Most of us know that running a business profitably presents its challenges, and the addition of AI projects makes it even more challenging to predict viable returns. Then, how would you still be able to know the return on AI investment, to learn that the implementation of AI is lucrative?
Many executives believe that it will take a while measured in at least three years, depending on the project to see that the technology begins returning the desired earnings. In some sectors of the economy, some returns prove easier to calculate. For example, in the energy production field, the use of AI to maintain and run the equipment would result in declining overall costs, decreasing the equipment downtime, and boosting the speed of outcome, and analysts can calculate all these indicators.
Likewise, for an e-commerce enterprise that makes the sales based solely on the internet and its search engine results pages, it becomes paramount to spot the location of the company on the list of results. Provided that for the specific e-commerce company, you know the average conversion rate to purchase the products/services, the location in the search engine results list determines the engagement of the viewers of that list. It means that you know how many will click to go to the e-commerce site, and based on the known conversion, you have the number of sales, and consequently, the total volume and amount of sales.
Since, as in some areas as described above, the benefits to cost prospects for implementing AI sound exciting, organizations are planning investments in AI at higher rates. They expect to increase revenue by over 30 percent, as per Accenture research, when managers employ traditional ROI, a significant portion of applications show complexity, segmentation, and unpredictability.
Consequently, large organizations that promote AI technology analyze customers’ requirements and provide staged implementation focused on the expected goals. In some cases, they acknowledge the existence of different data sets in large databases where AI identifies specific patterns. The patterns will be used to model the behaviour of the whole system. In other cases, the risk of introducing AI and taking away the existing equipment could shut down the company if the AI predictions do not occur. The team takes a simulation approach by running the two systems, the current and the AI, with the required benefits in parallel for a period deemed necessary to continuously produce the outcome. Other cases employ a narrow approach based on guaranteed results while using AI tech, and while achieving success, the objectives of the project broaden, and so do its consequences.
The different ways to commence implementing AI show that an already known approach can expand a system to get the results you want. It uses a combination of utilities to translate the existing structures to function along with AI, and it involves thorough customization to produce relevant, pertinent, predictable results.