# How organizations view AI-native transformation through better workflows, decisions, and organizational intelligence > Source: > Published: 2026-07-09 17:10:28+00:00 Organizations seem eager to bring AI into the workplace. Many begin the journey by focusing on the tool or the model. [VarOps](https://varops.com/), a systems-level AI advisory specializing in capability building in organizations, believes that’s the wrong place to start. According to VarOps, businesses should start by examining how work actually moves through the organisation, as this allows AI to deliver greater value when it supports a better [operating model](https://thenextweb.com/news/pit-16m-launch-andreessen-horowitz-ai-enterprise-operations). That philosophy provides the foundation for the company’s work and offers a different lens through which leaders can evaluate AI transformation. This perspective appears to be more relevant as [AI adoption](https://thenextweb.com/news/how-ais-capital-explosion-signals-opportunity-but-also-reveals-a-critical-need-for-measurable-roi-and-meaningful-impact) continues to expand across industries. VarOps founder [Ran Aroussi](https://aroussi.com/about) says, “Conversations usually begin with questions such as, ‘Which AI platform should we buy?’ or ‘Which chatbot should we implement?’ Those questions have their place, but a more valuable starting point may be understanding how decisions are made, where knowledge resides, and how information moves throughout an organization.” He adds that technology can then support those realities instead of defining them. [Recent research](https://www.anthropic.com/news/the-anthropic-economic-index) by Anthropic highlights why this distinction matters. Although AI use is spreading across the economy, with over one-third of occupations already incorporating AI into at least a quarter of their tasks, true scaling remains limited. Only about 4% of occupations use AI across three-quarters of their associated work. The report also found that AI is still used more often to augment human work (57%) than to fully automate it (43%). To me, that augmentation-heavy pattern is the telling part: it suggests organizational readiness and operational design matter just as much as the technology itself. “Those findings suggest that implementation plans and organizational readiness deserve as much attention as technology selection itself,” Aroussi says. He believes this principle extends beyond AI. “Technology earns its place when it solves a meaningful operational problem. The software is simply the vehicle; the business outcome is the destination,” says Aroussi. That philosophy echoes a longstanding product design principle, wherein successful technology begins with a human or business need instead of searching for a use case after the technology already exists. According to Aroussi, solutions that integrate into everyday work frequently create broader adoption because people can incorporate them into existing responsibilities with less friction. VarOps applies this thinking by first studying how an organization operates before recommending automation, [AI agents](https://thenextweb.com/news/asana-acquires-stack-ai-agent-builder-saas), custom software, or improvements to existing workflows. This focus on understanding work naturally leads to organizational knowledge. “Every day, businesses generate decisions, discussions, operational lessons, and institutional expertise across meetings, collaboration platforms, and internal communications. Much of that information becomes increasingly difficult to retrieve over time, limiting its value for future decisions,” Aroussi explains. VarOps regards this accumulated knowledge as a company’s organizational memory and has developed Varys to help organizations build a continuously evolving “company brain.” Instead of functioning as another [meeting transcription](https://thenextweb.com/news/granola-series-c-meeting-ai-enterprise-context) tool, note-taking assistant, or conversational chatbot, Varys is designed to identify operational patterns, reveal recurring bottlenecks through observable data, and provide leaders with a richer understanding of how work progresses across the business. For VarOps, operational intelligence offers an opportunity to strengthen decision-making because it builds upon how organizations already function. That philosophy also influences how the company views automation itself. AI, in VarOps’ experience, serves as an amplifier. Well-designed processes can become more efficient, while inefficient workflows often continue to produce similar challenges at greater speed. VarOps emphasizes that human judgment remains an important part of the equation, particularly when decisions involve context, priorities, or strategic direction. Automation may reduce repetitive administrative effort, allowing people to spend more time on analysis, collaboration, and leadership. Across examples such as engineering agents, executive assistants, IT automation, and operational workflows, VarOps views AI as a way to extend [human capability](https://thenextweb.com/news/luminadata-ai-transformation-finance-operations) instead of replacing organizational leadership. Aroussi says, “I believe AI scales whatever already exists inside an organization. It’s important not to automate everything on day one and treat every automation as an iterative process as the organisation gains confidence in the AI grounding and output.” That emphasis on capability also reflects broader adoption research. The same AI adoption report found that many organizations continue to experience differences in AI readiness, with skills, organizational preparedness, and clearly identified business needs influencing implementation decisions alongside technology itself. For VarOps, this leads to a broader objective than introducing another platform into the technology stack. The company works toward helping organizations develop the internal capability to understand, operate, and continuously improve their own AI-enabled systems. Organizational intelligence can remain with the business, allowing leaders to build upon accumulated knowledge as operations evolve. From that perspective, becoming AI-native represents an ongoing capability embedded within everyday decision-making. Overall, VarOps believes the organizations likely to realize the greatest long-term value may be those that begin by rethinking how work happens before deciding which technology belongs inside it. In that sequence, AI becomes part of a larger operational evolution that supports people, strengthens decisions, and helps knowledge remain an active resource across the organization. ## Get the TNW newsletter Get the most important tech news in your inbox each week.