But even if your name is not Jeff Bezos or Mark Zuckerberg, AI is transforming the business landscape in ways that are extremely applicable 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 affiliate the developing 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. Perhaps 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, almost 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.

 

Despite naysayers’ dire predictions about the effects of AI on the job market, the new wave of AI tech has unwrapped many solely 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. In reality, it's terribly 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 is likely to continue to attract talent hungry to work in an ideal industry, competition and headhunting are unlikely to slow down, either.

 

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

 

3. Data is everything—more is better.

 

For AI technology to make real results, it needs data — a great amount of data. To fully implement machine learning in your organization, you will 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 consistently 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, several consumers still aren’t quite sure how they feel about them. One 2017 survey discovered 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’s hard to blame consumers for some of these worries and misunderstandings in light of news stories about children ordering costly toys through Alexa and self-driving Ubers running red lights. While these experiences are the exception in place of the rule, they demonstrate that uncontrolled implementation of AI systems can present an enormous risk to a company’s image and even their bottom line. Any business implementing machine learning solutions has to carefully weigh risks and rewards — and above all, 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 fully 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? Now is the time to make sure your business is ready to keep up.

 

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

 

This article is originally posted on tronserve.com