5 strategies to optimize the use of AI in organizations

In a changing scenario like the one we live in, Artificial Intelligence offers the opportunity to analyze and optimize the data of companies, allowing them to know their real value in a more agile way and help them make better business decisions.

According to a Microsoft study conducted by EY, 65% of companies in Spain have Artificial Intelligence pilot projects but only 20% have actively incorporated AI into their business processes and tasks. However, by 2024, 69% of the routine work currently performed by company managers will be done through automation and Artificial Intelligence, Gartner analysts predict.

In this sense, the National Artificial Intelligence Strategy, one of the measures of the Digital Spain 2025 agenda endowed with 330 million euros in 2021 aims, over the next few years, to boost the process of digital transformation of the country. In fact, Artificial Intelligence can help small and medium-sized companies to grow and even compete against large companies, allowing to lighten the most mechanical and time-consuming processes and speeding up data access and decision making.

ANexllence, consulting and technology solutions division of Glintt Group, offers advanced Artificial Intelligence solutions hand in hand with its partner Thoughtspot. The company proposes five strategies that companies can incorporate into their strategy for integrating AI into business decision-making:


  1. Customer-centric view: although AI continues to play a leading role in optimizing the efficiency of operations or the supply chain, it is increasingly important to employ it in improving customer satisfaction and not so much in saving costs. If we look at some of the businesses most impacted by the pandemic, such as hospitality, we see how establishments that are using technologies to offer home delivery services or facilitate online reservations, with a consumer-centric view, are faring better during the pandemic. Overall, businesses that use AI to deliver an enhanced customer experience will come out stronger, whether it's providing digital experiences, simpler processes for returning products or canceling reservations, or personalized customer service across all possible channels, including text, chat and voice messaging.

  2. Combine data with business insight: many companies have been frustrated because they have not been able to get the information they expected from data, or because they have not been able to meet their objectives. According to MIT Sloan Review, up to 85% of big data and machine learning projects have failed to deliver ROI. Despite the growing knowledge of data and statistical processes, there is a lack of training in business applications that is impeding the achievement of these results. Placing a greater emphasis on programming and less on business has fostered this gap between technical and mathematical skills and business understanding or vision. Therefore, companies will need to put the emphasis on marrying deep data and AI knowledge with business acumen.

  3. Consider external data. According to an IDC report, less than half of organizations analyze more than 50% of data. However, analysts found that 52% of data originates from external sources. Companies that leverage external data are more competitive than those that do not. Traditionally, companies have leveraged external data such as weather, user behavior or the planning of their production chain. However, this has been done inefficiently, with little alignment between professionals and departments. In addition, collecting, preparing and analyzing external data has been a laborious, manual process, distributed in silos and replicated throughout the organization. Today, there are different sources of data that can be valuable to companies, such as mobility, card transactions, hiring trends or even air quality. It is critical to stop and consider the data sources that can provide valuable insight into consumer behavior, the supply chain, and contribute to agile and intelligent decision making.

  4. Do not neglect internal data: having quality data enables informed decision making and competitive advantage. For example, the facts initially generated by transactions become powerful business analytics indicators. Every product, customer or transaction that is generated in the business operation is reliable and valuable information about its activity. Using the right methodology, all this information can be transported to specific data repositories, from which in turn visualizations can be extracted to draw valuable conclusions.

  5. Search and AI at scale: choosing the right data analytics solution, to make it as useful as possible, is a matter of concern for many companies and data analytics experts. Having a centralized, search-driven tool capable of drawing conclusions from large volumes of data, such as ThoughtSpot, gives companies, and specifically users in each department, the ability to maximize their analytics capabilities without needing to know code or programming. Gartner's Magic Quadrant positions ThoughtSpot as a "visionary" among the top business intelligence and data analytics tools.

"Companies are betting intensively on Artificial Intelligence to optimize decision making," says David Faustino, Managing Director of Nexllence. "After this phase of digitalization acceleration brought about by COVID-19, it is essential to rethink the most appropriate ways to involve AI in processes and prioritize customer experience all along the way."".


Source: BigData Magazine