We are well past the hype of AI, and it is becoming clear that the technology’s greatest problems revolve around making profits instead of how to make it useable. AI can provide immense value to many companies thanks to the increasing number of AI specialists and machine learning services. Companies often fail to cover initial investment when deploying AI. This seems contradictory, doesn’t it?
According to a recent IBM study, only 21% are capable of integrating AI into their business operations. This is the core problem: It’s difficult to realize economic returns on technology that hasn’t been implemented into production. Even AI projects that are deployed don’t always deliver the expected results.
Let’s talk about the obstacles companies face in achieving AI profit-making.
AI is always data-intensive, so it is crucial that the culture of the adopting company is data-driven. Companies must address the problem of lack of data culture, which is unsurprising given the potential of AI.
AI projects will fail if the company’s top leaders and employees lack data expertise. If the staff does not apply data-driven decision-making methods, even well-built AI systems, they will not reach their full potential. Another common error in AI implementation is the lack of change management.
AI requires significant organizational changes, including in strategy and mindsets. Consider change management as an integral part of your AI implementation plan. Make sure that your company’s leaders are equipped with the knowledge and drive necessary to create an AI-centric culture.Also read: Best CRM software for 2021
Although goals are essential for any project’s success, many companies fail to define them when it comes to AI implementation. Clear expectations regarding the outcome of an AI project are essential. End-users are less likely to be involved in AI projects than the technical team. This means that even though AI systems can be built flawlessly, they have little business value. It is crucial to include all stakeholders in the project from the beginning.
AI projects can often provide unquantifiable benefits. It is often more difficult to track the cost and time savings of AI projects than employee satisfaction and customer experience. Let’s suppose you create an AI system that reduces the time taken by the IT department for categorizing tickets. The system will need to understand free-form text using NLP. This means that it won’t always be 100% accurate. Your team will need to calculate the ROI error rate and include it in your calculations.
Let’s take another example: let’s suppose there is an urgent issue that IT staff must address immediately. An AI system incorrectly labels this ticket as low priority. This complicates ROI calculations as it is difficult to quantify the negative consequences of such cases.
It is important to choose projects that can accurately calculate ROI. Many manufacturing companies have achieved economic returns from AI projects used for quality control. Their ROI is relatively easy to measure.
It’s tempting to create large-scale AI systems. However, it is more efficient to focus on the low-hanging fruits, particularly in the beginning. Robotic process automation (RPA) is a great option. It tends to be cheaper than AI and has a relatively quick ROI. RPA implementation is not disruptive, which means it doesn’t interrupt the flow of legacy systems as many AI solutions.
AI projects that are quick wins can be used to justify larger AI investments in the future and to secure stakeholder support.
Although it might sound trivial, AI can have a greater impact on companies that are more experienced and mature than others. These companies have more established data governance, detailed training programs, performance tracking, and clear project goals. These are the key differences between companies that succeed in AI implementation and those that don’t.
Because of the unpredictable success rates of projects, AI requires a solid foundation in key areas of management more than any other technology. Companies’ ability to track, measure, and organize their processes is often a predictor of their success with AI.
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