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Artificial Intelligence Technology Poses Implementation Hurdles

Artificial Intelligence Technology Poses Implementation Hurdles

Artificial Intelligence Technology is slowly but surely finding its way into more and more industries and a growing number of companies have already integrated it extensively into their business infrastructure. Artificial Intelligence has begun providing real value to organizations, specifically machine learning and deep learning techniques which show an exciting promise. However, there remains a minor section of the business world that seems to be exceedingly cautious about this “new” technology. Why is that? 

Artificial Intelligence Technology – not always a smooth ride! 

Artificial intelligence is the toast of every hi-tech business enterprise. Business establishments, these days, are taking AI to integrate a multitude this might sound, the path to adopting AI can be a rocky road. Here are some of the top challenges that businesses face while implementing an AI solution: 

1) Lack of computing power 

AI, more specifically, machine learning and deep learning techniques requires a huge number of calculations to be made very quickly. This means they utilize a lot of processing power. As data volumes continue to grow, and deep learning drives the automated creation of increasingly complex algorithms, the bottleneck will continue to slow progress. The answer to this is likely to lie in the development of the next generation of computing infrastructure, like quantum computing, which harnesses subatomic phenomena such as entanglement to carry out operations on data way more quickly than today’s computers.  

2) Integration challenges

Integrating Artificial Intelligence Technology into the existing systems is a process that is way more complicated than adding a plugin to your browser! Some of the rules are hard-coded. Testing the effectiveness of the developed AI, creating a feedback loop to continuously improve models based on people’s actions, and data sampling to minimize the amount of data stored and run models quickly while still producing accurate outcomes. To mitigate integration challenges, you will have to join efforts with your vendor to make sure that everyone is on the same page. And after you’ve successfully integrated AI into your system, you may still need to take the pain of having to train people to use the model.  

3) Dealing with model errors 

Artificial intelligence Technology is not completely error-free. Human prejudices do seep into its algorithms and sometimes the results are pretty biased. One of the main causes of AI’s mistakes is “Bad Reasoning”. As AI systems get more advanced, it can also get increasingly tricky to understand the processes in the network. So, when an AI system makes a mistake, it may be hard to pinpoint the exact problem area. However, scientists have now come up with what is called Whitebox Testing for Deep Learning Systems. It tests the neural network with a large number of inputs and tells where its responses are wrong so they can be corrected. To make sure that the errors made by AI are not critical, we need to ensure a high quality of input and appropriate testing. 

4) Ethical Challenges

Today, many tech players such as Google, Microsoft, Amazon, Facebook, IBM, etc. are  trying to develop cutting-edge AI technology. But this would mean that the Ethical Problems in Artificial Intelligence also need to be discussed. What are the dangers of developing AI? What would be their impact on the society? AI Bots are increasingly mimicking human conversations to the point of perfection. The humanoids like, Junko, Sophia, Nadine have perfected human emotions and caricature that is eerily realistic to accept.  Especially in today’s day and age, it has become extremely hard to ascertain whether the customer service agent you’re are chatting with is a human or a Bot! This sometimes poses an ethical and moral challenge, which makes the AI solution a tough technology to implement. 

5) The Cost Factor

Today, the development of technology is rising exponentially, bringing with it  a zillion possibilities. Artificial Intelligence is the buzz word in most companies, today,  with a myriad of AI-based solutions that improve business processes in several industries. Artificial Intelligence is quite expensive People who have implemented multiple AI systems will tell you that the dollar cost you set aside for the AI vendor or the budget you allocate for an AI team is only the tip of the iceberg. The costs start to stack up very rapidly when you think of the data and the training. It is usually the small and mid-size enterprises that struggle heavily as they try to implement AI solutions to their business processes.  

Artificial Intelligence Technology is Here to Stay  

If enterprises around the world learn to utilize AI effectively, it could drastically revolutionize businesses, improve processes, and increase employee productivity. Organizations will need to tap into the right approach to enable AI-driven solutions without the need to build everything from scratch. The reusability of data, models, and processing techniques is critical for expanding the use of AI. Rather than stay back weighing the negatives, organizations should work on how they can responsibly mitigate the risks posed by this path-breaking technology.   

The key here is to minimize the challenges and maximize the benefits through an extensive technology adoption roadmap that understands the core capabilities of artificial intelligence. At Techruit Solutions, we specialize in cybersecurity, cloud solutions, and managed security services, with a focus on Big Data, Cybersecurity, Networking, IoT, Cloud Computing, and UI Development. Our top-notch experts work closely with organizations, to help handpick the best talent for their unique business needs. We also host online training programs for experienced as well as non-experienced IT professionals 

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