Shadow AI: A silent helper in the background and a test of corporate culture

What is Shadow AI and why should you care 

Shadow AI (literally "shadow artificial intelligence") refers to the use of AI tools by employees without formal approval or supervision from the IT department. It is akin to the phenomenon of shadow IT (i.e., unofficial technologies within a company), but in the case of AI, the barriers to entry are even lower and the spread is faster. Today, employees can start using generative AI models like ChatGPT to facilitate their work within minutes, without management even knowing it. And this is not an exceptional occurrence: studies show that over 80% of organizations exhibit signs of such informal AI activity across all departments – from sales teams entering customer data into ChatGPT to HR uploading candidate resumes into public AI applications. Another survey revealed that more than half of employees (59%) have already used some unauthorized AI tool at work, and in most of these cases, their direct supervisor is even aware of it and quietly tolerates it. Thus, Shadow AI is not a marginal phenomenon, but a common reality even in Czech companies and at the same time a warning signal in terms of corporate culture. 

Why Shadow AI arises in companies 

Shadow AI does not arise because employees want to deliberately break the rules – on the contrary, they often have no other option in a situation where they need to perform. There are several main reasons why people turn to unofficial AI tools: 

• Hunger for efficiency and innovation: Employees are under pressure to do their jobs faster and smarter, and AI offers them immediate assistance in this respect. Many AI tools are also available for free or at a low cost, so their benefits (quick text summaries, generating proposals, automating routines) manifest almost immediately. According to recent findings, most employees perceive AI as a way to make their work easier, not to take someone else's job, and thus use it to increase their own efficiency. When they feel it will help them meet deadlines or complete tasks better, they do not hesitate, especially if the company currently offers no official alternative. 

• Slow internal processes and lack of support: Many organizations are still developing strategies, policies, and rules for the use of AI, which takes months. (If this topic interests you, read David Novák's article) Internal approvals of new tools, security checks, and procurement processes are often so cumbersome that employees have already found solutions on their own in the meantime. While management discusses AI strategy, people "on the front lines" are already using AI to solve real problems in real-time. As a result, a gap emerges between management's caution and individuals' initiative. Employees are optimizing their work for today, while leadership is still planning for tomorrow. The expansion of shadow AI is also fueled by the fact that official AI tools are either unavailable or do not meet needs. According to a survey, although 77% of companies have some policy for AI, only half of them provide employees with approved AI tools, and only a third of employees believe that these official tools actually meet their needs. Where official support is lacking or stalled, personal accounts and DIY "black" solutions come into play. 

• Performance pressure vs. rules: In the cultural undercurrents of many companies, a dual message resonates: on one hand, "be innovative, use AI, speed up", on the other "follow our procedures, wait for approval, adhere to regulations". These mixed, and sometimes even contradictory messages from the top place employees in a difficult situation. They feel strong pressure to be productive and see that competitors are using AI, so they do not want to fall behind. At the same time, however, they do not have clear tools or boundaries available, as official standards "will come in time". The result? To meet the demand for speed, they find their own way, even if it means circumventing existing rules. Employees are thus addressing a paradox where they have to simultaneously 1) innovate and speed up using AI, 2) wait for top-down directives, 3) protect the company from risks that cannot be fulfilled together, and so they improvise in favor of the first law while ignoring the second. 

Factors in corporate culture that promote shadow AI 

Shadow AI is a mirror of corporate culture. Its existence often points to "invisible friction" within the organization – obstacles and fears that lead people to prefer to innovate in secrecy. Certain cultural patterns may reinforce this phenomenon: 

• Lack of trust and psychological safety: If employees feel they might be penalized for using AI, they prefer to remain silent about it. If the corporate culture lacks openness to experimentation, innovation shifts "into the shadows." As AI consultant Dee Marshall says: "When people feel they have to hide how they work, it means they do not feel empowered or trusted to innovate openly." A corporate culture with a lack of trust paradoxically leads to greater secrecy – employees innovate secretly because they do not believe leadership would accept it positively. 

• Excessive control and prohibitions without explanation: Companies that respond to new technologies primarily with prohibitions and strict restrictions may inadvertently exacerbate the problem. Widespread blocking of AI tools drives the use of AI into illegality, causing organizations to lose oversight and influence over how employees use AI. If there is no constructive "safe path," unofficial usage becomes the path of least resistance, the easiest way to seek help. Culturally, this can make the company appear overly rigid. In contrast, organizations that provide boundaries and trust instead of prohibitions often find that people are happy to use official tools when they exist. The goal is not to break the rules but to get work done efficiently. 

• The role of IT security: The phenomenon of shadow AI is closely linked to the area of IT security. When employees use unapproved AI tools, it may lead to unintentional data breaches, circumvention of internal security measures, or disruption of the consistency of security processes. IT security teams often face the dilemma of how to protect the organization without supporting the movement of innovation "into illegality" with their restrictions. This is why it is essential that security policies are communicated clearly, explained, and accompanied by safe, approved alternatives. This significantly reduces the risk of shadow AI while also creating an environment where users can utilize AI responsibly and safely.  

• Silence and tabooing of the topic: Sometimes the culture sends a signal that AI is better left unspoken. This may occur when management ignores ongoing experiments or mid-level management actively discourages the use of AI by claiming it is dangerous or undesirable. As a result, instead of having an open discussion about the possibilities of AI, everything happens quietly, in isolation, without sharing insights. Shadow AI thrives in an environment where there is a lack of transparent communication: if there is no AI policy, or the topic never comes up in meetings, employees come to feel that the best approach is to do it their way and preferably not speak about it aloud. 

• Double standards and mixed signals from above: As previously mentioned, unclear or contradictory directives from leadership create fertile ground for shadow activities. A classic cultural shortcoming is demanding innovation but not providing the resources or rules in a timely manner. It is even shown that top management sometimes circumvents its own rules – in a survey, 93% of top managers admitted to personally using unauthorized AI tools. Such silent approval from above effectively legitimizes this approach. If a company formally prohibits something but informally everyone does it, it undermines trust in both the rules and leadership. Employees then take company policies with a grain of salt and do "what they need," as they see that even their superiors do not strictly follow them. 

It is noteworthy that shadow AI is not just an IT or compliance issue, but also a question of reliability and unity of the company. If tools are secretly used that can affect work outputs, it threatens coherence in decision-making and jeopardizes brand consistency. Experts warn that unaddressed shadow AI can undermine corporate culture and reputation. What signal does it send to employees when something is officially prohibited but unofficially tolerated? Or when AI is relied upon quietly in strategic decisions? In the long term, this leads to erosion of trust if the situation is not addressed openly. 

How to recognize that shadow AI exists in your organization 

It is very likely that some form of shadow AI exists in your company, especially if the company has yet to offer clear guidelines or tools. How can such hidden AI activity be recognized? Below are some signs and manifestations that may indicate shadow AI: 

1. Sudden increase in productivity or output quality: If an individual or team begins to complete tasks significantly faster than before or delivers outputs of unexpectedly high quality, silent assistance from AI tools may play a role. Extreme improvement in the speed of content creation or analytical results without any other apparent cause is often an early warning sign. Of course, sometimes it may also be a natural improvement in processes – distinguishing AI from human progress is not easy. However, a sudden change in performance certainly warrants attention. 

2. Change in style of work or communication: A marked shift in the style of writing documents, emails, or messages may indicate that part of the text is being generated by AI. For example, a monotonous, very consistent way of expression or the use of phrases that the given employee did not use before may be a clue from an LLM model. Likewise, if an analyst comes up with detailed analysis in no time, they may have used an AI tool for data processing. 

3. Reluctance to share workflows: Do you encounter evasive or vague responses to the question "How did you come to that conclusion?" Or do employees seem reluctant to show their intermediate products or work processes? This may signal that they are using tools that they believe management would not approve of. When people fear judgment, they prefer to hide their processes – and shadow AI is precisely that case. A corporate culture that values success but does not prefer to dissect the means to it provides fertile ground for hidden uses of AI. 

4. Bypassing official channels: Another sign is situations where employees use personal accounts or unsupported applications for work, whether mentioning it in passing or it coming to light indirectly. For example, a recruiter uploading candidate data to a public AI service (instead of the internal HR system) for faster processing of resumes. Or a salesperson generating a proposal with their personal version of ChatGPT. Sometimes this also shows up in accounting, e.g., in invoices where subscriptions to AI applications appear that have not been officially approved by anyone. If people are bypassing IT security (e.g., sending files to external services), it is already clear evidence that shadow AI is functioning in your organization. 

5. Silent approval from management: A specific "symptom" is a situation where mid-level managers or team leaders are aware of unofficial AI use and tolerate it. As shown in the aforementioned survey, in 57% of cases, direct supervisors are aware and do not object. They may see the benefits themselves and do not want to slow the team down in more productive work. However, for the overall culture, it is alarming if this happens secretly. It indicates a mismatch between official policy and everyday practice. Such silent approval serves as a signal that rule-breaking is acceptable if it leads to results, which undermines respect for rules in general in the long run. 

How leadership can work with Shadow AI 

A key question for leadership is how to respond constructively to the phenomenon of shadow AI and ensure that the company learns from it and reaps benefits, rather than simply imposing strict controls and sanctions. Experts at Kogi agree that the best approach is to strengthen trust, open communication, and employee engagement, rather than repression. Here are best practices and principles: 

1. Acknowledge that AI is living its own life in the company and destigmatize it.
The first step is to openly name the existence of shadow AI instead of pretending that nothing is happening. Managers should clearly state that they are aware that people are using some AI tools and declare that the aim is not punishment but finding a path to safe use. This statement in itself alleviates pressure, and employees will see there will be no “witch hunt.” Instead of prohibitions, express a willingness to partner: encourage teams to share what tools they are already using and for what purpose. Show appreciation for their initiative ("We see you are already finding clever solutions"), thereby showing respect for their motivation to innovate. Such acknowledgment removes stigma.  

2. Create an environment of psychological safety and trust.
It is essential to assure employees that sharing their experiences with AI or admitting to using an unauthorized tool will not have negative consequences for them. Remove the atmosphere of fear by perhaps declaring an "amnesty" for past rule violations in this area – the goal is to understand what people need, not to catch them. Reward openness and honesty (e.g., public praise for a team that came up with a creative AI solution), instead of blindly enforcing rule compliance at all costs. Building trust also means listening to concerns. Leadership should openly address these concerns and explain to people what role AI is meant to play (e.g., that it is a tool to improve their work, not to replace them). When employees understand why the company is implementing AI and that it is not against them, you will gain their cooperation. Trust and safety are the foundations without which any AI initiative can falter. In a culture of fear, people will prefer to continue "in illegality." 

3. Establish clear rules along with education.
Once the atmosphere is open, it is time to set clear boundaries for the use of AI. It is essential that the rules do not arise in isolation from practice. Ideally, involve those employees who already use AI in their creation. The AI policy should not just be a list of prohibitions but mainly guidelines – that is, a positive framework for what is allowed and under what conditions. For example, list approved tools and areas where they can be used, so there is no reason for people to flee elsewhere. It is advisable to build measures on explanations of reasons: when you prohibit something (e.g., sharing customer data with a public AI service), explain that it is for the sake of protecting client data, etc. People are much more likely to accept rules they understand and that make sense for the business. At the same time, you must educate employees – offer training, workshops, or resources where employees can learn how to use AI effectively and safely. Do not assume everyone is well aware of the risks of AI. Therefore, structure training as support, not a necessary evil for compliance. Train and coach instead of just policing. The goal is to learn new procedures together. 

4. Involve internal ambassadors and support experimentation.
In every company, there are enthusiasts for new technologies – those who are already experimenting with AI and voluntarily seeking more information about it. Find these “AI champions” across teams and engage them in the initiative. Give them the opportunity to test new tools on a small scale (pilot projects), hold internal "AI office hours" or mini-courses for colleagues. Encourage them to share their insights, perhaps through a corporate discussion channel about AI where tips and tricks can be discussed. Do not shy away from smaller experiments, even if they do not yield perfect results right away. A corporate culture that rewards curiosity and learning will gain an edge. The task of leadership is to create an environment where it is safe to try and make mistakes, while also monitoring risks. This will further strengthen trust – people will see that the leadership's goal is to learn together how to use AI responsibly, not to catch someone out. 

5. Communicate consistently and transparently.
Whatever measures you implement, their success relies on communication. Ensure that employees clearly understand what the goals of implementing AI are, what the rules are, and how decisions about AI in the company are made. Do not forget to share successes: if a team has expedited a process by 30% due to officially deployed AI or developed something new, publish it internally as a case study. Be open about mistakes or incidents (without targeting specific individuals). This demonstrates that transparency is more important than perfection. Uncertainty and ambiguity are fertile ground for continued underground AI usage. Clear, consistent communication reduces the need for clandestine activities. Every employee should understand how AI fits into the company's strategy and how they can contribute themselves. 

6. Proactively monitor and assess the situation.
It is beneficial to get a real picture of the current state and continuously update it. Conduct an anonymous survey or audit: ask people what tools they use to ease their work, or look at recent outstanding outputs and investigate whether they played a role in AI. You may discover more innovations (and potential risks) than you realized. Use this information positively – as input for further rule adjustments, for the offer of new training or for identifying areas where investments in official AI tools would make sense. Maintain an open feedback channel: encourage teams to report where official tools are insufficient and why they opt for alternatives. This allows you to get ahead, and instead of firefighting after security incidents, you will know in advance about employees' actions and be able to guide them in time. At the same time, it sends a signal that you care about support, not surveillance. 

How to transform shadow AI from a risk into a competitive advantage 

Shadow AI is not something that can be simply banned or ignored. It is feedback from your employees indicating where your organizational structures and processes are failing to keep pace with innovation. 

Shadow AI is, in practice, a conflict of motivations: management wants speed and innovation, the business wants performance and results, but security has KPIs set such that "the best incident is no incident at all." And when security is evaluated primarily on the basis that nothing happened, it naturally ends up with bans, because prohibition is the simplest way to reduce personal risk. However, the company does not create security this way, but a blind spot: the use of AI shifts outside of official tools and control, and the actual risk increases. 

Therefore, it is primarily not a technical question but one of governance and leadership. If management does not align goals (and responsibility) across roles, everyone will play "their own game": security will hinder, business will circumvent, IT will be frustrated, and employees will do it their way. The solution is to give AI a clear mandate from above, define an acceptable level of risk according to data types, and set KPIs to reward safe enablement, not blanket blocking.