Artificial Intelligence is one innovation that is amongst the best in terms of automation. Over the years, there have been many hit and miss for practical executions of AI development, making it a complicated decision for many enterprises. Yet, it remains to be a great integration into modern mobile applications.
According to Oberlo, AI has helped businesses grow by leaps and bounds with a growth of 270%. But, then, what is driving some companies away from AI?
Well!, some disasters scare businesses away from such innovations.
For example, Microsoft, in 2016, created a tweeting chatbot. It was designed to make the conversations more fruitful through tweets that resemble youth sentiments. Tay.AI(tweeting chatbot) was corrupted by several Twitter trolls infusing harmful emotions like racism, misogyny, and many more.
Another example, Florida’s push for AI in the transportation sector. So, if you are an app development company in Florida, Artificial Intelligence integration will be first on your checklist with a law passed on allowing autonomous vehicles’ development.
There is a catch here; a committee is reviewing the law several for the impact of automation on jobs. Let’s discover some practical issues with AI integrations and possible solutions to it!
Mobile Computing Challenges
Smartphones are now operating at much higher processing power with 8 Gigabytes RAM variants. But, when it comes to the ai app development, can enterprises rely on mobile computing power. There is a popular term for mobile devices among businesses known as the “Three-Watt Power.” It represents the limit of power that mobile devices can offer for computing complex processes.
The data processing in AI needs different layers of high power computing like the ones used in cloud computing paired with Fog computing. Achieving the same in a mobile device requires innovative technology along with a low latency bandwidth.
Here, the practical solution is designing the AI development process to execute the computing part in the cloud and rendered on smartphones. However, there will be a latency challenge, which may be resolved soon with the onset of 5G networks.
Data Security in AI
Data compliance and security are quintessential in the enterprise. Most businesses develop mobile applications with AI integrations to enhance user engagement, but data risks are still prevalent. Take an example of Facebook’s data misuse by Cambridge Analytica, where the social media giant had to face the heat of allowing an AI algorithm to analyze user data.
Enterprises looking to safeguard their data from misuse or leaking to unknown sources are reluctant to adopt the AI development process.
Cost of Integration
The integration of AI into mobile applications needs higher initial investments, especially in the infrastructure part. As the algorithms need high computational power and data centers to handle vast amounts of data, the integration cost is higher. The ROI for the development of AI and integrations need to be significant for enterprises to consider.
Take an example of a healthcare business such as In Vitro Fertilization or IVF that uses the AI algorithm to monitor patients’ daily growth through a mobile application. An average AI algorithm can cost around $20,000 to $100,000. Does this cost of integrating an algorithm for patient monitoring produces enough ROI? It is the question that induces doubt in CTOs and CEOs at major enterprises.
Downtime and Recovery
One of the biggest challenges for enterprises and businesses is to reduce breakdowns and increase data recovery. There is a whole market called IT Operation Analytics, or ITOA, focused on downtime and recovery. Now, let’s take an example of the AI prediction model used for ITOA to understand the issues.
Here, you can see that a predictive model helps detect the outage of IT operations. But, the question here is about accountability. Can businesses rely on AI? Here is an example: in 2017, Google Home devices suffered a sudden shutdown due to an integrated AI algorithm and got corrupted.
The best way to ensure lower breakdowns is to ensure that the AI development process is closely monitored, and there are failsafe systems to counter any breakdowns.
Lack of EQ
AI or Artificial Intelligence are often referred to as robots and having a low emotional quotient of EQ. Mobile application development is all about understanding the user’s perspective and create an enhanced experience. With the AI development process and integrations, most businesses aim to improve user experience.
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Take an example of a banking app chatbot. Now, in a scenario where a user is subjected to online fraud or a failed transaction, a chatbot conversation may seem to be lacking empathy. The user is asked to click on some link and rather than a straightforward resolution.
The best practice is to train the algorithms to empathize with the user according to the situation. An empathetic AI can improve the UX.
Conclusion
As AI technology evolves, there will be a clear choice for enterprises to make whether to go with it or not. But, rapid integrations of AI in mobile applications and other smart devices will make it inevitable. The best way forward for enterprises is to adopt the AI development process to secure, cost-effective, and user-friendly integrations. So, neglecting the AI is not an option anymore as it may be a necessity soon, then just a choice!
