- There will be a shift in focus from enterprise AI and everyday AI
With the growing confidence of organizations in AI, companies will employ robust data projects and processes at all levels of organizations, which will see the change emerging from enterprise AI to everyday AI. This change will make everyday AI an asset to the organization, which is pivotal to the success of enterprises, no matter the industry. The companies that will successfully adopt everyday AI will go beyond leveraging enterprise AI to sustain their businesses' future. Such companies will build a foundation by adopting data science, AI and machine learning at an organizational level.
- Enterprise IT operations and service issues will take advantage of machine learning
In the past decade, most IT departments were nervous about supporting machine learning models because of their complexity, potential for maintaining them, and resource-intensive nature. As we enter a new era, data science will be increasingly operationalized and democratized as it continues to become key in extracting value and helping in decision-making in organizations. IT will work hand in hand with machine learning models to solve issues associated with IT operations and improve service provision. This underpins the new avenues of investing in machine learning within enterprises.
- Organizations will improve in the deployment of models
With the success of the existing data science and AI initiatives, businesses are beginning to implement them in many parts of their businesses. This has led to the growth in confidence and increased experimentation. The rise in adoption gets AI leaders out of their comfort zones. However, with this evolution past this stage, new risks will emerge, mainly with the increased augmentation or automation, which will make data science less understandable. While such risks are inevitable, companies will use the challenges to forge ahead and unlock future data science and AI implementations. This will see efforts towards improving deployment models.
- The regulatory side of AI will emerge more in the future
While the regulatory side of AI is not a bad thing entirely, the capabilities and promises will change the attitudes towards how this technology is regulated and governed. With regulations, there is a bright future across the sectors. However, we will also see the rise of new practices that will align IT with the business needs and the changing landscape. The implementation of regulatory frameworks will ensure responsible use of data, which entails privacy, integration and privacy. We have already seen the rising discussion around ethics in AI from companies like Google.
- Climate AI startups will increase
Climate change is a major concern to the world today. As such, climate tech has become one of the top categories in the startup ecosystem, with massive amounts of venture capital pouring into this sector annually. Since this topic solves immediate problems affecting humanity, opportunities are abundant. Therefore, artificial intelligence has proven to be a great breakthrough in averting the climate crisis. Recently, various AI startups have come into the scene with funding. In 2022, some of these players will help fight the climate crisis. The leaders in the industry will be companies building tools for the carbon economy.
