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AI and Machine Learning: Top Priority with Corporate Executives

Many organizations have started adopting artificial intelligence (AI) and machine learning (ML) solutions, yet the C-level executives are neither data scientists nor AI experts.

AI is not a new concept. However, it is only in the last decade the technology has managed to make advancements worldwide. In the present day, we’re able to speak to our mobile devices and it responds, when we’re trying to book an Uber we receive multiple options with the price updates, or even when we’re streaming service on an online portal, we receive recommendations – we’ve all been experiencing AI-enabled solutions everywhere. It’s just that we tend to ignore them.

In the same manner, early AI adopters such as corporate executives with a fair knowledge of AI and machine learning have started embracing these technologies across enterprises.

And for those with zero knowledge in AI and machine learning have now started evaluating AI and machine learning measuring the risks versus return.

Besides these executives, we also find top data analyst organizations like PricewaterhouseCoopers (PWC), IDC, Forrester, Deloitte, and McKinsey towards early adoption of AI and machine learning. Multiple types of research showed, at the early stages of AI and ML adoption, there was quite a rush in implementing these technologies across enterprises.

As ML and AI products keep developing, the industry predicts around 73 percent of the corporate executives will start implementing AI and ML. Now, this step is taken not because they’re looking to sack workers from their jobs rather to effectively help them. Upskilling and retraining will not only keep these employees satisfied but also helps in filling a position that gets difficult with reduced cost.

Adoption of AI and ML to free human activities includes –

  • Sales process automation
  • Sales process recommendation
  • Customer service and support
  • Automated preventative maintenance
  • Fraud analysis and investigation
  • Intelligent process automation
  • Interactive advice and recommendations
  • Threat intelligence and prevention

Below are some of the AI and ML business strategies used by organizations to maximize business impact.

Voice and facial recognition – execute IoT devices

Both kiosks and IoT devices are used in making audible instructions. In addition to this, even the patients’ check-in data showcases the medical history as well. Before getting admitted, if at all there’s any data available, this could be used for further verification and can be updated if required. A card reader is used to scan data from the magnetic card strip.

Even with the help of a wearable IoT device data can be easily scanned and transferred using the front door monitoring system. You just need to walk through the door and the data gets transferred into the medical facility system. The next time you visit, only the required data gets transferred.

Even without the facial recognition or scanning device, your data can still get through the system. for instance, you’ve met with an accident and there’s an ambulance to pick you up. Even while going through the AI/ML door, data can easily get transferred because of the IoT wearable device you’re wearing. This helps capture every minute of information about the individual.

Retailers to accelerate sales to prevent fraud

A 2019 prediction states that the online sales will grow 44 percent to USD 35.8 billion while the global retail industry looks to invest around USD 5.9 billion in technologies like AI and ML last 2019, reports taken from PwC, IDC, McKinsey, and Deloitte.

Most of the retails have forged their plan in AI and ML, this is how they plan to go ahead with their business strategy. Doing so can help prevent the threat, frauds where the focus can be given more to customer services and sales.

Threat protection and fraud prevention have always been on the executives’ investment radar.

The AI and ML systems have been widely accepted by corporate executives for both healthcare and the retail sector.

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