Using AI for Technology Industry Solutions – Why AI is the New Internet

The use of AI for technology industry solutions in 2021 is similar to using the internet for commercial or economic purposes in the early 90s. Both have/had the capacity to transform global industries. Plus, the technology industry is the first to adopt these technologies rapidly. Here’s why early AI adoption can help businesses the same way internet adoption did in the 1990s –

AI and the Internet – Some Similarities

In the 1980s and 1990s, global technology was facing a major challenge – leading innovative programs while coping with old infrastructures’ side-effects. Wire and cable-based communication was taking a toll on their budgets and the environment. Then came the internet, making the processes of innovating much simpler.

Currently, the global technology industry is facing similar complexities. Outdated systems can’t cope with the amount of data being collected from consumers. Hence, modern IT environments are becoming super-complex and almost unmanageable. However, AI for technology industry solutions can have a similar effect as the internet.

Artificial Intelligence is a subset of computer sciences that generates a system that can automate various tasks like data gathering, speech/text recognition, low-level problem solving, etc. By using AI-powered systems, computers can finally handle and analyze vast amounts of data and provide valuable insights. This dynamic and hard-to-manage technological landscape can become a hundred times easier with such a system.

With global spending on such AI-powered systems already expected to reach over $50 billion in 2021, it’s high-time businesses pay attention to this technology and the AI-powered companies that can help them navigate this complex data-driven economic landscape.

How Can AI Systems Help Technology Companies?

Under the blanket of the collective term ‘AI technology,’ falls various technology segments, the most notable ones being –

  • Natural Language Processing: The AI-powered machine’s ability to cognize and maneuver natural language just like humans.
  • Machine Learning: Since intelligence without learning isn’t intelligence, this subset of AI focuses on parsing data and modifying itself without human effort. ML techniques provide better data-based outputs over time.
  • Deep Learning: DL falls under ML, but its capabilities aren’t comparable. Of all the subsets of AI, DL is the least-advanced technology. But it’s the most exciting one for the future.

Here’s how these subsets can combine to help businesses –

  • AI for Customer Service: Companies can utilize data resources more effectively and identify customer behavior touchpoints. The data amassed can be utilized to make customer service experiences faster, cheaper, and more efficient.
  • Supply Chain Planning: Advanced AI-powered data analysis companies can spot repetitive patterns within seconds. They can provide detailed insights on human errors that slow down supply chains, reasons why test times are increased, and easily recognize possible factors preventing the optimization of supply chains.
  • Public Data Analysis: AI systems can assess vast amounts of data collected from social media platforms and other public forums. Based on this data analysis, these systems can create market trend predictions and give valuable insights into customer behaviors. These tools can be used by businesses to recommend products to consumers, optimize sales processes, etc.