From Copilots to Autopilots: The Path to Autonomous Enterprise AI 

Cars today are safer than ever, thanks to sophisticated technology designed to help drivers. Features like adaptive cruise control and lane-departure warnings are increasingly standard equipment, with many models getting advanced options like surround-view cameras and heads-up displays that show relevant driving and navigation information directly on the windshield.

Sanjay Dhawan is the CEO of SymphonyAI

Before becoming CEO of enterprise AI SaaS leader SymphonyAI, I previously led two automotive industry companies, Cerence and Harman International. That gave me an inside look at the auto industry’s path from traditional driving to Level 5 fully autonomous driving and what it will take to get there.

The evolution of self-driving cars is an informative model I often use at SymphonyAI to explain the potential of AI in enterprise decision-making. Just as driver-assist technologies are charting a path from hands-on to autonomous, future enterprise AI applications could make autonomous decisions that reduce the need for human intervention.

To fully grasp the technological advancements and ethical implications that lie ahead in this transition, it helps to map AI autonomy in business to the auto industry’s experience with autonomous driving. For enterprises, that history starts with the traditional use of business intelligence tools designed to answer the question: What happened?

Even with impressive software, the user was always making the decisions and analyzing the data — in effect, the person operating the car was an informed driver but without automated assistance.

The next step for businesses was the introduction of predictive AI — technology that allows business users to not only understand what happened but also why it happened and what might happen next. In our driving analogy, predictive AI is like cruise control, an automated assistant controlled by the driver that helps the car maintain a steady speed. This level of driver automation is formally known as Level 1, with the driver engaged and supervising any support features.

The next phase of enterprise AI is marked by the explosive introduction of generative AI. In business, the next level of capabilities happens when predictive and generative AI are used together. That translates to AI models conducting what-if analyses and recommending next steps, interacting with users with intuitive copilots. In our self-driving car analogy, this is Level 2 autonomy, the current limit of most of today’s commercially available cars. Most leading AI-enabled businesses are also at this stage, as evidenced by how often “copilot” is used in new product names.

That brings us to the present, but what about the near future? The next act of enterprise AI will mean AI autopilots capable of independently making decisions and taking actions within defined parameters. It’s akin to the driverless cars from Waymo, Cruise, and Zoox that can be found in select cities in the United States.

Just as self-driving car companies have enough confidence in their AI vision algorithms to detect the road, weather conditions, traffic, pedestrians, cars, and the environment around them, enterprises will soon have the same confidence in the outcomes and predictions that mature AI algorithms can produce. At that point, AI is so intelligent that it’s not only making recommendations but also can implement them, albeit within specified ranges or guardrails.

This next stage of enterprise AI promises to enhance employees' productivity, agility, and creativity, but it does raise some logical questions. What happens to the worker who previously took action based on insights and recommendations, or who oversaw the AI-enabled software tool? How does his/her role develop? Once again, let’s look at some parallels in the auto industry for guidance.

• Gradual adoption: The auto industry started with features like cruise control and automatic emergency braking, eventually leading up to autonomous vehicles. Similarly, enterprises have moved from rule-based systems to AI chatbots, copilots, and other workspace tools that build trust ahead of more ambitious AI efforts.
• Human-machine collaboration: Human drivers work together with automated systems, intervening when necessary. The collaborative model will persist in enterprise AI, even with autonomous agents, when strategic decisions and human judgment are needed. In other words, the human is still in the loop.
• Regulatory considerations: The National Highway Traffic Safety Administration is one of the primary agencies that regulate the automotive industry. Governments around the world are currently weighing AI regulations around privacy, bias, transparency, and accountability.  In 2023 alone, 18 states and Puerto Rico adopted AI resolutions or enacted legislation, in addition to the Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence. In 2024, the EU AI Act became the world’s first comprehensive AI law.
• Continuous improvement: Waymo’s self-driving cars have racked up more than 20 million miles of real-world driving experience. Enterprise AI systems will also need to use data from real-world usage to adapt to changing business conditions, customer preferences, and market dynamics.

Just as self-driving cars are redefining our relationship with the road, autonomous enterprise AI is set to transform our interaction with and oversight of business decisions. By learning from the auto industry's journey—from cruise control to driverless vehicles—we can navigate the path from AI copilots to autopilots. The future is clear: AI won't just advise; it will decide and act, ushering in an era where human creativity and machine precision work harmoniously to drive business forward.

Sanjay Dhawan is the CEO of SymphonyAI, a leader in predictive and generative enterprise AI SaaS. He brings a reputation for rapidly growing technology companies and unlocking market value as an executive at Cerence, Harman, Symphony Teleca and Aricent.