"The thing with these changes is that nobody really likes them. I'd prefer it if changes were managed so that others change, but I stay the same," joked a good friend when the topic turned to AI adoption in companies. This friendly joke, however, reflects a deep truth: people often prefer their comfort zone, where everything is under control and familiar.
The key to successful change management is understanding that fears and resistance are natural and must be addressed by the entire organization, including management, through practical solutions and support. Attitudes towards change can also be influenced by previous experiences with technological innovations.
A lack of prior work experience with radically new types of technology can increase resistance. Many organizations are accustomed to dealing with small, gradually introduced new tools and methods. This places a great responsibility on the shoulders of leaders: to cope not only with technical changes but also with the challenge of transforming organizational culture.
To reduce resistance to change and alleviate fears, active and open communication is necessary, along with clear plans where all employees have the opportunity to participate and contribute. Transparent processes, consistent feedback, and practical training are key activities that help build trust and readiness in the change management process.
The Status Quo: How Things Have Been So Far
When we think about office work, it's clear that much of it relies on tools and techniques we've been using for over two decades. Email, Excel, Word, and web browsers have been our go-to tools since the late '90s.
It's easy to forget that when these tools were first introduced, adopting them was a big change. When email first entered the workplace, everyone needed training on how to use it. The same was true for the more complex programs like Excel and Word.
For years, the office work routine has remained relatively stable. While we've seen many minor updates and improvements, these have mostly been incremental enhancements to existing tools. Excel gets new features, email security is bolstered, but the core experience remains the same. Most of these changes have required some adjustment and adaptation, but not complete retraining or total overhauls of work practices.
Major technological leaps that completely transform our work style or methods have been few and far between. Perhaps the most significant change in recent memory was the forced shift to remote work due to Covid-19.
This general stability has shaped attitudes, leading many to view our tools as fixed and reliable. As a result, we've grown cautious, even sceptical, about the need for sweeping changes.
Today's Challenges in Implementing AI in Organizations
Implementing artificial intelligence in an organization is a challenge that requires a well-thought-out approach. Leaders responsible for AI adoption in their organizations face a range of tasks, some more complex and multifaceted than others.
But what are these specific challenges, and how can they be best overcome?
Lack of knowledge and experience
Many leaders, accustomed to small, gradual technological innovations, find themselves facing a new reality: AI implementation is much more complex and far-reaching. As AI is a relatively new and constantly evolving field, many leaders lack personal knowledge and experience in how to effectively deploy AI. Understandably, this can create uncertainty and fear of making wrong decisions.
Changing organizational culture
Integrating AI also means changing organizational culture, which is often the biggest and most difficult challenge. Employees must adopt new tools, learn new skills, and adapt to new work processes. Managing change at a cultural level requires the ability to engage and motivate employees so that they feel part of the innovation process.
Resistance to change
People have a natural tendency to resist change, especially when it affects their daily work processes and habits. Leaders must take this into account and develop strategies to mitigate this resistance. Proper communication, openness, and participation throughout the change management process are key factors here.
Technical complexity
Implementing AI technologies often requires specific technical knowledge and skills. Organizations may lack the necessary internal competence and often have to turn to external experts. This can make the process more complex and costly, and there is also a need to manage various third parties and coordinate their activities.
Ethical and legal considerations
The use of AI also involves several ethical and legal issues that leaders must consider. For example, data protection and privacy, AI hallucinations, and bias. These topics require careful consideration and the establishment of appropriate policies and procedures.
Proving ROI
Every change must be economically justified. Leaders must prove that AI implementation brings real benefits to the organization and is a worthwhile investment. This can involve complex ROI calculations and the design of metrics in a situation where there are no established standard practices and few case studies to rely on.
Short-term operational issues
Even well-thought-out and planned AI implementation can cause operational disruptions in the short term. This can be due to both technical problems and increased learning load and adaptation needs for employees.
Keeping these challenges in mind, leaders are in a unique position to successfully guide their organization through the AI integration process, provided they are willing to learn, adapt, and actively participate throughout the change process.
An Example of Today's Problems: The BYOAI Trend
![BYOAI - tehisintellekti juurutamine organisatsioonis rohujuure tasandilt](/images/blog/beyond-the-hype-a-pragmatic-gu-inline-1-ai_juurutamine_2.avif
BYOAI - grass-root initiatives of Applied AI
One of the recent notable trends in the adoption of artificial intelligence is "Bring Your Own AI" or BYOAI. This refers to a situation where employees bring their own AI tools and solutions into the company to perform their work tasks. According to the 2024 Annual Work Trend Index, 78% of employees use their own personal AI tools, not the ones provided by their organization. While this may seem like an innovative and proactive step by employees, it comes with several problems and challenges that management must address.
Lack of a unified approach
BYOAI often leads to a situation where different AI tools and solutions are used that are not coordinated or standardized with each other. This can cause data sharing and interoperability issues, as well as hinder the implementation of organization-wide strategies. If each employee uses a different AI solution, confusion and inconsistency in work processes can also arise.
Security risks
Each AI tool needs to be given access to certain data and systems, which means potential security risks. If employees use their own tools, they may not meet the company's security requirements or be sufficiently protected against cybersecurity threats. This can lead to data leaks, cyberattacks, or other security threats that jeopardize the organization's integrity and credibility.
Compliance and regulatory requirements
The use of different AI tools can also create problems in terms of compliance with regulatory requirements. Different solutions may follow different privacy and data protection standards, which can lead to a situation where the company unknowingly violates applicable laws. Meeting regulatory requirements and ensuring compliance requires clear guidelines and control mechanisms, which are difficult to ensure in the context of BYOAI.
Difficulty measuring effectiveness
If employees use different AI solutions, it can be difficult to measure the effectiveness and impact of these tools on the organization. This makes it harder for leaders to assess ROI and can lead to wrong decisions in the future. Standardized solutions allow for better oversight and analysis, which helps leaders make informed decisions.
Training and support
BYOAI brings with it the challenge of training and support. Each AI tool needs to be provided with appropriate training and support, which increases the burden and resource expenditure of the organization. A unified solution would make it much easier and more efficient to organize centralized training and provide support tailored to the needs of employees.
Lack of coordination in innovation and development
The adoption of different AI solutions can hinder the coordination of innovation and development within the organization. If each employee works with their own solution, it can be difficult to leverage collective knowledge and experience that could help develop and improve AI technologies together. A lack of unity and collaboration can slow down innovation and lead to a waste of resources.
The BYOAI trend, as a grassroots initiative, is welcome, but it is also important for management to adopt clear policies and guidelines on the use of AI tools while being open to employee ideas and suggestions. If leaders can strike a balance between an individualized approach and organizational needs, AI adoption can be more successful and efficient.
Change Management Frameworks
The challenges associated with the BYOAI trend underscore the importance of a structured approach to change management when implementing AI in organizations. To address these challenges and ensure an effective AI adoption process, organizations can turn to established change management frameworks.
Change management is a strategic process through which organizations implement changes to achieve desired outcomes. Implementing artificial intelligence represents a significant technological and cultural change, which is why it's important to use established and effective change management frameworks.
Below is the selection of the main change management frameworks that can be applied in the context of AI.
Kotter's 8-Step Change Management Model
: valmistu muudatusteks, loobudes vanadest töömeetoditest ja avades organisatsiooni uutele ideedele ja tehnoloogiatele.
- Change (Muuda): vii läbi muudatused, juurutades uusi tehnoloogiaid ja töömeetodeid.
- Refreeze (Kinnita muutused): stabiliseeri muudatused ja integreeri need organisatsiooni igapäevatöösse.
Bridges' Transition Model
William Bridges' model focuses on people's emotional and psychological transition during change. It consists of three stages:
- Ending: acknowledge and come to terms with the loss of the old and familiar way of working.
- Neutral zone: be prepared for a period of uncertainty and confusion as employees adapt to new tools and processes.
- New beginning: implement and reinforce the new way of working, encouraging and supporting employees in using the new methods.
McKinsey 7S Framework
 cycle provides a structured framework for implementing this process, allowing organizations to plan, test, evaluate, and refine changes on a small scale before implementing them more broadly.
Effective change management principles are equally important. Clear, transparent communication ensures that all stakeholders understand the reasons behind the changes and their impact on daily work processes. Involving employees from all levels of the organization promotes a sense of unity and collective adoption of new technologies and practices. Providing ongoing training and support is essential to ensure that employees are well-equipped to handle the changes and make the most of the AI applications being implemented.
Continuous improvement does not always require major overhauls; Lean IT encourages organizations to make small, well-thought-out changes on a regular basis. This approach allows for a more gradual and manageable transition, reducing the risk of disruption to daily operations.
In addition to these principles, organizations must prioritize data quality and accessibility, enabling employees to leverage the full potential of AI applications and make informed decisions based on the insights generated. Risk management is also vital, and Lean IT recommends using structured tools and methods for identifying and addressing potential risks associated with AI applications.
By embracing the principles of continuous improvement and effective change management, organizations can foster a culture of innovation and adaptability. This approach enables companies to successfully navigate the complexities of AI implementation and harness the full potential of these transformative technologies.
Conclusion
Implementing artificial intelligence in an organization brings up several challenges and changes that require a careful approach. The responsibility of leaders is to deal not only with technical aspects but also to overcome human and cultural barriers. Fear of the unknown and resistance to change are natural phenomena that need to be handled carefully. Transparent communication, consistent feedback, and active employee involvement are key elements that help build trust and readiness to cope with changes.
Historically, organizations have been more successful with small, gradually introduced changes. Therefore, especially for complex innovations like AI, it's important to start with small pilot projects and use an iterative approach. Lean IT methodologies and continuous experimentation help reduce risks and ensure that AI implementation brings real value to the organization.
When managing changes, it's important to choose a suitable and effective framework, such as Kotter's 8-Step Model, the ADKAR model, or the McKinsey 7S Framework. These structured approaches help organizations manage change systematically and successfully.
Implementing AI in an organization is usually not a one-time event but a continuous and iterative process. There is always an opportunity to improve systems and processes and learn from experiences and feedback. This requires resilience and readiness from leaders to experiment with new solutions.
In summary, AI implementation is simultaneously a complex and opportunity-rich step that requires strategic thinking, openness, and an innovative approach from management.If a balance can be maintained between the adoption of new technologies and resource conservation, AI implementation can offer organizations a competitive edge and create higher value for both employees and customers. It's important that leaders understand human barriers and are able to provide support and transparent communication throughout the process.