From Human to Hybrid Decision-Making
The key to success in business lies in creating value. This is not limited to financial value, but includes also areas such as increased market share, improved customer and employee satisfaction and minimization of operational risks. These goals are achievable when companies manage their processes effectively, thereby creating greater efficiency, sustainability and ongoing improvement.
The formula for value creation
Process intelligence technology, by identifying inefficiencies and risks in business processes, plays a key role in this value creation. It provides insight into where and why processes may be faltering and reveals opportunities for improvement. These insights provide a crucial basis for decision-making and allow companies to make the information-driven decisions essential for improvement and value creation. The formula for success can be summarized with a simple equation: insights x decisions x actions = value.
If any component of this value creation is missing or lacking, the result will fall short of expectations. Usable insights may, for instance, fail to be translated into effective decisions. Similarly, the best decisions not followed by appropriate actions will not produce tangible benefits.
The actions required to create value can be categorized into three main areas: people, processes and technology. These elements must function in harmony to maximize efficiency and minimize risk. Processes can be improved by training employees to work more effectively, adjusting the processes themselves, or optimizing operational flow. However, technology plays an increasingly prominent role in augmenting these efforts. Technology serves as a catalyst for efficiency and innovation, enabling companies to excel.
From dumb to smart
Throughout history, people have used technology to enhance their work. The advent of automation was a transformative moment, because it disconnected tasks from human labor and enabled scalability across industries. Yet, despite their strength and precision, machines have traditionally been considered ‘dumb’ because of the lack of ability for intelligent decisions. Intelligence and decision-making have long been seen as uniquely human abilities. But with the rise of AI, this paradigm is shifting.
AI has the potential to turn decision-making into a technological skill, extending intelligence beyond the human brain. Yet while AI can be a powerful tool, it remains limited in its ability to address company-specific processes unless provided with the accurate data. This is where internal AI systems, trained on business-relevant information such as process intelligence, come into play. By equipping AI with knowledge of a company’s processes, it can contribute to well-informed decisions.
Autonomous decision-making?
The implications of this development for companies are profound. The concept of an autonomous enterprise, where processes run with minimal human intervention, is a vision of the future. To understand the challenges and opportunities, it is helpful to draw a comparison with autonomous driving.
Modern cars are equipped with automation features and various assistance systems that support drivers. However, fully autonomous vehicles, capable of driving without any human input, are still in development. To make a truly autonomous vehicle, it needs an array of sensors to continuously monitor its surroundings and location. It also requires detailed maps, knowledge of 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.
The same principle applies to companies striving for autonomy. They need situational awareness of their current processes, a need that can be fulfilled by process intelligence. The rules and target processes are defined by process management, with AI analyzing the information, identifying deviations and suggesting corrective actions. Yet the most critical element at the heart of this system remains decision-making.
Human decision-making is limited by cognitive capacity. Moreover, complexity and stress can reduce this capacity. Experience, however, enables experts to process more information by helping them recognize relevant patterns more easily. 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 might be called hybrid intelligence - a combination of human and artificial intelligence. This collaboration is on the threshold of a revolution of the way in which decisions are made.
In this context, we can distinguish between three different types of decision-making.
1. Assisted decisions
In certain cases, human decisions are essential because they require empathy, ethics or other uniquely human qualities. Here, AI assists by organizing facts and providing analyses to support the human decision-maker.
This type of decision-making often occurs in areas where interpersonal skills are crucial. While AI can process vast amounts of data quickly and accurately, it cannot fully grasp complex human emotions, social contexts or moral dilemmas. Therefore, humans remain the central decision-makers, with AI acting as a tool to efficiently structure and present relevant facts. In these scenarios, the final responsibility lies with humans, as they must take ethical considerations, social implications or legal frameworks into account.
Consider, for instance, recruitment decisions. In this case, AI can help with initial candidate screening, but the final decision of who gets employed rests with the manager, who must assess factors such as personality, soft skills and cultural fit, which go beyond data analysis.
2. Augmented Decisions
In these situations, things are taken a step further with AI and humans collaborating, with AI preparing analyses and making suggestions, which the human can accept or reject. Alternatively, the human can guide AI to refine its recommendations.
Augmented decisions are an example of hybrid intelligence, combining the strength of humans and machines to produce well-informed decisions. Humans bring creativity, flexibility and contextual understanding, while AI excels at speed, precision and processing large amounts of data. This type of decision-making is applied in situations which demand both AI’s analytical power and humans' intuitive judgment, resulting in a decision-making process that is more efficient, accurate, and better-informed.
AI can, in logistic situations, run simulations to foresee bottlenecks or calculate optimal delivery routes. The human operator can refine these suggestions based on experience and knowledge of local conditions or external factors such as the weather. This collaboration creates an optimal combination of AI-driven analysis and human expertise.
3. Autonomous decisions
With autonomous decisions AI takes full responsibility for decision-making, with humans setting parameters and intervening only if exceptions arise.
Autonomous decisions are the ultimate aim for many AI-driven systems. These allow routine tasks or highly data-driven processes to be transferred to AI in full, which relieves human workload and boosts efficiency. For autonomous decision-making systems, it is essential to establish clear parameters and rules within which the AI must operate. This ensures that decisions align with the organization’s desired goals and ethical guidelines. Human intervention will, however, always be required for exceptions or unforeseen circumstances to prevent errors or undesirable outcomes.
Autonomous decisions are becoming more common across various domains. For example, in stock markets autonomous trading systems, running on AI algorithms, make independent buy-and-sell decisions based on market data, historical trends and pre-set trading strategies. These systems execute trades in milliseconds and can respond to market fluctuations instantly. Humans monitor the parameters and intervene only in the event of significant market disruptions or unforeseen events.
Evolution, not revolution
Looking to the future, we can confidently say that it will be more intelligent, as decision-making processes become more intelligent. This transformation will not be an overnight revolution; but rather a gradual, ongoing evolution that permeates all areas of life. In the business world, AI will become an essential tool for employees at every level, helping them make better and smarter decisions. The key to this transformation lies in ensuring that AI has access to the right information to generate these insights. When properly used, AI will usher in an era of smarter and more efficient business operations, and shape a future where decisions are made with greater precision, speed and impact.
This essay was published in Management Scope 10 2024.