I think we are no longer unfamiliar with the word artificial intelligence, he from a concept, gradually realized to application. Despite the hype and confusion surrounding AI today, it is powerful and potentially valuable to businesses. At this critical juncture, the industry must develop a strategy and vision for AI, and then start executing use cases to address the issues that have the greatest impact on achieving business goals.
You can’t just wait and see — plan and take root
Artificial intelligence proof-of-concept projects are also emerging; But many projects do not make it to the pilot or production stage because they do not focus on solving key business problems, have no support from senior executives, or have no expansion plans. If an AI proof-of-concept is not aligned with a specific business outcome and lacks the vision to apply it to a production environment, it is a waste of time and effort.
There is no fixed starting point between automation and AI. Where to start depends on the organizational structure of the enterprise, the business needs, the business problem to be solved, or the desired outcome. The emphasis should be on rethinking the overall process, rather than piecemeal application of AI. Ai projects should aim to solve problems that no other technology or approach can solve. Look for professional consulting agencies, re-think the overall process with the help of design thinking, joint investment to share risks, and explore innovation through collaboration.
Once you’ve successfully created a minimally viable AI product, you need to think about how to extend this model. At this point, enterprises usually give serious thought to data management. Because if it’s not extended, it’s just an interesting project.
Change your thoughts and ideas
Ai requires advanced talent who can understand the intersection of data and algorithms and their impact on process chains and workflows. When moving away from lower levels of RPA, you need to forget about the concept of plug and play. Advanced projects require highly specialized talent, resulting in a shortage of these skills.
Many businesses acquire the skills they need through a combination of training, recruitment and collaboration. Investment must be made in the training of people with both data engineering and data science technology and expertise to properly apply automation and analytics to consolidate data platforms, knowledge bases and machine learning. Without such talent, companies will be unable to expand their AI projects.
Change management is key. For now, we are still seeing a lot of layoffs as companies implement RPA and AI, which raises concerns about automation. To change the way of working and the role of talent, there needs to be a general change and cultural management. This is not a one-time event. The presence or absence of strong staff support is a key factor in successfully managing ongoing IT and business change.
Just a few steps away from effective artificial intelligence
Without access to a broad data set, AI has limited knowledge, can only perform specific tasks, and can’t generate insights at the scale or speed that corporate executives want. In order to align the vision with the execution of the strategy, there must be a clear understanding of the end state of AI and intelligent automation. While AI can bring significant benefits, it does not directly deliver business outcomes.
Rather, it is a booster to help companies achieve their goals efficiently and intelligently — digital organizations that can serve customers in real time, quickly meet their needs, and help predict changes in the business environment to stay ahead of the market. People, smart software, processes, and infrastructure converge to deliver a unified set of business results that will satisfy customers. Ultimately, ai will be most effectively optimized by companies with strong data management capabilities. Data in a myriad of forms eventually trains and implements cognitive abilities.