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Time of India
09-07-2025
- Business
- Time of India
Gartner warns of 40% agentic AI failure by 2027 - Industry leaders push back
In a recent report, Gartner has predicted that more than 40% of Agentic AI projects would be cancelled by the end of 2027, due to escalating costs, unclear business value or inadequate risk controls. Though making significant predictions, the report does not outline the nature of the study, the parameters or the methodology involved in reaching such conclusions.'Most agentic AI projects right now are early stage experiments or proof of concepts that are mostly driven by hype and are often misapplied,' says Anushree Verma, Senior Director Analyst, Gartner. 'This can blind organizations to the real cost and complexity of deploying AI agents at scale, stalling projects from moving into production. They need to cut through the hype to make careful, strategic decisions about where and how they apply this emerging technology.' "If we don't invest in the invisible guardrails that Agentic AI warrants or if we design Agentic AI by overlooking overall system design, costs can escalate and repatriation can start. Left shift is a must as far as security, performance and costs are concerned. These should be the considerations at the architecture stage and not at the monitoring stage. Not everything needs to become agentic and not everything needs to go on cloud," says Anjali Satam, Head of Engineering and Technology Director at IKS Health. Assessing Gartner's initial claims Gartner's initial prediction about agentic AI adoption—prior to June 2025—was highly optimistic, especially regarding customer service and enterprise automation. They also stated that agentic AI would autonomously resolve common customer service issues without human intervention, leading to a reduction in operational costs. "I agree with the current Gartner point of view. Because, the ability to provide flawless access to data for agentic AI to truly function as intended is a major challenge, because most data is not ready and the majority of organizations have not invested in the computing infrastructure needed to deliver on the real promise of agentic AI," says Rajendra Deshpande, former CIO at Intelenet Global Services and a technology consultant. Implementations should be driven by business objectives A few technology experts are of the view that the current agentic AI models don't have the maturity and agency to autonomously achieve complex business goals or follow nuanced instructions over time and may not be able to autonomously achieve complex business goals or follow nuanced instructions over time and therefore could lack value or return on investment (ROI). Moreover, some use cases positioned as agentic AI may not require agentic implementations to be successful. 'Most agentic AI propositions lack significant value or return on investment (ROI), as current models don't have the maturity and agency to autonomously achieve complex business goals or follow nuanced instructions over time. Many use cases positioned as agentic today don't require agentic implementations,' adds Verma. Understanding Gartner's hype cycle Such overarching statements are not unusual. Most technologies follow the hype cycle. The hype cycle is a method to assess how technology evolves along an S-curve. Gartner categorizes current trends into five buckets, each representing a different stage of maturity. This process involves combining technologies and grouping them according to their maturity by a team of experts helps them evaluate the status of each technology on the S-curve. Such observations have usually held true for almost all emerging technologies. Almost 40% of projects fail in the first 1000 days, whether it is blockchain, drones or generative AI. The reasons could be diverse, including factors such as maturity of use cases, readiness of the specific industry, the cost of technology acquisition and implementation, challenges of integration with the existing legacy systems, etc. Gartner explains this phenomenon through its concept of the hype cycle, which is a graphical tool that plots the maturity, adoption, and social application of emergent technologies, providing insights into their potential risks and benefits. It includes five phases: the innovation trigger, peak of inflated expectations, trough of disillusionment, slope of enlightenment, and plateau of productivity. "I am not sure if they use any statistical models, but a team of experts definitely reviews and evaluates the status of each technology on the S-curve. This means there is a level of subjectivity embedded in the assessment for sure. Again, it would be interesting to see how many of their predictions have actually come true. I think the hype cycle is more of a framework to assess the level of maturity of technologies, rather than a precise prediction method. It would be even more interesting to know if Gartner has any audited data about the accuracy of their predictions," observes Deshpande. A major driver of the current hype around agentic AI is the widespread rebranding of existing products by many suppliers, often without delivering the true, full capabilities that define agentic AI. "In 2000, dot-coms rose fast and fell faster. In 2010, many IT services projects grew big but couldn't scale well. In 2020, SaaS exploded, but too much of it lacked clear value. Every hype wave leaves behind good ideas that failed because of weak execution or no real go-to-market!" points out Kingshuk Hazra, Founder, LeadStrategus.
Forbes
28-06-2025
- Business
- Forbes
AI Agents And Hype: 40% Of AI Agent Projects Will Be Canceled By 2027
AI Agents: Fact vs Fiction The future of work is increasingly being defined by autonomy, not just for employees, but for the software systems that support them. Agentic AI, a class of intelligent systems capable of making decisions and taking action without constant human oversight, has captured the imagination of corporations, startup founders, and tech giants alike. But beneath the surface of this technological frontier lies a sobering truth: according to Gartner, more than 40% of agentic AI projects will be canceled by the end of 2027. The reasons range from surging costs to vague business outcomes and immature risk management frameworks. So while the allure of intelligent agents, agentic AI, and AI agents is strong, so is the risk of overreach. Hype, Haste, and the Harsh Reality The term 'agentic AI' has quickly become one of the most talked-about concepts in 2025 in enterprise tech. These are not just chatbots or static automation tools—they are systems designed to act independently, initiate tasks, and adapt over time. Yet according to Anushree Verma, Senior Director Analyst at Gartner, the enthusiasm is frequently misaligned with execution: 'Most agentic AI projects right now are early-stage experiments or proofs of concept that are mostly driven by hype and are often misapplied.' In a January 2025 Gartner survey of over 3,400 professionals, only 19% reported significant investment in agentic AI. Another 42% were investing conservatively, while a notable 31% remained on the fence or undecided. Despite its potential, most companies are proceeding with caution—if they're proceeding at all. As vendors rush to ride the wave, many are simply rebranding existing technology such as RPA, AI assistants, or simple chatbots—as 'agentic AI' without making substantive changes. Gartner estimates that out of thousands of so-called agentic AI providers, only around 130 offer solutions that meet the true definition of autonomous agency. This dynamic is creating confusion in the marketplace and inflating expectations among enterprise buyers. Additionally, the financial side of agentic AI is proving more challenging than anticipated. Beyond development and integration, organizations must contend with the high costs of compliance, infrastructure, workforce training, and workflow redesign. In many cases, legacy systems can't easily accommodate these autonomous agents without substantial reengineering. Without clear ROI metrics, projects lose momentum. As Verma notes, 'Many use cases positioned as agentic today don't require agentic implementations. The technology's not yet mature enough to deliver the business value companies expect.' The result? Projects stall in pilot stages. Teams lose faith. Budgets are reallocated. And the promise of transformation fades into a post-hype hangover. When Agentic AI Makes Sense Despite the grim outlook for many early projects, Gartner's long-term vision for agentic AI does remain optimistic. By 2028, the firm predicts that 15% of routine business decisions will be made autonomously by AI agents—up from virtually zero in 2024. Furthermore, a third of all enterprise software applications will feature embedded agentic AI by that time and the key for organizations will be to focus on high-impact areas such as: Furthermore, success stories will only emerge from firms willing to rethink workflows from the ground up. This might mean redesigning customer service journeys to allow agents to triage and resolve requests autonomously—or creating new internal operations models that pair human oversight with AI-driven decision-making and these shifts aren't minor—they require cultural, structural, and technical buy-in. Conclusion: Why 40% Will Fail and What the Other 60% Must Do Now Agentic AI is not a passing trend—it's a defining shift in how software interacts with the enterprise. But it's also a high-risk investment, vulnerable to exaggerated promises and weak execution as it stands. The projected 40% failure rate however is not a condemnation of the technology, but a reflection of how quickly hype can outpace operational readiness. To succeed, leaders must cut through the noise, build around clear business outcomes, and adopt an enterprise-first mindset. It's not about deploying agents for the sake of innovation—it's about using them to solve hard problems with measurable returns. The bottom line? Agentic AI isn't for the timid or the trend-chasers. It's for the disciplined, the strategic, and the visionary. Because in this next phase of AI, it's not about who starts first—it's about who finishes strong.
South China Morning Post
27-06-2025
- Business
- South China Morning Post
Over 40% of agentic AI projects forecast to be scrapped by 2027 due to lack of value
More than 40 per cent of agentic artificial intelligence (AI) projects will be cancelled by the end of 2027 because of escalating costs and unclear business value, according to a report by Gartner. Tech giants such as Salesforce and Oracle have embraced AI agents , systems that can autonomously complete goals and take action, pouring billions into the technology in the hopes of boosting margins and optimising costs. Many vendors were engaging in 'agent washing' – the rebranding of products such as AI assistants and chatbots without significant agentic capabilities, Gartner said, estimating that only about 130 of the thousands of agentic AI vendors were real. 'Most agentic AI projects right now are early stage experiments or proofs of concept that are mostly driven by hype and are often misapplied,' said Anushree Verma, senior director analyst at Gartner. 'Most agentic AI propositions lack significant value or return on investment, as current models do not have the maturity and agency to autonomously achieve complex business goals or follow nuanced instructions over time,' Verma said. Gartner predicted at least 15 per cent of day-to-day work decisions would be made autonomously through agentic AI by 2028, up from 0 per cent in 2024.
Phone Arena
25-06-2025
- Business
- Phone Arena
Good news? Research firm says AI agents not a big threat to employees
Gartner says AI agents are hyped up Do you fear losing your job to AI? Yes Nope Yes 0% Nope 0% Receive the latest Apps news By subscribing you agree to our terms and conditions and privacy policy AI threatens an already crumbling job market Agentic AI will continue to grow will Grab Surfshark VPN now at more than 50% off and with 3 extra months for free! Secure your connection now at a bargain price! We may earn a commission if you make a purchase Check Out The Offer Ever since generative AI has entered the spotlight, there's been concern over whether it will replace human employees and lead to mass layoffs. While such a thing is already happening — and some professionals, like artists, are being overlooked in favor of AI — research firm Gartner is downplaying AI to a new report , around 40 percent of agentic AI projects should be scrapped by 2027. The firm believes that many of these undertakings are usually built around a lot of hype, and not actual substance. Gartner also says that the return on investment for these projects is highly unreliable, which will further lead to their agents are, in very simple terms, digital employees that are replacements for human workers. They can interact with a computer and carry out tasks that would have previously required a Director Analyst at Gartner — Anushree Verma — claims that current AI models are unable to follow complex instructions. Verma also says that these models cannot help companies achieve business goals, and so human employees will still be required in the near current job market is, simply put, very discouraging to employees. There are too few openings for too many workers, and people are being forced to accept lower wages just to remain employed. Hiring culture has also seen a sharp decline, and a positive experience with a recruiter is like finding a is massively threatening this already volatile market. Artists, writers, and customer support representatives in particular are fearful of being laid off. Many companies — like Microsoft, Amazon, IBM, and even Duolingo — have already begun letting employees go in favor of AI replacements. Agentic AI further threatens these fields, and Gartner's report will elicit a sigh of relief from AI agents may not be as capable as human workers, the progress being made in this field cannot be denied. Gartner says that by 2028, at least 15 percent of daily work decisions will be made by agentic from a purely business perspective, it will be more profitable for companies to use AI agents. As such, providers of said agents will continue to pour money into improving their services. So, while many of these projects may shut down two years from now, the major offerings are here to stay.A friend of mine is working on such projects, and I've seen first-hand the advancements being made so rapidly in this sector. AI agents aren't perfect yet, and might not be for quite some time, but I definitely think that theyget there.
Entrepreneur
25-06-2025
- Business
- Entrepreneur
The Agentic AI Dilemma: Promise, Pitfalls, and Practicality
At this early stage, agentic AI can only be pursued where it delivers clear value or ROI Opinions expressed by Entrepreneur contributors are their own. You're reading Entrepreneur India, an international franchise of Entrepreneur Media. "A lot is happening in terms of Agentic AI." This headline is everywhere but is it truly happening, or is it just another misleading hype in the tech world? Gartner has termed this trend as "agent washing"—the rebranding of existing tools such as AI assistants, robotic process automation (RPA), and chatbots, without any substantial agentic capabilities. According to Gartner's latest report, more than 40 per cent of agentic AI projects will be cancelled by the end of 2027 due to escalating costs, unclear business value, or inadequate risk controls. Of the thousands of vendors claiming to offer agentic AI solutions, Gartner estimates only around 130 are legitimate. "Most agentic AI projects right now are early-stage experiments or proof-of-concepts driven largely by hype and often misapplied," said Anushree Verma, Senior Director Analyst at Gartner. "This can blind organisations to the real cost and complexity of deploying AI agents at scale, stalling projects before they move into production. They need to cut through the hype to make careful, strategic decisions about where and how they apply this emerging technology." The high cost of agentic AI One of the major barriers to meaningful adoption is cost. So how much does it actually take to build agentic AI? "For enterprises starting from scratch, developing Agentic AI internally could cost anywhere between INR 50 crore to INR 200 crore (approx. USD 6 million to USD 24 million) over 3–5 years, depending on scale, vertical, and use case," said Apurv Agrawal, Co-founder and CEO, SquadStack. "And even then, you're not guaranteed deployment success unless your systems are robust, your data flywheels are strong, and your change management is bulletproof." So, what will it take? Despite these early-stage challenges, the potential of agentic AI is immense. Gartner predicts that by 2028, at least 15 per cent of day-to-day work decisions will be made autonomously by agentic AI up from zero in 2024. Additionally, 33 per cent of enterprise software applications are expected to include agentic AI by 2028, compared to less than one per cent in 2024. To prepare for this transformation, organisations must adopt significant mindset and infrastructure shifts. The voices in the Indian AI space weighed in: Ganesh Gopalan, Co-founder & CEO, Gnani AI explained, "To effectively integrate Agentic AI into their operations, organisations must cultivate a mindset that embraces autonomy, continuous learning, and cross-functional collaboration. We often say that automation isn't AI and while many companies stop at basic task automation, what's truly needed is AI that works, understands, reasons, and adapts in real-time to deliver business outcomes." "To support this, companies need to leverage API-first, modular system designs, which enable flexibility, allow for rapid development, and orchestrate AI workflows seamlessly across business areas while maintaining an engaging, human-like experience. Ultimately, businesses must understand that AI is not just a tool for automated responses but a way to improve and personalise customer engagement," added Beerud Sheth, Co-founder & CEO, Gupshup. However, at this early stage, agentic AI can only be pursued where it delivers clear value or ROI. "To get real value from agentic AI, organisations must focus on enterprise productivity, rather than just individual task augmentation," Verma said. "They can start by using AI agents for decision-making, automation for routine workflows, and assistants for simple retrieval. It's about driving business value through cost, quality, speed, and scale."
