- More organizations will prioritize machine learning in their IT budgets
Despite the disruption occasioned by the coronavirus pandemic, 2020 was a good year for AI and its applications. The year saw organizations increasing their adoption to enhance their performance. According to a report by Enterprise Trends in Machine Learning, organizations are expanding their ML projects into a wide range of applications such as process automation and those that will improve customer experience. The study found out that 76 percent of companies will prioritize ML over other IT projects in 2021. The pandemic was responsible for the sense of urgency regarding the adoption and completion of AI and ML projects.
- Augmented analytics will transform businesses
Augmented Analytics uses artificial intelligence and machine learning technologies to assist in preparing data into meaningful information. This technology has proven to be crucial in assisting organizations in scaling their AI practices. As organizations continue looking for ways to optimize their workflows, most of them will ask their business intelligence (BI) teams to develop ML models to help them achieve this. Expect a new class of developers (AI-developers), who will develop new platforms to help BI teams in their operations.
- The number of data scientists will continue rising
Data scientists are an important part of AI and ML. As such, the demand for data scientists is higher than ever. This has led to a rise in the number of data scientists from last year. Between 2020 and 2021, the number of data scientists rose to 76%. This demand is expected to rise this year as organizations implement big data projects. AI and ML projects are driving the demand for skills at a 71% compound annual rate between now and 2025.
- ML and AI will trigger innovations
The next wave of digital transformation will be powered by artificial intelligence. This innovation will aim at reducing inefficiencies in organizations, generating data-driven insights, and automating business decision-making. These innovations will advance from “early adopters” such as organizations in the financial services sector such as insurance and manufacturing, to other industries. Expect machine learning to be embedded in more business functions across different industries to enhance efficiency. This will create new products and services that will increase income.
- Ethics, explainability, and interpretability will be important
As machine learning becomes increasingly adopted, ethics and explainability will form a crucial part of it. This will lead to a demand for more regulation, privacy, and more transparency to build trust. There will be a rise in concerns and risks posed by ML/AI models and automation. Interpretability will also form a crucial part of the ML/AI project’s decision-making. It will help organizations stay accountable for their actions and decisions regarding their projects. Therefore, as we move forward, expect more regulatory frameworks to guard privacy and improve understanding of AI projects.
In a nutshell, ML projects will increase in scale and sophistication from now on. As such, large corporations will hire Chief Analytics Officers (CAO) and invest in resources to leverage analytics. With this, AI companies will need to be more transparent about collecting, storing, and using people’s data. They must be mindful of how they use data fairly and equitably. Only then can an organization make the most out of AI and ML and deploy this technology to enhance production.