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Saturday 24th July 2021 10:33 PM

5 Ways Artificial Intelligence Is Transforming the Business Landscape


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Artificial intelligence (AI) is here, it is effective and it’s expanding. While the transformation is taking longer than some anticipated, the acceleration we have seen in recent years shows no signals of slowing down. Whether it’s Alexa ordering your groceries or the Facebook algorithm selecting what news you’ll find most relevant, AI and machine learning now drive many of the world’s biggest businesses.


But even if your name isn’t Jeff Bezos or Mark Zuckerberg, AI is modifying the business landscape in many ways that are significantly associated to your organization. Here are five ways that the machine learning revolution is transforming the playing field for businesses of every size and every type.


1. Automation is the new standard.


Many people associate the thriving wave of AI technologies with consumer-facing chatbot constructs like Amazon’s Alexa and Apple’s Siri. For most businesses, though, AI implementation happens behind the scenes. One of the most prevalent applications of AI is to automate common, basic tasks to free up employees’ time.


Some of the tasks that businesses can now delegate to AI programs include:


-Responding to simple customer inquiries

-Coordinating schedules, including team meetings

-Recording and transcribing meeting minutes

-Translating communications between team members who speak different languages

-Consolidating data and performing basic trend analysis

-Optimizing sales forecasts and inventory levels

-Monitoring productivity analytics and identifying areas for improvement


Gives credit to AI’s versatility in creating automation solutions, practically every business can improve productivity in some way through smart deployment of these technologies. And remember that if you’re not using them, your competitors probably are.


2. Jobs are being redefined.


Regardless naysayers’ dire predictions about the effects of AI on the job market, the new wave of AI tech has exposed several exclusively new job markets flush with openings. Machine learning development is one of the most sought-after skill sets in the job market today, and it is easy to see why.


The special struggles of becoming a machine learning expert make it one of the toughest skill sets to get a handle on. As a matter of fact, it's considerably problematic to learn that 80 percent of businesses cite “lack of requisite talent to drive AI adoption” as one of their top obstacles to developing functional AI systems. While the field likely will continue to attract talent hungry to work in a relevant industry, competition and headhunting are unlikely to slow down, either.


A less-recognized trend is the development of the “data labeler” as the blue-collar job of the future. Raw data is usually messy and challenging for machines to digest and learn from effectively. As a result, the data labeler: a position that involves manually grouping and cleaning data before it is fed into machine learning systems. A data labeler may well spend all day sorting pictures of cats and dogs or selecting news stories relevant to particular interests. Whether it’s at the upper echelons of the C-suite or in the nitty-gritty detail work of data processing, AI is altering the jobs market in ways that have consistently defied expectations.


3. Data is everything—more is better.


For AI technology to generate real results, it needs data — a good number of data. To fully implement machine learning in your organization, you’ll need serious data collection and management infrastructure. Many businesses are working on this right now, and it can be a struggle. Common challenges include:


-Identifying exactly which data points are relevant

-Finding trustworthy sources of data

-Collecting data without seeming invasive to consumers

-Tailoring data collection to fit specific use cases

-Developing data architecture capable of storing and utilizing collected data


It’s furthermore significant to recognize the ways in which other machine learning algorithms are frequently affecting the data you use every day. Identifying these key players can often enable your organization to piggyback on their algorithms to collect more actionable data.  Understanding these processes makes it easier to develop strategies that address new technologies, such as identifying keywords that will increase visibility in voice search through apps like Siri or Alexa.


4. Consumer interaction needs careful management.


Despite the ubiquity of voice assistants and other consumer AI technologies, various consumers still aren’t quite positive how they feel about them. One 2017 survey presented some interesting statistics about consumer perceptions of AI:


-84 percent of respondents had interacted with an AI program, but only 34 percent were aware that they had. Many consumers don’t realize that technologies like email spam filters, predictive search terms and Facebook-recommended news are all AI-based.


-Despite this lack of understanding, 72 percent of respondents were confident that they understood what artificial intelligence was.


-Consumer perception of AI varies widely by industry, but comfort levels remain low. 34 percent of consumers said they’d feel comfortable with an online retail business using AI to improve customer service, while only 20 percent said the same of financial services and only 15 percent of insurance or car dealerships.


-Perhaps unsurprisingly, knowledge about AI was a good predictor of comfort. People who had used AI technologies were 30 percent more likely than non-AI users to feel comfortable about a business using AI to interact with them.


It is rough to blame consumers for some of these worries and misunderstandings in light of news stories about children ordering highly-priced toys through Alexa and self-driving Ubers running red lights. While these experiences are the exception instead the rule, they demonstrate that uncontrolled implementation of AI systems can present a tremendous risk to a company’s image and even their bottom line. Any business implementing machine learning solutions needs to carefully weigh risks and rewards — and in particular, never push an AI technology out the door before it’s been thoroughly tested.


5. There’s room for growth.


Most businesses still have quite a distance to go toward thoroughly implementing AI technologies. If you feel like your organization has been lagging behind, there is still time to catch up. According to a 2018 EY survey, only 21 percent of business respondents had scalable, fully-implemented AI functionality with C-level support. But a combined 50 percent said they had either “emerging” or “functional” capability. The takeaway? The time has come to make sure your business is prepared to keep up.


Even more so than with other technologies, AI implementation has no one-size solution. What successful outcomes look like depends nearly wholly on your organization’s specific goals and needs. The one common thread for almost all businesses is that this technology is now here to stay—so it's the time to work out what it means to you.


This article is originally posted on The Network Effect


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Posted on : Saturday 24th July 2021 10:33 PM

5 Ways Artificial Intelligence Is Transforming the Business Landscape


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Posted by  Tronserve admin
image cap

Artificial intelligence (AI) is here, it is effective and it’s expanding. While the transformation is taking longer than some anticipated, the acceleration we have seen in recent years shows no signals of slowing down. Whether it’s Alexa ordering your groceries or the Facebook algorithm selecting what news you’ll find most relevant, AI and machine learning now drive many of the world’s biggest businesses.


But even if your name isn’t Jeff Bezos or Mark Zuckerberg, AI is modifying the business landscape in many ways that are significantly associated to your organization. Here are five ways that the machine learning revolution is transforming the playing field for businesses of every size and every type.


1. Automation is the new standard.


Many people associate the thriving wave of AI technologies with consumer-facing chatbot constructs like Amazon’s Alexa and Apple’s Siri. For most businesses, though, AI implementation happens behind the scenes. One of the most prevalent applications of AI is to automate common, basic tasks to free up employees’ time.


Some of the tasks that businesses can now delegate to AI programs include:


-Responding to simple customer inquiries

-Coordinating schedules, including team meetings

-Recording and transcribing meeting minutes

-Translating communications between team members who speak different languages

-Consolidating data and performing basic trend analysis

-Optimizing sales forecasts and inventory levels

-Monitoring productivity analytics and identifying areas for improvement


Gives credit to AI’s versatility in creating automation solutions, practically every business can improve productivity in some way through smart deployment of these technologies. And remember that if you’re not using them, your competitors probably are.


2. Jobs are being redefined.


Regardless naysayers’ dire predictions about the effects of AI on the job market, the new wave of AI tech has exposed several exclusively new job markets flush with openings. Machine learning development is one of the most sought-after skill sets in the job market today, and it is easy to see why.


The special struggles of becoming a machine learning expert make it one of the toughest skill sets to get a handle on. As a matter of fact, it's considerably problematic to learn that 80 percent of businesses cite “lack of requisite talent to drive AI adoption” as one of their top obstacles to developing functional AI systems. While the field likely will continue to attract talent hungry to work in a relevant industry, competition and headhunting are unlikely to slow down, either.


A less-recognized trend is the development of the “data labeler” as the blue-collar job of the future. Raw data is usually messy and challenging for machines to digest and learn from effectively. As a result, the data labeler: a position that involves manually grouping and cleaning data before it is fed into machine learning systems. A data labeler may well spend all day sorting pictures of cats and dogs or selecting news stories relevant to particular interests. Whether it’s at the upper echelons of the C-suite or in the nitty-gritty detail work of data processing, AI is altering the jobs market in ways that have consistently defied expectations.


3. Data is everything—more is better.


For AI technology to generate real results, it needs data — a good number of data. To fully implement machine learning in your organization, you’ll need serious data collection and management infrastructure. Many businesses are working on this right now, and it can be a struggle. Common challenges include:


-Identifying exactly which data points are relevant

-Finding trustworthy sources of data

-Collecting data without seeming invasive to consumers

-Tailoring data collection to fit specific use cases

-Developing data architecture capable of storing and utilizing collected data


It’s furthermore significant to recognize the ways in which other machine learning algorithms are frequently affecting the data you use every day. Identifying these key players can often enable your organization to piggyback on their algorithms to collect more actionable data.  Understanding these processes makes it easier to develop strategies that address new technologies, such as identifying keywords that will increase visibility in voice search through apps like Siri or Alexa.


4. Consumer interaction needs careful management.


Despite the ubiquity of voice assistants and other consumer AI technologies, various consumers still aren’t quite positive how they feel about them. One 2017 survey presented some interesting statistics about consumer perceptions of AI:


-84 percent of respondents had interacted with an AI program, but only 34 percent were aware that they had. Many consumers don’t realize that technologies like email spam filters, predictive search terms and Facebook-recommended news are all AI-based.


-Despite this lack of understanding, 72 percent of respondents were confident that they understood what artificial intelligence was.


-Consumer perception of AI varies widely by industry, but comfort levels remain low. 34 percent of consumers said they’d feel comfortable with an online retail business using AI to improve customer service, while only 20 percent said the same of financial services and only 15 percent of insurance or car dealerships.


-Perhaps unsurprisingly, knowledge about AI was a good predictor of comfort. People who had used AI technologies were 30 percent more likely than non-AI users to feel comfortable about a business using AI to interact with them.


It is rough to blame consumers for some of these worries and misunderstandings in light of news stories about children ordering highly-priced toys through Alexa and self-driving Ubers running red lights. While these experiences are the exception instead the rule, they demonstrate that uncontrolled implementation of AI systems can present a tremendous risk to a company’s image and even their bottom line. Any business implementing machine learning solutions needs to carefully weigh risks and rewards — and in particular, never push an AI technology out the door before it’s been thoroughly tested.


5. There’s room for growth.


Most businesses still have quite a distance to go toward thoroughly implementing AI technologies. If you feel like your organization has been lagging behind, there is still time to catch up. According to a 2018 EY survey, only 21 percent of business respondents had scalable, fully-implemented AI functionality with C-level support. But a combined 50 percent said they had either “emerging” or “functional” capability. The takeaway? The time has come to make sure your business is prepared to keep up.


Even more so than with other technologies, AI implementation has no one-size solution. What successful outcomes look like depends nearly wholly on your organization’s specific goals and needs. The one common thread for almost all businesses is that this technology is now here to stay—so it's the time to work out what it means to you.


This article is originally posted on The Network Effect

Tags:
artificial interlligence automated future