The AI and ML trends to watch for 2022
Chad Gouws, Data Analyst at PBT Group
Artificial intelligence (AI) and machine learning (ML) will play integral roles in driving organisational success in 2022 and beyond. These technologies will be indispensable as more businesses become data-driven and garner the insights necessary to differentiate themselves on the global stage.
Well-designed data architecture is the key to value-adding analytics and AI projects. They make the process simpler, faster and more robust, leading the way to the successful implementation and integration of such projects. Therefore, the technologies and data platforms that enable this architecture will continue to be adopted in 2022 and beyond.
However, the rate of adoption by South African corporates will be impacted by how effectively business and technology leaders can manage the regulatory requirements of the Protection of Personal Information Act (POPIA). This will see the need for responsible (or ethical) AI to eliminate the risk of any data being used to prejudice the decision-making process. Aspects like race, sexual orientation and political beliefs will become focal points if organisations are to avoid people from certain groups being negatively impacted by AI and ML automation even if this is through unconscious bias on the technology side.
On top of that, organisations must remain mindful of how best to secure their data. The rapidly evolving cybersecurity threat landscape means companies must do everything possible to keep customer data safe. This becomes more complex as customers demand to have access to their data with the rise in self-service features. It is therefore critical for any business to establish trust with customers that their data is safeguarded.
Fortunately, ML and AI will make great inroads on cybersecurity next year. The application of machine-driven models to automatically monitor for breaches and identify potential vulnerabilities will be a significant area for growth in 2022. Cybersecurity providers will leverage this to monitor customer networks in real-time and proactively identify weak points to mitigate against the risk of them being exploited.
In the future, all cybersecurity tools and processes will be augmented by AI to increase operational security. Those organisations not willing to invest in these systems now, will become easy prey. Of course, cybercriminals will also invest in these technologies to make it easier for them to identify vulnerabilities.
Beyond security, AI and ML will also give new impetus to data science and predictive analytics. This will be done through ML Ops that will require a diverse skill set from employees. This will lead to more emphasis being placed on staff with data science and ML skills, where they will also need to understand big data systems and software. These individuals will be difficult to find. ML engineers and others working in ML operations must ultimately ensure data science models and pipelines are working properly, scaling effectively, and do not fail to enable companies to benefit from AI and ML.
As part of this, there will be significant job opportunities opening in ML Ops. In turn, companies will build their data teams around these processes to manage thousands of models at a time. ML Ops will be an extremely important part of the data science process as AI and ML become more pervasive in developing models for data analysis.
There is significant potential for ML and AI to positively disrupt data analysis in the coming year. Companies need to embrace this and capitalise on the opportunities that will be created.
