(Source: GamePixel/stock.adobe.com;)
When the latest generation of agentic artificial intelligence (AI) desktop tools launched, it put much of the Software-as-a-Service (SaaS) and enterprise world into a tailspin. The tech markets took a hit too, with the software sector of the S&P 500 down roughly 20 percent year-to-date.[1] On the surface, it looked like this was the AI upgrade that would finally spell disaster for the job market. These new tools could interact with a wide range of other software and plugins, emulating the complex workflows that define white-collar work in sales, legal, accounting, and marketing. But the reality is more nuanced than the headlines suggest. The underlying model intelligence existed before these most recent releases, primarily used by those in more technical roles, so what actually changed is who had access to it. That accessibility shift has real implications for SaaS companies and workers alike, but both have more agency in the situation than the initial reaction implied.
The first models capable of reasoning started shipping in 2024 and 2025, which is the same timeframe that the term “agentic” emerged, along with workflows built around it. Those developments marked the shift from chatbots alone to systems that could answer more rigorously and slot into real workflows with meaningful utility. Software programmers, for example, started using coding assistants with the same level of agentic workflow automation and tool use long before these desktop apps showed up, and without anywhere near the same level of panic. These models could create and modify files, plan and execute multi-step updates, and interact with other coding tools and libraries to format and make requests to merge new code. In other words, these agentic AI models were smart enough to do meaningful work within the software developers’ day-to-day, but their arrival did not bring the same level of unrest.
So, what is markedly different this time round? Digging deeper, the inflection point appears to be accessibility. According to Microsoft, 75 percent of knowledge workers are already using AI tools, often without formal company deployment.[2] What's new, though, is that non-developers now have access to the same kind of agentic capability that was previously confined to developers and technical users. Low-code and no-code agent platforms mean that business users, not just engineers, can build and deploy agents. This is a genuine democratization moment, and it's the accessibility rather than the raw capability that made the market react the way it did.
The demo videos and functionality assessments of these tools do make it look like certain software categories are at risk, because now the AI appears to be able to do what some common software does. That concern was directed at SaaS companies in particular, with those providing tools in domains like legal, sales, and project management seeing their valuations fall. Investors were chiefly concerned that agentic AI could build the same convenience-type software in-house or reduce the need for paid seats, as individual employee productivity could be dramatically enhanced by AI. But the overlooked piece is that part of the power of these agentic tools comes from their plugins to the very apps the market dropped. Customer relationship management (CRM) platforms, project management tools, and analytics suites are all the connective tissue that agents plug into. The fact that they're considered staples across various domains—enough to warrant out-of-the-box support from many agentic AI providers—means it's unlikely they'll disappear.
The companies that successfully navigate this shift will be the ones that become essential nodes in the agentic stack, not the ones that target small friction and inconveniences that are easier to automate away now.
Workers worried about losing their jobs to AI would do well to think about this from a similar angle. According to PwC and Capgemini, while 79 percent of executives say AI agents are being adopted at their companies, only 27 percent of organizations trust fully autonomous AI agents, and 71 percent of users still prefer a human-in-the-loop setup for high-stakes decisions.[3] This shows that despite the advances, people still trust other people more than algorithms when the stakes are high, and that's an opportunity to establish real value.
The practical move then is to use these tools to automate the generic, easily replaceable parts of day-to-day work, and then double down on areas where judgment and context are critical and where useful outcomes are genuinely harder to automate. The productivity upside of doing this well is significant. A study from the Harvard Business School and Boston Consulting Group found that consultants using AI completed tasks 25 percent faster and produced 40 percent higher quality results compared to those working without such tools.[4] Those kinds of gains are available to anyone willing to learn the tools, and the advantage they bring makes the case for keeping a skilled human in the loop even stronger. Workers who position themselves as the people who know how to leverage AI effectively while still applying the expertise and critical thinking that these tools lack will be considerably harder to replace than those who ignore the shift entirely.
There is a meaningful shift in how AI tools are built and who they're built for, and that accessibility is what makes this moment different from previous waves of AI capability. The underlying intelligence is not new, but putting it in the hands of every knowledge worker with point-and-click simplicity is. The companies and workers who will come out ahead are those treating these tools as a new layer of infrastructure to build on, while leaning into the judgment, context, and trust that remain genuinely difficult to automate. The market reaction was understandable, but this is ultimately a story about a redistribution of capability. The next, and possibly most important, step is determining where humans matter most in that equation.
[1] https://finance.yahoo.com/news/why-software-stocks-are-getting-crushed-as-ai-casts-shadow-of-uncertainty-over-sector-160011783.html [2] https://www.microsoft.com/en-us/worklab/work-trend-index/ai-at-work-is-here-now-comes-the-hard-part [3] https://www.capgemini.com/insights/research-library/generative-ai-in-organizations-2025/; https://www.pwc.com/us/en/tech-effect/ai-analytics/ai-agent-survey.html [4] https://doi.org/10.2139/ssrn.4573321
Becks Simpson is a Machine Learning Lead at AlleyCorp Nord where developers, product designers and ML specialists work alongside clients to bring their AI product dreams to life. She has worked across the spectrum in deep learning and machine learning from investigating novel deep learning methods and applying research directly for solving real world problems to architecting pipelines and platforms to train and deploy AI models in the wild and advising startups on their AI and data strategies.
