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This article was originally published in German.
Variants of idea management
Dr. Hartmut Neckel describes idea management as a versatile tool between improvement, innovation, and employee participation, and shows how its role in organizations is often shaped implicitly. He also explains how AI is fundamentally transforming the balance between top-down and bottom-up approaches.
Author
Dr. Hartmut Neckel
Wikipedia defines “idea management” as follows: “Idea management encompasses the generation, collection, and selection of suitable ideas for improvements and innovations. The objective of idea management is to unlock performance potentoial by fostering a creative working environment, by involving all employees, to strengthen the competitiveness of the organization. Idea management complements innovation management, which typically only involves part of the workforce.”
This definition does not preclude the possibility that idea management may also generate ideas for innovation, in the sense of improved or entirely new products for the market. Nor does it exclude that, through this channel, all employees may thereby contribute to innovation management. In some highly innovation-driven companies, idea management may therefore be understood as an innovation tool, or the two terms may be used more or less interchangeably.
In other organizations, however, its primary purpose is seen above all as an improvement tool; where the focus is on improvements that generate financial savings, it may also be regarded as a cost-reduction instrument. In some companies, its value as a participation tool also plays a role.
Although only a limited number of companies make a conscious strategic decision about the primary purpose idea management should serve, an implicit and often unconscious positioning frequently results from its organizational assignment alone:
In more than 40% of the manufacturing companies that took part in the Idea Management Benchmarking survey in 2021, idea management was assigned to one of the more improvement- and savings-oriented functions, such as Operational Excellence, CIP, Lean, process optimization, the production system, or operations organization (compared with just under 17% among non-manufacturing companies).
A look at the actual savings achieved per employee shows that, among participants in the benchmarking studies of recent years, only around one quarter could credibly claim to use idea management as a true “savings instrument.” In the remaining three quarters, it is more convincingly positioned as a “self-financing improvement tool.”
Assignment to Human Resources ranked second among manufacturing companies at 19.9%, and even first among non-manufacturing companies at 31.5%. This may reflect its classification as a participation instrument, but it may also be a legacy from a time when the main emphasis was on rewarding ideas - combined with the fact that no more suitable department has since been identified that was willing to take over responsibility for the topic.
By contrast, only 3.9% of manufacturing companies assigned idea management to functions such as innovation, research, development, or technology, compared with 13% among non-manufacturing companies. This difference may be explained by the fact that, in highly innovation-driven businesses, a large share of employees are involved in programming, design engineering, or project management, which tends to place these companies in the non-manufacturing category.
Irrespective of how improvement or innovation processes subsequently unfold, the starting point is always the emergence of ideas. The methods used for this can be categorized according to whether topic selection and idea generation take place bottom-up or are organized and directed top-down by experts or by the company; whether participation is voluntary or mandatory; and whether, in principle, all employees or only a defined group are involved. The following are just a few examples, to which further variants could easily be added:
Classic idea management, which is open to all employees on a voluntary basis and in which employees are expected to identify topics with improvement potential [or innovation potential] and develop suitable ideas independently (without excluding the possibility that the company may provide thematic inspiration and that ideas may be further developed during the evaluation process)
CIP workshops or shopfloor meetings, in which participation is mandatory for all employees and where they are able to identify problems and topics with improvement potential, for which ideas are then developed jointly - usually after prioritization and with methodological support
Campaigns focused on top-down defined topics (or narrowed focus fields), in which relevant ideas can be developed bottom-up on a voluntary basis by all employees, or alternatively by a limited and exclusive group of participants
CIP or Lean workshops [or creativity workshops] in which selected employees are required to participate. In these sessions, participants work together; using structured methods; to develop improvement or solution ideas [or innovation ideas] for specific topics or problem areas defined by the organization.
Whether this type of idea generation should still be considered “bottom-up” is open to debate. The situation is clearer in Six Sigma projects: while employees may contribute information and, where possible, ideas, the responsibility for developing the final solution remains with the Six Sigma expert, regardless of how valuable those contributions may be.
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Ideation with AI as a new communication participant
To understand what is currently changing at a fundamental level in idea management, it is worth considering two particularly relevant fields of research. The first is neurobiology and cognitive science; the second is sociological systems theory.
Neurobiology and cognitive science help explain - in the sense of neuro idea management - how employees come to identify topics with improvement potential in a bottom-up way in the first place. After all, employees do not come to work with the explicit intention of having an idea for the idea management system that day.
In reality, employees absorb far more signals from their environment in passing than ever reach conscious awareness: of the roughly one million bytes transmitted per second by the sensory organs, only around three bytes per second are consciously perceived by the cerebral cortex.
The vast majority of these signals is absorbed subconsciously and processed through predominantly unconscious neurobiological and cognitive mechanisms in such a way that “ideas” may emerge almost “by chance” regarding how something could be improved.
This kind of sensory-based, holistic perception also enables people to perceive atmospheres physically, even where these can scarcely be pinned down in terms of concrete objective criteria. Anyone entering a room can immediately sense whether tension is in the air or not.
Bottom-up topic identification and selection effectively use employees as “mobile sensors for the holistic capture of the company’s physical and psychological state” and rely on employees communicating suitable outcomes of this processing (“ideas”) in as purposeful a form as possible, ideally as developed “suggestions.” Top-down selection works the other way around: here, the starting point is usually formed by visible discrepancies between target and actual values, in order to identify and prioritize topics consciously, rationally, and wherever possible in a data-based way (in Six Sigma, for example, this happens explicitly in the Define phase), before addressing them systematically.
Digital data capture and processing have massively expanded the volume of data available for conscious, data-based management - to such an extent that this abundance of signals and information can now only be meaningfully accessed and utilized through digital tools, above all artificial intelligence. The fact that these digital tools have now also learned to communicate in ways similar to humans makes it worthwhile to consider social systems theory as well.
Sociological systems theory regards communication as the key constitutive element of social systems such as organizations, associations, and families. Without communication, a company ceases to exist as a social system, regardless of whether its physical infrastructure or legal entity still remains in place.
The communication forms that are essential to idea management are the “communication of the idea” (the “suggestion”), the “decision” (which may include the “assignment” to implement it), the “feedback” to the idea submitter (possibly in the form of a reward notification), and the motivating and supportive “advice” provided by idea management (both for employees / submitters and for those responsible for processing the ideas). Regardless of whether this communication took place in analog or digital form, both the sender and the recipient have until now always been human.
As already noted, the rise of the Internet of Things and artificial intelligence has not only led to a further explosion in the volume of communicated content-data, signals, and information-but has also introduced fundamentally new types of communication participants.
This does not refer to technical systems that have long existed and “actively communicate” by automatically issuing fault messages, maintenance alerts, reminders, or predefined notifications. From the perspective of a social system, such “communications” represent information about the system’s environment rather than components of the system itself. What matters here instead is communication with chatbot avatars or agents that increasingly accompany-or even replace-interaction with human beings.
When an AI is trained on company data and given access to current information—for example from MES/BDE, ERP, CAQ, or CRM systems, meeting minutes, project reports, patent databases, and ultimately all information and communication content that leaves digital traces within the organization, including emails and chat messages in Teams or employee apps—it can do far more than a “language machine” trained only on general data and capable of generating texts based on the mainstream of its training material. It becomes a company-specific AI, and communication with it can relate directly to concrete situations and conditions within the organization. In this way, it acquires functions that are relevant to the company itself, meaning that such communication becomes part of the social system.
This represents an entirely new and additional element, fundamentally different from any form of communication previously conducted between people. Quite simply, no human being has ever been able to communicate on the basis of processing such vast quantities of data and information. The knowledge asymmetry between the communication partners therefore has a completely different quality from anything that could arise between two people.
The fact that a corporate AI can also take into account signals from the market-and from other parts of the system’s environment, such as politics and regulation, culture, and society-adds another dimension to the scope of capabilities described above. This is particularly relevant for innovation ideas but may also support the further development of improvement ideas aimed at internal practices.
As with all systems, social systems exhibit properties and behaviors that arise from the interaction of their parts and cannot be derived solely from the properties of the individual components: the whole is more than the sum of its parts. Speculating about what new properties and behaviors may emerge in companies as communication with company-specific AI becomes more common is not the aim of this article. Nor is it necessary to elaborate further on the fact that the time- and location-independent availability of an almost “all-knowing” communication partner can be extremely useful and valuable for everyone involved in idea and innovation management. What is important, however, is to reflect on both the risks and the opportunities.
One risk is that these unprecedented possibilities for conscious, data-based topic selection in improvement and innovation may tempt organizations to rely too heavily on top-down control. For those steering the process, this initially promises greater predictability compared with the apparent “randomness” of bottom-up processes. This may appeal to managers who prefer the company to operate with the precision and reliability of clockwork. At the same time, the willingness to engage in dialogue on equal terms may decline, since AI supposedly provides a comprehensive overview of what is happening. As a result, only those topics already visible on the data-based radar of those in control may become subjects for improvement and innovation.
The opportunity, by contrast, lies in recognizing the limits of AI and therefore the need to complement it with human experience. AI may be able to describe very well what it feels like to be thirsty and then to quench that thirst - but it has never actually experienced either. All of AI’s knowledge is second-hand; none of it has been lived. In this sense, AI resembles a very large head without a body. It is the human body, with its sensory organs and its holistic perception, that enables experiences an AI can never have. Human intuition and empathy arise from sensory experiences and from their largely unconscious; though partly conscious; processing.
Of course, AI can simulate all of this. It can behave and communicate as if it were thirsty and then as if that thirst had been satisfied. It can behave and communicate as if it possessed intuition and empathy. But that is not the point. The real question is whether AI can, within a continuous flow of real situations, experience the specific perceptions and impressions that an employee encounters over the course of a working day. At present, this is not in sight. For this reason, the open bottom-up channel of idea management remains essential, ensuring that topics can still be brought forward and developed into ideas - even when they cannot be identified through data alone.
At the same time, the same capabilities of AI that help those steering the process identify topics can also support submitters in further developing and qualifying their initial ideas, which often arise “by chance” without targeted direction - and, where appropriate, discarding them again. This alone will significantly reduce the effort required for evaluation and decision-making. In addition, the support AI can provide directly to evaluators and decision-makers will further decrease the workload. Many of the criticisms sometimes raised about spontaneous bottom-up ideas are therefore likely to become obsolete.
The opportunity therefore lies in recognizing and valuing the equal importance of serendipity - making productive use of chance - and the “method of unconscious perception in passing” that underpins bottom-up variants of idea management. These approaches act as a counterbalance to purely data- and knowledge-driven control. This insight also suggests that the metaphor of the “company as clockwork” must be complemented by other perspectives - such as the “company as an organism” or the “company as a political system”- in order to better reflect organizational reality.
About the author
Dr. Hartmut Neckel is the founder of Dr. Neckel Consulting based in Bonn. He is the author of several industry books, numerous professional publications, and a well-known blog on idea management.
He also conducts the “Idea Management Benchmark Study” (Kennzahlenvergleich Ideenmanagement), the only annual benchmark study in the DACH region, based on data from more than 250 companies.
In the fields of idea management, innovation, and continuous improvement, Hartmut Neckel is regarded as one of the most recognized thought leaders and experienced practitioners.
