The innovation facade on LinkedIn: Why it's not just companies that invoke change and organize stagnation

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Published on: November 2, 2026 / Updated on: November 2, 2026 – Author: Konrad Wolfenstein

The innovation facade on LinkedIn: Why it's not just companies that invoke change and organize stagnation – creative image on the topic, with AI: Xpert.Digital

Lots of talk about the future, little change? What's really holding companies back?

Why companies often remain stagnant despite ambitious innovation plans

AI and Innovation: Why new technology alone does not bring about change

Innovation is omnipresent on LinkedIn, in strategy presentations, and at leadership conferences. Companies talk about artificial intelligence, new business models, and the changing world of work. But there's often a gap between this commitment to the future and the changes taking place in day-to-day operations. New ideas are presented, technologies are tested, and a willingness to change is demanded—while decision-making processes, incentive systems, and routines remain surprisingly familiar.

This discrepancy cannot simply be explained as personal hypocrisy. Often, organizational structures, limited resources, and conflicting incentives come into play: day-to-day operations must run reliably, potential risks are immediately apparent, while the benefits of innovation are often uncertain and distant. Thus, many stakeholders can act rationally from their own perspectives, yet collectively hinder change.

The rapid spread of artificial intelligence also demonstrates why public attention is not synonymous with widespread adoption or measurable economic success. What matters is not how often companies talk about innovation, but whether they solve concrete problems, allocate resources, and evaluate results. Those who look beyond the rhetoric of innovation realize that renewal doesn't begin with grand promises, but with the willingness to critically examine even established processes – and to make changes where they truly make sense.

Everyone wants a future – as long as nothing changes at their own desk

Innovation is part of the standard vocabulary of business. On LinkedIn, in strategy presentations, and at leadership conferences, it's easy to get the impression that almost every company is in the midst of a fundamental transformation. Content producers explain the next technological leap, team leaders demand greater willingness to change, and executives present ambitious visions of the future. At the same time, decision-making processes, incentive systems, and workflows often remain remarkably familiar.

The pointed criticism that everyone opportunistically demands innovation while continuing to operate according to established patterns within their own sphere of influence touches a raw nerve. However, it wouldn't hold water as a general judgment about all stakeholders. Not every publicly visible advocate of innovation acts inconsistently. Not every unchanged routine is outdated. And not every delayed introduction of a new technology demonstrates a lack of courage.

The economically significant core issue lies deeper: A considerable gap can arise between public commitments to renewal and the actual change in business decisions. This gap is not solely a matter of personal credibility. It can also be understood as a result of incentives, power dynamics, uncertainty, and organizational dependencies.

The crucial thesis, therefore, is this: Companies don't necessarily fail because they lack new ideas. They can just as easily fail because their existing structures reward the protection of the tried and tested more than the verifiable testing of the new. Anyone who wants to understand this dynamic must focus less on innovation rhetoric and more on resources, responsibilities, and consequences.

The crucial boundary lies between announcement and application

First, the debate needs a sound definition of innovation. Not every new idea is an innovation. Not every workshop transforms a company. And not every piece of software acquired improves a business process.

From an economic perspective, it makes sense to define innovation by an actual change that has been implemented: a new or significantly improved product that is available to potential users, or a correspondingly modified process that is in practical use. Implementation distinguishes innovation from a mere invention, declaration of intent, or vision of the future.

This distinction may seem unremarkable, but it has far-reaching consequences. An organization can engage in numerous innovation activities without having yet implemented an innovation. It can conduct research, provide training, evaluate technologies, and develop prototypes. All of this can be necessary and economically sound. However, it is something different from making changes during ongoing operations.

Equally important is the distinction between introduction and success. Even a successfully implemented innovation can be economically disappointing. Innovation is not a seal of approval that automatically guarantees higher profits, better working conditions, or more satisfied customers. It initially denotes a relevant change, the benefits of which must then be evaluated.

Mixing these levels creates favorable conditions for a facade of innovation. Activities are then treated like results, and results are prematurely sold as successes. A presentation replaces implementation, a launch replaces a cost-benefit analysis, and the number of employees involved replaces the question of whether a relevant problem has actually been solved.

Why visible approval is easier than binding change

Public support for innovation is relatively inexpensive. A presentation on artificial intelligence doesn't tie up a multi-year investment budget. A speech about entrepreneurial courage doesn't change any approval guidelines. A commitment to a culture of learning from mistakes doesn't obligate anyone to treat a failed attempt fairly in the next performance review.

This results in a fundamental asymmetry. Communicating about change can quickly generate attention, recognition, and a sense of strategic modernity. Implementation, on the other hand, immediately causes effort, conflict, and risks. Its potential benefits often lie further in the future and are initially uncertain.

From an economic perspective, this resembles a signaling problem. If virtually every actor can claim a willingness to innovate, the claim loses its distinctiveness. Actions that actually cost something become more meaningful: reserved working time, a dedicated experimentation budget, foregoing an outdated control step, or the willingness to question an existing measure of success.

This doesn't mean that communication is worthless. Change can also fail without clear goals and public guidance. Communication only becomes problematic when it becomes a substitute for action. Its purpose is to prepare and explain decisions, not to permanently mask the lack of decisions.

The toughest test of a commitment to innovation is therefore not how convincingly someone speaks about the future. It is what concrete decision that person makes differently within their own sphere of responsibility than before.

What the spread of AI actually shows – and what it doesn't

The development of artificial intelligence provides a vivid example of the gap between public awareness and actual adoption in companies. In OECD countries for which relevant data is available, around 20.2 percent of companies reported using AI in 2025. This figure was 14.2 percent in 2024 and only 8.7 percent in 2023. Within two years, the share more than doubled.

These figures allow for two conclusions simultaneously. First, AI is indeed spreading. Its development is therefore by no means merely a communication phenomenon. Second, despite the high level of public attention, its use is far from widespread. An intense debate about a technology should therefore not be confused with its complete economic penetration.

The differences between sectors are considerable. By 2025, 57.3 percent of companies in the information and communication sector were using AI. For freelance, scientific, and technical services, the figure was 36.8 percent. A uniform picture of the entire economy would obscure these significant differences.

Furthermore, there is an important methodological caveat: A usage rate initially measures whether companies are using AI. It does not automatically answer how intensively that usage is being done, which business processes are affected, and whether the investments are paying off. An occasionally used assistant and a deeply integrated planning system can have very different organizational and economic significance.

Likewise, non-use does not automatically prove backwardness. A company can justifiably reject a use case because data is lacking, the error costs are too high, or another investment promises greater benefits. Neither individual hypocrisy nor the quality of a single investment decision can be deduced from distribution statistics.

The problem of scale behind the supposed lack of courage

Company size also affects the initial situation. For 2024, or the most recent year available, OECD data shows AI usage rates of around 40 percent for companies with at least 250 employees. For companies with 50 to 249 employees, it was 20.4 percent, and for companies with 10 to 49 employees, it was 11.9 percent. These size categories should not be directly used as benchmarks for comparison with the overall figures for 2025.

The gap is nonetheless revealing. It shows why simply demanding greater innovation falls short. A small business may have to manage the introduction of a new technology without its own data department, specialized legal counsel, or substantial financial reserves. A large corporation is more likely to have such functions, but it also bears other coordination burdens.

The fundamental prerequisites for innovation include more than just money and technology. They also encompass management skills, qualifications, existing knowledge, experience, and access to external partners. These resources influence which changes a company can meaningfully pursue.

Therefore, the blanket comparison of courageous innovators and complacent resisters is analytically inadequate. Some organizations could change more but choose not to. Others want to change but lack the necessary prerequisites. Still others invest heavily but fail due to the connection between technology and business processes.

A serious evaluation must distinguish between these cases. Otherwise, criticism of innovation becomes a moral judgment that neither explains the causes nor enables practical improvements.

When sensible individual decisions combine to create a standstill

A company can block change even though many stakeholders are acting understandably from their respective perspectives. The finance department protects liquidity, IT limits security risks, sales avoids disruptions to customer business, and operations management ensures delivery capability.

None of these tasks are inherently detrimental to innovation. On the contrary: without financial stability, security, and reliable performance, a company can hardly finance new ideas sustainably. The problem arises when each function focuses solely on its local risks and no one bears responsibility for the company-wide benefits of change.

A hypothetical example illustrates the mechanics. A new digital sales process could save time overall. However, the sales department would first have to clean up customer data, the IT department would have to create interfaces, and the controlling department would have to accept new key performance indicators. The potential benefits are distributed, while individual departments immediately incur additional workload.

Under such conditions, each party involved can find good reasons to postpone the next step. The overall result, however, remains unsatisfactory. The organization protects its individual interests so effectively that it hinders their shared development.

The solution is not to dismiss technical objections. It lies in balancing benefits and burdens. Anyone demanding cross-departmental change must also organize responsibilities, resources, and compensation mechanisms across departments.

Why the existing business almost always has the better arguments

There is a fundamental conflict of objectives between improving existing processes and exploring new possibilities. Existing processes provide experience, familiar customer relationships, and relatively predictable results. New approaches, on the other hand, require research, experimentation, and a willingness to accept initially incomplete knowledge. This tension is described in organizational research as the relationship between exploration and exploitation.

In day-to-day operations, existing businesses have a structural advantage. Their revenues are transparent, their cost centers are established, and their managers are in place. A new solution, on the other hand, often has to prove its value before it gains access to the prerequisites that would even allow for reliable proof.

This creates a circular argument: without proven benefit there are no resources, without resources there is no meaningful experiment, and without experimentation there is no proof. The organization can then claim that it saw no convincing alternative.

Conversely, it would be equally wrong to give priority to the new in principle. A company that constantly experiments and neglects its reliable performance jeopardizes the financial foundation of its own future. Innovation therefore requires a deliberately balanced approach and not a blanket devaluation of day-to-day business.

A useful consequence is the separation of different evaluation logics. A mature standard process should be judged on its stability and efficiency. An early attempt, on the other hand, should be evaluated on the quality of its questions, its limited risks, and its insights. Anyone who evaluates both using identical criteria systematically favors either stagnation or disorder.

Content producers caught between gaining knowledge and interchangeable rhetoric about the future

The innovation gap is particularly evident among content producers. Their own sphere of influence includes topic selection, research, presentation style, quality control, and handling feedback. They can demand innovation and yet repeatedly publish the same ideas in slightly modified packaging.

However, this should not become an unsubstantiated claim about all content on LinkedIn. Without a systematic investigation, it's impossible to reliably quantify what proportion is actually superficial or opportunistic. The relevant question is rather what incentives a particular business model provides.

If a provider's livelihood depends primarily on short-term visibility, it can be economically attractive to quickly address well-known hot topics. However, an independent analysis requires research time, methodological rigor, and often the willingness to accept a less sensational result. A conflict of objectives can therefore arise between providing added value in terms of expertise and achieving short-term marketability.

This distinction is particularly important for B2B content. An article about AI doesn't provide guidance simply because it contains new terms. It becomes useful when it explains conditions of use, costs, data requirements, risks, and verifiable results.

Genuine change in one's own field of work could therefore mean making fewer claims and offering more robust comparisons. Or correcting a frequently repeated thesis based on practical experience. It is not the volume of a call for innovation, but rather the ongoing development of one's own research that determines its credibility.

Middle management is playing out the conflict in their calendars

Team leaders often find themselves at the intersection of strategic expectations and limited working hours. They are expected to ensure smooth day-to-day operations, develop employees, meet quality standards, and simultaneously test new ways of working.

If change is to occur alongside fully planned tasks, its failure is already organizationally inevitable. A team cannot sustainably allocate the same capacity simultaneously to ongoing performance, training, data cleansing, and process restructuring without shifting priorities.

This doesn't mean that every innovation necessarily requires additional staff. It does mean, however, that time is a real resource. Anyone introducing a new process must identify which existing tasks will be reduced, postponed, or discontinued.

It becomes particularly problematic when team leaders are penalized for short-term deviations, while long-term improvements are only vaguely welcomed. In such cases, it's understandable for individuals to keep experiments to a minimum, report risks upwards, and defend established ways of working.

Credible leadership therefore demands concrete relief rather than abstract encouragement. It protects limited trial periods, defines escalation procedures, and distinguishes between avoidable negligence and a professionally prepared experiment with an open outcome. Only then does the demand for a willingness to change become a genuinely usable scope for action.

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The role of resource allocation in corporate innovation

At the executive level, the budget determines the stance

At the top of a company, a willingness to innovate is primarily demonstrated in the allocation of resources. Strategic statements only become binding when they are reflected in investments, personnel decisions, responsibilities, and the handling of conflicting objectives.

A board of directors cannot simultaneously demand profound change and expect every change to bypass an untouched cost structure. Likewise, a new business model cannot be seriously developed if it is judged solely on the short-term profit metrics of the established business.

However, differentiation is necessary here as well. A difficult liquidity situation can limit investments. A high debt ratio can reduce risk-bearing capacity. Not every postponed project is a sign of a lack of foresight. What is crucial is whether the limitation is transparently justified and linked to a comprehensible prioritization.

Problems arise when renewal is deemed strategically indispensable but consistently treated as a low priority in terms of funding. This creates a downward contradiction: the organization is expected to change without management altering its own allocation decisions.

A verifiable leadership achievement therefore also consists of knowing when to end something. Which existing initiative loses its budget? Which reporting obligation is eliminated? Which product will be discontinued? Which management preference must give way to a better solution? Without such decisions, the future will be placed on an already overburdened organization.

Why technical performance does not guarantee operational benefit

The debate surrounding AI clearly demonstrates how easily technical possibilities can be confused with actual results. A powerful system initially creates an option. Whether this translates into economic benefit depends on its specific application.

A study on the phased implementation of a generative AI assistant among 5,179 customer support employees, published as a working paper, found an average productivity increase of approximately 14 percent, measured by customer service requests resolved per hour. For inexperienced and lower-skilled employees, the increase was around 34 percent. For experienced and high-performing employees, the effect was significantly smaller.

These differences are precisely what matters. They contradict the notion that a tool works the same everywhere, regardless of task, experience, or context. A system can be particularly helpful where it makes existing knowledge accessible and supports less experienced employees in making recurring decisions.

However, such a result does not guarantee productivity for entire companies. Customer support is not automatically comparable to design, purchasing, management, or industrial maintenance. Even within a single company, data availability, the consequences of errors, and work structures can vary significantly.

The economically sound conclusion, therefore, is not to immediately introduce the same tool everywhere. It is to identify suitable tasks, measure the initial situation, and then examine under which conditions improvements actually occur. This is less spectacular than a grand transformation announcement, but significantly more informative.

The underestimated work lies between the tool and the work process

When evaluating new technologies, attention is easily drawn to purchase prices and visible features. However, the real work of change often lies in complementary activities: employees need to be trained, data structured, processes adapted, and responsibilities clarified. Innovation-related activities therefore encompass not only research, but also, among other things, software development, databases, training, design, and innovation management.

This breadth is crucial for cost-benefit analysis. An inexpensive tool can become costly if it generates high testing costs. A more expensive solution can pay off if it reliably integrates into existing processes and permanently reduces manual labor.

There are also transition costs. During implementation, the old and new processes may temporarily run in parallel. Employees have to learn, correct errors, and answer questions. Therefore, a short-term loss of efficiency can be compatible with a worthwhile long-term investment.

However, this shouldn't become an unlimited excuse. Those who accept temporary burdens need a clear understanding of when and how improvements will be evident. Without verifiable milestones, it's nearly impossible to distinguish whether a company is undertaking necessary development work or artificially prolonging an unsuitable project.

The crucial difference, therefore, lies between patient development and uncritical adherence to the status quo. Both may appear similar on the surface. Only transparent assumptions and verifiable results make them distinguishable.

A model calculation shows why saving time does not automatically mean profit

A simplified, hypothetical example illustrates the relationships. Let's assume a company employs 20 people in a knowledge-intensive process. Each person is allocated 1,500 working hours per year relevant to the tasks under consideration. In total, the process thus comprises 30,000 hours.

If a new way of working were to actually save ten percent of this time, this would theoretically result in a potential saving of 3,000 hours. Assuming a full cost rate of 50 euros per hour, this would correspond to a capacity value of 150,000 euros per year.

However, this value would not yet translate into additional profit, nor would it be automatically paid out into the business account. For that to happen, the freed-up time would have to be used economically: for example, through additional billable services, fewer overtime hours, avoided new hires, or a genuine reduction in other costs.

Assuming that software, training, integration, and additional quality assurance cost a total of €90,000 in the first year, fully utilizing the calculated potential would result in a surplus of €60,000. However, if only half the capacity value could be realized, the benefits would be €75,000, offsetting the same €90,000 in costs.

This example is not a forecast. It illustrates an evaluation logic: Crucial factors are not only the technically feasible savings, but also implementation costs, utilization rate, and economic viability. A sound innovation analysis must differentiate between these factors.

Increased output can even worsen a bad process

Not every acceleration is an improvement. If a company automates a poorly designed process, it can reproduce its weaknesses more quickly and on a larger scale. The crucial question, therefore, is not just how much more is produced, but whether the additional output is needed.

This difference is particularly easy to demonstrate in the content area. The number of published posts is initially just a metric for output. It says little about whether customers better understand the connections, whether qualified inquiries are generated, or whether decisions are made more easily.

The same applies to automatically generated contact requests in sales. More messages can be beneficial if they reach relevant customers with suitable offers. However, they can also create additional review effort and reputational risks if the approach is inappropriate.

A similar distinction is necessary in industrial processes. Higher throughput at a single machine does not automatically improve the overall result if another bottleneck awaits afterward or additional inventory is generated.

This leads to an uncomfortable consequence for the innovation debate: some supposedly modern measures are merely the technologically enhanced continuation of a questionable process. True innovation, on the other hand, can consist of reducing or completely eliminating an activity. Innovation is not always more. Sometimes it is the justified end of something superfluous.

The pilot operation can become a safe hiding place

A limited trial is generally a good idea. It reduces risks and allows for learning before a company makes larger commitments. The pilot project only becomes problematic when it lacks a clear transition to a decision.

A project can remain promising for a long time without it being clear which results will be sufficient to continue. Presentations, interim reports, and further testing phases are then produced, while the actual decision is postponed.

The economic damage lies not only in the direct project costs. Employees invest time, other projects are pushed aside, and the organization becomes accustomed to equating activity with progress. At the same time, an ongoing pilot project can easily be presented publicly as evidence of a willingness to innovate.

A reliable experiment therefore requires a precise question to be formulated before it begins. What problem is to be solved? What is the initial situation? What quality requirements apply? What costs are being considered? And under what conditions will the experiment be terminated, modified, or expanded?

Not every answer needs to be decided on the first day. But the decision-making process should be clear. Otherwise, the pilot project won't become a tool against uncertainty, but rather an organizational shield against responsibility.

Good key performance indicators make progress visible without inventing it

The number of ideas, workshops, or trained employees can provide information about activities. However, it is not sufficient to assess the success of a change. Therefore, innovation measurement makes sense by distinguishing between goals, activities, and results.

For a new sales process, relevant metrics might include processing time, error rate, rework, and conversion rate. For a technical assistance solution, additional factors such as technical reliability and the need for human intervention would need to be considered. For a knowledge platform, the time until users can independently complete tasks might be more meaningful than the number of stored documents.

A balanced selection of metrics is crucial. A single metric can create perverse incentives. Focusing solely on speed risks compromising quality. Rewarding only error prevention can discourage any change. Tracking only usage doesn't reveal whether that usage is actually effective.

Comparability is also crucial. Improvements must be assessed against a verifiable baseline. Changes in demand, case complexity, or staffing levels can influence results and should not be prematurely attributed to a new technology.

A good key performance indicator (KPI) doesn't simplify things arbitrarily. It makes a relevant relationship verifiable and simultaneously reveals where the limits of its explanatory power lie. This is precisely what distinguishes data-driven management from decorative numbers in a presentation.

Resistance can be about protecting vested interests – or valuable information

Not every objection to change carries the same weight. Some objections serve to protect power, habits, or personal advantages. Others point to real risks that were overlooked in optimistic project planning.

An employee who critically evaluates a new solution might, for example, identify specific sources of error. A specialist department might explain why certain data is not reliable enough. A manager might point out an economically unsound sequence. Such contributions are not automatically indicative of an outdated attitude.

The difficulty lies in distinguishing between objective review and blanket obstruction. A helpful objection identifies a specific risk, its potential consequences, and a condition under which it would be acceptable. A mere blockade, on the other hand, often remains vague or continually postpones the requirements.

This distinction protects both sides. It prevents legitimate concerns from being dismissed with the label of a lack of innovation. At the same time, it requires critics to provide verifiable reasons for their rejection.

A learning organization therefore doesn't have to welcome every innovation or overcome every resistance. It must be able to evaluate the reasons behind it. This is more demanding than a culture of general enthusiasm, but significantly more sustainable economically.

A culture of learning from mistakes needs boundaries, otherwise it remains just a slogan

A credible culture of learning from mistakes does not mean letting every wrong decision go unpunished. It must distinguish between a justifiable experiment, inadequate preparation, and the deliberate ignoring of known risks.

A meticulously planned experiment can fail and still yield valuable insights. Conversely, a successful approach achieved by chance may be methodologically weak. Those who judge solely by the final result may reward risky luck and penalize careful study.

Therefore, in practice, limited risk tolerances are necessary. An experiment with internal text drafts must be treated differently than a change that affects safety-critical decisions or significant customer consequences. The scope, control, and approval process must be proportionate to the potential damage.

Equally important is how we deal with negative results. If a team realizes that a proven solution is not economically viable, early termination can be a sound management decision. If every termination is portrayed as a defeat, the incentive to continue weak projects and downplay problems increases.

The quality of a culture of learning from mistakes is therefore not demonstrated by sympathetic language. It is demonstrated by whether people can report bad news in a timely manner and whether an organization responds with analysis rather than knee-jerk blame.

Those who receive the benefits influence the willingness to change

Innovation not only creates overall benefits, it also changes their distribution. A new way of working can offer customers better services, enable owners to generate higher returns, and relieve the burden on employees. However, it can also lead to certain groups bearing additional burdens or losing their previous position.

This issue of distribution should not be obscured by general promises for the future. If employees have to assume that every increase in productivity will only lead to a faster work pace, their reluctance is understandable. If managers lose influence due to a new process, their resistance may also stem from personal reasons.

This does not guarantee that every change will benefit all parties equally. Economic adjustments can require difficult decisions. However, it does necessitate openly discussing the consequences and consciously shaping transitions.

Further training, new responsibilities, and transparent participation can support implementation. Crucially, the promised prospects must align with the actual decisions. A company damages trust when it promises relief and then demands only additional work.

A willingness to innovate is therefore not just an individual trait. It also depends on whether people perceive the change process as professionally meaningful, predictable, and appropriate.

The most meaningful innovation often begins with an unspectacular question

Public debate favors grand concepts: disruption, transformation, platform, autonomous organization. In day-to-day business, the most economically significant entry point can be considerably smaller.

Why is the same information recorded multiple times? Why does a simple decision require multiple approvals? Which data is consistently missing? Where do queries, waiting times, and rework arise? Which customer problems are repeatedly solved individually, even though a standardized solution could help?

Such questions are not automatically anti-innovation simply because they relate to existing business practices. A significant process improvement can be a genuine innovation, provided it differs significantly from the current process and is actually implemented.

The advantage of a problem-oriented approach lies in its verifiability. A concrete bottleneck can be described, its costs can be at least approximately estimated, and improvements can be observed. An abstract demand to finally become more innovative, on the other hand, easily remains without consequence.

Focusing solely on what is immediately measurable should not overshadow long-term exploration. Companies also need room for possibilities whose benefits are not yet fully foreseeable. Crucially, a conscious distinction must be made between concrete process improvement, strategic exploration, and purely communicative needs.

Credibility arises where someone forgoes their own comfort

The initial criticism of innovation-driven actors is particularly justified where the demand for change is consistently directed at others. Employees are expected to become more flexible, while managers fail to examine their control practices. Customers are expected to use digital channels, while internal processes remain unchanged. Competitors are expected to rethink their strategies, while the company's own offerings have only been rebranded for years.

The problem is not a lack of originality in every single step of the process. No company needs to constantly reinvent its entire organization. Rather, credibility arises from the willingness to subject one's own area of responsibility to the same scrutiny that one publicly demands of others.

For content producers, this concerns the quality and originality of their content. For team leaders, it concerns the distribution of tasks and decision-making authority. For company management, it concerns capital, priorities, and power. Responsibility is distributed differently and should therefore not be equated across the board.

Those who actually wield more influence also bear greater responsibility for structural conditions. It would be convenient to look for the innovation gap solely in the willingness of individual employees to change, when budgets, incentives, and approvals are determined by others.

No more rhetoric about innovation, but a different decision-making order

The economically viable response to the facade of innovation lies neither in blanket disdain for LinkedIn nor in blind technological optimism. Both miss the point. Public communication can provide guidance, and new technologies can enable significant improvements. The crucial question is whether this leads to sound decisions and genuinely usable changes.

An innovative organization is therefore not recognized solely by its language. It is recognized by its ability to identify relevant problems, provide resources for addressing them, and evaluate different projects using appropriate criteria. It can initiate an experiment without feigning success. It can end it without treating the learning process as a failure. And it can scale up successful approaches instead of preserving them in a pilot phase.

The rationale, therefore, is this: The greatest obstacle is not the existence of established routines in principle. It is their inviolability. Routines are economically valuable as long as they reliably deliver benefits. They become a problem when their continuation requires no justification, while every alternative must provide virtually impossible proof.

Innovation begins where this asymmetry is corrected. Not everything new deserves a lasting chance. But neither does the existing, simply because it already exists. Anyone who takes this seriously must talk less about the future and more about changing the present.

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