AI agents tackle 101 enterprise tasks as tech teams build confidence in autonomous business

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."