Category: General

 
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Automation, AI, and Machine Learning Are Changing Business Operations

Machine learning the concept that, once data is introduced to a computer, it can make decisions based on the input, has grown by leaps and bounds in the past few years. Machine learning enables predictions based on large quantities of data. The more data, the better the predictions. Add in AI and robotic automation, and the future of business looks very interesting.

AI and Machine Learning
While machine learning and AI are often conflated, they are two different concepts. It’s not quite the same. AI is meant to simulate intelligent thought, while machine learning is more about using data for prediction. AI like IBM’s Watson can use machine learning for analyzing big data and sharing its insight across a company, utilizing then-unheard of amounts of data to draw conclusions.
“The data that is used in these algorithms can include everything from customer spreadsheets, past buyer information, murder rates, loaner information, census information, survey information, diabetes rates, website visiting rates and much more,” according to the University of California, Riverside. “Machine learning can not only reveal trends about this information, but can also give insight toward predicting things about future behavior, such as who is likely to pay back their loans or what customer base a specific marketing campaign should target.”
Here’s a relatable example: You are searching for something to watch on Netflix. There’s a “recommended for you” section based on previous movies and shows you have watch. Algorithms have used the data — what you have watched — to predict other movies and show you might be interested in watching.

Natural Language Processing
Another major application that is still evolving is speech recognition, and natural language processing in particular. Think of one of your smart home devices, such as Amazon’s Alexa or a Google Home. Machine learning can learn how you phrase a particular request, parse the idea into one of its normal commands, and execute the command. By the same token, automated systems are using NLP to help route callers to the correct department and customer service representative. For example, calling an insurance company will pose an automated prompt of which kind of insurance you are seeking and route your call accordingly.
AI and machine learning in tandem are also changing the face of marketing. In the search engine optimization world, there are tools using NLP to create stories that read as if a real person wrote them, rather than being computer-generated, all aimed at ranking higher on Google. Even news agencies such as the Associated Press use NLP tools to create articles quickly, such as business earnings reports and localized election coverage.
Robotic Process Automation.


RPA is a technology where software robots, like Watson, perform routine and repetitive tasks normally done by humans.
Because it’s a robot, it doesn’t have to take breaks or go home at 5 p.m. It can be extremely efficient, but complex tasks may be out of its purview. For example, it can provide lesser IT support but may not be able to solve a complicated problem and elevate to a human IT specialist. A global company might have multiple large offices but a small IT department. These lesser problems can be filtered out, saving time and money on having to have a large IT team.
The same system can be used to update user preferences or obtain billing data. It can be done as a chatbot on the company’s website, with a database of questions and answers to pull from.
RPA can even standing in for parts of an HR department, partly automating the hiring and firing process while also managing payroll. It can filter out resumes lacking certain keywords, and when a decision is made, automatically fill out and file paperwork. There are additional benefits, such as promoting anti-discriminatory hiring practices, taking much of the human bias out of the hiring process.
Combine these concepts, and we can see the technology is evolving quickly. Putting them together creates an exciting future outlook. Imagine a future where AI algorithms can predict the outcomes of CRISPR-Cas9 gene editing, carry out the gene edits, and perform other minor surgery without humans needed for anything more than oversight. The AI analyzes the data, uses tools, and does the surgery. There have already been more than 3 million robot-assisted surgeries in the past two decades.
AI, machine learning, and robotic process automation are all current but evolving technologies. They are shaping how businesses will operate in the future, and hold promise to improve a company’s efficiency in anything from HR to marketing, data analysis to customer service.

Machine learning the concept that, once data is introduced to a computer, it can make decisions based on the input, has grown by leaps and bounds in the past few years. Machine learning enables predictions based on large quantities of data. The more data, the better the predictions. Add in AI and robotic automation, and the future of business looks very interesting.

AI and Machine Learning

While machine learning and AI are often conflated, they are two different concepts. It’s not quite the same. AI is meant to simulate intelligent thought, while machine learning is more about using data for prediction. AI uses machine learning for analyzing big data and sharing its insight across a company, utilizing then-unheard of amounts of data to draw conclusions.

“The data that is used in these algorithms can include everything from customer spreadsheets, past buyer information, murder rates, loaner information, census information, survey information, diabetes rates, website visiting rates and much more,” 

“Machine learning can not only reveal trends about this information, but can also give insight toward predicting things about future behavior, such as who is likely to pay back their loans or what customer base a specific marketing campaign should target.”

Here’s a relatable example: You are searching for something to watch on Netflix. There’s a “recommended for you” section based on previous movies and shows you have watch. Algorithms have used the data — what you have watched — to predict other movies and show you might be interested in watching.

Natural Language Processing

Another major application that is still evolving is speech recognition, and Natural Language Processing in particular. Think of one of your smart home devices, such as Amazon’s Alexa or a Google Home. Machine learning can learn how you phrase a particular request, parse the idea into one of its normal commands, and execute the command. By the same token, automated systems are using NLP to help route callers to the correct department and customer service representative. For example, calling an insurance company will pose an automated prompt of which kind of insurance you are seeking and route your call accordingly.

AI and machine learning in tandem are also changing the face of marketing. In the search engine optimization world,  that read as if a real person wrote them, rather than being computer-generated, all aimed at ranking higher on Google. Even news agencies such as the Associated Press use NLP tools to create articles quickly, such as business reports and localized election coverage.

Robotic Process Automation

RPA is a technology where software robots, like Watson, perform routine and repetitive tasks normally done by humans.

Because it’s a robot, it doesn’t have to take breaks or go home at 5 p.m. It can be extremely efficient, but complex tasks may be out of its purview. For example, it can provide lesser IT support but may not be able to solve a complicated problem and elevate to a human IT specialist. A global company might have multiple large offices but a small IT department. These lesser problems can be filtered out, saving time and money on having to have a large IT team.

The same system can be used to update user preferences or obtain billing data. It can be done as a chatbot on the company’s website, with a database of questions and answers to pull from.

RPA can even standing in for parts of an HR department, partly automating the hiring and firing process while also managing payroll. It can filter out resumes lacking certain keywords, and when a decision is made, automatically fill out and file paperwork. There are additional benefits, such as promoting anti-discriminatory hiring practices, taking much of the human bias out of the hiring process.

Combine these concepts, and we can see the technology is evolving quickly. Putting them together creates an exciting future outlook. Imagine a future where AI algorithms can predict the outcomes of CRISPR-Cas9 gene editing, carry out the gene edits, and perform other minor surgery without humans needed for anything more than oversight. The AI analyzes the data, uses tools, and does the surgery. 

AI, machine learning, and robotic process automation are all current but evolving technologies. They are shaping how businesses will operate in the future, and hold promise to improve a company’s efficiency in anything from HR to marketing, data analysis to customer service.


Another major application that is still evolving is speech recognition, and natural language processing in particular. Think of one of your smart home devices, such as Amazon’s Alexa or a Google Home. Machine learning can learn how you phrase a particular request, parse the idea into one of its normal commands, and execute the command. By the same token, automated systems are using NLP to help route callers to the correct department and customer service representative. For example, calling an insurance company will pose an automated prompt of which kind of insurance you are seeking and route your call accordingly.
AI and machine learning in tandem are also changing the face of marketing. In the search engine optimization world, there are tools using NLP to create stories that read as if a real person wrote them, rather than being computer-generated, all aimed at ranking higher on Google. Even news agencies such as the Associated Press use NLP tools to create articles quickly, such as business earnings reports and localized election coverage.
Robotic Process Automation
RPA is a technology where software robots, like Watson, perform routine and repetitive tasks normally done by humans.
Because it’s a robot, it doesn’t have to take breaks or go home at 5 p.m. It can be extremely efficient, but complex tasks may be out of its purview. For example, it can provide lesser IT support but may not be able to solve a complicated problem and elevate to a human IT specialist. A global company might have multiple large offices but a small IT department. These lesser problems can be filtered out, saving time and money on having to have a large IT team.
The same system can be used to update user preferences or obtain billing data. It can be done as a chatbot on the company’s website, with a database of questions and answers to pull from.
RPA can even standing in for parts of an HR department, partly automating the hiring and firing process while also managing payroll. It can filter out resumes lacking certain keywords, and when a decision is made, automatically fill out and file paperwork. There are additional benefits, such as promoting anti-discriminatory hiring practices, taking much of the human bias out of the hiring process.
Combine these concepts, and we can see the technology is evolving quickly. Putting them together creates an exciting future outlook. Imagine a future where AI algorithms can predict the outcomes of CRISPR-Cas9 gene editing, carry out the gene edits, and perform other minor surgery without humans needed for anything more than oversight. The AI analyzes the data, uses tools, and does the surgery. There have already been more than 3 million robot-assisted surgeries in the past two decades.
AI, machine learning, and robotic process automation are all current but evolving technologies. They are shaping how businesses will operate in the future, and hold promise to improve a company’s efficiency in anything from HR to marketing, data analysis to customer service.

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Artificial intelligence in business

Many businesses take up artificial intelligence (AI) technology to try to reduce operational costs, increase efficiency, grow revenue and improve customer experience.
For greatest benefits, businesses should look at putting the full range of smart technologies – including machine learning, natural language processing and more – into their processes and products. However, even businesses that are new to AI can reap major rewards.

Artificial intelligence impact on business
By deploying the right AI technology, your business may gain an ability to:
• save time and money by automating routine processes and tasks
• increase productivity and operational efficiencies
• make faster business decisions based on outputs from cognitive technologies
• avoid mistakes and ‘human error’, provided that smart systems are set up properly
• use insight to predict customer preferences and offer them better, personalised experience
• mine vast amount of data to generate quality leads and grow your customer base
• achieve cost savings, by optimising your business, your workforce or your products
• increase revenue by identifying and maximising sales opportunities
• grow expertise by enabling analysis and offering intelligent advice and support

According to a recent study, the main driving force for using AI in business was competitor advantage. After that, the incentive came from:
• an executive-led decision
• a particular business, operational or technical problem
• an internal experiment
• customer demand
• an unexpected solution to a problem
• an offshoot of another project

AI opportunities for business
Whatever your reason for considering AI, the potential is there for it to change the way your business operates. All it takes to start is an open-minded attitude and a willingness to embrace new opportunities wherever and whenever possible.

Keep in mind, however, that AI is an emerging technology. As such, it is changing at a fast pace and may present some unexpected challenges.

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Examples of artificial intelligence use in business

Artificial intelligence (AI) is all around us. You have likely used it on your daily commute, searching the web or checking your latest social media feed.

Whether you’re aware of it or not, AI has a massive effect on your life, as well as your business. Here are some examples of AI that you may already be using daily.

Artificial intelligence in business management

Applications of AI in business management include:

  • spam filters
  • smart email categorisation
  • voice to text features
  • smart personal assistants, such as Siri, Cortana and Google Now
  • automated responders and online customer support
  • process automation
  • sales and business forecasting
  • security surveillance
  • smart devices that adjust according to behaviour
  • automated insights, especially for data-driven industries (eg financial services or e-commerce)

Artificial intelligence in e-commerce

AI in e-commerce can be evident in:

  • smart searches and relevance features
  • personalisation as a service
  • product recommendations and purchase predictions
  • fraud detection and prevention for online transactions
  • dynamic price optimisation based on machine learning

Artificial intelligence in marketing

Examples of AI in marketing include:

  • recommendations and content curation
  • personalisation of news feeds
  • pattern and image recognition
  • language recognition – to digest unstructured data from customers and sales prospects
  • ad targeting and optimised, real-time bidding
  • data analysis and customer segmentation
  • social semantics and sentiment analysis
  • automated web design
  • predictive customer service

These are only some of the examples of AI uses in business. With the pace of development increasing, there will likely be much more to come in the near future.  Please share with us if you have another example and email info@winyourbrand.com

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Artificial intelligence in business

Artificial intelligence (AI) is not new. It has been around for decades. However, due to greater processing speeds and access to vast amounts of rich data, AI is beginning to take root in our everyday lives.

From natural language generation and voice or image recognition to predictive analytics, machine learning and driverless cars, AI systems have applications in many areas. These technologies are critical to bringing about innovation, providing new business opportunities and reshaping the way companies operate.

What is artificial intelligence (AI)?

Artificial intelligence (AI) is a branch of computer science. Its main goal is to create smart machines that can learn on their own and are capable of thinking like humans.

Definition of artificial intelligence
The term ‘artificial intelligence’ commonly applies to devices or applications capable of carrying out specific tasks in human ways, by mimicking cognitive functions such as:
• learning
• reasoning
• problem-solving
• visual perception
• language-understanding
• Different types of artificial intelligence
• There are two main types of AI:

Applied AI – is more common and includes systems designed to intelligently carry out a single task, eg move a driverless vehicle, or trade stocks and shares. This category is also known as ‘weak’ or ‘narrow’ AI.

Generalised AI – is less common and includes systems or devices that can theoretically handle any task, as they carry enough intelligence to find solutions to unfamiliar problems. Generalised AI is also known as ‘strong’ AI. Examples of true strong AI don’t currently exist, as these technologies are still in very early stages of development.
Most modern AI applications are enabled through a discipline known as ‘machine learning’.

What is machine learning?
Machine learning (ML) is a core part of AI. It is based around the idea that machines can detect patterns in data and adjust their program actions according to these patterns. For example, ML applications can:
• read a text and decide if the author is making a complaint or a purchase order
• listen to a piece of music and find other tunes to match the mood
• recognise images and classify them according to the elements they contain
• translate large volumes of text in real time
• accurately recognise faces, speech and objects

In the most basic terms, ML enables computers to learn without being explicitly programmed.

How are AI and machine learning used in business?
Over the years, AI research has enabled many technological advances, including:
• virtual agents and chatbots
• suggestive web searches
• targeted advertising
• pattern recognition
• predictive analytics
• voice and speech recognition
• face recognition
• machine translation
• autonomous driving
• automatic scheduling

Many of these are now commonplace and provide solutions to a great number of business challenges and complex, real-world problems.

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Artificial intelligence in business

Artificial intelligence (AI) is steadily passing into everyday business use. From workflow management to trend predictions, AI has many different uses in business. It also provides new business opportunities.

Application of artificial intelligence in business

You can use AI technologies to:

  • Improve customer services – eg use virtual assistant programs to provide real-time support to users (for example, with billing and other tasks).
  • Automate workloads – eg collect and analyse data from smart sensors, or use machine learning (ML) algorithms to categorise work, automatically route service requests, etc.
  • Optimise logistics – eg use AI-powered image recognition tools to monitor and optimise your infrastructure, plan transport routes, etc.
  • Increase manufacturing output and efficiency – eg automate production line by integrating industrial robots into your workflow and teaching them to perform labour-intensive or mundane tasks.
  • Prevent outages – eg use anomaly detection techniques to identify patterns that are likely to disrupt your business, such as an IT outage. Specific AI software may also help you to detect and deter security intrusions.
  • Predict performance – eg use AI applications to determine when you might reach performance goals, such as response time to help desk calls.
  • Predict behaviour – eg use ML algorithms to analyse patterns of online behaviour to, for example, serve tailored product offers, detect credit card fraud or target appropriate adverts.
  • Manage and analyse your data – eg AI can help you interpret and mine your data more efficiently than ever before and provide meaningful insight into your assets, your brand, staff or customers.
  • Improve your marketing and advertising – for example, effectively track user behaviour and automate many routine marketing tasks.

Depending on the type of AI technology (ie applied or generalised), smart programs can perform:

  • specific individual tasks, such as medical diagnosis, electronic trading, robot control, etc
  • complex, cognitive tasks based on their understanding of how certain things (eg a language or a behaviour) work
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Risks and limitations of artificial intelligence in business

Artificial intelligence (AI) involves giving machines and programs the ability to think like a human. Businesses are increasingly looking for ways to put this technology to work to improve their productivity, profitability and business results.

However, while there are many business benefits of artificial intelligence, there are also certain barriers and disadvantages to keep in mind.

Limitations of artificial intelligence
One of the main limitation of AI is the cost. Creation of smart technologies can be expensive, due to their complex nature and the need for repair and ongoing maintenance.

Software programs need regular upgrading to adapt to the changing business environment and, in case of breakdown, present a risk of losing code or important data. Restoring this is often time-consuming and costly.

Other AI limitations relate to:
• implementation times, which are often lengthy
• integration challenges and lack of understanding of the state-of-the-art systems
• usability and interoperability with other systems and platforms

If you’re deciding whether to take on AI-driven technology, you should also consider:
• customer privacy
• potential lack of transparency
• technological complexity
• loss of control over your business decisions and strategy

AI and ethical concerns
With the rapid development of AI, a number of ethical issues have cropped up. These include:

• the potential of automation technology to give rise to job losses
• the need to redeploy or retrain employees to keep them in jobs
• fair distribution of wealth created by machines
• the effect of machine interaction on human behaviour and attention
• the need to eliminate bias in AI that is created by humans
• the security of AI systems (eg autonomous weapons) that can potentially cause damage
• the need to mitigate against unintended consequences, as smart machines are thought to learn and develop independently

While these risks can’t be ignored, it is worth keeping in mind that advances in AI can – for the most part – create better business and better lives for everyone. If implemented responsibly, artificial intelligence has immense and beneficial potential.

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