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Benefits and Risks of Artificial Intelligence (+4 Industry Examples)

Instead of giving credence to fear-mongering books and films about artificially intelligent beings taking over the world and turning on humans (The Terminator, anyone?), it’s time we took a hard look at the proven benefits and potential risks of some real-world applications of artificial intelligence.

For instance, it has become commonplace to see AI used in: These industries (as well as others) have opened the door to increased opportunities and advancements that AI can bring as a complement or supplement to human thought and labor.

Image courtesy of Medicalview.org AI factors into radiology by using deep learning algorithms – specifically convolutional neural networks (CNN) – to help with detecting and diagnosing diseases.

For now, AI’s purpose within radiology is to act as a second set of eyes – or a second opinion – that can aid with the level of accuracy of an initial diagnosis made by a physician.

Not only does Buoy provide the user with an illness that most closely matches the patient’s symptoms, it also provides suggestions on how soon to see a primary care physician and nearby clinic options based on the user’s zip code or computer location.

On top of improved accuracy and speed, industrialized machines can be programmed to identify recurring patterns that may result in issues with quality of output – patterns a human may not catch for quite some time.

Dubbed a “virtual financial assistant,” Erica can take care of customer needs and concerns in a few key areas such as tracking users’ spending habits, managing bill payments, and locking debit cards, to name a few.

In 2019, Chase introduced the JPM Coin, a “digital coin [that represents] a fiat currency” using blockchain technology for instant “transfer of payments between institutional accounts.” Chase is the first bank to create its own cryptocurrency.

Chatbots function similarly to customer service representatives (CSR) for base-level questions by using natural language processing (NLP) to respond to customer concerns in a timely and accurate manner.

For instance, if a company’s working hours are 8 a.m.-6 p.m. (CT), but someone from a different part of the country or different country altogether needs to contact a CSR, there may never be a viable time that works for both the customer and the company;

To keep up with online-only retailers like Amazon, many brick and mortar stores have implemented artificial intelligence chatbots as a way to communicate with customers or assist in customer buying decisions.

Some retailers, such as Sephora, not only prompt the customer with a pre-written greeting message, but also allow the customer to make self-driven decisions about using their “try on looks” tool or reaching out to a human customer service agent.

To put things in perspective, over the past few years, China has implemented a social credit system that heavily tracks and evaluates people’s social credit based on a combination of mostly minor offenses such as jaywalking, smoking in non-smoking areas, not putting one’s dog on a leash, running stop signs, not paying taxes on time, or not giving up one’s seat on a train to someone else.

This social credit system tracks citizens by their internet activities, including social media profiles, financial records, private messages, health background, dating history, and consumption of media (books, TV, video games.)

Image courtesy of Kevin Hong Obviously this system has negative implications for societal “wrongdoers.” People with low social credit have been barred from purchasing plane tickets to travel, enrolling their children in elite schools (despite their children’s skills and abilities), or even leaving the country in some circumstances.

It is evident that AI-fueled machines do not have the emotional capacity that humans have despite many advancements the field (including the creation of realistic humanoid bots with facial expressions and the ability to converse.) Because of this, mechanized employees are ideal given that they cannot possibly be affected by emotional occurrences inside or out of the workplace.

GIF courtesy of Amazon via Medium.com Though machine error can occur (as a result of human error in terms of programming), human error in a workplace can lead to greater losses in revenue for a company over time.

Many world leaders believe that AI will become more ingrained in society in the coming years, thus providing opportunities to integrate artificial intelligence into military force by means of autonomous weaponry.

final concern regarding AI-programmed machines for military usage is what might happen if weaponized machines gain “a mind of their own.” If a machine once programmed to kill and be used solely for purposes of war goes rogue, it may not be able to discern “enemy” from “friend.” Granted, this idea is theoretical and more far-fetched than tangible;

Regardless of how many industries decide to integrate AI as an addition to human skill, thought, and labor, the common denominator is clear: human beings remain the springboard off of which AI has existed, does exist, and will continue to exist in the future.

8 Uses of Artificial Intelligence to Boost Customer Experience Measurement

In a recent Forrester survey, only 35% of CX professionals reported that they are measuring how their most important customers feel about their most important experiences.

In this blog post, I detail eight uses of artificial intelligence to boost your customer experience measurement program by improving your understanding of the rich customer experience data you’re already collecting and making that data actionable.

Customers today interact with companies in a variety of ways, leaving a massive trail of experience data in the form of call recordings, chat transcripts, comments on websites, and more.

The traditional rules-based approaches that are currently used by text analytics platforms are not very efficient, as they require considerable time and skilled resources to use.

While it’s difficult to accurately determine sentiment and intent using rules-based text analytics approaches, AI-based text analytics platforms perform much better.

These algorithms provide all the auditory cues that you need to reveal valuable insights from customer feedback, so you can take action to improve customer experience throughout the customer journey.

“By 2022, your personal device will know more about your emotional state than your own family.” — Gartner The ability to understand and accurately predict your customers’ emotions is precious to you as a customer experience professional.

When it comes to customer feedback, emotional signals can provide valuable insights based on changes in facial expressions, tone and pitch of voice, body language, and even neurophysiological activity conveyed through biometric markers.

Customer service teams could then use these insights during a customer call to ask follow-up questions, resolve any complaints, accept compliments, thank customers or offer sincere apologies, as the case may be.

Most organizations struggle to show how the customer experience metrics they collect impact business performance or how they are using it to improve customer experience.

The following uses of artificial intelligence can make a real difference by making customer experience metrics actionable: Customer experience professionals need to measure customer feedback gathered across end-to-end customer journeys from multiple sources like customer surveys, social media platforms, call center recordings, etc.

AI-based customer journey analytics tools can be used to rapidly and easily integrate customer data from a variety of sources to achieve a single customer view.

This view reveals actionable insights from customer experience data that enables you to take direct actions that will maximize benefits for your organization, improve your customers’ experiences, take your customer experience measurement program to the next level, and make your organization more customer-centric.

Sometimes, an anomaly may occur that represents an entirely new and unexpected pattern of behavior, so the customer experience team won’t know to look for it.

The power of using an AI-based customer journey analytics tool for customer experience measurement is that it can sift through a much larger and more complex data space to uncover unanticipated issues or anomalies.

Using real-time analytics through an AI-based tool, customer experience teams can effectively train employees on how to handle customer complaints in a way that satisfies customers and helps improve retention.

An AI-based customer journey analytics tool, for instance, can help create a consolidated visual interactive dashboard that presents all your customer experience metrics in real-time and monitors them over time.

Instead of struggling through the data, if you could simply query, ‘How many customers were affected by the app outage today between 11 a.m. to 1 p.m.?’ it could provide you results much faster and help you deal with the issue more rapidly.

Before you begin to applying the various uses of artificial intelligence given above for analyzing customer data, have a plan in place for where you will be getting that data from.

Once you start using the artificial intelligence methods described above, it is important to track the results in terms of their impact on customer experience measurement.

As a customer experience professional, you should start preparing today to utilize artificial intelligence in customer experience measurement through the ways described above.

AI today: definition, use cases, risks, and unexpected consequences on society

Although latest advances in AI improved accuracy and efficiency, smart algorithms are inherently uncertain as there is no machine learning technique 100% accurate except for minor problems (Weir et al., 2017).

Nicholas Carr, author of “The Glass Cage, who needs humans anyway?”, stresses a deeper issue: when using highly sophisticated tools making life easier, people turn from actors to observers, which inhibits the development of expertise (2013).

He further explains that, when people have a small role in a task and end up functioning as mere monitors, they become passive watchers of screens which is a job that humans are especially not good at with their notoriously wandering minds (Carr, 2013).

However, there is a growing consensus that the way AI systems are designed, along with the data used to train algorithms, perpetuate and amplify the same biases already present in our culture, leading to even more discrimination (Whittaker et al., 2018).

This recognition comes in the wake of a string of examples, including evidence of bias in risk assessment for sentencing (Angwin et al., 2016), healthcare benefits (Lechter, 2018), hiring process (Goodman, 2018) or visa fraud detection (Sonnad, 2018).

In the sentencing case, ProPublica, a non-profit newsroom that produces investigative journalism, found that the COMPASS presented significant racial disparities, as the algorithm was “particularly likely to falsely flagged black defendants as future criminals at twice the rate as white defendants”.

The truth is that data can be biased, as they are often incomplete, skewed or drawn from non-representative samples, and developers can encode the bias, consciously or unconsciously, when programming the machine learning models (Campolo et al., 2017).

It is especially problematic when automated decision systems are used in the public sector and complex social systems as it may disproportionately affect disadvantaged people and reinforce existing inequalities, regardless of the intentions of the developers (United Nations, 2018).

In health care, a Medicaid program suddenly cut hours of caretaker for people with heavy disabilities without any valid reason to do so, and both people affected by the decision and assessors using the tool were unable to understand why (Lechter, 2018).

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