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As news articles tout how big advances in technology will make certain jobs obsolete, fear of technology is growing. Think robotics and blockchain. This fear of displacement is real and, to some extent, legitimate. New technologies have been making some jobs obsolete since man began to innovate and invent. Scribes lost their jobs when the printing press was invented. Women who wove cloth using hand-held spindles lost their jobs with the invention of automated spinning machines. The telephone displaced the jobs of telegraph operators. The list of workers displaced by inventions and innovations can probably reach the sun. But, of course, with each new invention that eliminates some jobs, other jobs are created. For example, the rise of artificial intelligence will eliminate jobs, but it will surely also create jobs. The question then is only one of being nimble to learn and evolve.
According to Science Daily, artificial intelligence or AI is defined as “an intelligent system that perceives its environment and takes actions which maximizes its chances of success.” John McCarthy, who coined the term in 1956, defined it as “the science and engineering of making intelligent machines.” But a better way to think about artificial intelligence is in its application such as when a machine mimics the cognitive functions that humans associate with other human minds, such as learning and problem solving. While most don’t realize it, applications of AI have been around since the 1990s in things such as optical character recognition in newspapers, computers that play chess, and computers that can label pictures with text descriptions. More recent applications include self-driving cars, facial recognition in images and computers that can translate text from one language to another. It makes sense then that companies will use AI to improve sales and marketing. Already many applications are being developed. Consider how some of these might help turbo boost your business.
AI in Business
The ways to apply AI to improve business sales, marketing, operations, security, customer service, inventory, and more are increasing daily. Here are some ways that they are already being used (and you may not even realize), which may prompt you to consider other applications for your own company. The sooner you adapt and embrace AI, the sooner you will have a leg up over your competition. He who is last to adopt and adapt will be first to become extinct.
1. Curating Content
A common use of AI is to unite information from diverse datasets. This can be useful in gathering information about customers and then tailoring online information that speaks to the specific customer.
Imagine if a business could serve up different versions of a web page to different audiences, depending on their specific profile? For example, in response to a search for “face cream” by a 21 year old female, Avon cosmetics could serve up their “face cream” page featuring its skin care moisturizers that are light and help block UV rays at the top of the page. The page would feature images of iGen women rollerblading and Millennial women playing tennis, while protecting their skin from sun damage. However, in response to a search for “face cream” by a 51 year old female, that same page would feature its anti-aging creams and retinol formula creams that diminish the look of dark marks. Instead of images of 20-somethings engaged in rollerblading, it might show GenX women taking a Yoga class at a park and Baby Boomer women playing a round of golf. The concept and layout of the page would be the same, but the organization of products, choice of text and selection of images would be tailored to speak to the specific audience visiting that page.
One company that is already helping companies to leverage data in order to curate content is Kickfire. (Note: Their products target the B2B sector, not B2C.) They place a tracking tag on a website to uncover the anonymous businesses visiting the site, and see the content those site visitors are viewing. That allows their clients to view firmographic data about those companies (e.g. company name, headquarter address, employee range, revenue range, and industry descriptions). Kickfire’s clients can then track leads, view full click path details, and set up lead scoring and lead alerts. They can integrate that information with their website’s own Google Analytics to help look behind anonymous site visitors to see who is actually viewing their site content. Then, with a server-to server-call, KickFire can then pass back company (B2B) firmographic information so KicKFire’s client can use that data to deliver curated content pages.
This type of content curation is being used by many companies. Predictive analytics allows Netflix to surface and finesse recommendations for its subscribers. The best part is that this kind of clustering algorithm continually improves suggestions. The more information is gathered, the better and more aligned is the content delivery.
The applications for how to curate content for customers will vary. But one easy application that any company can use right now is software such as Persado that tailors emails using emotional language that resonate with each individual recipient based on that recipient’s past behavior opening emails and website use. So a company that sends one email to 200,000 contacts will tailor each and every email to use language that speaks to that client specifically.
2. Prevent Fraud and Data Breaches
There are currently three major ways that AI is being used to spot and prevent fraud  .
- Rules and reputation lists
- Supervised machine learning
- Unsupervised machine learning
Computer programs combined with rules help spot fraud. They’re easy to implement and understand. A “rule” is a human-encoded logical statement that is used to detect fraudulent accounts and behavior. For example, Chase Manhattan Bank will contact a banking customer if their credit card is used in two locations that are geographically distant within a short window of time, such as a charge in Lansing, Michigan at 8:00am and another charge using the same card number in Tampa, Florida at 10am. Or they might flag a string of purchases made within a short window of time. Those are rules.
Reputation lists work the same way. There the focus is on what is known to be bad or fraudulent. It might consist of a list of specific IPs, device types, email addresses, or other single characteristics. Then, if a transaction involves a flagged email address or IP address, it is blocked.
This kind of AI has been around for over a decade. Most all credit card companies and banks today use AI to help spot inconsistencies that might indicate fraud. The problem with rules and reputation lists is that they are rigid and highly manual. That’s where supervised and unsupervised machine learning can help.
Supervised machine learning involves machines that learn from experience. Rather than look at a few features and rules, all the features and rules are looked at together more holistically. To work, the company must feed historical information into the system, so that the machine is able to know the difference between what good accounts and normal activity looks like versus what fraudulent accounts and activity looks like. The problem with this is that the software is only able to detect fraud that follows past patterns. It doesn’t allow for criminal creativity and innovation, and most fraudsters are quite creative.
So now there is unsupervised machine learning. This is useful because unlike supervised machine learning which relies on past patterns of fraud, unsupervised machine learning just looks for patterns. While normal client behavior is chaotic and unstructured – each client communicates and operates in different ways because they are human — fraudsters actually work in patterns, even if they don’t realize it. They work fast and scale up their efforts through volume. Such activities create patterns that unsupervised machine learning detects. So if a fraudster is sending out lots of emails within an organization to find the person who will “take the bait”, the AI-driven software is able to detect that pattern (a volume of emails going to many people within one organization, for example) and catch the attempt much earlier.
3. Predictive Customer Service
Another way that businesses are using artificial intelligence to turbo boost business is by helping to predict what customers need and want and then delivering it every time.
Imagine if you purchased coffee for the office from a company online. The sales rep would provide excellent customer service, answering questions about the product, shipping costs and delivery time. She would answer her mobile phone if you called. Better yet, she was way better at remembering when you needed coffee again based on your office’s general pattern of consumption. She would have good scheduling software that tracked how often your company purchased coffee, and would call just days before your office ran out of coffee. She’d ask, “Do you need coffee for next week?” When you check, you see that you do.
Your initial reaction to a hyper-personalized and predictive world might be that it’s creepy, and perhaps an invasion of privacy. Yes and yes. But it is the reality coming soon to practically every business everywhere.
According to R. Scott Raynovich of Predictive Analytics Times, “Big data and analytics platforms are merging with customer experience technology such as web content management systems (CMS) and customer relationship management (CRM) software. That means the machine is constantly learning and digesting information about you — and keeping it in a database that can be referenced for your benefit. The goal, in the end, is to produce organizations that know so much about you and are so predictive that they can always make you happy.”
How might this type of customer service intuitiveness cultivated by AI from your data be applied to your business? That is the question.
More importantly, in what other ways can AI be used to supercharge your business? It will surely kill some jobs and create new ones, but which? Those are the questions that business owners need to consider as they prepare for quickly growing world infused with AI. Think about it.
Quote of the Week
“By far the greatest danger of Artificial Intelligence is that people conclude too early that they understand it.” Eliezer Yudkowsky
 Reference Terms, Science Daily, https://www.sciencedaily.com/terms/artificial_intelligence.htm
 April 19, 2016, By Ben Davis, 15 Examples of Artificial Intelligence in Marketing, Ecoconsultancy.com, https://econsultancy.com/blog/67745-15-examples-of-artificial-intelligence-in-marketing/.
 February 18, 2017, Catherine Lu, How AI is Helping to Detect Fraud and Fight Criminals, Guest Contributor, Venture Beat, https://venturebeat.com/2017/02/18/how-ai-is-helping-detect-fraud-and-fight-criminals/
 January 19, 2017, By Dena Hamilton, NCR Corporation, Business Insider, http://www.businessinsider.com/could-ai-be-the-ultimate-weapon-in-the-fight-against-fraud-2017-1
 March 3 2014, R. Scott Raynovich, The Future of Customer Service: Predictive, Personalized; Predictive Analytics Times, Originally published at www.CMSwire.com, http://www.predictiveanalyticsworld.com/patimes/future-customer-service-predictive-personalized/3383/
© 2018, Written by Keren Peters-Atkinson, CMO, Madison Commercial Real Estate Services. All rights reserved.