Currently, Artificial Intelligence is the buzz word and its use and applicability has been growing considerably. The finance sector has adopted artificial intelligence to enhance their revenue and process automation which considerably reduces their time. Each organization that is willing to adopt artificial intelligence will have to go through a few obstacles and difficulties which are needed to be overcome to ensure a fruitful journey to implement artificial intelligence. Any organization which moves from the legacy system to a new system has to go through a few challenges. Let us analyze the challenges to implement artificial intelligence and try to find solutions to address them.
If we broadly divide the various challenges, it can be related to the following:
One of the key considerations for artificial intelligence / machine learning would be the quality of data. As the word prescribes, the machine learning through the processing of the data. The machine tries to understand the pattern and try to develop logic from the same. For the machine to understand, it needs huge amount of data. The data needs to be analyzed for their accuracy and presentability. The data can be structured or unstructured. In case certain data are missing, it can be obtained from the public domain. Thus, there is requirement of proper data strategy.
The challenge can be addressed by knowing what data you have and what data is needed. This will enable to verify the ways of expanding data sets which will work best for you.
Data privacy & data security could be another aspect where there are huge data available which can be used for wrong purpose. It is important to understand as to which data can be used for the training (from where the machine can learn) and which is the data which can be actually used for the operational purpose.
The data can be used on separate devices which is separate from the main server and then on successful result, used on the main server.
Data labelling is one of the challenges as the data could be heterogeneous. The data can be structural or textual. With the internet becoming an inevitable part of our life, the data can be in the form of video or images. The fact that we produce vast amounts of data every day doesn't help either; we've reached a point where there aren't enough people to label all the data that's being created.
Adopt various data labelling approaches like data programs or internal person who would identify and label the data.
Biased data can be one of the challenges where there could be prejudiced due to race, ethnicity, sex, skin colour etc. has the capacity to look at the data in a biased manner. This can result from number of factors starting from collecting the data. In many of the cases, it may be possible that the data set may not represent the whole population.
Adopt different models for the data set to ensure that the data is representative of the whole population.
Any new technology generally faces restrictions from the people who are going to use them. People tend to go into their comfort zone while using their traditional approach and are generally reluctant to changes. Artificial intelligence is considered to be highly technical term and there is this myth that only data scientist can be able to successfully use this new technology. Lack of AI know-how hinders the adoption of artificial intelligence in many fields. Another mistake which generally tends to happen is lack of understanding and setting of impossible goals.
Creating awareness and educating people about the Artificial Intelligence basically explaining that to use artificial intelligence, you need not be data scientist.
Lack of requisite skill set can be one of the reasons for adoption of artificial intelligence. Though artificial intelligence is the technology which can replace many mundane works of human, it requires a specialize understanding. The AI expert needs to have the knowledge to apply the technology to a given business problem. So is the number of good data scientist in general.
Small and medium enterprises may fall short on the idea of AI adoption because of their limited budget. However, outsourcing a data team is now an option as well.
Integration of artificial intelligence with the existing system is a process which is more complicated than adding add-on or plug ins to the existing system. The elements to address your business needs has to be set – up. The needs like the data infrastructure, data storage, labelling feeding the data into the system.
In order to overcome the possible integration challenges, it is imperative to have joint efforts with the vendor to ensure that the business and the vendor have a clear understanding of the process. When the implementation of artificial intelligence is done in a step-by-step manner, the risk of failure is mitigated.