendor makes false, misleading claims The US Federal Trade Commission (FTC) found that one large language model (LLM) vendor, Workado, was deceiving people with flawed claims of the accuracy of its LLM detection product. It wants that vendor to “maintain competent and reliable evidence showing those products are as accurate as claimed.” Customers “trusted Workado’s AI Content Detector to help them decipher whether AI was behind a piece of writing, but the product did no better than a coin toss,” said Chris Mufarrige, director of the FTC’s Bureau of Consumer Protection. “Misleading claims about AI undermine competition by making it harder for legitimate providers of AI-related products to reach consumers. “…The order settles allegations that Workado promoted its AI Content Detector as ‘98 percent’ accurate in detecting whether text was written by AI or human. But independent testing showed the accuracy rate on general-purpose content was just 53 percent,” according to the FTC’s administrative complaint.  “The FTC alleges that Workado violated the FTC Act because the ‘98 percent’ claim was false, misleading, or non-substantiated.” There is a critical lesson here for enterprise IT. GenAI vendors are making major claims for their products without meaningful documentation. You think genAI makes stuff up? Imagine what comes out of their vendors’ marketing departments.