Misconceptions swirl about what AI is, what machine learning does, its capabilities and its purpose in our society. However, our world is benefiting from the technology’s implementation, and it’s driving positive cultural and societal change, such as helping doctors predict the next global health outbreak or enhancing diagnosis and treatment capabilities. Yet despite these advancements, many are still concerned about AI’s capabilities and how the tech will affect the future of work. Job automation, for example, has been covered tirelessly by the press, and headlines take both sides of the ever-swaying media spectrum – “AI is taking our jobs,” “automation is complementary to our jobs,” or “don’t fear the factory robots.”
The problem isn’t automation itself, but a general lack of education surrounding the topic, and understanding a future where computers are extensions of our own capabilities. Because of their wide-reaching impact on society, healthcare, insurance and retail are three industries that are prime candidates for AI implementation. While they’re already employing AI, it’s important for consumers to know why.
The $3.2 trillion dollar healthcare industry has been ripe for disruption, and today’s health challenges are complex; they require multi-branched, collaborative and technology-enabled approaches to yield more positive results for the modern patient. This innovation in care delivery starts with identifying problems with pinpoint accuracy that older technologies can’t detect.
Data collection and processing with AI also gives physicians a better picture of the patient profile, allowing them to spend more time with patients and diagnoses. AI and big data is also available to identify risks, recommend treatment, and piece together more cohesive medical reports that are accessible by all healthcare providers. For example, platforms like Genoox, which manage the genetic sequencing process, can interpret DNA within minutes to offer a holistic genetic profile of the individual, and provide clinical, actionable insights for physicians and healthcare professionals. Platforms like Genoox are especially valuable for children and NICU units, since these populations often can’t vocalize their symptoms, and DNA testing can allow professionals to identify the root cause of the illness.
Additionally, many of today’s medical machines and devices are quite complex, and as a result, field technicians over prepare and bring several tools and repair parts to fix problems when these machines break. AI deployed on mobile devices can allow technicians to determine what component or plug was needed, and greatly reduce the excess inventory associated with ordering and storing unused parts and speed time to repair.
This kind of information isn’t just useful for doctors – it’s extremely valuable to understanding, accounting for and addressing the health of future generations.
AI has huge potential to give insurance companies a better picture of damages for more accurate reporting, mainstreaming claims processing, and calculation of economic losses. The estimated damage from Hurricane Harvey alone ranged from $65 billion to $190 billion, and because of limited human capital, claims processes are oftentimes delayed – thus leading to slower reimbursement times – when using more traditional methods. Furthermore, the number one driver for CSAT within insurance is the claims processing experience, the opportunity to improve this, even marginally, can have huge customer experience implications.
In order to maximize efficiency when it comes to disaster relief, there are innovative AI resources insurance companies can adopt. A report by Tata Consultancy Services estimates insurance companies will each spend an average of $90 million on AI in the next three years, and for good reason; adapting these new tools can make the process more accurate, streamlined and safer for all parties. Recognition software with learning capabilities, for example, can identify items in a photograph, understand the image and accurately tag and file damage at scale. Through machine learning, AI can identify what’s in an image and prepopulate from the information, reducing the amount of times humans need to analyze it.
Other operational efficiencies include drones for damage assessment, and AI mobile SDKs – on-device machine learning that puts AI in the palm of claims agents’ hands on a device such as on a cell phone, whether they are online or offline.
The U.S. apparel industry is currently valued at $12 billion, and with the high number of dollars pouring into the retail economy, brands are looking to technology to foster deeper connections with consumers (I’m aware of the irony). Previously an underutilized resource in the fashion industry, retail tech is now offering personalized shopping experiences that suit the needs of a diversified pool of consumers.
Macy’s and IBM’s pilot program expedites and streamlines the in-store shopping experience for customers. Watson, IBM’s AI technology, can answer questions about inventory, store navigation, FAQs and specific questions regarding store locations (sportswear brand The North Face did the same).
Understanding AI is critical to facilitating changes in infrastructure and transformative technologies, which can further innovate when AI is understood. Bringing AI to the masses is important to this process, as everyone should have the power to use it for the improvement of life, regardless of budget, infrastructure or technical skill level. This is why independent AI companies will be most successful in 2018 and beyond, since they aren’t competing with their clients for advertising dollars.
It’s our duty as a society to make sure that our children and our grandchildren are properly informed and educated- through industry and government collaboration – so they know the training and workplace implications the technology poses. Similarly, businesses have a responsibility to choose technologies that will improve customer experience and must partner with trustworthy partners to ensure that both the ethical and business benefits are understood and properly implemented. The greatness of AI is ours to build, and we’re on the road to solving many of the world’s biggest problems.