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6 Exciting Applications of Artificial Intelligence

Automation, together with the ongoing coronavirus pandemic, is creating a "double-disruption" scenario for workers, states the latest Future of Jobs Report by the World Economic Forum. According to the same report, growing technological adoption by companies, coupled with the economic contractions brought about by the pandemic, is projected to transform jobs, tasks, and skills by 2025.

While 85 million jobs are projected to be displaced by automation by 2025, 97 million new jobs will likely be created by the new division of labor between humans, algorithms, and machines.

Despite such optimistic projections about the future of work and the role that artificial intelligence (AI) will play, some experts continue to warn the public about the dangers of AI, fearing that the development of full-fledged AI (also known as strong AI) would likely spell the end of humanity.

Like the British economist Roger Bootle, other pundits argue that robotics and artificial intelligence are more likely to work alongside humans rather than replace them.

Regardless of where you stand on the AI debate, the development of strong AI is likely still decades away. Most of today's so-called AI systems are merely advanced machine learning software equipped with extensive behavioral algorithms.

Despite its current limitations, weak AI is still making a positive impact in many industries, including healthcare, financial services, e-commerce, cybersecurity, digital marketing, and the services sector. Read on to learn more about these exciting applications of AI.

6 Current Applications of Artificial Intelligence

  1. Anticipate consumer behavior with predictive analytics,
  2. Improve the quality of care in the healthcare industry,
  3. Improve processes in the financial services industry,
  4. Improve digital marketing strategies,
  5. Strengthen security systems & prevent theft and fraud,
  6. Improve the quality and precision of app- and computer-based services.

1.Anticipate consumer behavior with predictive analytics. 


Predictive analytics analyzes current and historical facts to make predictions about future or otherwise unknown events. This branch of advanced analytics utilizes an array of statistical techniques—including data mining, machine learning, AI, and predictive modeling—to make predictions.

Predictive analytics is utilized in industries as diverse as e-commerce, insurance, fintech, entertainment, and social networking to anticipate consumer behavior and other areas of interest.

Usage of predictive analytics in CRM

In customer relationship management (CRM), various types of analytical CRM are applied to customer data to construct a holistic view of customers. Analytical CRM can be applied to all stages of the customers' lifecycle (i.e., acquisition, relationship growth, retention, and win-back).

When it comes to big data, such as customer records and sales transactions, predictive analytics can help analyze key metrics to formulate more efficient cross-selling and upselling to customers and predict the likelihood of existing customers terminating their contracts. 

Usage of predictive analytics in entertainment

Streaming media services like Netflix have expanded into new markets and gained legions of loyal customers partly through predictive analytics. The streaming service is very good at developing TV shows and movies that attract loyal viewers by diving into big data and analytics.

At least 75% of viewer activity is motivated by personalized recommendations. Netflix collects several data points to craft detailed profiles of its subscribers. These profiles are far more detailed (and accurate) than the personas created via conventional marketing.

Netflix also collects customer interaction and response data, including the time and day a subscriber watches a show, how often the show was paused and at which points, and whether certain portions were rewound or skipped.

To collect all of this data and translate it into meaningful and actionable information, Netflix harnesses analytics (including predictive analytics) and algorithms. For example, a recommendation algorithm is used to recommend content that is likely to resonate with subscribers, while another algorithm is used to customize content and increase engagement rates.

Usage of predictive analytics in healthcare

Predictive analytics is finding many practical and life-saving applications in healthcare. It's being used to determine which patients are most at risk for developing conditions like diabetes and asthma. It can also be used to detect early signs of patient deterioration in the ICU and general ward and identify at-risk patients at home to reduce rates of hospital readmissions.

In a healthcare setting, predictive analytics aims to educate caregivers and clinicians on the likelihood of events occurring before they happen. By fusing AI with the Internet of things (IoT), healthcare professionals now have access to algorithms that can be fed with real-time and historical data to make meaningful predictions.

2.Improve the quality of care in the healthcare industry. 

AI is finding many other uses in the healthcare industry, in applications as diverse as diagnostic pathology, medical imaging, robot-assisted surgery, and safeguarding medical records against cybercriminals.

More specifically, AI (in the form of machine-learning algorithms and software) is utilized in healthcare settings to analyze and interpret data and make recommendations. When combined, key clinical health AI applications could potentially create $150 billion in annual savings in the United States healthcare economy by 2026, according to a report from Accenture.

"Unlike legacy technologies that are only algorithms/tools that complement a human, health AI today can truly augment human activity—taking over tasks that range from medical imaging to risk analysis to diagnosing health conditions," states the Accenture report.

Among the top applications of artificial intelligence in healthcare include robot-assisted surgery, virtual nursing assistants, administrative workflow assistants, fraud detection, dosage error reduction, and clinical trial participant identification.

Notable companies that are trailblazing innovations with AI help include PathAI (which uses machine-learning algorithms to help pathologists analyze tissue samples to make more accurate diagnoses) and Atomwise (which uses AI and deep learning to facilitate drug discovery).  

3.Improve processes in the financial services industry.

The financial services sector has long utilized AI to assist them with fraud detection. The sector also relies on AI to provide accurate, real-time reporting and process high volumes of qualitative data.

As a result of AI's proven accuracy and ability to optimize processes, the financial services sector is incorporating chatbots, automation, algorithmic trading, and other facets of AI into financial processes.

A growing trend among financial planning services is the use of robot-advisors to manage portfolios and other investment services. Robo-advisors use AI and sophisticated algorithms to scan data in the markets and predict the best stocks or portfolios based on clients' preferences.

Robo-advisors collect information from clients about their financial situation and future goals via online surveys. This data is then used to offer advice and invest client assets. Compared to human financial planners, robo-advisors are inexpensive, giving more people access to investment vehicles, retirement planning, and wealth management.

Notable companies in the robo-advisor niche include Betterment (an automated financial investing platform that uses AI to learn more about their investors and build customized profiles based on their financial plans) and AlphaSense (an AI-powered financial search engine that can scan millions of data points and generate actionable reports, saving analysts hours of work).

4.Improve digital marketing strategies.

AI is transforming digital marketing strategies by providing valuable customer insights and support, leading to more profitable decisions and outcomes.

Usage of AI chatbots in customer service

Advancements in AI semantic recognition, natural language processing, and voice conversion technology have enabled AI chatbots to deliver improved customer service. Compared to the traditional "one-on-one" approach provided by human customer service specialists, AI chatbots can simultaneously support multiple customers at different time zones and in real-time. These, and other advantages, make AI chatbots very cost-efficient for businesses interested in optimizing their conversational marketing campaigns.   

Usage of AI in email marketing campaigns

Artificial intelligence has been successfully used to personalize email marketing campaigns based on user behaviors. This allows marketers to send targeted emails to customers' inboxes, each with distinct subject lines, messaging, product recommendations, and calls-to-action.

Digital marketers can also use AI copywriting tools like Phrasee to dynamically optimize email campaigns, leading to greater open rates and conversions. Compared to the traditional method of A/B testing copy and design (which can take days or weeks to complete), AI copywriting tools radically shorten the testing and optimization phase.

Usage of AI in digital advertising

AI's data-driven approach to marketing and decision-making makes it the ideal tool for platforms that rely heavily on digital advertising, like Facebook and Google Ads. Both platforms collect, store, and analyze users' information. This data is used to develop automated systems and customer profiles to more effectively target audiences and markets.

5.Strengthen security systems & prevent theft and fraud.

In applications and industries that require high levels of security, artificial intelligence is proving invaluable. Among other applications, AI has been used to strengthen security systems by using facial recognition technology, preventing theft and fraud in the banking sector, and bolster cybersecurity.

Usage of AI in facial recognition technology

Facial recognition is the process of identifying or verifying the identity of a person using their face. In many systems, this is often accomplished by matching faces from digital images and video frames against a database of faces.

The world's top facial recognition tech companies utilize artificial intelligence, image recognition, and face analysis in a variety of applications, including:

  • Security and law enforcement (including check-points and other secured locations)
  • Locating missing children and disoriented adults
  • Identifying victims of sex or human trafficking
  • Identifying and tracking criminals and terrorists

Usage of AI in fraud prevention

Fraud in the banking and financial sector remains a major issue, which is why AI is being harnessed to reduce fraudulent transactions, phishing scams, fraudulent insurance claims, and other aberrations. 

To prevent fraudulent transactions (including the theft of bank account and credit card details), banks are now deploying machine learning models to flag suspicious transactions in real-time, preventing theft, and alerting the authorities. 

As for phishing scams, Google now deploys advanced machine learning models to alert users to phishing emails or automatically sends them to the spam folder. According to the tech giant, its machine learning models block more than 10 million malicious and spam emails every minute.

Usage of AI in cybersecurity

The AI component in cybersecurity solutions is vital for protecting organizations and individuals from cyber threats and emerging forms of malware. AI also helps organizations reduce response times to threats and helps them comply with security best practices.

Machine learning algorithms in cybersecurity solutions automatically detect and analyze security incidents, with some even programmed to respond automatically to threats. 

6.Improve the quality and precision of app- and computer-based services

AI is radically transforming many of the services that consumers access via their smartphones and computers. Some of the innovations introduced by AI include:

  • Conversational user interfaces (which enable users to interact with computers on human terms),
  • Automated reasoning (which enable AI algorithms to solve users' problems very quickly),
  • and speech recognition technology (which converts human speech into a format that virtual assistants like Siri and Cortana can understand).

Other AI technologies used in-app- and computer-based services include:

  • AI chatbots
  • Natural language processing
  • Biometrics
  • Machine learning
  • Deep learning
  • Big data
  • Neural networks
  • Image, emotion, and text recognition

Usage of AI in ride-sharing apps

Machine learning algorithms are being used to improve the experience of drivers and riders who use Uber.

Uber's data scientists have devised the One-Click Chat feature (which can be found on UberChat) to facilitate fast, dynamic, and personalized smart responses. After a passenger sends a message to their driver, Uber's back-end service automatically sends it to a machine learning platform. Here, natural language processing is used to pre-process and encode the message, then generates prediction scores for possible intent.

Uber then provides the top four suggested replies based on prediction scores via its reply retrieval policy. These are sent back to the Uber driver, who can now respond to the rider's query with a single click. Using this system, Uber drivers can spend more time paying attention to the road and less time typing responses on their smartphones.

Usage of AI in remote executive assistant services

Compared to fully automated virtual assistant services, companies that provide remote, managed executive assistant services are able to provide premium support by merging human expertise with innovative technology and artificial intelligence.

Sigrid.AI, which specializes in personal process outsourcing solutions, offers its clientele many of the privileges traditionally reserved for the C-suite directly on their computers and smartphones, minus the exorbitant costs associated with traditional personal assistants.

They're able to do this by offering subscription-based, fractional services and by leveraging the power of their AI-powered collaboration platform (known as MySigrid). Team members use this platform to solve problems and complete tasks collectively.

Using machine learning, Sigrid.AI can guarantee its clients uninterrupted service since the MySigrid platform captures all information gathered about the client. Hence, even if the client's primary EA goes on leave, another EA can step in and provide services continuously and seamlessly.


Conclusion: Human Expertise is still Needed to Manage AI-Based Services 

Despite AI's superior processing and analytical powers, humans are still needed to manage most AI-based services. To put it plainly, artificial intelligence isn't as sophisticated or cognizant as human minds, and they're simply not evolved enough to manage people's complex everyday lives.

At Sigrid.AI, we fuse human intellect and empathy with big data and machine learning to provide our clients with an unparalleled level of service. Aside from fulfilling Roger Bootle's utopian vision of humans and machines working side by side towards common goals, our unique approach to remote personal assistance enables us to offer timely and accurate services at affordable prices.

Our range of services include:

  • Specialized market research
  • Logistics management
  • Travel management
  • Bookkeeping
  • Calendar and meeting management
  • IT support
  • Less complex life tasks (such as online shopping, vendor procurement, proofreading, data entry, and tracking parcels).

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