Implementing Artificial Intelligence seems to be the way forward for modern enterprises. Trends suggest that there has been a significant rise in the implementation of AI in businesses as they continue to learn new things about this technology.
Especially after the outbreak of COVID-19 in 2020, Machine Learning, Artificial Intelligence, and Deep Learning came at the forefront of business practices. It allowed brands to manage their workforce and uphold the quality of services and products.
As modern industries continue to expand rapidly, adopting AI in one form or the other has become inevitable for businesses of any size. But it’s not as simple as it sounds.
Implementing AI is a Challenging Task
For many reasons, adopting Artificial Intelligence is still a significant challenge for businesses. However, given that AI can potentially change the landscape of medical, manufacturing, engineering, and many other industries, companies are keen to overcome these challenges to make the most of this state-of-the-art technology.
But what are the main challenges that industries face? Here is a quick look:
- Data Security Challenges with Larger Data Sets
Artificial intelligence feeds on data. The more data you can provide to AI or ML algorithms, the better output you will receive. But it means that businesses must prepare the infrastructure to effectively store such humongous data.
Since the ML and AI models depend on this data, security becomes a critical concern. Imagine that your development team requires more extensive data sets for more accurate models. Ultimately, it will require your development team to analyze system requirements. Only then can you integrate more efficient data management environments and set adequate budgets for their implementation.
- Replacement of Older Infrastructure
AI systems are designed for the future, so you must have the infrastructure that provides enough computational speed. Otherwise, it’s essentially no use to invest in artificial intelligence. This becomes a significant challenge because AI systems have a substantial cost based on certain factors. However, that’s a price businesses must pay if they wish to harness the technology.
Moreover, the venturing businesses must remain flexible and plan a robust infrastructure that can support AI-based applications and tools later on.
- Complex AI Models and Working with Datasets
AI models are necessarily simple. If you want to develop intelligent systems through AI, you must be ready to work with complex algorithms and ML models.
This is where businesses must understand the use of the right data sets and have a general clarity about where they want to go with this technology. Otherwise, you can’t expect your business to reap the best benefits of AI technology.
Moreover, once you have implemented AI algorithms, you will need sufficient manpower to handle the continuous updating of data sets and their subsequent algorithms.
- Model Errors
Since AI runs on whatever data is available, you can’t expect it to be completely error-free. Often, datasets can be incredibly biased, so the expected output will be erratic. It means inaccurate results and false predictions.
Now, combine the errors with a complex AI system, and you have an even more significant challenge to deal with: How do you figure out where the system has made a mistake?
While errors with AI may be manageable for some companies, they can be disastrous for autonomous car manufacturers, potentially causing fatal results.
Therefore, scientists are currently working on Whitebox testing to eliminate modeling errors.
As mentioned earlier, Ai requires high-performance GPUs and processors to work with complex software. So, it’s expectedly more costly than standard tech solutions. Costing becomes a key parameter that enterprises must consider before calling to shift to an AI-based infrastructure.
Generally, businesses need to stretch budgets and take services from professional AI service providers to implement the technology at a reasonable cost.
Considering that AI can revolutionize the future of industries, businesses are keen on facing the challenges posed by AI to prepare for the future. Therefore, they are ready to take on challenges like model training, integrating, acquiring manpower, and understanding the core principles of artificial intelligence.