AI Adoption in Enterprises: Challenges, Strategies, and the Future of Work

AI usage is stalling out at work from lack of education and support

A new survey of enterprise use of artificial intelligence released Thursday by the Boston Consulting Group shows the technology has stalled in its deployment, and the most-hyped new area, "agents," has seen low adoption within companies so far. The obstacles to greater AI usage are primarily a lack of training, limited access to tools, and insufficient encouragement from management, the group states. These obstacles are raising concerns about job loss and lack of oversight, the Boston Consulting Group found. Also: 15 new jobs AI is creating - including 'Synthetic reality producer' "AI is now part of our daily work life -- but frontline employees have hit an adoption ceiling," according to the report, "Momentum Builds, But Gaps Remain," based on a survey of 10,635 employees at companies around the world from May 13 through June 4. The study included employees at companies with more than $5 billion in revenue, as well as at those with less than $500 million, so it was not limited to large enterprises. The survey covered three classes of employees: so-called frontline workers, managers, and "leaders" in organizations. Use of AI is "strong" overall, the survey finds, with 72% of workers using the technology regularly, defined as "several times a week or daily." While 51% of frontline employees use AI -- up from 20% in 2018 -- that figure is actually down one point from 52% last year. The study authors identified three impediments to greater usage. The first is training: just over a third of employees report receiving adequate AI training, meaning "trained on the skills needed for AI transformation." The group advises that employees with five or more hours of training were more likely to use AI regularly, as were those who received in-person training (as opposed to virtual) and those who had access to an AI "coach" -- a human who can advise them. The factors "significantly boost employees' confidence in AI and improve the quality of AI-enabled work outputs," the study advises. Also: Tech prophet Mary Meeker just dropped a massive report on AI trends - here's your TL;DR On the second point, almost 40% of employees "say their company is not supplying the right tools," the survey revealed, though it did not specify which tools. Over half of those employees said they resorted to bypassing corporate restrictions to use other AI tools "when corporate solutions fall short," and as many as 62% of Gen Z employees said they did so. That poses security risks for corporations, the group advises. The third impediment was lack of support from the top, the group found. People were more likely to use AI regularly "with clear leadership support" for the technology. The survey found that "only 25% of frontline employees experience it," referring to C-suite support. Aside from individual use, the survey reveals that AI usage is rather basic, not advanced or sophisticated. Of the companies surveyed, 72% said they were deploying ChatGPT, Microsoft Copilot, and French startup Mistral's generative AI. Also: AI could erase half of entry-level white collar jobs in 5 years, CEO warns That percentage is well above the 50% of firms that reported they are "reimagining" corporate functions by redesigning "processes." Only 22% of companies said they were developing something entirely new with AI. The group warns that merely deploying ChatGPT and similar rudimentary solutions is not ambitious enough. "According to BCG studies, companies that create the most value with AI focus 80% of their investment on Reshape and Invent, in a few core processes." The firm states that companies which train workers and receive support from senior management are more likely to use AI to reimagine or invent processes rather than simply deploy a chatbot. Also: Don't be fooled into thinking AI is coming for your job - here's the truth "As a result, employees at those companies," the authors say, "save more time" and "shift to strategic tasks," and are more likely to "think their company will make better decisions thanks to data." Reimagining processes, however, has a downside. When asked if their job will be replaced by AI, 41% said yes, a number that rose to 46% in companies focusing on reimagining processes. That prompted the survey authors to conclude, "Employees in companies reshaping their workflows feel most vulnerable to job loss -- reinforcing the need for clear communication and proper upskilling." The sense of job loss was even higher in regions where AI is more prevalent. In parts of the "global south" -- such as India, Spain and the Middle East -- where usage is highest, respondents answered affirmatively by 48%, 61% and 63%, respectively, when asked if "their job will certainly or probably disappear entirely in the next ten years." Also: Open-source skills can save your career when AI comes knocking Those surveyed reported other concerns about AI generally within their organizations. Concerns included "decisions taken without human oversight" (46%), "unclear accountability when mistakes occur" (35%), and "bias or unfair treatment introduced" (32%). Also somewhat stalled are the much-ballyhooed agentic AI technologies: only 13% of companies currently have agentic AI in their "workflows," despite 77% of respondents believing they "will be important in the next three to five years." Over a third of respondents do not fully understand agent technology, the group found. "Employees see potential, but don't fully understand AI agents," they write. The countries most active in putting agents "into their workflows," according to their employees, are Brazil, India, Spain, the US, and the UK. Also: Your next job? Managing a fleet of AI agents "Three out of four employees believe AI agents will be critical to future success -- but only 13% say they've seen them integrated into workflows," said David Martin, the global lead of people & organization at Boston Consulting Group, in an email to me. "That tells us we're at the very beginning of the adoption curve. What we've found is that when people understand how agents work, they stop seeing them as threats and start seeing them as teammates. Education, not just experimentation, will be the unlock." The BCG report echoes other studies of the market that have emphasized obstacles and pitfalls in corporate AI deployment. For example, a January report from consulting firm Deloitte found that the majority of companies are not ready to put generative artificial intelligence into production.

Do these 4 things before betting on AI in your business - and why

While AI can democratize access to information and boost worker productivity, business leaders at Snowflake Summit 2025 said you need to get a few key things right first. The unstoppable march of AI continues to gather pace. Analyst Gartner recently forecast that half of all business decisions will be fully automated or at least partially augmented by AI agents within the next two years. Also: 4 ways to turn AI into your business advantage Some organizations have experimented more than others. Four business leaders who have explored AI shared lessons learned at a recent media roundtable event at Snowflake Summit 2025 in San Francisco. Here's what they had to say. Wayne Filin-Matthews, chief enterprise architect at AstraZeneca, explained how his organization is pioneering AI implementations in several areas. The pharma giant has developed an AI-enabled research assistant that boosts the productivity of scientific researchers by focusing on the reproducibility of scientific methods and the development of new medicines. AstraZeneca partners with leading academic institutions, such as Stanford University, to run agentic AI experiments. "We're thinking about how you can have a team of agents that can support the traditional scientists who do their research," said Filin-Matthews. Also: 4 ways your organization can adapt and thrive in the age of AI The company is also exploring how to apply AI in commercial areas. AstraZeneca operates in 126 markets, and serving those varied locations with content is a complex challenge. That's where AI comes in. "We've leveraged the technology from an AI perspective to automate the creation of marketing material and information about drug development," he said. While these experiments have highlighted the benefits of AI, they've also shown the importance of solid data foundations. Also: Integrating AI starts with robust data foundations. Here are 3 strategies executives employ Filin-Matthews said companies can only solve problems with AI if they've built a strong underlying cloud infrastructure. "There are so many use cases where the benefit is becoming clear as we've gone on this journey," he said. "We're definitely in the era of AI-enabled decision-making. But the key for me is you can't forget those other underlying elements. You cannot be AI-first without being cloud-first." Amit Patel, chief data officer for wholesale banking at Truist, said he learned two key lessons from rolling out AI use cases. Number one was the importance of the underlying data foundation. "As a bank, we have to prove, 'Where did the data come from? Is it correct? Is it governed? Do I have lineage? Do I have metadata? Do I have data quality checks?' I have to prove those points to an external regulator," he said. "I can't just release a large language model (LLM) into the wild, right? And I can't point it at just any sources that I have internally. It's got to be a governed source. It's got to be an authorized provisioning point." Also: This free Google tool turns AI into your research assistant Patel said this focus on regulated sources helped elucidate a common problem point for CDOs: getting your data in order. "Through that process, I've discovered that I don't have as many reliable sources as I would like to point to," he said. "I've got to enable that foundation first, and then I can build on top." Patel said the second thing he learned is that people who use AI at home assume it will be easy to deploy LLMs in an enterprise environment. "It's not that simple," he said. "You have to define guardrails around what the models can look at. You should define the metadata to guide the models' interpretations. And that process takes time." Also: Is your business AI-ready? 5 ways to avoid falling behind Patel said his team has addressed staff misconceptions about the time to exploit AI through expectation-setting exercises. "As we've started to enable use cases, people have started to understand that it's not as easy as a point-and-click process," he said. "While implementing technology is faster than it used to be, it's still challenging, and it requires time and thought around how you put governance and structure around AI before you enable it for work." Anahita Tafvizi, chief data and analytics officer at Snowflake, said her team helps the tech company develop the AI-enabled products its customers use. However, Tafvizi said her company doesn't just sell these products -- the organization also gets to experiment with these technologies. "The interesting thing about being the CDO at a data company is that I get the privilege of being the very first customer of a lot of our products," she said. Tafvizi drew attention to Snowflake Intelligence, a technology launched at Summit that allows business users to create data agents. Also: The top 20 AI tools of 2025 - and the No. 1 thing to remember when you use them Her team partnered closely with the product team to develop an AI-enabled assistant for the internal sales organization. She recognized that implementing new AI tools brings challenges, particularly when it comes to balancing the velocity of innovation with governance requirements. One crucial consideration is quality. As her team pushed the tool to the sales team, they pondered important questions, such as, "Is 95% quality good enough?" Tafvizi advised other business leaders to think carefully about these challenges, as staff must trust the outputs of AI experimentation. "The focus on quality has been important for us," she said. "The right governance structures, access controls, lineage, metadata, and semantic models are also critical. We constantly think about those things as part of the tension between innovation and velocity." Thomas Bodenski, chief data and analytics officer at finance technology specialist TS Imagine, said his company has been using AI to reduce employee workloads since October 2023. However, while the focus of AI is often on automating manual processes, his experiences suggest business leaders should recognize the technology also produces other benefits. "Using AI is not just about reducing effort," he said. "You get to do things faster, better, and have an unbelievable coverage improvement as well." He explained how TS Imagine buys data from specialist vendors that send emails about upcoming product changes. Also: 10 strategies OpenAI uses to create powerful AI agents - that you should use too The company receives 100,000 of these emails a year. Each email has to be read and its implications understood. Traditionally, that work-intensive process has consumed, on average, two and a half full-time equivalents per year. "It's stressful because you can't make mistakes," he said. "If we miss information in an email, our systems will go down. Thousands of traders cannot trade and thousands of risk managers can't assess their exposure, so it's potentially catastrophic." To avoid this scenario, Bodenski said the company uses Snowflake's AI models to complete this time-intensive work. "Now, we never miss the result," he said. "Those two and a half full-time equivalents can do knowledge work rather than manual data curation or entry." Bodenski said AI can also manage what was previously a weak spot: ensuring customer requests are dealt with on Saturdays. "Nobody worked on those days. Now, there's AI, and she will respond to customer inquiries and assign the ticket to the right person," he said.

OpenAI's API lead explains how enterprises are already succeeding with its Agents SDK and Responses API

Join the event trusted by enterprise leaders for nearly two decades. VB Transform brings together the people building real enterprise AI strategy. Learn more At VentureBeat's Transform 2025 conference, Olivier Godement, Head of Product for OpenAI's API platform, provided a behind-the-scenes look at how enterprise teams are adopting and deploying AI agents at scale. In a 20-minute panel discussion I hosted exclusively with Godement, the former Stripe researcher and current OpenAI API boss unpacked OpenAI's latest developer tools -- the Responses API and Agents SDK -- while highlighting real-world patterns, security considerations, and cost-return examples from early adopters like Stripe and Box. For enterprise leaders unable to attend the session live, here are top 8 most important takeaways: Agents Are Rapidly Moving From Prototype to Production According to Godement, 2025 marks a real shift in how AI is being deployed at scale. With over a million monthly active developers now using OpenAI's API platform globally, and token usage up 700% year over year, AI is moving beyond experimentation. "It's been five years since we launched essentially GPT-3... and man, the past five years has been pretty wild." Godement emphasized that current demand isn't just about chatbots anymore. "AI use cases are moving from simple Q&A to actually use cases where the application, the agent, can do stuff for you." This shift prompted OpenAI to launch two major developer-facing tools in March: the Responses API and the Agents SDK. When to Use Single Agents vs. Sub-Agent Architectures A major theme was architectural choice. Godement noted that single-agent loops, which encapsulate full tool access and context in one model, are conceptually elegant but often impractical at scale. "Building accurate and reliable single agents is hard. Like, it's really hard." As complexity increases -- more tools, more possible user inputs, more logic -- teams often move toward modular architectures with specialized sub-agents. "A practice which has emerged is to essentially break down the agents into multiple sub-agents... You would do separation of concerns like in software." These sub-agents function like roles in a small team: a triage agent classifies intent, tier-one agents handle routine issues, and others escalate or resolve edge cases. Why the Responses API Is a Step Change Godement positioned the Responses API as a foundational evolution in developer tooling. Previously, developers manually orchestrated sequences of model calls. Now, that orchestration is handled internally. "The Responses API is probably the biggest new layer of abstraction we introduced since pretty much GPT-3." It allows developers to express intent, not just configure model flows. "You care about returning a really good response to the customer... the Response API essentially handles that loop." It also includes built-in capabilities for knowledge retrieval, web search, and function calling -- tools that enterprises need for real-world agent workflows. Observability and Security Are Built In Security and compliance were top of mind. Godement cited key guardrails that make OpenAI's stack viable for regulated sectors like finance and healthcare: Evaluation is where Godement sees the biggest gap between demo and production. "My hot take is that model evaluation is probably the biggest bottleneck to massive AI adoption." OpenAI now includes tracing and eval tools with the API stack to help teams define what success looks like and track how agents perform over time. "Unless you invest in evaluation... it's really hard to build that trust, that confidence that the model is being accurate, reliable." Early ROI Is Visible in Specific Functions Some enterprise use cases are already delivering measurable gains. Godement shared examples from: Other high-value use cases include customer support (including voice), internal governance, and knowledge assistants for navigating dense documentation. What It Takes to Launch in Production Godement emphasized the human factor in successful deployments. "There is a small fraction of very high-end people who, whenever they see a problem and see a technology, they run at it." These internal champions don't always come from engineering. What unites them is persistence. "Their first reaction is, OK, how can I make it work?" OpenAI sees many initial deployments driven by this group -- people who pushed early ChatGPT use in the enterprise and are now experimenting with full agent systems. He also pointed out a gap many overlook: domain expertise. "The knowledge in an enterprise... does not lie with engineers. It lies with the ops teams." Making agent-building tools accessible to non-developers is a challenge OpenAI aims to address. What's Next for Enterprise Agents Godement offered a glimpse into the roadmap. OpenAI is actively working on: These aren't radical changes, but iterative layers that expand what's already possible. "Once we have models that can think not only for a few seconds but for minutes, for hours... that's going to enable some pretty mind-blowing use cases." Final Word: Reasoning Models Are Underhyped Godement closed the session by reaffirming his belief that reasoning-capable models -- those that can reflect before responding -- will be the true enablers of long-term transformation. "I still have conviction that we are pretty much at the GPT-2 or GPT-3 level of maturity of those models....We are still scratching the surface on what reasoning models can do." For enterprise decision makers, the message is clear: the infrastructure for agentic automation is here. What matters now is building a focused use case, empowering cross-functional teams, and being ready to iterate. The next phase of value creation lies not in novel demos -- but in durable systems, shaped by real-world needs and the operational discipline to make them reliable.

From chatbots to collaborators: How AI agents are reshaping enterprise work

Join the event trusted by enterprise leaders for nearly two decades. VB Transform brings together the people building real enterprise AI strategy. Learn more Scott White still marvels at how quickly artificial intelligence has transformed from a novelty into a true work partner. Just over a year ago, the product lead for Claude AI at Anthropic watched as early AI coding tools could barely complete a single line of code. Today, he's building production-ready software features himself -- despite not being a professional programmer. "I no longer think about my job as writing a PRD and trying to convince someone to do something," White said during a fireside chat at VB Transform 2025, VentureBeat's annual enterprise AI summit in San Francisco. "The first thing I do is, can I build a workable prototype of this on our staging server and then share a demo of it actually working." This shift represents a broader transformation in how enterprises are adopting AI, moving beyond simple chatbots that answer questions to sophisticated "agentic" systems capable of autonomous work. White's experience offers a glimpse into what may be coming for millions of other knowledge workers. The evolution has been remarkably swift. When White joined Anthropic, the company's Claude 2 model could handle basic text completion. The release of Claude 3.5 Sonnet enabled the creation of entire applications, leading to features like Artifacts that let users generate custom interfaces. Now, with Claude 4 achieving a 72.5% score on the SWE-bench coding benchmark, the model can function as what White calls "a fully remote agentic software engineer." Claude Code, the company's latest coding tool, can analyze entire codebases, search the internet for API documentation, issue pull requests, respond to code review comments, and iterate on solutions -- all while working asynchronously for hours. White noted that 90% of Claude Code itself was written by the AI system. "That is like an entire agentic process in the background that was not possible six months ago," White explained. The implications extend far beyond software development. Novo Nordisk, the Danish pharmaceutical giant, has integrated Claude into workflows that previously took 10 weeks to complete clinical reports, now finishing the same work in 10 minutes. GitLab uses the technology for everything from sales proposals to technical documentation. Intuit deploys Claude to provide tax advice directly to consumers. White distinguishes between different levels of AI integration: simple language models that answer questions, models enhanced with tools like web search, structured workflows that incorporate AI into business processes, and full agents that can pursue goals autonomously using multiple tools and iterative reasoning. "I think about an agent as something that has a goal, and then it can just do many things to accomplish that goal," White said. The key enabler has been what he calls the "inexorable" relationship between model intelligence and new product capabilities. A critical infrastructure development has been Anthropic's Model Context Protocol (MCP), which White describes as "the USB-C for integrations." Rather than companies building separate connections to each data source or tool, MCP provides a standardized way for AI systems to access enterprise software, from Salesforce to internal knowledge repositories. "It's really democratizing access to data," White said, noting that integrations built by one company can be shared and reused by others through the open-source protocol. For organizations looking to implement AI agents, White recommends starting small and building incrementally. "Don't try to build an entire agentic system from scratch," he advised. "Build the component of it, make sure that component works, then build a next component." He also emphasized the importance of evaluation systems to ensure AI agents perform as intended. "Evals are the new PRD," White said, referring to product requirement documents, highlighting how companies must develop new methods to assess AI performance on specific business tasks. Looking ahead, White envisions AI development becoming accessible to non-technical workers, similar to how coding capabilities have advanced. He imagines a future where individuals manage not just one AI agent but entire organizations of specialized AI systems. "How can everyone be their own mini CPO or CEO?" White asked. "I don't exactly know what that looks like, but that's the kind of thing that I wake up and want to get there." The transformation White describes reflects broader industry trends as companies grapple with AI's expanding capabilities. While early adoption focused on experimental use cases, enterprises are increasingly integrating AI into core business processes, fundamentally changing how work gets done. As AI agents become more autonomous and capable, the challenge shifts from teaching machines to perform tasks to managing AI collaborators that can work independently for extended periods. For White, this future is already arriving -- one production feature at a time.

Lessons learned from agentic AI leaders reveal critical deployment strategies for enterprises

Join the event trusted by enterprise leaders for nearly two decades. VB Transform brings together the people building real enterprise AI strategy. Learn more Companies are rushing AI agents into production -- and many of them will fail. But the reason has nothing to do with their AI models. On day two of VB Transform 2025, industry leaders shared hard-won lessons from deploying AI agents at scale. A panel moderated by Joanne Chen, general partner at Foundation Capital, included Sean Malhotra, CTO at Rocket Companies, which uses agents across the home ownership journey from mortgage underwriting to customer chat; Shailesh Nalawadi, head of product at Sendbird, which builds agentic customer service experiences for companies across multiple verticals; and Thys Waanders, SVP of AI transformation at Cognigy, whose platform automates customer experiences for large enterprise contact centers. Their shared discovery: Companies that build evaluation and orchestration infrastructure first are successful, while those rushing to production with powerful models fail at scale. The ROI reality: Beyond simple cost cutting A key part of engineering AI agent for success is understanding the return on investment (ROI). Early AI agent deployments focused on cost reduction. While that remains a key component, enterprise leaders now report more complex ROI patterns that demand different technical architectures. Cost reduction wins Malhotra shared the most dramatic cost example from Rocket Companies. "We had an engineer [who] in about two days of work was able to build a simple agent to handle a very niche problem called 'transfer tax calculations' in the mortgage underwriting part of the process. And that two days of effort saved us a million dollars a year in expense," he said. For Cognigy, Waanders noted that cost per call is a key metric. He said that if AI agents are used to automate parts of those calls, it's possible to reduce the average handling time per call. Revenue generation methods Saving is one thing; making more revenue is another. Malhotra reported that his team has seen conversion improvements: As clients get the answers to their questions faster and have a good experience, they are converting at higher rates. Proactive revenue opportunities Nalawadi highlighted entirely new revenue capabilities through proactive outreach. His team enables proactive customer service, reaching out before customers even realize they have a problem. A food delivery example illustrates this perfectly. "They already know when an order is going to be late, and rather than waiting for the customer to get upset and call them, they realize that there was an opportunity to get ahead of it," he said. Why AI agents break in production While there are solid ROI opportunities for enterprises that deploy agentic AI, there are also some challenges in production deployments. Nalawadi identified the core technical failure: Companies build AI agents without evaluation infrastructure. "Before you even start building it, you should have an eval infrastructure in place," Nalawadi said. "All of us used to be software engineers. No one deploys to production without running unit tests. And I think a very simplistic way of thinking about eval is that it's the unit test for your AI agent system." Traditional software testing approaches don't work for AI agents. He noted that it's just not possible to predict every possible input or write comprehensive test cases for natural language interactions. Nalawadi's team learned this through customer service deployments across retail, food delivery and financial services. Standard quality assurance approaches missed edge cases that emerged in production. AI testing AI: The new quality assurance paradigm Given the complexity of AI testing, what should organizations do? Waanders solved the testing problem through simulation. "We have a feature that we're releasing soon that is about simulating potential conversations," Waanders explained. "So it's essentially AI agents testing AI agents." The testing isn't just conversation quality testing, it's behavioral analysis at scale. Can it help to understand how an agent responds to angry customers? How does it handle multiple languages? What happens when customers use slang? "The biggest challenge is you don't know what you don't know," Waanders said. "How does it react to anything that anyone could come up with? You only find it out by simulating conversations, by really pushing it under thousands of different scenarios." The approach tests demographic variations, emotional states and edge cases that human QA teams can't cover comprehensively. The coming complexity explosion Current AI agents handle single tasks independently. Enterprise leaders need to prepare for a different reality: Hundreds of agents per organization learning from each other. The infrastructure implications are massive. When agents share data and collaborate, failure modes multiply exponentially. Traditional monitoring systems can't track these interactions. Companies must architect for this complexity now. Retrofitting infrastructure for multi-agent systems costs significantly more than building it correctly from the start. "If you fast forward in what's theoretically possible, there could be hundreds of them in an organization, and perhaps they are learning from each other,"Chen said. "The number of things that could happen just explodes. The complexity explodes."

The hidden scaling cliff that's about to break your agent rollouts

Join the event trusted by enterprise leaders for nearly two decades. VB Transform brings together the people building real enterprise AI strategy. Learn more Enterprises that want to build and scale agents also need to embrace another reality: agents aren't built like other software. Agents are "categorically different" in how they're built, how they operate, and how they're improved, according to Writer CEO and co-founder May Habib. This means ditching the traditional software development life cycle when dealing with adaptive systems. "Agents don't reliably follow rules," Habib said on Wednesday while on stage at VB Transform. "They are outcome-driven. They interpret. They adapt. And the behavior really only emerges in real-world environments." Knowing what works -- and what doesn't work -- comes from Habib's experience helping hundreds of enterprise clients build and scale enterprise-grade agents. According to Habib, more than 350 of the Fortune 1000 are Writer customers, and more than half of the Fortune 500 will be scaling agents with Writer by the end of 2025. Using non-deterministic tech to produce powerful outputs can even be "really nightmarish," Habib said -- especially when trying to scale agents systemically. Even if enterprise teams can spin up agents without product managers and designers, Habib thinks a "PM mindset" is still needed for collaborating, building, iterating and maintaining agents. "Unfortunately or fortunately, depending on your perspective, IT is going to be left holding the bag if they don't lead their business counterparts into that new way of building." Why goal-based agents is the right approach One of the shifts in thinking includes understanding the outcome-based nature of agents. For example, she said that many customers request agents to assist their legal teams in reviewing or redlining contracts. But that's too open-ended. Instead, a goal-oriented approach means designing an agent to reduce the time spent reviewing and redlining contracts. "In the traditional software development life cycle, you are designing for a deterministic set of very predictable steps," Habib said. "It's input in, input out in a more deterministic way. But with agents, you're seeking to shape agentic behavior. So you are seeking less of a controlled flow and much more to give context and guide decision-making by the agent." Another difference is building a blueprint for agents that instructs them with business logic, rather than providing them with workflows to follow. This includes designing reasoning loops and collaborating with subject experts to map processes that promote desired behaviors. While there's a lot of talk about scaling agents, Writer is still helping most clients with building them one at a time. That's because it's important first to answer questions about who owns and audits the agent, who makes sure it stays relevant and still checks if it's still producing desired outcomes. "There is a scaling cliff that folks get to very, very quickly without a new approach to building and scaling agents," Habib said. "There is a cliff that folks are going to get to when their organization's ability to manage agents responsibly really outstrips the pace of development happening department by department." QA for agents vs software Quality assurance is also different for agents. Instead of an objective checklist, agentic evaluation includes accounting for non-binary behavior and assessing how agents act in real-world situations. That's because failure isn't always obvious -- and not as black and white as checking if something broke. Instead, Habib said it's better to check if an agent behaved well, asking if fail-safes worked, evaluating outcomes and intent: "The goal here isn't perfection It is behavioral confidence, because there is a lot of subjectivity in this here." Businesses that don't understand the importance of iteration end up playing "a constant game of tennis that just wears down each side until they don't want to play anymore," Habib said. It's also important for teams to be okay with agents being less than perfect and more about "launching them safely and running fast and iterating over and over and over." Despite the challenges, there are examples of AI agents already helping bring in new revenue for enterprise businesses. For example, Habib mentioned a major bank that collaborated with Writer to develop an agent-based system, resulting in a new upsell pipeline worth $600 million by onboarding new customers into multiple product lines. New version controls for AI agents Agentic maintenance is also different. Traditional software maintenance involves checking the code when something breaks, but Habib said AI agents require a new kind of version control for everything that can shape behavior. It also requires proper governance and ensuring that agents remain useful over time, rather than incurring unnecessary costs. Because models don't map cleanly to AI agents, Habib said maintenance includes checking prompts, model settings, tool schemas and memory configuration. It also means fully tracing executions across inputs, outputs, reasoning steps, tool calls and human interactions. "You can update a [large language model] LLM prompt and watch the agent behave completely differently even though nothing in the git history actually changed," Habib said. "The model links shift, retrieval indexes get updated, tool APIs evolve and suddenly the same prompt does not behave as expected...It can feel like we are debugging ghosts."

Enterprise giants Atlassian, Intuit, and AWS are planning for a world where agents call the APIs

Join the event trusted by enterprise leaders for nearly two decades. VB Transform brings together the people building real enterprise AI strategy. Learn more This dawning age of agentic AI requires a total rethink on how we build software. Current enterprise APIs were built for human use; the APIs of the future will be multi-model, native interfaces. "We need to build the kind of APIs that will work well with agents, because agents are the ones that are now going to interact with APIs, not humans," Merrin Kurien, principal engineer and AI platform architect at Intuit, said during the Women in AI breakfast at this year's VB Transform. Kurien had a dynamic discussion on the present and future of AI agents with fellow AI practitioners Mai-Lan Tomsen Bukovec, engineering and product leader for storage and compute services at AWS, and Tiffany To, SVP of product for platform and enterprise at Atlassian. "I would like to think five years from now, agents will be mainstream," said Kurien. "A lot of the challenges we face today probably will be overcome with better tooling, if the last two-and-a-half years is any indication. How prepared will you be? It's dependent on your investments today." How Intuit is getting invoices paid and AWS is supporting faster migration Intuit has been using agents and seeing "amazing progress," Kurien reported in the onstage panel, which was moderated by Betsy Peretti, partner for innovation and design at Bain. Notably, the financial technology platform company has incorporated automated invoice generation and reminders into its QuickBooks offering, which is popular among small and medium businesses (SMBs). "We have seen businesses get paid on an average five days faster, and there's 10% more likelihood that invoices get paid in full," said Kurien. AWS has also seen success with AWS Transform, an agile infrastructure that migrates .NET, mainframe, and VMware workloads into AWS, said Tomsen Bukovec. The traditional migration scenario, as she described it: A customer would go to the application owner and request to, for instance, move their Windows application to a Linux-based application running on AWS. "And guess what they would say? 'Take a number. You are priority number 42.'" But now, enterprises can do the majority of those migrations with AI assistance. "Your generalist teams are able to do way more work on their own, and it reduces the ask to the specialist," said Tomsen Bukovec. "That is changing migration as an industry." Ultimately, how AWS and others evolve will be closely tied with how customers are using AI, she said. She marveled that incredible advancements in AI are "really making us take a new look, a hot take" on how to build applications. "When we build agentic infrastructures and we incorporate AI into the mission of our businesses, we're not just taking technology and putting it to work," said Tomsen Bukovec. "We are actually changing the nature of the workplace, the workforce." She added, "We're seeing this happen right now. We're seeing this happen at warp speed." How Atlassian is learning from experimentation internally and with customers Atlassian is taking a thoughtful inside-out approach to AI agents, said To. For instance, the project management platform has launched an onboarding agent to help new employees access to all the materials they need to get started with their jobs. In the first month of launch, the agent fielded 7,000 requests. Now, it's just a regular part of the onboarding process, To said. Meanwhile, the company's go-to-market team has numerous interface points with customers, which can make it challenging to gather all the necessary context. Atlassian built a customer agent that pulls all that data together, and To reported that it is one of its most popular agents, used by 80 teams across the company. "I use it quite a bit before I talk to customers," she acknowledged. At Atlassian, there is a strong responsibility to 'dog food' -- using one's own products and services -- and iteratively experiment to help guide customers as they evolve with AI, To explained. That work can then be translated into what Atlassian ships to customers out of the box. "It's not only going to come from engineering; it's going to come from across your entire organization," she said. "So what can you do programmatically to bring the creativity of everyone cross-functionally, to bring ideas together, to design workflows?" The company recently introduced its 'Teamwork Collection,' a curated selection of apps -- Jira, Confluence and Loom -- managed by 'rovo agents.' This is built into its platform and supports various aspects of the collaborative process. For instance, before a meeting, the agent will pull together a "really nice summary" based on Confluence pages and JIRA tickets. "So when you go into that meeting, you now have all that shared context," said To. "You're not trying to update each other, you can actually spend time on important strategy decisions." Atlassian estimates that that particular use case saves at least four hours per person per week. Customer HarperCollins, in particular, has used it to "great effect," To noted. Customers are using AI agents in varying complexities, she said: Sometimes they're just offloading work, gathering data or writing release notes; other times they're getting deep into raw data and pre-building strategic roadmaps. To explained that Atlassian has built a graph layer on top of its data that provides deeper intelligence on how data is connected. For instance, enterprises can analyze their goals alongside team structuring and projects in progress. "It's not just an HR org chart," said To. "When you think about how people build their software development lifecycles right now, a huge part of that is creating roadmaps and prioritizing strategies," she said. "But that can be very dynamic, and taking into account all of that data is hard for humans to do. The agents we're seeing become really popular now with customers are actually pre-building those strategic roadmaps." To emphasized the importance of creating feedback loops with customers, noting that, in just the last three months, Atlassian users have customized 10,000 different versions of the company's out-of-the-box agents. "It's a really great pool of feedback data that then helps us understand how they're embedding these agents into their workflows," said To. "I think part of what is really exciting about this wave is it's such a collaborative process in designing with customers." Earning trust, building it right from the get-go Trust is the cornerstone of any product and that should be no different with AI, Kurien emphasized. Customers want to know what the agent is doing behind the scenes and have control over its actions. This requires stringent review processes. "With new waves came new vulnerabilities," she said. "We have built a robust process where we are identifying the lifecycle in which an agent fits in and creating the right processes of reviews for that phase." To underscored the fact that it's more than raw technology; people must collaborate, build complete solutions together and tap into experience. The industry must invest in strong data architecture and have the right data context so that AI agents can make the powerful decisions we'll be asking of them. "Where it becomes really exciting is when it is a superpower in your organization, when it's able to help you make better decisions, release better products, re-sort your goals, be more competitive as a company," said To. She noted that there have been many waves of innovation over the years, but this one with AI is one all its own. "I feel like with AI, it's a tidal wave. It's moment after moment after moment, right? AI is just completely different from all of the other waves." Editor's note: As a thank-you to our readers, we've opened up early bird registration for VB Transform 2026 -- just $200. This is where AI ambition meets operational reality, and you're going to want to be in the room. Reserve your spot now.

Confidence in agentic AI: Why eval infrastructure must come first

Thys Waanders, Shailesh Nalawadi, Shawn Malhotra, Joanne Chen, As AI agents enter real-world deployment, organizations are under pressure to define where they belong, how to build them effectively, and how to operationalize them at scale. At VentureBeat's Transform 2025, tech leaders gathered to talk about how they're transforming their business with agents: Joanne Chen, general partner at Foundation Capital; Shailesh Nalawadi, VP of project management with Sendbird; Thys Waanders, SVP of AI transformation at Cognigy; and Shawn Malhotra, CTO, Rocket Companies. A few top agentic AI use cases "The initial attraction of any of these deployments for AI agents tends to be around saving human capital -- the math is pretty straightforward," Nalawadi said. "However, that undersells the transformational capability you get with AI agents." At Rocket, AI agents have proven to be powerful tools in increasing website conversion. "We've found that with our agent-based experience, the conversational experience on the website, clients are three times more likely to convert when they come through that channel," Malhotra said. But that's just scratching the surface. For instance, a Rocket engineer built an agent in just two days to automate a highly specialized task: calculating transfer taxes during mortgage underwriting. "That two days of effort saved us a million dollars a year in expense," Malhotra said. "In 2024, we saved more than a million team member hours, mostly off the back of our AI solutions. That's not just saving expense. It's also allowing our team members to focus their time on people making what is often the largest financial transaction of their life." Agents are essentially supercharging individual team members. That million hours saved isn't the entirety of someone's job replicated many times. It's fractions of the job that are things employees don't enjoy doing, or weren't adding value to the client. And that million hours saved gives Rocket the capacity to handle more business. "Some of our team members were able to handle 50% more clients last year than they were the year before," Malhotra added. "It means we can have higher throughput, drive more business, and again, we see higher conversion rates because they're spending the time understanding the client's needs versus doing a lot of more rote work that the AI can do now." Tackling agent complexity "Part of the journey for our engineering teams is moving from the mindset of software engineering - write once and test it and it runs and gives the same answer 1,000 times - to the more probabilistic approach, where you ask the same thing of an LLM and it gives different answers through some probability," Nalawadi said. "A lot of it has been bringing people along. Not just software engineers, but product managers and UX designers." What's helped is that LLMs have come a long way, Waanders said. If they built something 18 months or two years ago, they really had to pick the right model, or the agent would not perform as expected. Now, he says, we're now at a stage where most of the mainstream models behave very well. They're more predictable. But today the challenge is combining models, ensuring responsiveness, orchestrating the right models in the right sequence and weaving in the right data. "We have customers that push tens of millions of conversations per year," Waanders said. "If you automate, say, 30 million conversations in a year, how does that scale in the LLM world? That's all stuff that we had to discover, simple stuff, from even getting the model availability with the cloud providers. Having enough quota with a ChatGPT model, for example. Those are all learnings that we had to go through, and our customers as well. It's a brand-new world." A layer above orchestrating the LLM is orchestrating a network of agents, Malhotra said. A conversational experience has a network of agents under the hood, and the orchestrator is deciding which agent to farm the request out to from those available. "If you play that forward and think about having hundreds or thousands of agents who are capable of different things, you get some really interesting technical problems," he said. "It's becoming a bigger problem, because latency and time matter. That agent routing is going to be a very interesting problem to solve over the coming years." Tapping into vendor relationships Up to this point, the first step for most companies launching agentic AI has been building in-house, because specialized tools didn't yet exist. But you can't differentiate and create value by building generic LLM infrastructure or AI infrastructure, and you need specialized expertise to go beyond the initial build, and debug, iterate, and improve on what's been built, as well as maintain the infrastructure. "Often we find the most successful conversations we have with prospective customers tend to be someone who's already built something in-house," Nalawadi said. "They quickly realize that getting to a 1.0 is okay, but as the world evolves and as the infrastructure evolves and as they need to swap out technology for something new, they don't have the ability to orchestrate all these things." Preparing for agentic AI complexity Theoretically, agentic AI will only grow in complexity -- the number of agents in an organization will rise, and they'll start learning from each other, and the number of use cases will explode. How can organizations prepare for the challenge? "It means that the checks and balances in your system will get stressed more," Malhotra said. "For something that has a regulatory process, you have a human in the loop to make sure that someone is signing off on this. For critical internal processes or data access, do you have observability? Do you have the right alerting and monitoring so that if something goes wrong, you know it's going wrong? It's doubling down on your detection, understanding where you need a human in the loop, and then trusting that those processes are going to catch if something does go wrong. But because of the power it unlocks, you have to do it." So how can you have confidence that an AI agent will behave reliably as it evolves? "That part is really difficult if you haven't thought about it at the beginning," Nalawadi said. "The short answer is, before you even start building it, you should have an eval infrastructure in place. Make sure you have a rigorous environment in which you know what good looks like, from an AI agent, and that you have this test set. Keep referring back to it as you make improvements. A very simplistic way of thinking about eval is that it's the unit tests for your agentic system." The problem is, it's non-deterministic, Waanders added. Unit testing is critical, but the biggest challenge is you don't know what you don't know -- what incorrect behaviors an agent could possibly display, how it might react in any given situation. "You can only find that out by simulating conversations at scale, by pushing it under thousands of different scenarios, and then analyzing how it holds up and how it reacts," Waanders said.

From hype to hard truths - UiPath's Ed Challis on what's holding agentic AI back

More than 90% of enterprises say they've deployed generative AI. But over 80% report it's made no measurable difference to revenue or margin. That contradiction isn't just a growing pain - it's a pretty glaring sign about enterprise AI adoption today. For all the enthusiasm around large language models (LLMs) and agentic automation, most organizations remain trapped in what Dr Ed Challis, Head of AI Strategy at UiPath, calls the "unrealized zone" - a gap between impressive technical capabilities and actual business impact where promising pilots fail to scale into measurable returns. Speaking at UiPath on Tour London, Challis laid out both the remarkable progress in AI capabilities and the persistent barriers preventing enterprise value creation. Challis began by establishing the unprecedented scale of AI progress. "We've achieved something really remarkable, which is a 400% annual growth rate in the size and the complexity of our AI model," he explains, noting this represents a dramatic acceleration beyond the 40% annual growth that followed Moore's Law for the first 40 years of computing. The numbers are staggering, with Challis noting that the largest models now have 2 trillion parameters, with training requirements so intensive that using the best supercomputer from the year 2000 to train today's models would take around 36 billion years. A critical breakthrough has been synthetic training data generation, as Challis explains: AI models that can create their own training data work by giving models reasoning challenges, like math challenges, coding challenges, and they can take many attempts to solve these problems, hundreds of attempts, and then we can keep only those solutions where it gets it right. The performance gains are measurable. Current models now outperform humans on GPQA (graduate-level Google Proof Q&A benchmark) challenges, leading to what professors describe as intelligence that "feels like a very good graduate student." Goldman Sachs economists estimate this technology could add seven percent global GDP over a decade. Yet the deployment reality tells a different story. To understand the deployment gap, Challis distinguished between traditional rules-based software and agentic systems. He explains: We have had for decades now, traditional rules-based software... defined by a sequence of conditional logical statements. And now we have this new type of software which is goal-based. An agent, he defined, requires three components. First, "the large language model at the heart of an agent can do the reasoning and deduction." Second, "tools - because without tools, the agent is nothing more than a kind of next word chat interface." Tools allow the LLM to interact with other systems, access data and retrieve new information. Finally, a goal or an objective that the LLM is seeking to solve. Despite these capabilities, UiPath's survey of over 250 executives revealed consistent implementation challenges. Security concerns topped the list, but extend beyond basic data hosting questions. Challis noted: Security concerns covered a much broader range of issues around access control, monitoring, logging, really understanding those reasoning chains about how certain decisions are made. Development complexity ranked second. Challis continued: If this technology can only be developed by extremely expensive, extremely rare AI engineers that are aware of all of the different state of the art packages. This just isn't going to be scalable. Integration challenges proved equally persistent. These findings align with third-party research highlighting the need for autonomy, integration, orchestration, scalability, and reliability in enterprise deployments. Following his presentation, I sat down with Challis to explore these challenges in greater detail. He expanded on why current implementations fail to deliver measurable value, even when individual productivity improvements are evident. "I don't think what businesses have done is bad," he emphasized, noting his personal ChatGPT usage: It's just that it's very, very hard to quantify the value that ChatGPT delivers. Even rigorous studies showing 20% faster development speeds don't necessarily translate to business outcomes: "Does that actually mean that like features get out the door 20% faster?" The challenge extends to daily productivity, as it may help to write a better email but that doesn't necessarily equal more productivity. The solution requires transformation at scale, Challis notes: For real values, we realized we need a whole workflow, a whole process, the whole business critical thing to be deeply transformed. And that requires integration, the people integration with all of the systems, or the right contextual data, and maybe, in many cases, a redesign of how we do that work. UiPath has announced forward deployed engineers as one response to the implementation gap, which Challis explains are: people that are deeply technically capable but highly embedded with the client and their problems that will ultimately help us deliver higher value solutions to the customer, but also provide much higher quality feedback to our product engineering teams. Challis explained how agents could improve through operational experience, using an employee analogy that holds true from the last time we spoke: You build an agent for a specific task and put it into production. It gets 60% of them right and 40% of them wrong, but for those 60% it got right, you confirm it by giving it a positive signal, so those can go back into its memory. When asked whether agents will eventually compose workflows rather than just perform them, Challis sees a hybrid evolution: I think businesses want certainty. Businesses want design authority, particularly given that human experts... have known about the claims adjudication process they've worked on for 50 years, and an AI model just doesn't have that contextual knowledge. However, AI's role in continuous improvement appears more promising. Challis sees orchestration and process mining coming closer together, with AI generating hypotheses about process improvements rather than requiring humans to make the imaginative deductive leaps about what could be changed. UiPath is exploring self-healing capabilities, which he explains as "an agent that looks at the error logs, thinks about what could be done to fix it, and either comes back with a proposal or actually fixes it, depending on how significant that change is." The integration challenge that Challis highlighted in his presentation remains central. UiPath's approach involves integrating new buyers with a strategy that he says is "ultimately agnostic. So if you have an agent from Salesforce or an agent from some other company, and you want to orchestrate that. We want to be the best platform for helping to achieve that." While UiPath has invested in training programs (I had an excellent conversation at the UiPath Community booth on the expo floor) access alone proves insufficient. As Challis emphasized: It's one thing to say the tools there, the Learning Academy is there, but it's another thing to say we value you by actually prioritizing that workforce to learn. That's a cultural challenge. Looking forward, Challis believes organizations underestimate both the technology and the speed of change. He predicts: This technology will cause huge disruption to incumbents, and I think you'll see very large significant businesses diminish if they don't adopt the technology, just like we saw with the Internet." The conversation between presentation insights and interview details reveals both the remarkable potential and persistent challenges of agentic AI. While model capabilities have reached new levels of performance and synthetic training approaches promise continuous improvement, the gap between technical sophistication and business impact remains substantial. The limiting factors center on integration complexity, cultural adaptation, and the challenge of moving from shallow, isolated deployments to deep, process-transforming implementations. Success requires addressing security frameworks, development scalability, legacy system integration, and organizational change management. The intelligence exists. The question now is whether enterprises can orchestrate the surrounding elements needed to unlock its transformative potential. The willingness of organizations to embrace change, and the authenticity of vendors to actively work with their customers through those challenges will determine whether the 90%/80% contradiction resolves into measurable business transformation or remains a cautionary tale about the gap between technological capability and operational reality.

How agentic AI adoption is reshaping enterprise workflows - SiliconANGLE

Agentic AI accelerates innovation, but trust and governance remain critical Agentic AI adoption is transforming workflows across industries, helping companies accelerate development and decision-making. But as agentic AI adoption grows, trust is becoming essential to realizing lasting business value. One sector feeling this shift firsthand is financial services. There, generative AI aids in summarizing complex documents, while agentic AI enhances data retrieval and improves quality across diverse sources, according to Jayeeta Putatunda (pictured, middle), lead data scientist and director for AI center of excellence at Fitch Group Inc. "We all know retrieval was tough. Connecting all the knowledge bases, web searches, different variations of data points that we might have," Putatunda said. "We are just ... making sure that there's no garbage in, garbage out problem that we are still seeing with generative AI." Leaders in software and tech marketplaces are also seeing agentic AI reshape the landscape. For them, now is the time for brutally honest assessments of what's really happening on the ground as companies invest in and build out AI agents and agentic AI initiatives. Putatunda; Paul Sciaudone (left), vice president of print cloud platform engineering and quality at HP Development Company LP; and Tim Sanders (right), vice president for research insights at G2.com Inc., spoke with theCUBE Research's Scott Hebner for the AI Agent Builder Summit, during an exclusive broadcast on theCUBE, SiliconANGLE Media's livestreaming studio. They discussed how agentic AI adoption is streamlining workflows while raising new challenges around trust and governance. (* Disclosure below.) Agents are also taking software development to the next level. They're changing how applications and services are built, reducing the need for traditional coding and development, according to Sciaudone. "You've now got an ecosystem of agents that essentially become the LEGO blocks to build new capabilities and new services. You think about prototyping, getting software out there quickly, you don't even need to be a software developer to do it with agents," he said. "You can get product people building things quickly, rapid prototyping, even our customers can work with us to prototype together." Agents solve the delivery problem that generative AI doesn't, according to Sanders. Unlike generative AI, which offers suggestions, agents take action continuously. "You really couldn't do that with generative AI. It was more Waze; agents is more Waymo," Sanders said. Moving forward, a key development will be greater standardization and interoperability between agents and platforms. Google LLC's recent announcement of its Agent2Agent Protocol is a big step forward in making it easier for companies to integrate agentic capabilities at scale, according to Sciaudone. "I think what's been happening in the past 12 to 18 months, is everyone's been rushing to get out there first, and build out these new agent capabilities," he said. "Now, with that proliferation, [there] needs to be some sort of standardization to build that trust and security, and make sure it is reliable and standardized, so enterprises can actually rely on building agentic capabilities that isn't going to change three months from now." In industries such as financial services, which requires strict guardrails and governance requirements, there's still much to be considered. Agentic AI must be layered with human-in-the-loop review and grounded in trusted processes to ensure responsible implementation, according to Putatunda. "I think it's a great exciting world, but we have to kind of balance it just to make sure we sell to our clients, as well as leadership, a trustworthy solution," she said. Here's the complete video interview, part of SiliconANGLE's and theCUBE's coverage of the AI Agent Builder Summit:

Enterprises now have strategic AI budgets, but the agentic revolution's timeline will take longer according to NASSCOM

Some 88% of enterprises now have dedicated AI budgets and emerging AI expert teams focused on building scalable AI applications, with AI spend as a percent of tech budgets inches up with two-thirds of companies spending over 15% dedicatedly on AI. Overall awareness and usage of gen AI is strong, with over 60% respondents focusing on gen AI tools, infrastructure readiness and platform-based solutions. But direct usage of gen AI models is still relatively low, with just over half (51%) of companies actively working on or with foundation models or Large Language Models (LLMs).Those are some of the topline findings from Enterprise Experiments with AI Agents - 2025 global trends, a wide-ranging new report from NASSCOM, the trade association of the Indian IT sector. It's based on a global survey of 100 large/medium-sized enterprises with 69% of respondents from Western Europe. 18% India, 14% Australia and 12% North America. Job functions of respondents go from CIO (46%) and CEO (29%) to Business Unit Head (14%) and Chief AI Officer/Head of AI (nine percent). It's an interesting mix, particularly geographically as the findings are not as US-dominated as many other global studies are. We've noted Indian tech firms ambitions when it comes to carving out a leading role in the AI space, pondering: While the likes of Accenture have been running a tally on their gen AI revenues to date, neither Infosys or Wipro elected to open that particular kimono to scrutiny. Will India be able to undercut and outperform its US rivals in this field? The main conclusion from the study is that AI spend is now strategic as opposed to tactical. AI budgets as part of tech budgets are climbing, from less than 10% prior to 2023 to 16-20% on average in 2025. Spend is currently being allocated to gen AI productivity tools (73%), AI-ready infrastructure (66%), and fine-tuned commercial LLMs (51%) But what about agents? Surely they have to be climbing up the spending priority list? According to the study, there is strong organizational intent in investing in agentic tech, with the current focus on task-based and process agents for internal usage rather than on external client offerings. Some 88% predict that they will allocate dedicated spending to agentic AI solutions in 2025, up to 20% of AI spend in the case of 55%, or 10% in another third (33%). Only 13% of respondents don't buy into the allure of agents and reckon they will have no impact on business. Just over half (52%) believe they will make a difference within 12-18 months, while a further 10% postulate a 2 years timeline. Meanwhile an optimistic 25% expect to see a difference within six months. The study notes that views are becoming polarized around what happens next: The long-term (five-10 years) future of enterprises mulling agentic AI adoption is to either not act and get fully disrupted by competition in the near-mid term or it is to move rapidly beyond 'agent washing' to build truly collaborative human + AI agentic systems With that in mind, 62% of the global enterprises are experimenting with AI agents, ranging from proofs-of-concept (PoC) through to some degree of production at scale. Hi-tech companies and those with strong AI budgets are moving fast: Over 25% of enterprises in either subset have reported PoC-to-production deployments, while over 35% have indicate successful controlled production and potential for scaling agents. So-called 'client zero' deployments - AKA 'eating your own dog food first' - are the main focus for 76% of the companies. Of these, 81% center such activities on their IT operations teams piloting agents, followed by IT helpdesk functions (49%), with only 31% saying they use AI agents for customer service and support, despite this being an application area most commonly cited as perfect for agentic tech. In terms of business sectors, the most active users are in manufacturing - industrial manufacturing 71%, hi-tech manufacturing 69% - followed by travel/hospitality (69%), insurance (63%), banking (62%) and energy/utilities (62%). Public sector comes in lowest on 43%, despite this screaming out as an area badly in need of the kind of operational and cost benefits agentic tech promises. In terms of what organizations are looking for in terms of benefits from agents in 2025, rapid information-to-intelligence for accelerated decision-making comes top (57%). Followed by better ability to respond to new business opps( 55%), and reduction in failure rates from human-led process optimizatons (47%). But overall, it's early days all round for agentic adoption. In terms of timelines, 53% of organizations are working on the assumption of adoption schedules of year with the remaining 47% looking further ahead to 18 months+ delivery. The more optimistic forecasts are coming from larger organizations with revenue scale, tech readiness, and greater AI budgets and teams. That said of the many companies in 'wait-n-watch' mode, large organizations with revenues above $5 billion are taking a more cautious approach, with 25% of them planning PoCs in next 6-12 months. All of which brings us to the inevitable question of barriers to adoption - and these should by now be familiar to anyone who's picked up whatever AI survey has been published on a day ending in a 'y'. These are data security risks (60%), fear of self-learning AI (56%), lack of regulation (51%) and cost of foundational infrastructure (48%). If you're having a fit of the vapors about the hype around the Digital Labor workforce as Phil Wainewright is, then some encouragement may be taken from the fact that most enterprises today are focused on optimizing existing processes, but with a heavy dose of Human Intelligence to the fore, such as process automation with regular human interaction (65%) and tasks at the interface of physical or industrial AI and humans (60%). Only 27% are working on empathetic, intent-based tasks that mimic human behavior. Of course, this week Salesforce CEO Marc Benioff, one of the more articulate Digital Labor proponents, has grabbed attention with a claim that AI is doing "30% to 50% of the work at Salesforce now", with engineering and customer support being two prime areas where this is being seen. But Salesforce is still a pioneer here it seems. Contrary to the hype, only 39% of respondents to the NASSCOM survey believe that using agentic AI systems will free up workforce time for high-order work. The findings imply that AI agents will need sustained human involvement and oversight for a long time to come, so the robots aren't coming for your job just yet. Significantly, 44% of respondents cite mindset resistance to humans and AI existing in a digital workforce. So what is the path to a Digital Labor future for most? NASSCOM suggests a multi-year timeline: Well worth a read - a grounded mix of acknowledged potential for agentic tech, leavened with pragmatic realitism about the situation today. Having a majority of respondents coming from outside of the US undoubtedly adds a welcome perspective to the agentic debate.