Implementing AI requires much more than a decision to get a paid tool and a few words of encouragement: try it and share. Its effective use requires a change in mindset and constant reflection: “Could artificial intelligence help me in this task? How?”. Dagmar’s project manager Julia Huusko, an enthusiastic user of artificial intelligence and a developer of expertise, gives tips for the introduction of artificial intelligence.
AI experiments require time and space
Taking root in artificial intelligence as part of an organisation’s everyday life requires more than just technological solutions – it requires a change in mindset. As the mindset changes, AI becomes an integral part of work processes and its use becomes part of our daily tools such as Slack, Teams or email. No one is “going to use AI” separately – it becomes a natural part of workflows.
According to the dusty way of thinking, innovations are seen as top-down. In the case of artificial intelligence, the situation can easily be just the opposite. The most important thing is not that the management sets an example, but that it gives room for use and experimentation. In a modern organization, AI spreads at its best as a wave of transformational power, where those who use AI most effectively inspire others – regardless of their role or title. In other words, the adoption of AI does not necessarily proceed hierarchically, but in a decentralized manner and even from the bottom up.
The three phases of deployment
1. Planning and inspiring
- Management commitment and support – The successful implementation of AI is not just a technological decision – it is a change in the operating culture that management must actively support and promote. Without this, the change may remain a separate experiment.
- Set clear goals and metrics – AI needs to solve real problems. Define concrete benefits, such as time savings, faster decision-making, or improved quality. Also decide how the impact will be measured – for example, a reduction in errors or a smoother flow of work..
- Prioritise the most effective applications – Not everything can and should not be done right away. First, focus on the AI solutions that bring the fastest and most tangible benefits. Could artificial intelligence free up employees’ time from routine tasks or make reporting more efficient?
- Boost and inspire with the power of example – Show concrete successes: how artificial intelligence has helped other companies or your own organization. Pilot with a low threshold. Share success stories and let your staff share their own experiences.
- Some are already ahead of others – take advantage of internal AI ambassadors – Give forerunners visibility and encourage them to openly share information about their own experiences. Encourage experimentation and create a culture where successes and challenges are openly discussed.
- Collaboration with technology and IT – IT ensures that AI solutions integrate seamlessly with existing systems, meet security requirements, and are scalable for future needs.
Define concrete benefits, such as time savings, faster decision-making, or improved quality.
2. Experimentation and implementation
- Apply in practice, create specific examples for those working in different tasks – The benefits of artificial intelligence will only be realised when people see how it makes their own work easier in practice. At the same time, the implementation takes place more naturally and enthusiasm grows.
- Modify processes with the help of artificial intelligence – Analyze current operating models and identify the opportunities that artificial intelligence brings. Ensure that AI supports employees. The renewal of processes must not make work more difficult, but the task of artificial intelligence is to make it easier and more efficient.
- Ask and find out how AI can help me in my daily work tasks – AI is most useful when it supports employees in their own, concrete tasks. Everyone has a different role and area of responsibility, so the most important thing is to think about where in my work AI could make things easier, save time or bring new insights.
- Create a safe framework for piloting and experimentation – Enable low-threshold experiments. A culture of experimentation is a key part of the utilisation of artificial intelligence – the more you learn through practical experiments, the faster genuinely useful solutions are found.
- Monitoring the impact of artificial intelligence and developing job descriptions – How will artificial intelligence change job descriptions? How should competence be updated? The introduction of artificial intelligence is not a one-off change, but a continuous process that shapes job descriptions, competence needs and operating models. In order for AI to be used effectively, it is important to monitor its impacts, identify new skills needs and support employees in the change.
Artificial intelligence requires a change in mindset: Could artificial intelligence help me in this task? How?
3. Consolidation and continuous development
- Share successes and lessons learned – keep going – The introduction of artificial intelligence does not happen with a single training or one successful experiment – it requires constant communication, examples and repetition. People adopt new ways of working at different speeds, so communication must be continuous and multi-channel in order for the use of artificial intelligence to become a normalised part of everyday life.
- Dispel suspicions, ensure security – Build trust and keep risks under control. Trust in artificial intelligence is created by openness, information and clear information security practices. When AI is deployed in a more controlled and responsible manner, it is seen as a useful tool rather than a threat.
- Educate and engage – Make AI a tool for everyone. The real benefit of AI for the organization will only arise when all employees feel that its use is meaningful and know how to apply it in practice. Use low-threshold learning solutions – Short videos, webinars, workshops, and prompt-a-thons make learning flexible.
- Use best practices from outside your own industry – See what others are doing and apply what you learn to your own industry. The development and application of artificial intelligence progress at different paces in different industries, and often the best ideas and innovations come from surprising directions.
- Take AI into processes in small steps – Don’t try to change everything at once. Focus on single, impactful use cases that quickly translate into tangible results. This enables learning along the way and reduces resistance to change.
- Measure and develop – Utilizing AI is not a one-time project, but a continuous development process. When deploying AI, it is important to understand its impacts, assess success, and identify areas for improvement. This can only be achieved if the effects of artificial intelligence are systematically monitored and informed decisions are made based on it.
- Ethical principles and accountability – The use of AI brings huge opportunities, but at the same time, it places a responsibility on companies and organizations to ensure that technology is used in a fair, transparent, and ethically sustainable way. This is particularly important in expert organisations, where customer trust, data reliability and responsibility are critical factors.
People adopt new ways of working at different paces and in different ways, so communication must be multi-channel and learning solutions must be light.
The introduction of artificial intelligence is not just a technical update, but a journey towards a new way of working. It is continuous learning, bold experiments and insights that make everyday life smoother and work more effective.
As the mindset evolves, AI isn’t just a tool – it becomes a superpower that helps us all do our jobs smarter!
Encourage experimentation and create a culture where successes and challenges are openly discussed.
Do you need more understanding of artificial intelligence in your organization? Would you like to start testing and utilizing the use of artificial intelligence, but don’t know where to start?
Our experts are happy to help – contact us and we will arrange a meeting!
AUTHOR
Project Manager
Julia manages both client and internal projects seamlessly. As a time-saver, she values ease of execution, process efficiency, and open communication. A solution-oriented innovator excited about AI’s possibilities, she runs year-round – and dreams of moving to Sicily.
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