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ZDNET’s key takeaways
- Industries are undergoing a profound transformation as products, factories, and companies adopt autonomous machine models.
- Autonomous companies use AI agents to sense, understand, decide, and act with minimal human intervention.
- They adopt software-driven hardware, extended connections, wide contextual awareness, and continuous learning.
Industries are undergoing a profound transformation as products, factories, and companies adopt the autonomous machine design model, treating each element as an integrated system that can sense, understand, decide, and act (SUDA business operating system) independently or in coordination with other platforms.
As exemplified by Elon Musk’s view of factories as products, this convergence is now evident across leading organizations and technologies.
Back in 2016, Elon Musk made one of his more famous gnomic utterances: “We realized that the true problem, the true difficulty, and where the greatest potential is — is building the machine that makes the machine. In other words, it’s building the factory. I’m really thinking of the factory like a product.”
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Musk was articulating a way of thinking about business and industrial systems that had already implicitly started to revolutionize the design of both, but that had remained relatively obscure and unspoken. However, it is now clear that we are witnessing a fundamental shift in how successful systems are designed. Products, factories, and companies are converging toward a common architectural model, one that treats them all as autonomous machines capable of sensing, understanding, deciding, and acting with minimal human intervention.
This convergence isn’t accidental. This shift reflects the reality that, in an increasingly complex, fast-changing world, the competitive advantage goes to systems that can learn, adapt, and optimize themselves continuously. Whether we’re talking about a Tesla navigating traffic, a smart factory adjusting production in real-time, or a company responding to market changes, the underlying design principles are remarkably similar.
Autonomous products
Products were the first to embrace this transformation. Compare a 1995 Honda Civic to a 2025 Tesla Model 3. The Civic was a mechanical system with fixed capabilities, offering the same performance throughout its life, no external connectivity, and maintenance based on rigid schedules.
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The Tesla is a cyber-physical system that improves over time through software updates, learns from millions of other vehicles, and can predict maintenance needs before problems occur. Autonomous machines operate in three integrated modes:
- Independent: Full autonomy in basic tasks (for example, a vehicle navigating on its own, or a robot cleaning a room).
- Choreographed: Coordination with similar units for optimal overall performance (for example, fleets of delivery robots working together).
- Orchestrated: Alignment with centrally managed systems to execute broader strategies, such as enterprise-wide planning, network-wide optimization, or participation in city or market-level initiatives.
Here are some brief case studies of the shift from products to factories:
- Tesla Model 3: Learns and adapts through software upgrades, network connectivity, and fleet data, anticipating needs and optimizing driving experience with minimal intervention.
- Roomba: Maps its environment, adapts cleaning logic based on past runs and room layouts, receives algorithm updates, and can even coordinate with other cleaning robots within a home.
- iPhone: Suggests actions, automates daily tasks, and continually learns from user and aggregated data, providing a personalized assistant experience with seamless connectivity and predictive support.
- Nest Thermostat: Optimizes indoor climate using real-time sensing, predictive models, and learning from occupants’ schedules and preferences, automatically coordinating with other home systems or utility provider programs.
Autonomous factories
Manufacturing operations quickly adopted similar principles. Modern smart factories use cyber-physical systems, with digital twins, predictive maintenance, and real-time optimization. Production lines that once required manual adjustment now reconfigure themselves based on demand signals and quality data.
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Even software development has evolved into factory-like operations through DevOps and agile methodologies, offering continuous delivery pipelines that deploy small changes frequently, with automated testing and rollback capabilities. Modern smart factories represent this model at a massive scale:
- Tesla Gigafactory: Fuses hardware and real-time software systems for quality control, demand signals, and supply chain integration, capable of rapid, data-driven adaptation.
- Amazon Fulfillment Centers: Robots and algorithms predict demand, optimize storage placement, adapt logistics, and coordinate across the global network for exceptional delivery speed and efficiency.
- Netflix: Content delivery and production are managed by algorithms that learn from viewing trends, audience engagement, and predictive planning, driving both individualized recommendations and strategic investments.
- Software Development Pipelines: Automated systems deploy features, test code, resolve issues, and integrate feedback with minimal human oversight, accelerating innovation and quality control.
Autonomous companies
Here’s where the gap becomes stark. While companies have successfully applied autonomous machine principles to their products and operations, they continue to organize themselves using industrial-era models:
- Products: Detect anomalies in milliseconds, self-correct through updates, and optimize continuously.
- Factories: Predict equipment failures, adjust production in real-time, and reconfigure automatically.
- Companies: Discover problems through quarterly reviews, take months to implement changes, and run the same processes for decades.
The companies that win the next decade will be those that close this gap and design themselves as autonomous machines, not just organizations that use autonomous machines. Some key principles are crucial to the development of autonomous machines:
- Software-Driven Hardware: The new generation of machines blends physical components with modifiable software, allowing continuous updates, improvements, and new features, whether in cars, household devices, or industrial robots.
- Connectivity: Autonomous systems thrive on networked intelligence, syncing data locally and globally to optimize performance, share learnings, and coordinate activities. This connectivity can be peer-to-peer (as in robotic swarms), cloud-driven (as in smart factories), or integrated within local smart networks (such as homes or offices).
- Contextual Awareness: Advanced systems use sensors and data feeds to understand current conditions and make predictions about future scenarios. Whether it’s a thermostat tracking weather forecasts or a factory anticipating supply chain bottlenecks, contextual awareness leads to smarter decisions and resource use.
- Adaptivity and Prediction: Autonomous machines automatically adjust their behavior in response to changing conditions. They don’t just react to problems; they anticipate needs and dynamically reconfigure themselves, such as a factory recalibrating production to inventory shifts or a digital platform predicting trends for personalized recommendations.
- Continuous Learning: At the core of these systems is the capacity for continual improvement. Data from millions of cycles, such as driving habits, cleaning routines, viewing patterns, or software deployments, feeds back into machine-learning algorithms, which enhance system performance and user experience.
Agentic enterprises
The journey for companies to become autonomous machines will begin with the adoption of AI agents as they become agentic enterprises. An agentic enterprise refers to an organization that uses autonomous AI agents to drive and execute business processes.
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These AI systems can act independently, adapt to changing situations, and make decisions without constant human supervision. The agentic enterprise represents a significant leap beyond earlier forms of AI, which were limited to predictive analytics (recommending a next action) or generative AI (creating content from a prompt).
The Agentic Enterprise Index by Salesforce suggests consumer-facing industries, such as retail, travel, hospitality, and financial services, are the top adopters of AI agents. Travel and hospitality saw AI and agent actions grow at a monthly average rate of 133% in the first half of 2025.
Retail saw AI and agent actions grow at a monthly average rate of 128% in the first half of 2025, while financial services saw AI and agent actions grow at a monthly average rate of 105% in the same period.
The rise of the machines
Despite success in products and factories, most large companies are still organized with outdated, hierarchical models, such as manual coordination, slow reviews, and quarterly planning cycles. The next breakthrough lies in re-architecting the company itself as an autonomous machine:
- Software-Defined Processes: Automating policy, decision-making, and workflow in code replaces manual forms and approvals with instant, systemic execution.
- Unified Connectivity: Real-time dashboards, live market signals, and seamless integration with suppliers and customers foster an agile enterprise that senses and responds in minutes, not months.
- Adaptive Organizational Design: Rapidly formed, skill-based teams, instant resource reallocation, and continuous workflow optimization become the norm.
- Predictive Management and Learning: Internal machine learning captures best practices, experiments in business processes, and enables organizational intelligence to compound across the entire enterprise.
As companies adopt more AI agents, portions of their businesses will become self-driving (autonomous). As AI agents gain momentum, service professionals anticipate rapid growth in the share of cases resolved by AI. By 2027, 50% of service cases are expected to be resolved by AI, up from 30% in 2025.
Gartner notes that agentic AI isn’t just another gen AI feature; it’s a fundamental shift in how software behaves. According to Gartner, by 2028, one-third of gen AI interactions will involve autonomous agents. These agents don’t just respond; they act, make decisions, and execute tasks independently.
In the next decade, the six levels of autonomous work will drive augmentation capabilities, followed by full replacement of tasks, roles, teams, and, ultimately, lines of business. A continuously improving set of AI resources over the next decade will have a two-fold impact on business and the human workforce.
Initially, AI will have a broadly augmentative effect, taking over low-value tasks and empowering humans to focus their efforts on more strategic and creative jobs. However, at some point, and likely over the next five years, AI will take over entire job roles, starting with the most procedural or rules-based jobs.
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Eventually, AI will acquire enough decision-making and orchestration capabilities to take over entire teams and even lines of business. The design for the ‘machine company’ is a journey of assistive and AI augmentation use cases and fully autonomous AI powering a digital labor force that can learn, reason, act, and continuously improve with minimal to no human intervention.
Competitive implications
Companies that make this leap will sense market shifts almost instantly, deploy resources and teams with new speed, personalize customer experience across all interactions, and learn and improve as entire systems rather than isolated units.
A 2025 survey of chief financial officers (CFOs) found that, on average, CFOs report dedicating 25% of their current total AI budget to AI agents. That said, the momentum for embracing digital labor is not uniform across business leaders.
Research suggests that only 34% of organizations have policies for using gen AI, and even fewer have effective training programs in place. There are many important lessons that businesses can learn as they design their companies to be autonomous machines. Salesforce has documented key lessons after more than a million conversations with AI agents, including the importance of machine empathy.
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The path to becoming an autonomous enterprise, using a hybrid workforce of humans and digital labor powered by AI agents, will require constant experimentation and learning. Go fast, but don’t hurry. A balanced approach, using your organization’s brains and hearts, will be key to success.
Once you start, you will never go back. Adopt a beginner’s mindset and build. Companies that are built like autonomous machines no longer have to decide between high performance and stability. Thanks to AI integration, business leaders are no longer forced to compromise. AI agents and physical AI can help business leaders design companies like a stealth aircraft. The technology is ready, and the design principles are proven in products and production.
The fittest companies are autonomous companies. The big question is who will be the first enterprise to transform its organization into a true autonomous machine and reap the enormous strategic advantage that follows?
This article was co-authored by Henry King, co-author of Boundless and a new book, Autonomous, Wiley October 2025.
