By Robin Austin, Contributor, CIO | 23 JULY 2019 05:19 PT
From around every corner, from behind every door and through every window our lives are being disrupted by artificial intelligence with embedded machine learning. The escalation and widespread global availability of AI/ML solutions in every possible emergent embryotic product has triggered it as the 'top of mind' blueprint with the majority of global corporate executives with many retail and healthcare Executives as early adopters.
Early adopters embrace AI/ML
Only early adopter corporate executives with a better than average understanding of the AI/ML cognitive processes have whole heartedly embraced AI/ML implementation across the enterprise. The majority of the rest of corporate executives are evaluating AI/ML in baby steps measuring risk vs. return. Following those executives and measuring AI/ML concerns and prospective adoption are top data analyst organizations like Deloitte, McKinsey, PricewaterhouseCoopers (PWC), Forrester and IDC. Surveys have shown that during early stages of AI/ML development, there was a rush to implement across the whole enterprise.
Recently, adoption has slowed with executives beginning AI/ML pilots in predictable successful segments of their operations. As AI/ML products and services are developed the industry predicts at least 73% of corporate executives will implement some form of AI/ML not to eliminate people but to more effectively reposition them and fill gaps in a cost-effective manner. Retraining not only keeps the employee loyal and growing within the company but fills a position desperately needed that has been hard to fill with less cost.
Corporate executives are hoping to augment and possibly free up human activities with AI/ML in 1) customer service and support, 2) sales process automation and recommendation, 3) threat intelligence and prevention, 4) fraud analysis and investigation and 5) automated preventative maintenance. Others to mention are intelligent process automation along with AI/ML systems focused on interactive advice and recommendations.
Retail looks to increase sales and prevent fraud
With the 2019 prediction of online sales to increase 44% from 2018 to $35.8 billion, the Global retail industry will invest approximately $5.9 billion in AI/ML for 2019 based on respected analyst organizations like IDC, PWC, Deloitte and McKinsey reports. Banking plans to invest $5.6 billion in 2019. Retail is focused on AI/ML systems for customer service, sales, threat protection and fraud prevention. The hopes are that customer satisfaction will improve due to faster real-time service and support response times. Retail’s executives are looking to have AI/ML improve accuracy and by the addition of AI/ML expert shoppers’ buying recommendations, to increase sales. With ongoing AI/ML real-time customer education, the belief is, and the industry experts agree, that educated buyers create loyal customers and increased sales.
Fraud prevention and threat protection are also on Retail executives’ AI/ML investment radar. With online sales predicted to be over $591 billion by 2020 and over $735.4 billion by 2023, fraud loss can be about 10% of revenue and up to 85% of revenue depending on whether a breech has occurred with ransomware penetration. That wide range is totally unpredictable despite enhanced online cyber security efforts because despite increased online protection investments, one never knows.
Ultimately, every customer pays for theft and fraud because all losses are ultimately passed along to the customer in some way or another. Should corporate executives subscribe to their own private tribal community much like Facebook, it would not only increase customer interaction and hence sales but could help police fraud. Through flagging threat behaviors from an AI/ML fraud prevention system that appear within the community, appropriate action based on policies can be initiated. By including AI/ML, the retail company will miss less sales opportunities due to charge backs or declines and will surpass current customer delivery expectations therefore increasing customer satisfaction. Automated customer service and support agents, typically chatbots, are widely being adopted at a rapid pace throughout just about every retail company with an online presence.
Competition for Alexa, AI/ML voice recognition systems will begin to extend beyond mobile and landline phones into the household. By offering personalized FAQs and answers the prediction is more orders with less steps and fuss for the customers. Automated preferences can be facilitated by audio automation providing product descriptions, possible suggested product uses along with any warnings necessary. A purchase may be made through voice recognition and activation completing the process with acceptance of credit card information and automatically sending an email or text with expectant delivery date. If Alexa can order pizza from human commands, then it can order pizza through AI/ML automated commands. Issues could arise with this freedom and flexibility through. You wouldn’t want 100 pizzas showing up at your door with the accompanying charges on your charge card. Chinese researches have already hacked both Alexa and Siri to open bank accounts and make major purchases without intervention.
AI/ML may help healthcare save lives
Healthcare executives are rapidly embracing investment in AI/ML with a prediction of $36.1 billion by 2025. Analysts report that this year (2019) more than one-third of health care provider executives are investing in AI/ML. Current data analysis shows that utilizing AI/ML for arduous administrative tasks including gathering medical history during hospital or clinic check-in through a kiosk is a possible cost saving option. Not only is a kiosk faster but it can be seen as being more compliant having the patient entering his or her personal data. The health care professionals hope that with patients inputting his or her personal data, entry errors and possible inaccuracy can be prevented. Healthcare administrators believe this option might reduce governance risks. The data will be verified in real-time for accuracy by checking against former data check-in records if available.
Other data source integrations are being discussed like integrations with law enforcement data bases to enhance law enforcement’s tracking of and the arrest of criminals. Although there is much privacy push-back discussions with privacy winning in some cases over convenience, your choice may be to opt-in to the medical facility and be treated, or not be treated; just like your need to opt-in online to enter a website or not receive admittance.
The thinking is that transformation and adoption of IoT devices will incorporate AI/ML transparently. Personal data is available through wearable devices, implanted arm “chips” or even on magnetic strips on cards and is an enormous benefit for both the patient and for the medical facility. Along with past and present medical data residing on some device carried on your person, the next health enhancement proposed is real-time symptom monitoring. AI/ML systems collecting real-time vital signs like temperature, blood pressure, heart rate and breathing can alert a real-time monitoring center when your medical thresholds are exceeded.
A person or another AI/ML system can audibly reach out through a Global System for Mobile (GSM) and Global Positioning System (GPS) verifying your need for assistance. After all, we already have OnStar, Life Alert and ADT security monitoring platforms performing similarly. Of course, a person has to agree to be monitored through GSM and GPS systems and through your opting-in, your medical history, real-time vital signs, symptoms, as well as your location will be made available by your permission to those systems. Again, opting-in will facilitate your permission and acceptance and could save your life.
Facial and voice recognition execute IoT devices
Of course, with an IoT wearable device, data could transfer into the system through a front door monitoring system. Just by walking through the door or a room scan for AI/ML medical devices, data records could automatically transfer into the medical facilities system whether you are entering the facility as a patient or visitor. When you do require assistance, only data necessary to be updated will be transferred the next time you are present.
To avoid your data showing up without you, facial and voice recognition systems can enhance security and provide real-time accuracy. Picture having an ambulance deliver you after an accident rolling you through the AI/ML door. The door monitoring system would engage your IoT AI/ML wearable watch capturing your personal information, your medical history and vital signs. The Emergency Medical Technicians (EMT)s could also be carrying another AI/ML device that was measuring your vital signs, symptoms and any additional data captured while in route to the hospital. The hospital monitoring systems could then prioritize their doctors, nurses and medical technicians scheduling your procedures and assigning you a room. Tests and procedures that were formerly unavailable to an EMT could now be possible deepening care.
Many of you will be afraid of your loss of anonymity concluding this type of system is an intrusion of your personal freedoms calling it “big brother” and a violation of privacy. As a response to that, let me ask you, “If you are incapacitated and your life is on the line, will you be in uproar or even think about who might have access to your data?” For me, I would be extremely happy that a second was not lost on saving my life. Funny, how the immediacy and reality can put things in a proper perspective so bare it in mind when you disagree.
Data privacy is an illusion with the cost of compliance adding cost overhead and a distraction from necessary care. Those who ethically need the data for your benefit suffer at the hands of those with malicious intent increasing YOUR cost. Metaphorically, do you really think that if someone wants to break down your door, you can keep them out? Also, metaphorically, someday, AI/ML may offer a solution to even this; in a matter of speaking.
We already trust devices to take our vital signs and much more. Additionally, AI/ML systems can provide proper instructions at a kiosk to gather this data. Although AI/ML has not yet evolved to be completely trusted as a diagnosis and treatment platform, IBM’s Watson has already demonstrated proven oncology diagnoses accuracy expressing treatment options with what many doctors agree is a high success rate. Watson for Oncology has been accepted at Memorial Sloan Kettering Cancer Center (New York, USA) as a trained cognitive computing system that uses natural language processing and machine learning to provide treatment recommendations. IBM Watson has absorbed both structured and unstructured data from medical literature, treatment guidelines, medical records, imaging and lab and pathology reports, while availing itself from expertise of Memorial Sloan Kettering medical staff to formulate therapeutic recommendations.
AI/ML systems for both Retail and Healthcare have been widely accepted by early adopter corporate executives and are expanding at a rapid pace as proven successful outcomes are shared. Almost all industries will be affected by AI/ML systems as it evolves in accuracy and proficiency. Companies will retrain and retain personnel to fill gaps in order to maintain resilience and relevance with more efficiency. Although it is possible that AI/ML will eliminate positions, it is also possible that AI/ML will enable personnel additional education and higher paid positions benefiting all.
Embrace constant education and growth for yourself and keep abreast of what value you can bring to your company. Engage your leadership about retraining asking for guidance and offering suggestions as to your possible value in the future economy. Begin now and develop a game plan for your career path including AI/ML as leverage.