With most companies in the world working on artificial intelligence and more and more companies coming to realize the strength of AI for effective business operations. The realization that the money spent on AI will be better at the end; cutting down the bills on long manual tasks that humans would have been doing otherwise (and maybe even half heartedly). But as they always say that money is not everything and they also say time is money, so here is another effectiveness of AI in business operations it saves time as well. Many tasks like tracking and data analysis were done by the humans hands in the past (like a minute ago and it still isnâ??t finished).
According to 96 percent of companies think and what they is in the next two years machine learning projects will continue to skyrocket touching new heights and milestones (is my job safe). The truth of the day is AI does bring a level of accessibility and closeness to the data processes which unparallel to the attempts made in the past.
Brandon Purcell (principal analyst in customer insights at Forrester explain it like â??Most manifestations of AI in business today revolve around machine learning, and the use cases are quite vertically dependent, further adding to this statement he says "Many customer-facing companies use machine learning to glean insights about their customers."
For example, "manufacturers and utilities use machine learning for predictive maintenance, and retailers use it to optimize inventory levels; companies are building chatbots to handle routine customer service inquiries; companies are also using speech and text analytics on customer service and feedback data to identify pain points and improve the customer experience," said Purcell
While AI opens the opportunity for many exciting possibilities across industries, many implementation challenges arise. Previously, problems with AI execution have commonly been attributed to employees' lack of experience with the technology, resulting in a learning curve for business professionals. Often, companies have to reach for outside talent to help get the most out of their resources, said Purcell. However, humans are not solely to blame for AI's limitations.
Here are three limits to AI's use in the enterprise that tech and business leaders tend to overlook.
In order for AI to do its job, models need to be trained on data. However, data brings quite a few obstacles to the table. "The most pervasive limitation to AI adoption is data. AI needs data to learn to perform its function," said Purcell. "Unfortunately, I've yet to speak to a company that has its data house completely in order. In most companies, data is typically siloed and rarely consistently catalogued and governed. Without good, relevant training data, a company will find it quite hard to get started with AI."
Often, companies think they may not have enough data to work with AI in the first place. The key here, though, is to remember that it's not about having enough general data, it's about having "actionable data that will help them learn, that is suitable for whatever task they have in mind," said David Parmenter, head of data science at Adobe.
Another data-related limitation has to do with data standards and regulations. Companies need to determine whether the data has the right parameters, said Whit Andrews, agenda manager for AI and distinguished analyst at Gartner. Organizations need to make sure that their data is able to be shared with different companies based on federal, state, and internal requirements for those organizations, Andrews said.
2. Lack of knowledge
Another limitation to AI is that machines often don't know what they don't know, said Parmenter. While AI is fantastic for interpreting large volumes of information, there is no guarantee that the technology will understand all the data.
A great example the occurred in September involves the Nest doorbell, said Parmenter. The doorbell locked a man out of his house because he was wearing a shirt with Batman's face on it, and the doorbell didn't recognize Batman. "It's very funny, but it's real world," added Parmenter.
Hidden bias is present in both people and data, and oftentimes bias is transferred to data because of people. "We can't do these jobs without getting data. Then you go shopping around for data, and the data may have a bias in it that you don't even know about," said Parmenter. "You're just blind to it."
One example is from the world of autonomous cars, Parmenter said. "You're going to get more data in wealthy neighbourhoods, because that's where autonomous cars are going to go first," he added. "I really don't think any practitioners in my field are bad actors, but we really have to be open to the implications of what we're doing and making sure that we are fair and even-handed."
The biggest thing companies need to remember when adopting AI is why they want it. "Don't do AI for the sake of AI," said Purcell. "Start with a business case grounded in customer insights from behavioural analytics and market research." Companies will end up wasting a lot of time and money trying to implement AI for no good reason. Make sure your company has the data and reasoning first, and then execute, added Andrews.