6 reasons why autonomous enterprises are still more a vision than reality
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ZDNET's key takeaways
- True AI autonomy is still only seen in a minority of companies.
- Tech professionals need to learn new ways of delivering value.
- Agent orchestration is needed, and only 3% have achieved this status.
The buzz about artificial intelligence taking over everything has reached a fever pitch. The latest panic-inducing essay was just published by AI entrepreneur Matt Shumer, who suggested AI will start sweeping away all human work within a matter of months.
Such talk brings about a question: could enterprises really operate without employees? Not likely anytime soon, but we will see more "autonomous" enterprises in which people leverage AI to speed up tasks and innovation, according to a report from tech services specialist Genpact.
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True AI autonomy only exists in a minority of companies and may remain there for the foreseeable future. Genpact's survey of 500 senior executives found that about one in four firms expect self-managing business processes that run with minimal human oversight could become a reality within three years.
At least 12% of companies are advanced with this effort. In addition, only 35% of executives indicated that select AI applications are very effective at delivering measurable business value. "Translating AI investments into confirmed financial outcomes remains a significant challenge, underscoring the magnitude of progress still needed to realize tangible impact," according to report author Sanjeev Vohra.
The path to greater AI autonomy is three-pronged, Vohra, chief technology and innovation officer at GenpactAI agents, empowering AI practitioners, and reimagining their enterprise architectures.
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"Autonomous enterprise" could mean many things, and the term has been used for decades. Apple, for example, opened an autonomous factory in 1984 to produce its Macintosh computers, which closed two years later due to production and machinery inefficiencies. However, AI may make the difference this time.
"AI is the first technology that allows systems that can reason and learn to be integrated into real business processes," Vohra said. "Agentic AI introduces intent and goal-directed behavior, so systems can reason across data sources, learn from outcomes, and adapt their actions without waiting for new rules."
At the same time, it does not mean an enterprise will run entirely without human oversight, he emphasized. Rather, the shift to autonomy is more of a human-machine cooperative. "Autonomy does not mean the absence of humans, but rather it enables humans to move faster," Vohra said.
Autonomous organizations, he continued, "are built on human-AI agent collaboration, where AI handles speed and scale, leaving judgment and strategy up to humans." They are defined by "AI systems that go beyond just generating insights in silos, which is how most enterprises are currently leveraging AI," he added. Now, the momentum is toward "executing decisions across workflows with humans setting intent and guardrails."
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Vohra likens the scenario to "a symphony of agents, where individual agents perform specialized tasks, an orchestration layer acts as the conductor, and humans write the sheet music."
Such a model "does not remove humans; it elevates them," he said. "Task workers become task managers, enabling immense productivity gains."
The survey highlighted that work is required to help develop agents. Only 3% of organizations -- and 10% of leaders -- are actively implementing agentic orchestration.
"This limited adoption signals that orchestration is still an emerging discipline," the report stated. "The scarcity of orchestration is a litmus test for both internal capability and external strategic positioning. Successful orchestration requires integrating AI into workflows, systems, and decision loops with precision and accountability."
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In other words, not exactly an overnight project. In Genpact's report, Vohra identified several factors that may keep tasks in human hands for a while:
- Executives remain cautious about handing high-stakes, judgment-driven decisions, such as problem-framing and final decision-making, to AI: "Strategic decision-making continues to be people-led, reflecting a deep-rooted trust in human intuition and accountability."
- Architectures are complex: When it comes to scaling AI, 61% of technology professionals and enterprise architects say that the complexity of their technology architecture is a major or moderate challenge. In addition, only 25% of of the most advanced organizations have fully adopted a real-time data infrastructure. The Genpact research found that the most frequently cited challenge is difficulty integrating AI into existing workflows, followed closely by broader technology limitations. "The constraint is not just aging systems, but how work is structured around them," said Vohra. Issues that arise include "fragmented ownership, handoffs, and operating models that were never designed for AI. That challenge is compounded by organizational inertia, or workforce resistance to change."
- Scaling autonomous AI is a challenge: "People often underestimate the time and organizational effort required to translate individual productivity gains, such as using ChatGPT to craft emails, into enterprise-wide performance improvement," he said. "Scaling those gains across end-to-end processes, operating models, and systems has proven more complex."
- Governance is way behind the curve: Almost all executives (99%) said they don't "have adequate governance models and structures in place for autonomous or agentic AI systems and associated risks." In addition, 40% identify fragmented ownership and accountability as key challenges. "While leaders have done more to overcome these barriers, they've not eliminated them yet," the survey noted.
- AI skills are also behind the curve: Workforce capability gaps continue to be the most frequently cited organizational constraint to AI adoption, as reported by six in 10 executives -- yet only 45% say their organizations offer AI training for all employees.
- Technology professionals need to relearn their craft: These employees need to "redirect how they apply their expertise and unlearn how work has traditionally been done," said Vohra. "As AI takes on more execution and pattern recognition, human value increasingly shifts toward system design, integration, governance, and judgment -- areas where trust, context, and accountability still sit firmly with people."
Using software engineering as an example, the value of autonomous AI is measured by "how efficiently individuals could write, test, and maintain code," said Vohra.
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"Today, AI can generate, refactor, and optimize code much faster than a human. As a result, software engineers are evolving into system architects and orchestrators, designing how AI-enabled components interact, setting guardrails, validating outcomes, and ensuring systems are secure and scalable."
Such a shift requires engineers to "unlearn purely code-centric workflows and adapt to a hybrid human-AI, system-oriented way of working. The same pattern will play out across other technology roles. In the autonomous enterprise, career opportunities expand for those willing to work confidently at the intersection of humans, AI, and enterprise-scale systems."
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