AI has been a useful tool for the generation of actionable insights. In a sea of data, most of us lose track of the right data which can be utilized for analysis purposes resulting in useful and result-oriented insights for the organization. AI works like an assistant in making the proper usage of these data for the benefit of the organization. But there are certain data limitations of AI which has to be analyzed carefully for a proper resolution.
B2B organizations have been investing heavily on AI for gaining useful perspectives from the enormous data available in their database; thus it has become increasingly important for organizations to look into the following limitations:
1. Data: All the organizations in the industry have enough data, but the problem arises when that data is not enough to give you the insights which can be implemented for the growth of the company. Most of them are unaware of such scenarios and encompasses a huge amount of futile data in their already overloaded database. This can lead to confusion in the process as irrelevant data becomes a time-consuming process for the organization.
2. Shortage of Knowledge: Although machines give faster and better output, one cannot guarantee that it will provide 100% effective results. AI gets programmed by human beings and also if they are self-learning AI, they will execute actions without understanding it. The bottom line is that machines cannot think, they cannot acquire knowledge through discussion or by other procedures. The data which is fed to them by human beings are the only source of knowledge for them. So, there is a misalignment of the information which the system needs and the information provided to them. This shortage of knowledge for the AI can hamper your planned business results.
3. Perfection: Over the years we have built up an idea that machines are better than human beings. Yes, they work faster but are they perfect it’s still a question?
Professionals working in companies think that any data given to machines will lead to perfect results. One should not implement AI just for the sake of its implementation. Companies have to understand why they need AI and what all benefits it can provide to them in the long and short term. The data which is available in a particular organization might be of irrelevance to the AI and companies might be completely unaware of these cases. This has to be worked upon as only relevant data is useful for the growth of the organization.
4. Emotional Intelligence: Machines lack emotional intelligence. The data provided to them will churn out a statistical point of view to develop the plan but will lack the emotional intelligence insight. Although the natural language processing (NLP) will help in comprehending the information which human beings want to convey but still it will be more logical and data- oriented.
5. Lack of Strategic Approach: Companies lack of strategic approach is also one of the reasons for the data limitations. There is no plan for a structural and streamlined approach for the collection of the right data for producing insightful results.
Conclusion: Artificial intelligence has been the buzz word of the 21st century. People connected to technology use this term more often in their daily business discussions. The adoption of AI innovation has already been helping leading organizations in the increased sales and revenue, but the over expectations from AI has not also been undermined.
The data helpful for the insights has to be highly relevant so that AI can make useful predictions for the B2B businesses. The cut-throat competition in the market is not providing ample time for these organizations to give a second thought on this scenario so that they can gather the right data for analyzing. This has to be overcome with better strategic planning so that businesses can generate the required profit and revenue.