Innovation is a way of solving problems in new ways. But not all problems require the same type of thinking, and not all organizations approach them in the same way. In today’s world, where both complexity and the pace of change are increasing, the difference between algorithmic and heuristic innovation is becoming increasingly clear.
It is not just about the methods but about how we understand and relate to systems, relationships and uncertainty.
Two different logics for innovation
The algorithmic logic is based on structures, processes and standardized approaches. Here, clear steps are followed: identifying needs, generating ideas, testing, validating and implementing. This form of innovation is often effective in stable systems where cause and effect are known and where there are established frameworks to improve within. It is the type of innovation that lends itself to improvements, creation, process change and development within a certain context.
The heuristic logic, on the other hand, is more exploratory. It is based on intuition, experimentation and relationships. There are no ready-made formulas or processes to develop here, but you learn by trying, observing and reevaluating. It is about understanding the system while you are working in it, rather than following a predetermined path. Heuristic innovation is central when you want to create system shifts and radically new solutions. It involves more perspectives than one individual can define.
System innovation as a framework
From a system innovation perspective, the difference between these approaches becomes clear. Algorithmic innovation fits in well-defined systems, where the goal is to do something better. Heuristic innovation is needed when the system itself no longer works or when the assumptions that were previously true no longer apply.
An example can be seen in the energy transition. Algorithmic innovation improves or renews existing energy systems. More efficient turbines, smarter control systems, lower costs. Heuristic innovation questions the structure itself. What happens if energy is no longer produced centrally but in distributed microsystems, or if energy becomes free but ownership of data becomes the new currency? The entire energy system and its current components are fundamentally questioned.
System innovation often requires a combination of algorithmic and heuristic innovation. To change the system, you usually need to understand the existing system, and to understand it, a structured algorithmic analysis is required. But to create the new system, you have to step outside of logic, use relationships, real creativity and even more exploratory thinking.
Relationships as the heuristic component
In heuristic innovation and system innovation, relationships are central. Since solutions do not exist in advance, they must be built through interaction with others. These can be relationships between actors in an ecosystem, between research areas or between disciplines.
An illustrative example is how the construction of sustainable cities is done today. Previously, a city could be planned based on an algorithmic model. Traffic flows, water pipes, living space, etc. Now we know that cities are living systems. Creating sustainable cities requires dialogue between residents, technologists, biologists and decision-makers. It is in these meetings, where knowledge and worldview collide, that heuristic innovation arises.
Relationships also enable social learning innovation. By trying together, reflecting together and creating together, knowledge arises that no one has alone. It is a form of innovation that is difficult to replicate but necessary for building resilient and self-organizing systems.
The individual dimension
Which approach one prefers is often linked to personality and experience. Some people thrive best with structure, methods and a clear process. They appreciate that innovation can be systematized, measured and repeated. Others are driven by questioning, challenging and testing new paths. They see innovation as a way of breaking logic rather than following it.
The interesting parts arises when these two styles of thinking meet. In an organization that wants to work with system innovation, both are needed. The methodological ones are needed to create order, stability and the ability to implement and make something happen. The exploratory ones are needed to create renewal, risk-taking and creativity.
The problem is that organizations often prioritize the algorithmic. They want predictability, plans, and measurability. But what really creates disruption and innovation often occurs in the heuristic, where you don’t know the outcome in advance.
Algorithmic innovation and heuristic innovation as a model
If we imagine a four-dimensional figure, we can see two axes. The horizontal axis shows the degree of algorithmic and heuristic innovation, respectively. The vertical axis shows how much people like to break rules to create disruption, from low to high.
In the lower left field is the low degree of both heuristics and rule-breaking. Here, improvement, so-called incremental innovation, often within routines and processes. It can involve streamlining logistics or automating simpler tasks. The innovation is stable, but the level of innovation is low and not innovative.
In the lower right field are those who are algorithmic but more likely to break rules. Here are the methodical rebels, those who follow innovation processes, use tools and methods and dare to challenge boundaries within the system. They can create major advances within existing frameworks, such as when a company revolutionizes a product or a process but without changing the business model.
In the upper left field are the heuristic but rule-conservative. Here are thinkers who are happy to experiment but within given values and norms. They can create innovations within established industries but do not change the playing field itself.
In the upper right field are the heuristic and rule-breaking innovators. This is where the truly disruptive changes occur. This is where new systems, business models and paradigms are born. They see rules as temporary agreements rather than obstacles. It is this category that creates system innovation and transformation that affects many.
Examples of applications
In product innovation, algorithmic innovation can be about making existing products better, faster or cheaper. Heuristic innovation is about redefining the need itself. For example, camera manufacturers have long algorithmically improved their analog cameras with better lenses, more features and even a leap in digitalization. Then came the mobile camera, a heuristic innovation that changed the very definition of what a camera is and how photos are collected and used.
In business model innovation, the same logic works. An algorithmic change could be introducing digital payments in a store, a major impact but no change in the system. A heuristic change could be questioning why the store needs to exist at all, which led to the rise of e-commerce and consumer-to-consumer commerce via sharing.
When sustainability and circularity come into play, heuristic innovation becomes particularly central. It is no longer about making linear processes more efficient, but about creating completely new flows, business logics and value chains.
When methods become both support and an obstacle
Today, there are many standards and methods for innovation, such as ISO standards for innovation, design thinking, lean startup, TRIZ and many more. They are invaluable for creating structure and a common language when innovating. But they rarely capture disruptive thinking. When a method becomes too controlling, it risks creating security and routine instead of new thinking.
This does not mean that methods should be avoided, but that they should be used with awareness. An organization can use algorithmic methods to create ideas, but needs to supplement them with heuristic working methods to drive change.
System innovation requires both logics
The major societal challenges such as climate, health, resources, social inequality, cannot be solved algorithmically. They require system innovation, where both structure and exploration interact. Algorithmic innovation helps us understand the present and build processes for change. Heuristic innovation makes it possible to discover future possibilities and create systems that do not yet exist.
Organizations that manage to balance these two logics become those that can both improve and innovate. They can hold two minds at the same time, creativity and innovation, control and chaos, analysis and intuition.
Algorithmic and heuristic innovation are therefore not opposites but complements. One is about understanding, improving and innovating within a system. The other is about questioning, experimenting and transforming the entire system.
When we learn to switch between them, we can create innovation that is both innovative and bold. That is when we reach the system level where innovation is no longer about solving a problem in a new way, but about redefining what the problem really is.
