‘Cause we’ve already passed artificial intelligence Hype, it’s becoming clear that the biggest problems in tech revolve around gain profit rather than figuring out how to make it useful.As more and more artificial intelligence experts and Machine Learning Services, artificial intelligence can bring enormous added value to many organizations. However, when deploying AI, companies often cannot even pay for the initial investment. Sounds a bit contradictory, doesn’t it?
A recent study by IBM showed that, Only 21% of companies Ability to integrate artificial intelligence into their operations. Herein lies the root of the problem: there is no economic return for technology that is not put into production. And, even deployed AI projects often fail to deliver the expected value.
Let’s discuss the hurdles businesses face on their path to AI profitability and how to overcome them.
Prepare the workforce
Since AI is always data-rich, adopting an organization’s culture is data-driven critical. Not surprisingly, lack of data literacy is one of the most common issues companies have to face in order to fully exploit the potential of AI.
AI initiatives are likely to fail if a company’s leaders and key employees have insufficient data expertise. Even AI systems built by experts cannot reach their full potential if employees do not take a data-driven approach to decision-making. Lack of change management is another common mistake in AI implementations.
Often, AI requires significant changes to organizational structures and strategies, as well as employee mindsets and skills. Therefore, consider change management as a core part of your AI implementation roadmap and ensure that your company’s leaders have the knowledge and willingness to foster a change-focused culture.
set specific goals
While goals are a fundamental prerequisite for the success of any project, many companies still fail to clearly define them when implementing AI. It is critical to have clear expectations about the outcome of an AI program. End users typically do not actively participate in AI projects. So when tech teams build flawless AI systems, they provide little business value. This is why it is crucial to involve all stakeholders from the very beginning of the project.
Additionally, AI projects often provide unmeasurable value. For example, improving employee satisfaction or improving customer experience is harder to track than saving time or money. Or, say you’re building an AI system to reduce the time IT takes to triage tickets. First, since the system has to use NLP to understand free-form text, it won’t be 100% accurate, especially at first.Therefore, your team needs to determine the allowable error rate and incorporate it into king calculate.
Here’s another example – let’s say there’s a critical issue that requires immediate attention information technologist And the AI system incorrectly identified the ticket as low priority. This complicates the calculation of the return on investment, as it is difficult to measure the negative consequences of this situation.
That’s why it’s important to start with projects where ROI expectations can be calculated correctly. For example, many manufacturing companies have successfully realized the economic return of AI initiatives applied to quality control because their return on investment is relatively easy to measure.
While it’s tempting to build large-scale AI systems, targeting low-hanging fruit is often a more effective strategy, especially in the beginning. It might be a good idea to start with Robotic Process Automation (RPA), which tends to be more affordable than AI and offers a relatively quick return on investment. RPA implementation is non-intrusive, which means it doesn’t disrupt processes in legacy systems like many AI solutions do.
AI projects that prove to be quick wins can also help justify more ambitious investments in AI and ensure stakeholder support in the future.
AI calls for maturity
As trivial as it may seem, more mature and experienced companies are more likely to realize the benefits of AI. These companies typically already have established data governance practices, developed training programs, performance tracking systems, and clear project goals. These are the key differences between companies that have successfully implemented AI and those that have not.
Given the volatility of project success rates, AI, more than any other technology, requires a solid foundation in key management areas. The degree to which companies can track, measure and organize processes is often correlated with their likelihood of benefiting from AI.