Global catastrophes have historically brought moments of truth for all fields of business. In such times, their inner workings, strengths and weaknesses are laid bare for the whole world to see, as organizations rapidly alter their processes to come to terms with the new reality.

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SwissCognitive, AI, Artificial Intelligence, Bots, CDO, CIO, CI, Cognitive Computing, Deep Learning, IoT, Machine Learning, NLP, Robot, Virtual reality, learningBusinesses that can make bold moves during such challenging times can quickly turn the misfortune into a benefit. So early indications are that businesses that value information as a currency, and have been quick to adapt machine learning and advanced data analytics, have emerged better from the economic aftermath of the pandemic.

AI and business optimization

The coronavirus pandemic that continues to ravage the world has forced small businesses into building online ventures. It has also compelled them to adopt AI-enabled platforms that offer consumer insights and help enterprises to deliver “hyper-personalized” products to online buyers.

Additionally, AI has so far helped companies that are struggling to create safe and contagion-free work environments by setting up on-demand online labour forces.

In a business context, AI has the potential to perform automated, repetitive tasks that would otherwise have been left for humans. It helps improve efficiency, reduces cost and saves time that could be invested in other business functions.

However, AI requires massive amounts of data to learn about consumer trends, predict consumer behaviour and find the “next best action” to enhance customer satisfaction and boost sales. But this predictive intelligence can also foresee demand and supply behaviours and help in quality control processes in manufacturing facilities.

The trouble is that gathering and storing this data securely while safeguarding the interests of your stakeholders is no easy task. In the age of digital transformation, where everything is interconnected and shared online, internet of things (IoT) security poses a significant risk for users.

In recent years, there has been a dramatic increase – 15% to 21%, according to various estimates ­– in security breaches. Leading platforms such as Facebook, Twitter, and Yahoo have become victims, compromising millions of dollars’ worth of users’ data.

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This does not only mean people must set better passwords, but also mandates that these platforms have higher standards for cybersecurity. Since data science and AI will be shaping the next stages of IoT development, data-rich companies must create efficient and trustworthy approaches to turn data into useful and actionable insights.

Cybersecurity in the age of AI

Data collection and AI algorithms are becoming the cornerstone of the cybersecurity industry. Automated decision-making and evaluation processes provide a wider range of protection from malicious activity than legacy solutions. For instance, AI can be proactive and monitor devices for suspicious activity, instead of relying on slowly updated malware databases.

Gartner, one of the world’s leading research and IT companies, predicted that 60% of digital businesses could suffer major losses due to the inability of their security teams to manage digital risks.

After the high-profile security breaches of the past two years, businesses are becoming more aware of risk-management techniques. In fact, 79% of global executives of companies rank cybersecurity risk management as one of their top priorities.

Most CISOs have actively started to make transformations in their security and IT culture by dispersing security responsibility throughout the organization. Therefore, AI developments in cybersecurity should emphasize on making systems safer and more secure for consumers to use. Let’s take a look at how to do this right: […]

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