Now that the AI buzz has worn off, the technology’s major problems are focused on making profits rather than finding out how to make it helpful. AI can offer great value for many enterprises, thanks to the expanding number of AI professionals and machine learning services. When it comes to AI, however, corporations frequently fail to recoup their initial expenditures. Moreover, the first question they ask before starting the implementation is “How to get returns from AI investments?”
According to a recent IBM study, just 21 percent of businesses can successfully integrate AI into their operations.
The foundation of the problem is that it is impossible to earn economic returns on technology that has not yet been put into production. Furthermore, even when AI programs are launched, they frequently fail to deliver the desired results.
However, companies who want to get higher returns on AI investments should first consider that AI is data-driven
Surprisingly, a lack of data culture is one of the most common issues that businesses encounter when it comes to reaching AI’s full potential. AI attempts will almost certainly fail if the company’s management and senior workers lack data competence. Even the most carefully developed AI systems will fall short of their full potential if the workforce does not use data-driven decision-making. Another common AI implementation problem is a lack of change management.
Artificial intelligence (AI) frequently requires considerable changes in organizational structure and strategy, as well as personnel attitudes and abilities. As a result, companies should make the change of management a key aspect of any AI deployment strategy, and make sure the company’s leaders have the skills and motivation to develop an AI-centric culture.
While it is tempting to construct large-scale AI systems, focusing on low-hanging fruit is typically a better strategy
It could be a good idea for a company to first start with robotic process automation (RPA), which is less expensive than AI and has a faster return on investment. RPA is non-invasive, which means it does not disturb the flow of existing systems as many AI solutions do.
Small steps and wins in AI initiatives can also assist justify more ambitious AI expenditures and guarantee stakeholder buy-in in the future.
How to define proper objectives when it comes to AI deployment.
Clear expectations regarding the consequences of an AI program are critical. End users seldom participate actively in AI initiatives, so when the technical team creates faultless AI systems, they add minimal value to the business. Therefore, it is vital to include all stakeholders in the project from the start.
Furthermore, AI initiatives frequently provide benefits that cannot be quantified. Improved employee happiness or a better customer experience, for example, is significantly more difficult to monitor than cost or time savings. Let’s imagine a company wants to develop an AI solution to help the IT staff categorize tickets faster. First, because the system will have to decipher free-form text using natural language processing, it will not be perfect at the very beginning. As a result, the team will need to calculate the acceptable mistake rate and factor it into the ROI calculation.
In addition, if there is a major issue that requires quick attention from IT workers, and an AI system incorrectly classifies the ticket as low priority, this will complicate ROI calculations because it is difficult to quantify the negative consequences of such a situation. Hence, it is crucial to start with initiatives that can be adequately quantified in terms of ROI. Many industrial organizations, for example, are able to achieve economic returns on AI quality control programs since their ROI is quite simple to calculate.
To conclude, the AI implementation journey requires high AI maturity and a great focus on business analysis.
So, how to get returns from AI investments? Generally, firms that are more mature and experienced have a higher chance of benefiting from AI. Data governance standards, sophisticated training programs, performance monitoring systems, and specific project goals are common features of such businesses. These are significant distinctions between firms that thrive and those that fail when it comes to AI deployment.
Given the unpredictability of project success rates, AI, more than any other technology, needs a strong foundation in key management areas. The ability of a company to track, measure, and manage operations is typically linked to its likelihood of benefitting from AI. Do you need help with your AI implementation? Book a free call or look at our portfolio of services!
