AI, unlike any other initiative, is a business transformation tool, not an implementation of another technology system that needs to be educated for business users. Traditionally, companies have opted for either the classic waterfall approach to linear problems or the agile approach where teams review and evaluate solutions as they test them.
However, successfully integration of AI is a rather difficult process — mostly because it requires a very different approach.
To ensure success in the transformation period, businesses need to use the following three concepts.
1. Understand the human influence
The introduction of AI is not like the introduction of new software; it will surely affect employees and customers work, behave and make decisions. AI technologies will open up new opportunities for employees to acquire new skills. Employee’ domain knowledge is key to building the right AI, and employees are also valuable in extreme cases where the AI does not have the right context, capabilities, or parameters to respond appropriately. It is important to correctly identify the human contribution on which successful AI projects depend and adjust the roles of employees to provide this support. It’s not just about embedding a new technology; it changes the business model and requires deeper change management.
2. Use design thinking
Understanding human influence is just the beginning of steadily focus on the end-user AI implementing. Companies need to understand the goals they are trying to achieve at the human level, not just at the business process level. How will the app improve the experience of a person, as an employee or a client? In the past, engineers independently created new technologies in their workplaces based on a several requirements. This linear approach development is no longer enough. Due to the complexity of AI, a new non-linear approach is needed.
Design Thinking is an iterative process of observation, idea generation, prototyping, and testing that ensures that the end-user makes all technology decisions.
3. Learn to accept failure.
AI requires a higher tolerance for failure. Companies should expect to fail and learn from the consequences, not abandon their efforts before the start of a new fiscal quarter. Each failure leads to new discoveries that ultimately create value for the company. This optimism is especially important because enterprises will be working with unstructured data when they are working with artificial intelligence technologies. Unstructured data seems chaotic, but it can open up new opportunities for the entire enterprise.
SYPWAI is proud to be involved in the development of technological progress and can be useful not only for businesses but for people.