The adoption of AI solutions is progressing rapidly – through both newly built applications and features embedded into existing systems. At the same time, companies are struggling with how to make these solutions generate real business value.
MIT’s State of AI in Business 2025 report highlights a worrying phenomenon: 95% of organizations do not gain any financial benefit from their AI investments. This is where change management plays a crucial role. Models can be technically excellent, but everyday use often falters if AI does not solve the practical problem that hinders productivity. The issue is not only about technology, but about change management and how people, processes, and culture are integrated into the development.
Preconditions for Success
Comprehensive change management in AI utilization begins even before actual development projects are formed and launched. A holistic change journey for leveraging AI consists of three key stages through which companies can achieve sustainable improvements in efficiency and productivity. These stages are explained in more detail in the following sections.
1. Building the Strategic and Cultural Foundation
Companies have strategic objectives and derived business goals in which AI solutions can provide significant advantages by improving efficiency and productivity. Successful AI adoption requires that people understand the significance of the change, receive sufficient support, and can participate in the journey from the very beginning. A strong data culture, continuous competence development, and ongoing communication create the foundation. In addition, the example set by leadership carries great weight: without commitment and a clear message, AI solutions easily remain at the pilot stage. Therefore, increasing management’s understanding of AI is key at the early stage. Naturally, defining the AI strategy and objectives that connect to the company’s overall strategy is the responsibility of top management.
Real AI utilization begins with a precise understanding of business needs and pain points. Identifying, designing, and prioritizing potential AI solutions require vision, agile methods, and continuous oversight. Development projects must be carried out in the right order to maintain overall visibility of the development roadmap and existing AI solutions. All of this requires seamless cooperation between business and IT.
At the preparatory stage, it is also important to establish frameworks and principles for responsible and ethical AI development. In addition, the organization should take a position on the balance between IT and governance-driven (centralized) and individual-level (decentralized, citizen AI) development, allowing and supporting small-scale experimentation where appropriate.
2. Continuous Change Management as Part of AI Development and Implementation
Development projects are continuous processes that require clear direction, ongoing prioritization, and value measurement. This also supports MIT’s observation: only organizations that combine technology with a learning, adaptive process truly benefit from AI.
Change management must be integrated into every phase of a project:
Before the project: It is important to identify the change, communicate it clearly, and engage key people. The target state – how AI is intended to transform processes and roles – must be understandable to everyone. It is critical to build a sponsorship and support network in which leadership and key individuals are actively involved.
During the project: Keep key users and business stakeholders closely involved in planning. Communication serves as a continuous bridge-builder: explain what, why, and how, and clarify how the change affects people. Ensure that training and support are included from the beginning, not just at the end of the project. Introduce feedback and dialogue channels that employees can use in real time to express concerns, questions, and suggestions.
At the implementation phase: Plan training and competence development systematically. Provide practical support, support personnel, chatbots, and provide easily accessible guides. Monitor how the solution spreads and highlight success stories. Tangible benefits build trust.
3. Embedding AI into Everyday Work
Ultimately, the goal is to embed AI use into the company’s ways of working and ensure continuous development. AI utilization and development should be linked to goals, metrics, and management. Continuously develop skills and monitor the value created by AI solutions. If monitoring through business metrics is not possible, use surveys or employee satisfaction indicators instead. Use these results as tools for both development and communication.
Build an internal “AI Neighbors Network” (AI Center of Excellence) to support change in everyday work. It is easier to ask a nearby colleague for help than a central development team. Continuous communication, celebrating successes, and sharing stories should also be part of everyday life.
In addition to all this, the company should, of course, centrally manage the operational governance of AI: maintaining the development portfolio, continuously identifying new technological capabilities, monitoring projects and usage, ensuring maintenance, and overseeing responsibility and compliance with AI regulations and legislation.
Utilizing AI in Change Management
AI can and should also be utilized in change management itself. There are several ways to do this, such as:
- Analytics and forecasting: AI can model how change affects different parts of the organization and identify risk areas in advance.
- Real-time monitoring: AI can collect and analyze data to show how well the change is progressing and where additional resources are needed.
- Communication and training: Chatbots based on natural language processing can provide continuous employee support, while personalized learning platforms ensure that everyone receives the right information at the right time.
This way, change management shifts from reactive to proactive. Problems can be addressed before they grow larger.
Summary
Successful utilization of AI solutions is not primarily a technical challenge, but a change management challenge. Organizations that understand this and build a strong culture, competence, and leadership for AI transformation will gain real value from their investments. This ensures that AI becomes a permanent part of everyday operations.
Key elements of comprehensive change management include:
- Creating a shared direction, foundation, and environment that encourage AI utilization
- Agile methods for defining and prioritizing business requirements and AI use cases
- Continuous communication, employee engagement, and knowledge sharing
- Systematic competence development and encouragement of experimentation
- Support and training for solution implementation
- Measuring business value, not just technical metrics
At Norrin, we are both comprehensive AI solution providers and experts in change management. We are happy to assist in planning, implementing, and orchestrating the various aspects of change management as part of, or even prior to, your organization’s AI initiatives.
Get in touch and let’s start the next transformation together: tomi.bergman@norrin.com
