Autonomous Value Creation

The evolution of work: from tools to automation and artificial intelligence

Since the earliest days of human history, people have used tools to make their work easier. Even in the Stone Age, our ancestors used simple tools such as spears and stones to hunt animals, gather food and defend themselves. Tools and aids were crucial for accomplishing complex tasks more efficiently and with less effort. Over time, humanity evolved, and with it the tools it used. Simple tools evolved into ever more advanced devices, machines and technologies.

The next big step in this development was automation. Automation allowed work that was previously carried out by human hands to be taken over by machines. These machines, whether in factories, on construction sites or in agriculture, perform tasks faster, more precisely and often around the clock, without human fatigue. One example is the industrial revolution, in which steam engines and looms revolutionized production. Other examples include assembly line production in the automotive industry and the use of robots in modern manufacturing.

But despite these advances, the machines were “stupid”. They carried out predefined tasks without thinking for themselves or making decisions. All important decisions, from planning to troubleshooting, were reserved for humans. Thinking and decision-making were always closely linked to human knowledge and intuition.

The rise of Artificial Intelligence (AI)

However, this separation between man and machine is beginning to change. With the advent of AI, we have created a tool that not only helps us to perform tasks, but also to think and make decisions. AI models that are able to analyze large amounts of data, recognize patterns and make predictions can help us make informed decisions faster and more accurately. And this turns the human ability to think and make decisions into a technological skill that decouples thinking from people and makes it scalable, just as automation has decoupled work from people and made it scalable.

However, one major limitation remains: AI knows a lot about topics that are accessible on the internet, but it knows nothing about a company's internal processes, as this data is not publicly accessible. But by feeding an internal AI with specific information and process data, we can train the AI to help us make internal decisions. And it is these very process data that we make available with Celonis Process Intelligence. Thousands of companies around the world already have full transparency of current processes, but also of the optimal and not-so-optimal course of processes in the past. This data represents enormous value, as it can be used to train AI and enable it not only to automate work and orchestrate processes, but also to act as an advisor in the decision-making process.

The emergence of an “Autonomous Enterprise”

But what if we were to combine the automation of machines and processes with automated decision-making through AI? This could lead to the emergence of an “Autonomous Enterprise” - a company that is able to control large parts of its business processes autonomously and make decisions within the guidelines set by humans, but otherwise without human intervention.

An “Autonomous Enterprise” can be compared to autonomous driving. In autonomous driving, the vehicle must be able to recognize, understand and react to complex environments by making decisions in real time, such as adjusting speed or avoiding obstacles. An autonomous company would require similar capabilities: it would need to be able to analyze business processes, make predictions and react flexibly to changes. Just as a self-driving car has to pay attention to road conditions and traffic, an autonomous company would have to be able to react to market changes, customer requirements and internal process developments.

An autonomous company could automate tasks such as production planning, inventory management or even customer service. It would be able to make decisions independently on the basis of data, optimize production processes or adapt logistics chains. The implications of this development for businesses are profound. The concept of an autonomous enterprise, where processes run with minimal human intervention, is a vision of the future. To better understand the challenges and possibilities, it is instructive to draw a comparison with autonomous driving.?

The advent of “Autonomous Driving”

Many cars today are equipped with automation functions and various assistance systems that support the driver. However, fully autonomous vehicles that can drive without human intervention are still under development, even though companies such as Waymo already have autonomous cabs on the roads in San Francisco and other cities. For a car to be truly autonomous, it must have a suite of sensors that constantly monitor its environment and location. It also requires detailed maps, traffic rules, and the ability to make split-second decisions. Crucially, the vehicle’s central intelligence must have access to all operational systems, such as steering, acceleration, and braking.

This same principle applies to businesses seeking autonomy. They require situational awareness of their current processes, a need fulfilled by Celonis Process Intelligence. Process Management defines the rules and target processes, while AI analyzes this information, identifies deviations, and suggests corrective actions. Once a decision is made, Process Automation and the Celonis Orchestration Engine ensure that these actions are implemented by interfacing with operational systems like Enterprise Resource Planning (ERP) and Customer Relationship Management (CRM) platforms. Yet, at the heart of this system, the most critical element remains decision-making.

Human decision-making is limited by cognitive capacity. Studies show that people can effectively consider only 5 to 9 pieces of information at once. When faced with too many variables, humans experience cognitive overload, leading to suboptimal decisions. Furthermore, complexity and stress can further diminish decision-making capabilities. However, experience allows experts to process more information, as they can more easily identify relevant patterns. This is where AI can provide significant support. AI excels at pattern recognition and can process vast amounts of information simultaneously, far exceeding human capacity. This makes AI a valuable partner in decision-making processes, creating what could be called Hybrid Intelligence — a combination of human and artificial intelligence. This collaboration is poised to revolutionize how decisions are made.

Hybrid intelligence: humans and AI together

Hybrid intelligence is a close collaboration between humans and AI, where humans control the decision-making process and use AI as a tool for support. This collaboration can be divided into three categories:

1. Assisted Decisions: In this form of decision-making, AI provides humans with suggestions or recommendations, which humans then evaluate and implement. One example would be an e-commerce company that uses AI to receive suggestions for price changes based on market trends. Another example is medical systems that support diagnoses by alerting doctors to possible illnesses based on image data. Financial systems that calculate risk forecasts for investments also fall into this category.

2. Augmented decisions: Here, AI enhances the decision-making ability of humans by performing complex calculations or analyses in real time. One example is AI-supported traffic control in smart cities, which optimizes traffic and thus avoids traffic jams. Other examples include real-time analyses in stock market trading, where AI trading systems support investors in finding the optimal time to buy or sell shares, or AI-based production planning in the manufacturing industry.

3. Autonomous decisions: In this most advanced form of decision making, the AI makes the decision completely on its own, without human intervention. One example would be the autonomous control of warehouse robots that efficiently sort and dispatch goods. Autonomous drones that independently carry out inspections of industrial plants also fall into this category. There are also autonomous financial systems that carry out small transactions or automatic budget adjustments based on predefined parameters.

Autonomous Value Creation: A Human-AI Collaboration

The concept of autonomous value creation through AI represents a profound change in business processes. However, this change does not mean that humans are excluded from the process. Rather, it represents a future in which AI handles both the execution of tasks and strategic decision-making in collaboration with humans.

In an autonomous value creation model, AI can take on repetitive, data-driven tasks and react faster than humans alone to market changes, changes in customer behavior and disruptions in the supply chain. While AI executes at an unprecedented speed, humans will focus on higher-level strategic decisions while ensuring AI systems remain ethical and transparent so that AI operates within the desired moral and societal framework and makes decisions that align with corporate values and regulations. This balance between human insight and AI efficiency allows companies to capitalize on opportunities or mitigate risks with a precision that would be impossible for either side alone.

In the future, companies will emerge where AI takes over routine tasks and decision-making autonomously and humans play the critical role in guiding strategy, creativity and ethics. These companies will work around the clock, leveraging AI's ability to continuously optimize operations, while humans take care of innovation, customer relationships and long-term growth. This collaboration ensures that companies are not only fast and efficient, but also ethical, innovative and aligned with human values. AI alone cannot innovate or make ethical decisions; humans are still essential to guide AI systems and ensure that they create value in a way that aligns with society's broader goals.

In conclusion, the future of autonomous value creation lies in the synergy between AI and humans. AI will take over the fast-moving, data-intensive aspects of operations, while humans provide guidance, creativity and ethical oversight. Together, this partnership will revolutionize the industry and drive innovation and value creation in a way that is both efficient and human-centric.