Artificial Intelligence predictions in 2022 for businesses

Artificial Intelligence has many advances every day. PwC has broken down what will be the most relevant trends in the field of AI, which range from new methods to measure and grow Return on Investment (ROI) to possible applications in the virtual world or metaverse. Companies that have the ability to take advantage of them will be able to sustainably grow their profits and achieve their purpose as a company:

AI will converge with data and the cloud. On its own, data does not have great value. It needs to be organized, analyzed, and used on a large scale, something that can be achieved through the use of AI. However, for AI investment to be truly cost-effective, it must be integrated into systems and applications that run 24 hours a day, which need power that can scale up and down to efficiently respond to different information demands. With these requirements, it is clear why leading companies are increasingly investing in AI and cloud as a single system.

When data, AI, and cloud are put to work together, end-to-end, the result is a flexible and powerful system that helps you identify exactly the data you need, gather or synthesize it, and use it to mitigate risks and find new business opportunities.

Simulations will uncover the full power of AI. AI-generated simulations can help business managers test countless scenarios to make the right decisions. AI, for example, can create digital twins that, when combined with other types of AI, are able to predict the behavior of certain consumer groups. On the other hand, there are other types of large-scale AI-based simulations that can recreate and forecast the potential behavior of financial assets and markets.

Similarly, AI will be a key enabler as the technology matures into the metaverse, a convergence of technology trends that will allow users to experience the digital world in a new way and with a new level of autonomy.

Bye to messy data. Data is the raw material for AI, but it has to be collected, sorted, validated, labeled, and standardized for AI to take advantage of it. This is changing, as AI is evolving in such a way that it can turn messy, unstructured data into something it can use.

AI can be used to collect data from multiple sources, turn unstructured data into information, verify and standardize it for ease of use and management, and make it available to the right people at the right time. As progress is made in the application of AI in data management, a data system can be created. Some data-centric companies are going even a step further, restructuring their organizations to create a data network, allowing them to quickly develop and produce products based on personalized consumer information.

The ability to calculate and estimate the value that AI brings. It is often difficult to predict the return on investment in AI. Fortunately, new valuation methods can capture both hard benefits and costs, such as increased productivity or hardware costs; and soft benefits and costs, such as improved employee experience or reduced workload for subject matter experts.

AI will have a strong impact on sustainability. It will be important to ensure that the development of AI within the company has been done in a reliable, ethical, and trustworthy manner, always providing users and consumers with appropriate levels of information. Once the environmental, social, and governance (ESG) risks of AI have been mitigated, it is time to consider its benefits. For example, the power of simulations enables more concrete decisions on how to reduce environmental impact or improve the life of a community. Enabling more virtual reality tools can also make it easier for people with disabilities to participate fully within workforces in companies.

AI will be too important to be run by AI experts alone. Managing AI means facing very unique challenges, and management teams may not have the technical and management skills to keep pace with the technology, and AI specialists may not understand what is being asked of them from the business. The solution is to make integrated management of the data lifecycle, artificial intelligence, and the cloud. In this way, the aim is to integrate risk, AI, and business managers. Managers, for their part, will need to learn some of the fundamentals of AI and data science. Only then will they be able to shape AI systems to ensure the right business outcomes and drive true digital transformation.

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