AI and machine learning are quickly changing the way businesses of today operate. Here are some things to be aware of.
Artificial intelligence is here, it’s powerful and it’s growing. While the transformation is taking longer than some predicted, the acceleration we’ve seen in recent years shows no signs of slowing down. Whether it’s Alexa ordering your groceries or the Facebook algorithm deciding 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 altering the business landscape in ways that are tremendously relevant 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.
Automation is the new standard.
Many people associate the growing 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 widespread 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
- Monitoring productivity analytics and identifying areas for improvement
Thanks 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.
Jobs are being redefined.
Despite naysayers’ dire predictions about the effects of AI on the job market, the new wave of AI tech has opened several entirely 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’s easy to see why.
The unique challenges of becoming a machine learning expert make it one of the toughest skill sets to get a handle on. In fact, it’s so difficult 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 will likely 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 emergence of the “data labeler” as the blue-collar job of the future. Raw data is often messy and difficult for machines to digest and learn from effectively. Thus, the data labeler: a position that involves manually sorting and cleaning data before it’s fed into machine learning systems. A data labeler might spend all day sorting pictures of cats and dogs or identifying 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 transforming the jobs market in ways that have consistently defied expectations.
Data is everything—more is better.
For AI technology to produce real results, it needs data—lots 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 also important to recognize the ways in which other machine learning algorithms are constantly 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. For example, it’s surprisingly easy to use existing algorithms from around the web to find useful SEO keywords. 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.
Consumer interaction needs careful management.
Despite the ubiquity of voice assistants and other consumer AI technologies, many consumers still aren’t quite sure how they feel about them. One 2017 survey revealed some interesting statistics about consumer perceptions of AI:
- Eighty-four 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. Thirty-four 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 expensive toys through Alexa and self-driving Ubers running red lights. While these experiences are the exception rather than the rule, they demonstrate that uncontrolled implementation of AI systems can present a significant 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 above all, never push an AI technology out the door before it’s been thoroughly tested.
There’s room for growth.
Most businesses still have a long way to go toward fully implementing AI technologies. If you feel like your organization has been lagging behind, there’s 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 prepared to keep up.
Even more so than with other technologies, AI implementation has no one-size solution. What successful outcomes look like depends almost entirely 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 time to figure out what it means to you.