AI Agents Are Reshaping Enterprise Workflows as $234 Billion in SaaS Revenue Faces Disruption

Agent confidence on the technical frontier

A ranking of 101 agent tasks reveals where workflows are trending and where connected intelligence is critical. Enterprise investment in AI is booming. Gartner is calling 2026 an "inflection year" for organizations to align their AI projects with strategic business objectives. As the pressure to prove ROI mounts, executives and technology leaders are looking to agentic AI to drive the measurable financial outcomes their businesses seek. A prime opportunity for AI agents exists in the tech function, where IT infrastructure costs are projected to grow two to three times by 2030, even as budgets remain unchanged, according to McKinsey. And in the last 18 months, tech teams -- the engineers, developers, architects, and other practitioners who are building, deploying, and continually improving their organizations' infrastructure and applications -- are clearly putting agents to work. DOWNLOAD THE REPORT The ultimate promise of agents is not only to automate tasks but to manage and coordinate entire workflows, pursuing business goals in a way that allows humans and agents to work together. Given the risks involved in automated decision-making, teams cannot delegate the work that agents do without confidence that they are fully capable of performing the task and that it will do so in a safe, reliable, and secure manner. Among technology experts, our research shows that teams are exceedingly confident about using agentic AI across a significant amount of AI, data, and cloud tasks. Where agent readiness drops is largely due to a lack of business context being supplied to agentic systems. The more complex the task, the more reasoning capability an agent requires and the greater its need for business context. Such context-generation capabilities for agents are still at an early stage of development, especially in situations where enterprise data is difficult to wrangle and connect into the agent lifecycle at the speed and quality in which developers and executives need it. Human oversight is a key factor of success in deploying agentic AI. Knowing that tech teams are in a pivotal position to lead this transformation, the experts we interviewed expect agent confidence to accelerate as experience with agents deepens and business environments mature. "As we design agents to operate within the same operational boundaries, identity systems, and governance models that teams already use, they start to behave more like the systems organizations already trust," says Jeremy Winter, corporate vice president and chief product officer at Microsoft Azure Platform. This report, based on a survey of 300 global technology experts, ranks 101 tasks across AI, data, and cloud workflows based on respondents' confidence in agents acting on their behalf. It also examines how technology teams view the opportunities and challenges related to agentic AI, along with the potential for the technology to enhance their careers. Key findings from the report include: Confidence in agents is surging for measurable tasks and growing in areas of complex judgment. Technology experts overwhelmingly believe agents help with everyday work including streamlining processes, improving performance, and reducing repetitive tasks. Confidence is highest for processes like generating reports and boilerplate code, and there is clear opportunity where tasks involve multistep workflows and advanced reasoning to make decisions. Data workflows are the breakthrough domain. Tech teams trust agents most where structure can provide a reliable foundation for decisions. This includes areas such as data quality monitoring, visualization anomaly detection, real-time data stream monitoring, and data profiling. This is where domain experts closest to the point of data generation can provide context to allow agents to act and deliver trusted outcomes. Download the full report. Read the Microsoft Cloud blog by Amanda Silver, corporate vice president of Microsoft 365 Core and Work IQ, which underscores the importance of keeping humans in the loop and how systems thinking advances careers. And for a deeper dive into data workflows as a breakthrough use case for agents, check out the Fabric blog to hear from Kim Manis, corporate vice president of Product for Microsoft Fabric. This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review's editorial staff. It was researched, designed, and written by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review.

AI agents are your new colleagues - how to get the best results

Follow ZDNET: Add us as a preferred source on Google. ZDNET's key takeaways * Your next work team will include humans and agents. * Experiment and benchmark AI tools to check value. * Stay open to new agentic solutions to fresh challenges. Worrying whether the person next to you is pulling their weight professionally is no longer your only concern. For people who want to meet tight targets and deliver great results, your team is likely to include a broad mix of human colleagues and agentic counterparts. We are entering the age of the autonomous business, where new combinations of technology and data mean some of the roles we take for granted today -- from basic operational tasks up to decision-making responsibilities -- are fulfilled by agents that discover, negotiate, and transact autonomously. Also: 12 rules of agentic AI for successful enterprise transformation Tech analyst Gartner suggests companies are increasing their investments in agents, with AI agent software spending set to reach $206.5 billion and $376.3 billion in 2027, up from $86.4 billion in 2025. Some companies already use agents in their operational activities. Three digital leaders at the Snowflake Summit 2026 in San Francisco recently explained how their organizations are putting agents into production. After the panel session, ZDNET asked the participants what they'd learned about working successfully with their agentic colleagues. They suggested three areas are crucial: benchmarking agents, staying open to new ideas, and focusing on the right areas. Benchmark your tools Madeleine Want, VP of data at sports specialist Fanatics, recognized that delivering great results across agentic and human colleagues is a tough ask, so her organization tracks and traces benefits across the data practitioner community. She said Fanatics is an aggressive and early adopter of AI for data, where the organization tests tools, compares features, runs previews, and develops design partnerships. "We benchmark how you are using these tools, what type of tasks you are using them for, how much time you feel that they are saving you, and what you are doing with the time -- all of these kinds of self-reported value-based questions," she said. Also: 40% of enterprises will scrap AI agents - 3 ways to ensure yours don't fail Want, who manages data engineering, data science, and machine learning across the betting and gaming division at Fanatics, told ZDNET the benchmarks show agentic input saves human time. "Every business analyst out there will tell you some version of, 'I wish I could be doing more strategic work, but I am bogged down in routine reporting,'" she said. "What we are seeing is that the more routine reporting tasks are the ones that often lend themselves best to automation through AI, so we are seeing staff get that time back and then reapplying it to work that's more human and more strategic, which is kind of the dream outcome that you would hope for." Want said the successful application of agentic AI is about getting hold of better tooling to work with, so you can get the necessary parts of work done and focus on the more interesting areas you do best. Also: AI is causing cognitive fatigue. Here's how to work with more haste and less speed However, while certain tools might work in the present, she recognized that agentic AI is a work in progress, and her company's commitment to adopting and testing tools means professionals might be exposed to new services regularly. Want said her organization's philosophical approach to agents means deployment involves a back-and-forth process between managers and professionals as new AI-enabled ways of working are discovered. "There's a lot of expectation management to say, 'This is not your traditional enterprise technology multi-year transformation project,'" she said, advising other professionals to stay open to exploration and change. "We are not adopting well-tested, well-trodden technologies that, once rolled out, will never be rolled back. We're in an experimental phase right now, and so, adopt early and try things, but also hold it lightly, because we're going to need to stay agile." Stay open to new ideas Matt Luizzi, VP of analytics at wearable technology specialist Whoop, is another digital leader who was eager to help his team make better use of their time, even before the rollout of agentic AI. "I was trying to understand where my team was spending their time, and people were saying they're spending between 50% and 60% of their time just answering random questions from around the business that came in," he said. "'What were sales yesterday? How does that differ by region? Why were our web sessions up?' Those are disruptive things that people want to go away. Those are tasks that people would be happy to get off their plates. It also happens to be where agents excel right now." Also: The autonomous business is coming. Here's why that shift is good news for professionals Luizzi told ZDNET that his business has seen that introducing agents means human counterparts can spend more time with their professional colleagues on strategic work that adds incremental value. "We've also seen real revenue impacts from this technology already, with people being able to identify things proactively, root cause them with AI, troubleshoot what's going on, and take action much faster before the ship has left the station." Luizzi suggested that the march of agentic AI will continue to gather pace, particularly for tasks that can be easily automated. "We'll continue to see advances that unlock new capabilities for where humans are spending time, but we need to continue to push the boundaries," he said. Also: Forget productivity: Here are 5 strategic shifts that drive real AI value To that end, Luizzi suggested that no single employee is likely to hold the key to agentic success. Great ideas can bubble up from anywhere, and all professionals must be ready to make a mark. "Some organizations are going to be bottom-up, where the junior-level workers are taking on new technology, taking risks, and making time," he said. "Some of these initiatives are going to be top-down, coming from leaders like us, coming to conferences and hearing what other customers are doing, and being able to persist those throughout the organization, and identify and pattern match where those solutions solve problems that their team faces." Find new problems to solve Sriram Sitaraman, CIO at software specialist Synopsys, said he manages fairly large amounts of engineering and corporate data. One thing that's become clear across both areas is that agents are showing how they will help to boost human capabilities. "If you look at the volume of data available, the concept of the next best action you can take used to be a conversation between a bunch of humans based on current priorities," he said. "Now, with AI, you can truly make a data-driven, profitable action." Sitaraman said his company has recognized the potential for AI agents to fulfill the tasks of junior employees, such as running quick queries, creating graphs, and deriving insights. Also: How to beat the AI algorithm and get the job of your dreams He also gave the example of deciding which new features to build for an application. He said employees can work alongside their agentic colleagues to sift ideas and surface commercially viable propositions. "You don't need a team of people having the conversation. It's a smaller team of people looking at a large amount of data," he said. "Many efforts to reconcile data sources for decisions are now focused on how humans take advantage of AI. That effort is about trimming down the large volumes of data to actionable next steps." Sitaraman said agentic AI gives time back to human workers. For example, by picking up level one data-sorting and sifting tasks, staff can move to higher-level, value-creating work. "It's a hierarchical thing. The models will keep pushing tasks downstream to AI, and the complexity of tasks AI can manage will increase as the models get better," he said. "So, in six months, I see AI solving different types of problems -- not the same types of problems as now but different types, and that's going to evolve continually."

From automation to autonomy: Building zero human ops with Agentic AI

Businesses are moving beyond basic automation towards 'Agentic AI,' enabling autonomous systems to handle complex workflows and decision-making. This shift promises 'Zero-Human Ops,' freeing human talent for strategic tasks by automating mundane operations. Organizations can build this future by starting with small, high-volume tasks and ensuring clean data, ultimately leading to faster decisions and reduced costs. Investments in technology notwithstanding, a fully automated operational enterprise remains an enigma. Organisations have inadvertently created mechanical assembly lines where our brightest minds are tethered to the manual construction of spreadsheets and presentations, which seek to inform stakeholders, including management and investors, on various operational aspects of a company, ranging from capital allocation to performance-monitoring. As per some estimates, over 100 man-weeks could be spent at mid-sized business enterprises in just compiling such material. Transactional automation has significantly reduced manual friction in day-to-day operations at organisations. However, their governance apparatus has not kept pace; it remains trapped in a cycle of siloed decision-making and manual vetting that lacks long-term continuity. We must address the cultural fallacy that 'manual' equals 'secure' -- the idea that a report is only credible if it has been personally, and often laboriously, vetted by a human intermediary. Thanks to Agentic AI, a fundamental shift is occurring. The era of generative AI assistants is giving way to Agentic AI -- autonomous systems capable of reasoning, planning, and executing entire workflows. For executives, this isn't just a technological upgrade; it is the key to unlocking the 'Zero-Human Ops' organization, where mundane, high-volume operational tasks are handled completely by AI, freeing human talent for high-value strategic work. The Shift from Reactive to Autonomous The transition from traditional Robotic Process Automation to Agentic AI marks a fundamental evolution from rigid automation to the era of the digital coworker. While legacy systems execute scripted tasks, AI agents integrate a reasoning layer to interpret goals, a knowledge layer to synthesize business context, and an action layer to execute across software ecosystems via APIs. The true paradigm shift lies in automating the decision to act, rather than just the workflow itself. For example, where a security analyst once manually investigated alerts, an autonomous agent now detects, contextualizes, and mitigates threats in milliseconds; elsewhere, agents transform complex data into a curated portfolio of strategic options, freeing human leaders to exercise judgment rather than labor over choice-building. By shifting the burden of cognitive execution to the action layer, Agentic AI serves as the definitive conduit to "Zero Human Ops," allowing the enterprise to prioritize strategic outcomes over operational processes. Building the Foundation for Zero Human Ops Building this future requires rethinking operational architecture. 1. Start with "Small" Autonomy, Not Small Automation: Avoid trying to automate everything at once. Begin with high-volume, low-judgment tasks where data is structured, such as tier-I IT support, invoice reconciliation, or HR onboarding. 2. Audit Data for Autonomous Decision-Making: Agentic AI is only as good as the context it trusts, and this aspect of agentic AI may prove to be the Achilles heel for organisation, since context setting an agent is not a trivial task. The enterprise's operations team must ensure data resides in a unified, clean, and accessible format (a non-negotiable condition). While agents with proper data and context are your best friends and partners, agents without context can become your dangerous fault-lines. 3. Deploy Agentic Task Forces: True power lies in multi-agent systems where specialized agents collaborate. A supply chain agent can talk directly to a compliance agent, which then triggers a financial forecasting agent, autonomously navigating complex supply chain scenarios. Operationalising Trust Zero Human Ops does not mean zero oversight. It means creating a robust 'human-in-the-loop' framework, where humans set the guardrails, audit the decision logs, and handle edge cases. This process should be of utmost importance as the more human-agent interaction happens initially, the higher the chances of building an Agentic apparatus that enhances efficiency and productivity. For example, an Agent in the vendor management and procure-to-pay function can be made autonomous to compare vendors, assess past performance, check for possible fraud and offer recommendations with a vendor score for the person responsible to finally accept the recommendation. The more the agent can explain the reasoning and logic, the more trustworthy and efficient they become. nature. The Strategic Imperative Organizations that embrace agentic AI now to build zero-human ops capabilities will achieve a significant competitive advantage through faster decision-making, significantly reduced operational costs, and 24/7 productivity. The goal of Agentic AI is not to replace human beings. It is to release us from the tyranny of repetitive operations. It is time for leaders to stop managing tasks and start orchestrating autonomous teams. The author is Group Chief Operating Officer, Jio Financial Services (Disclaimer: The opinions expressed in this column are that of the writer. The facts and opinions expressed here do not reflect the views of www.economictimes.com.)

How Agentic AI Is Growing -- and how enterprises can make the best of it

Enterprise software has spent the last two years shifting from AI that answers to AI that acts. Agentic AI - systems that plan multi-step work, use tools, and make decisions with limited supervision -- has become the defining enterprise technology story of 2026, and the numbers behind it are hard to ignore. A market growing faster than almost anything before itGartner forecasts that 40% of enterprise applications will embed task-specific AI agents by the end of 2026, up from under 5% a year earlier. Estimates of the agentic AI market's current size cluster around $10 billion, with most analysts projecting compound annual growth of above 40% for the rest of the decade. Adoption intent is sharper still: Gartner's 2026 CIO survey found that while only about 17% of organisations have deployed agents so far, more than 60% expect to within two years -- the steepest adoption curve of any emerging technology it tracks. The appeal is straightforward. Instead of jumping between a dozen browser tabs, employees can hand repetitive, cross-system work to an agent that triages requests, updates records, drafts replies, and routes tasks to the right owner. Early adopters report meaningful returns: faster decisions, lower documentation time, and double-digit cost savings. The gap between pilots and productionGrowth, though, is not the same as value. Much of the market remains stuck in experimentation. IDC has found that a large majority of AI proofs of concept never reach wide deployment, and Gartner expects more than 40% of agentic AI projects to be cancelled by the end of 2027 -- usually because of unclear business value, runaway costs, or weak risk controls. Researchers consistently note that agent failures rarely stem from the model itself. They come from ambiguous goals, missing tools and data access, and no discipline for evaluating agents once they are live. How enterprises can make the best of itThe organisations pulling ahead share a few habits. They start with well-defined, high-volume workflows -- customer support, operations, lead enrichment -- rather than a vague ambition to "automate everything." They treat data and tool access as the real bottleneck, connecting agents securely to the systems where work actually happens. They keep humans in the loop for consequential actions, and they instrument everything so agents can be audited and improved. Crucially, they measure success against concrete outcomes before scaling. Governance is now a first-class concern. With only a minority of firms reporting mature controls for autonomous agents, the winners are building permissions, approval gates, and audit trails into deployments from day one rather than bolting them on later. Where platforms like onetab.ai fit inThis is the gap that purpose-built agentic platforms aim to close. onetab.ai, founded in late 2023 by Saket Dandotia, Sonal Dandotia, and Alok Patil, positions itself as India's first full-stack AI agent builder. In April 2026 it launched its Enterprise AI Agentic Solutions suite, built on a proprietary agent-builder engine that integrates with more than 150 enterprise tools and draws on multiple foundation models from Anthropic, OpenAI, and Google. Its design reflects the lessons above. Agents run inside the permissions an admin sets -- granted tool by tool, read-only or read-write -- and any action that sends, deletes, or pays pauses for human approval. Every read and write is recorded and exportable for audit, and the company says customer content is never used to train models. Across deployments in healthcare, HR, banking, insurance, real estate, and operations, its clients report roughly 40% lower operational costs and up to 80% time savings on manual workflows. For enterprises, the lesson of 2026 is that agentic AI rewards focus, governance, and tight integration far more than raw ambition. Technology is no longer the hard part. Choosing the right workflows, wiring agents safely into existing tools, and measuring real outcomes is what separates the firms capturing value from the majority still experimenting -- and it is where the next wave of enterprise advantage will be won.

Agentic AI Puts $234 Billion in Software Spend at Risk, Gartner Warns

Agentic Arbitrage Breaks the Traditional SaaS Seat-License Model Agentic AI is set to disrupt enterprise software revenue models, with up to $234 billion of enterprise application spending exposed to agentic arbitrage between now and 2030, according to Gartner, Inc, a business and technology insights company. By 2030, this will account for roughly 20% of enterprise application software-as-a-service (SaaS) spending. Agentic arbitrage happens when AI agents complete tasks across multiple systems, reducing the need for users to interact with multiple traditional software interfaces. "Agentic AI changes the economics of software," said George Brocklehurst, Managing Vice President at Gartner. "Agentic systems deliver outcomes directly, bypassing traditional user experience (UX)-heavy applications and making the software invisible. This breaks the link between user growth and revenue growth for many enterprise software vendors." This shift is already underway and will refactor how software is built, priced and consumed. "It will also lead to a redefinition of 'Saaspocalypse', the disaggregation of the legacy SaaS market as we know it today," said Brocklehurst. This is less an apocalypse and more of a metamorphosis. SaaS will not be destroyed; it will emerge in a different form. This metamorphosis represents threats and opportunities for both incumbents and new challengers. Buyers Shift Focus from Features to Outcomes Gartner analysts said expectations are changing. "Enterprise buyers will deemphasize buying more new tools or dashboards," said Brocklehurst. "They want better outcomes and adding more AI features often creates more cost, not better outcomes. Better outcomes from AI require systems that can retain deep institutional memory and customer context over time." Some vendors are already offering agentic solutions that deliver autonomous end-to-end workflow execution, cross-system orchestration and capture customer context and knowledge, which help to foster business results and ROI. Today this typically requires heavy services engagement. "As organizations increasingly use agentic AI systems, the user interface is no longer a differentiation," said Brocklehurst. "Legacy SaaS market share will be cannibalized by incumbents and taken by new entrants delivering horizontal agentic platforms." Direct Risk for Incumbent Vendors and Revenue Opportunity for Service Providers To remain competitive and achieve growth opportunities, incumbent software vendors must move from interface-based value to outcome-based value, embed agentic capabilities at the point of execution into their offerings to defend their position in the value chain, capture and retain customer-specific knowledge, not just data. "While this shift is posing an existential threat for vendors who are defending legacy dashboards and seat-based models, it creates a substantial revenue opportunity for vendors who are enabling and developing services and platforms to support agentic enabled cross-domain workflows," said Brocklehurst. AI-native startups and service providers can act as the agentic layer across enterprise systems, deliver measurable outcomes instead of features and assist organizations redesign workflows around AI. "Ultimately, they can capture not just existing spend, but incremental budget unlocked through ROI upside," said Brocklehurst.