How Will Autonomous AI Systems Change the World?

Will autonomous AI disrupt businesses and lives? Or are the predictions of far-reaching disruption hype?

Until now, most businesses have used AI-powered systems to make existing processes better and faster. AI resembles a knowledgeable employee who does not speak until spoken to and someone who does his brief well but does not think out of the box.

Enter autonomous AI.

Autonomous AI systems make decisions and perform complex tasks on their own. These systems decide based on the underlying programming logic and available data, with little or no human intervention.

Autonomous systems are a win-win for all stakeholders. At the enterprise end, these systems improve the scope of intelligent automation and boost efficiency. It reduces operational overheads, especially labour costs. Employees get powerful assistants and get more free time for higher-value things. Customers get faster and more accurate service delivery. Improved customer satisfaction has a multiplier effect. Happy customers come back. They also become brand advocates and refer the business.

The Increasing Application of Drones and Robots

Logistics companies such as Amazon and UPS deploy autonomous drones to deliver packages to customers. These drones drop off packets in designated areas, from where customers can verify and pick up the parcels. The delivery speed improves, and the ecological impact of the delivery reduces.

Drones have also made their way into agricultural operations. Autonomous systems analyse soil health and climatic conditions to optimise planting schedules.

Warehouses and fulfilment centers deploy autonomous mobile robots to automate tasks. These robots perform complex tasks such as picking and sortingt with high accuracy. Human error reduces, and safety improves.

Self-driven vehicles have been around for long. Such vehicles now find widespread use in controlled environments such as supply chain warehouses and mines. Advancements in sensor technologies enable these vehicles to navigate more complex environments. Robotaxis has already made its appearance in several cities across the world. The use of robotaxis will increase in 2025.

Prototypes of robotic systems with infinite memory are close to reality. Such systems will be able to orchestrate entire business processes.

The advances in technology that lead to better autonomous robots and drones include:

The maturity of Lidar technology. Lidar uses lasers to measure distances and creates 3D maps of the environment. Autonomous systems use Lidar to perceive their surroundings. Improvements in Lidar technology enable faster scanning speeds, longer ranges, and higher resolution.
Easy availability of high-quality radars. Radar systems determine the distance, speed, and direction between objects by emitting radio waves. The availability of more sophisticated, durable, and cheaper radars improve sensing capabilities. Advancements in radar technology enable deploying smaller and more energy-efficient sensors. Robust radars operate in adverse weather conditions. This becomes especially handy for drones and autonomous vehicles.
The increasing sophistication of machine learning algorithms. AI models become more capable and more useful over time. Convolutional neural networks identify and classify objects more accurately than ever before.
Availability of more powerful hardware. Powerful GPUs and TPUs accelerate the development and training of complex AI models.
Availability of richer data. The self-learning capabilities of AI make autonomous systems better with every interaction. AI systems learn from the ever-increasing data generated by sensors and other sources. Reinforcement learning allow AI systems to learn through trial and error as they engage with the real world environment.

The Rise of Agentic AI

Traditional chatbots are reactive and answer user queries. Autonomous agents go many steps ahead. These AI-powered systems can initiate actions, set and achieve goals, and solve a wider range of customer needs.

Next-level agents can reason over other information sources. For instance, the autonomous agent can

Access the company’s returns policy, apply its reasoning to it, and decide for itself whether to accept the return or not.
Understand unstructured email text, interpret it, reason over it, and take action, such as generating a reply or filling out form fields.

Microsoft Copilot vision offers a preview of the possibilities on the anvil. Another mainstream example is Salesforce’s Agentforce. Agentforce manages workflows, schedules appointments, does data analysis, and much more.

The biggest disruption agentic AI will deliver is mainstreaming innovation.

Agentic AI will enable new ways of doing things for better results. For instance, autonomous agents will add a layer of business logic between the user interface and the backend. The agent will extract and analyse information from multiple databases and systems. It treats all sources on its merits, without the user having to identify specific backend data sources. With such an AI tier in place, the backend can be anywhere or anything. The agent will, for instance, access customer data from the CRM, POS systems, sensors, social reviews, and other public resources. The need for maintaining dedicated business applications such as CRM will reduce.

Agentic AI is in its early stages, though. Human direction and oversight remain critical.

The Security Issue

Autonomous systems, like all other computational systems, remain susceptible to cyber attacks. But the risks associated with security breaches are much greater in autonomous systems. The breach of an automated robot, for instance, may cause accidents and even deaths.

Attackers now deploy innovative attacks to target autonomous systems.

In data poisoning attacks, attackers manipulate the training data. The algorithm then generates inaccurate outputs.

In adversarial attacks, attackers modify inputs subtly, to make the systems misinterpret information. Such attacks can have disastrous consequences for systems such as self-driving cars. For instance, a small, almost imperceptible change to a stop sign could trick an autonomous vehicle’s perception system.

Attackers may also steal models outright to replicate them for malicious purposes. They can also extract sensitive information embedded within them.

The mainstreaming of autonomous systems depends on:

Developers designing more resilient and immune models.
Ensuring foolproof security of training data
Deploying monitoring systems to counter attacks in real-time.
Adopt comprehensive security measures, such as t he Zero Trust approach.

McKinsey estimates AI will automate up to 40% of work within the next 20 years. The global robotics industry will generate $910 billion in revenue by 2040. Another Capgemini survey underscores the growing popularity of autonomous systems. 24% of senior executives and 43% of investors consider AI-driven automation and robotics as a top technology trends for 2025.

Early adopters of autonomous systems can gain a valuable head start and reap rich competitive advantages.

Durjoy

Durjoy is a hands-on expert in Software Automation, Infrastructure and Fintech at DigiLeap Technologies, a specialist solutions provider for Chatbots, RPA, Process Automation and Financial Compliance. He has previously worked in the mobile payments sector and before that has had a long stint at Citibank across multiple roles in their operations and technology division. He loves picking on business processes in various sectors and transforming them through automation. Durjoy can be contacted at durjoyb@digileap.net.