Imagine an organisation where artificial intelligence produces new code and solutions at double or triple the speed, but old processes remain unchanged. Meetings would need to be held even more frequently, sprint reviews would stretch for hours, and decision-making would become congested. The speed of AI would not be an advantage, but rather a source of stress and wasted potential.
AI – whether it involves generative tools or other automation solutions – has not only transformed software development but also other functions and tasks at various levels of the organisation. For example, HR, sales, and customer service must also adapt to a faster pace. Cultural and structural leadership practices determine how well an organisation leverages the opportunities brought by AI.
Human-centred methods to support organisational change
The speed brought by AI and the productivity growth it enables affects work processes, decision-making, and indeed the entire organisational strategy.
Technology giant Google recommends combining a consistent vision with a culture of experimentation in its Generative AI Leader certification programme when building an AI strategy. This is an effective approach, as experts working on solutions often first identify areas where processes are stuck or where AI can add value.
It is important to bring their observations and insights to the attention of management so that the organisation can make quick and informed decisions. Human-centred methods, such as service design, support this process by providing a structure for modelling new processes and collecting and analysing information from different roles’ perspectives.
Implementing new practices step by step
Utilising AI in an organisation requires a systematic approach, where new practices are introduced gradually. When planning new practices, it is good to consider that the adoption of AI does not lead to internal fragmentation or excessive dependence on individual suppliers.
Operating models should support the creation of common principles and guidelines so that teams can experiment boldly, but the overall picture remains manageable. This enables rapid learning and adaptation to changing needs.
The limits of old practices are quickly reached if we do not renew processes and operating models.
Here are five key steps to help organisations effectively harness the speed and productivity brought by AI:
- Identifying bottlenecks to support management: Strategic design makes visible where the real obstacles in the organisation are. Management thus gets a clear picture of what slows down the flow of work and what processes need to be changed so that the speed produced by AI is not wasted.
- Identifying and automating manual routines: In many organisations, a lot of time is still spent on repetitive, manual tasks such as reporting, data consolidation, or decision preparation. Strategic design helps identify these hidden everyday obstacles and assess where automation can handle rule-based, repetitive tasks and where AI can support more complex decision-making. As manual work decreases, decision-making can be restructured.
- Designing new collaboration and decision-making models: As work speeds up, traditional meetings and approval chains no longer work. Design helps plan new ways of collaborating and making decisions, such as lighter approval models, flexible feedback channels, and clear divisions of responsibility, so that teams can operate faster and more independently.
- Visualising workflows: Often, organisational processes are scattered and invisible. Design helps make workflows visible so that it is easier to understand and share information about where work stops and where there are opportunities to improve efficiency. This shared visibility makes changes concrete and helps engage the entire organisation.
- Facilitating and measuring experiments and learning: Utilising AI requires quick experiments, but experiments must be done in a controlled manner and for the purpose of gathering insights. Experiments can involve a new technological solution or a way of making decisions. For example, decision-making models can be tested with a small group before scaling them to the entire organisation. This reduces risks and allows for faster learning.
By identifying the right targets for automation and ensuring that processes and decision-making models support the entire organisation, the speed of AI can be turned into a controlled competitive advantage.
Effective AI development starts with a standardised environment. Explore the Gofore AI Landing Zone: a unified, secure, and scalable platform that enables you to develop and deploy AI applications in a centralised and efficient manner.
