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In the world of technology, the mantra "innovate or die" is truer for organizations than ever, and artificial intelligence (AI) is redefining industries by providing greater personalization to users, automating processes, and disrupting how we work. Like the adoption of cloud computing five years ago, the adoption of AI and the speed of its deployment varies according to industry. Here we look at some of the places where dispution from AI is already being felt.
Alexey Sapozhnikov, co-founder and CTO of Tel Aviv, Israel-based prooV points out that while virtually every industry is embracing AI, it's the sectors that are stymied by well-worn processes and regulations — such as healthcare and government — that are likely to lag in AI adoption. “From the Food and Drug Administration’s stringent policies surrounding AI diagnosis software to developing complex proposals for government cybersecurity challenges, these processes can pose a huge stumbling block for organizations. Fortunately, many companies are realizing the importance of catching up to AI technology, lest they be left behind,” he said. So what industries are using AI, and what ones are likely to be disrupted by it. Here are 11 industries — in alphabetical order — that are experiencing disruption already.
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1. Agriculture
Jason Behrmann has worked as a communications strategist at two Montreal AI startups, one in business analytics (Enkidoo), the other in healthcare (Aifred Health). He said that industries that are suffering labor shortages are also likely to be heavily impacted by AI. “Most think that AI will be a job-killing disruptive technology, but for industries experiencing labor shortages, the automation and efficiency gains from AI will, in fact, strengthen these industries and preserve jobs in the long run," he said.
Behrmann says that one sector hit hard and suffering from labor shortages is agriculture. Few people want to work in this industry and recent populist backlashes against immigration have contracted the population of available farm workers in many industrial countries. This situation in Canada is a great example. “We estimate that Canada will suffer from a deficiency in 100,000 farm workers soon. Adopting AI and related automation technologies is a matter of survival for the agriculture industry,” he said.
2. Call Centers
According to Cristian Rennella, CEO and co-founder of Colombia-based elMejorTrato.com, AI will replace the call center industry.
He said that after nine years working with an internal marketing team to answer their client's questions through live chat, the company started developing its own chatbot. “Thanks to Artificial Intelligence through deep learning with Google's TensorFlow platform, we were able to automate 56.9 percent of queries. In this way, the user receives their response in seconds and our team only has to answer those questions that were never consulted before,” he said. “We believe that in two years we will be able to replace our entire call center with AI.
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3. Customer Experience
Ryan Lester is director of customer engagement technologies at Boston-based LogMeIn. He said that customer experience is emerging as an early success story for AI across industries. While retail is the most prevalent sector leveraging AI today, others are also taking notice. Travel companies, for example, are seeing real value in leveraging chatbots to create always-on, personalized concierge level service at scale. From airlines and hotels to travel agencies, AI is helping mitigate frustration during challenging travel situations by understanding the context of the customer’s circumstance and providing contextually relevant options to resolve the issue.
Kimberly Nevala is director of business strategies for SAS best practices agrees. She said there are early adopters in all sectors — particularly by disruptors born digital. However, industries making significant inroads currently include those with high-touch customer service requirements or engagement such as finance and banking. A visible example is the proliferation of customer service chatbots for basic inquiries and common transactions.
4. Energy and Mining
Oil and gas is one of the largest industrial segments and is a natural fit for AI, according to AJ Abdallat CEO of Glendale, Calif.-based Beyond Limits. Removing friction from port scheduling operations requires a rare form of machine intelligence called cognitive intelligence (or human-like reasoning). Cognitive AI is now being applied to track tankers to determine when they leave port, where they’re going, and how much petroleum or LNG they are transporting. Predicting what is being shipped, plus refinery destination and arrival times, will help traders make smarter decisions. This involves the fusion of the key cognitive capabilities of multi-agent scheduling with reactive recovery, asset management, rule compliance, diagnostics, and prognostics to ensure seamless autonomous operation.
The value an AI system can bring to the energy market is tremendous. When machine learning is applied to drilling, information from seismic vibrations, thermal gradients, strata permeability, pressure differentials, and more is collected. By analyzing this data, AI software can help geoscientists better assess variables, taking some of the guesswork out of equipment repair and failure, unplanned downtime, and even help determining potential locations of new wells. According to Abdallat, AI brings better predictive technology and efficiency to mining operations.
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5. Healthcare
Healthcare is a sector where AI has endless possibilities, according to Vineet Chaturvedi co-founder of Bengaluru, India-based Edureka. AI is currently used by healthcare innovators to predict diseases, identify high-risk patient groups, automate diagnostic tests and to increase speed and accuracy of treatment. It can also be used to improve drug formulations, predictive care, and DNA analysis that can positively impact quality of healthcare and affect human lives.
6. Intellectual Property
Brisbane, Australia-based TrademarkVision is applying AI to help with issues around intellectual property in the image recognition space. In a statement to CMSWire, the company pointed out that the sheer volume of visual data available is a challenge for brands today, particularly in the area of design recognition and protection.
The company is using technology in 2D and 3D image recognition and artificial intelligence to provide visual search solutions. They don’t just scan objects for their likeness using data and codes, but through a combination of proprietary search algorithms and machine learning, the technology understands and thinks like humans, contextualizing and recognizing if one thing is visually like something else.
7. IT Service Management
Marcel Shaw is AI evangelist, IT blogger and a federal systems engineer for Ivanti. He said that with AI technology making its way into corporate and government networks, like it or not, it is going to be a dominant solution for IT service management (ITSM) in the future. There simply aren’t enough resources for analysts to get personally involved with so many requests and incidents that are coming in. “We will see many organizations turn to chatbots with AI capabilities as a means to handle, for example, front line IT support calls. Further, although ITSM solutions are rapidly evolving, service management will never go away as long as IT exists. By implementing AI technology, IT service management will experience a disruptive change that will alter the way humans are involved with the service management process.”
8. Manufacturing
Manufacturing — vehicle manufacturers in particular — is also using AI focus on automation and optimization. Industries dealing with complex knowledge requirements such as pharma and healthcare are vigorously planning and testing for the near future, although much talking and POC still abound currently.
Here, emerging applications focus on augmenting decision-making. For example, using AI to parse complex medical data and research to better inform the practitioner's diagnosis and treatment recommendations. As you might suspect, access to integrated data is a key enabler and barrier.
9. Technical Support
Mark Brewer is global industry director for service management at IFS. He said that the service-sector will experience much of this AI adoption throughout 2018 — specifically the integration of AI-powered voice assistants. AI-powered voice assistants represent a second major opportunity for service organizations in 2018.
Many calls into a service helpdesk are uncomplicated queries, like establishing opening hours, or determining when an engineer is due to arrive, which means they are simple enough to be answered by a bot. This drives significant potential for companies to connect AI-powered voice assistants behind the scenes to enterprise software with capabilities such as self-service diagnostics or scheduling optimization engines, to automatically offer appointment slots.
10. Retail
Implementing chatbots will enable retailers to dramatically increase the amount of data they can collect about the customer, giving them a competitive advantage over those who do not implement chatbots. When customers use verbal requests to navigate websites and to make purchases, Chatbots will be able to capture audible reactions, improve conversation capabilities, and over time, provide analytics to the retailer that can be associated with the emotions and the mood of their customers while online.
As a result, analytics will provide retailers enough data to predict emotional responses to their customer’s online experience. Thus, enabling retailers with the ability to tailor and personalize the customer experience with a focus on making the customer happy, which increases the chances that the customer will return in the future.
11. Software Development
For Paulo Rosado, CEO of OutSystems, AI, has the potential to transform the entire software lifecycle where AI assistants help with everything from modeling new applications with the right architecture and user experiences to analyzing the business value and impact for the organization. A combination of AI technologies like advanced machine learning, deep learning, and natural language processing, and business rules will have an impact on all steps of the software development life cycle, helping developers build better software faster.”
