The healthcare industry is perhaps second only to finance when it comes to the sheer amount of historical data available for use with artificial intelligence. Data from EMRs, insurance claims, clinical trials, and drug research and development can all be pulled into a machine learning algorithm to generate insights on patient behavior, patient risk, and […]
McKinsey estimated that embarking on digital transformation to restructure value chains and drive R&D innovation across the pharmaceutical industry could be worth $50–150 billion of earnings before interest, taxes, depreciation, and amortization. In particular, machine learning is likely to continue finding a place in the pharmaceutical industry. Pharmaceutical companies have found applications for machine learning […]
Sensors and mobile devices are in many ways working with AI software for business intelligence purposes in a few industries, including insurance and oil and gas. In the healthcare space, mobile devices and wearables allow patients to receive information on possible diagnoses for their symptoms and to monitor metrics such as their heart rate. There […]
Accenture reports that in 2017, the 16 top biopharmaceutical companies in the world had an aggregate global revenue of $428 billion, which was nearly half the global pharmaceutical market by net sales. The report also revealed a shift to specialty drugs for hard-to-treat diseases. AI has numerous applications in healthcare broadly, and with the help […]
Accenture forecasts that the retail industry could grow operating profits to $2.95 trillion by 2025. We’ve covered AI in retail extensively on Emerj, and in this report, we dive into the Asian startups that are offering AI solutions to retailers. Asian AI vendors seem to offer retailers solutions for marketing, sales, operations, and making purchases easier, among other areas. […]
Forrester estimated that online sales in Western Europe will grow at an average of 11.9% annually until 2022. Over this period, 21% of non-grocery retail sales will be online. We see AI as continuing to find its way into the retail industry. This report specifically focuses on innovation in the European retail industry. In it, […]
Accenture forecasts that growth in the AI healthcare market is expected to reach $6.6 billion by 2021 from $600 million in 2014, growing at an annual compound rate of 40%. We’ve covered AI in healthcare extensively on TechEmergence, but in this report we’ll be looking at four European companies offering more niche solutions for both […]
According to the International Health, Racquet & Sportsclub Association, the global fitness industry earned revenues estimated at $83.1 billion in 2016, up from $81 billion in 2015, and growing by 2.6%. If it continues at this rate, it should reach $87.5 billion in 2018. As of now, numerous AI vendors claim to assist gyms with signing […]
According to Deloitte, global healthcare spending is expected to grow annually by 4.1% from 2017-2021, up from just 1.3% in 2012-2016. The report suggests this growth will be fuelled by aging, rising populations, the growth of developing markets, advances in medical treatments, and rising labor costs. Hospitals and healthcare companies will want to make more […]
Accenture forecast the Industrial Internet of Things could contribute $10 trillion to the global economy by 2030. The report also suggested that sensors, material tracking mechanisms, 3D printing, automated product design, robotics, and wearables could help manufacturers reduce costs and increase productivity. Predictive asset maintenance could potentially reduce equipment and machinery maintenance costs by up to 30% […]
Episode Summary: In this episode of the AI in Industry podcast, we interview Rajat Mishra, VP of Customer Experience at Cisco, about the best practices for adopting AI in the enterprise and how business leaders should think about the man-machine balance at their companies. Mishra talks with us about how the executive team should be able […]
Episode Summary: In this episode of the AI in Industry podcast, we interview Nikhil Malhotra, Creator and Head of Maker’s Lab at Tech Mahindra, about how artificial intelligence changed the nature of IT services and business services in general. Malhotra talks about what businesses should consider to make themselves relevant for the future. In addition, he discusses the […]
Modern AI and machine learning software require large sets of data in order to train its algorithms to make judgments, make predictions, and take actions. Data is a critical part of bringing artificial intelligence to life in different industry sectors. The applications we’ve highlighted below involve organizing historical and real-time data from existing businesses in […]
McKinsey reported that most oil and gas operators have not maximized the production potential of their assets. A typical offshore platform, according to the 2017 report, runs at about 77% of its maximum production potential. Industry-wide, the shortfall comes to about 10 million barrels per day, or $200 billion in annual revenue. To help optimize […]
Episode Summary: Prominent technology companies like Google and Amazon lead the way in the B2C world, having access to streams of searches, clicks, and online purchases. They have access to large volumes of consumer data points numbering in the billions that can be used to train machine learning algorithms. A typical B2B company, on the […]
Allied Market Research estimated the value of the global autonomous vehicle (AV) industry to reach $54.23 billion in 2019, increasing to $556.67 billion by 2026 at an annual growth rate of 39.47% during that period. It follows that AI would find its way into the autonomous vehicle world. We detailed our own timeline for self-driving cars, pooling quotes and insights from […]
Episode Summary: In this episode of the AI in Industry podcast, we interview Grant Ingersoll, CTO of Lucidworks, about AI developments in enterprise search, and the common challenges of AI adoption in enterprise. Ingersoll talks about how companies have massive amounts of siloed data, making it difficult to find within enterprise systems. We hope businesses might take away from […]
A recent PricewaterhouseCoopers study revealed that the global market for drone-powered business solutions was valued at $127.3 billion in 2016. For agriculture, prospective drone applications in global projects were valued at $32.4 billion. The same study forecast that agricultural consumption would increase by 69% from 2010 to 2050 due to the predicted increase in population […]
Forrester surveyed call center business leaders and found that 46% of them expect their business to grow by 5%-10% in 2019. To make this happen, Forrester reports that companies will increasingly explore the use of AI-driven chatbots and voice services. Previously, we released a report on chatbots for customer service. In this report, we expanded the […]
International Data Corporation reports that the global wearables market continued to grow in the second quarter of 2018 as shipments reached 27.9 million units, an increase of 5.5% year-on-year. This growth translated to $4.8 billion year-on-year for the quarter. Smartwatches continued to be the most popular wearables. We researched the use of AI in wearable […]
Episode Summary: We receive a lot of interest from business leaders in the domain of data enrichment, and we’ve executed on a few campaigns for these businesses. At the same time, our audience seems particularly interested in the collection of data to train a bespoke machine learning algorithm for business, asking questions related to how […]
Several factors have contributed to the advancement of AI in the pharmaceutical industry. These factors include the increase in the size of and the greater variety of types of biomedical datasets, as a result of the increased usage of electronic health records. We researched the use of AI in the pharma space to better understand where […]
Episode Summary: Over the last year, we’ve covered a lot of marketing applications, including a survey of the landscape of machine learning in marketing applications and which industries will be affected first. But marketing doesn’t tell the whole story when it comes to B2B sales. At some point, we need to take these clicks and […]
MicKinsey estimated that by 2030, up to 15% of cars sold will be autonomous vehicles. We detailed our own timeline for self-driving cars, pooling quotes and insights from executives at the top 11 global automakers. Additionally, in an article about the future of AI and self-driving cars, Stanford professor, mechanical engineer, and founder of the Center for […]
Reuters referenced an Orbis Research figure estimating the global cosmetics market to be worth around $$805.61 billion by 2023. According to the research, products for skin care, hair care, and fragrances are the most-sold products online. Skincare has the largest market share currently, while oral cosmetics will be the fastest growing sector during the forecasted […]
As of now, numerous companies claim to assist security firms, the military, and consumers prevent crime and defend their buildings, homes, and personal belongings. We researched the space to better understand where AI comes into play in the physical security industry and to answer the following questions: What types of AI applications are currently in use […]
Episode Summary: This week on AI in Industry, we speak with Amir Saffari, Senior Vice President of AI at BenevolentAI, a London-based pharmaceutical company that uses machine learning to find new uses for existing drugs and new treatments for diseases. In speaking with him, we aim to learn two things: How will machine learning play […]
In this report, we look at how gestural recognition is being implemented into home automation, healthcare, cars, and virtual reality. […]
Episode Summary: We usually discuss the impact of artificial intelligence on a business’s bottom line, but, in addition to law enforcement, governments and NGOs are also considering AI as a mechanism for improving society. We spoke to Anandan Padmanabhan, CEO of the Wadhwani Institute for Artificial Intelligence in India, as part of our research report […]
In this post, we highlight how VR and AI are being used in shopping. We focused on the specific areas of clothing fitting, makeup previews, home improvement, and merchandising. […]
Episode Summary: AI, specifically natural language processing, has made it easier to understand text as a medium in a deeper, more efficient way and at scale. With video, the situation is quite different. AI is already being used to help industries that work in the video medium. However, searching for content within videos is more […]
In this report, we explore the uses of augmented reality combined with artificial intelligence in the retail areas of home products, home improvement, apparel, and automotives. […]
In this report, we explore how virtual mirrors and computer vision claim to offer new business use cases and how they could change the industry landscape. […]
Episode Summary: When we think of recommendation engines, we might think of Amazon or Netflix, but while consumer goods and entertainment might be the most prominent domains for recommendation engines, there are others. This week, we speak with Madhu Gopinathan of MakeMyTrip, one of the few Indian unicorn companies, about recommendation engines for travel companies. […]
With this report, we aim to give business leaders a insights on AI-driven personalized marketing applications through case studies, leadership background and other information. […]
Episode Summary: In this episode of the podcast, we interview AIG’s Chief Data Science Officer, Dr. Nishant Chandra, about natural language processing (NLP) for internal and team communication. Dr. Chandra talks about how NLP can help with sharing documents with specific team members whose roles warrant viewing those documents. Instead of a broad memo that […]
As reports of nursing shortages continue, we look at 7 AI applications which claim to complete simple daily tasks to free up human nurses for critical work. […]
With the development of AI-powered recommendation engines, recent research shows that recommendation engines are becoming more common in the areas of fashion and apparel. […]
The listed applications provide a variety of retail solutions using AI technologies that include computer vision, machine learning and robotics. All still require inputs from the individual shoppers or enterprise operators, whether it is providing personal data, email address, uploading images. […]
A collaborative report by Moore Stephens and WARC estimated the size of the martech industry around $34.3 billion dollars in 2018. It follows that AI would find its way into the marketing world. Marketing experts agree that AI will have a significant impact on the marketing world in the coming years. As of now, numerous […]
Episode Summary: This week’s episode of the AI in Industry podcast focuses on two main questions. First, how should business leaders determine the most fruitful, potential applications of AI in their business? Second, how do they choose the right one into which to invest resources?
This week, we interview someone who has spoken with a number of CTOs and CIOs about early adoption strategies for machine learning for customer service, marketing, manufacturing and other applications. He is Madhusudan Shekar, Principal Evangelist at Amazon Internet Services.
<link to podcast>
Guest: Madhusudan Shekar, Principal Evangelist, Amazon Internet Services Pvt. Ltd.
Expertise: Cloud, mobility, analytics, artificial intelligence, Internet of Things
Brief Recognition: Madhusudan Shekar is Principal Technical Evangelist at Amazon Internet Services Private Limited (AWS) and has been working on cloud computing technologies since 2008. He works with organizations of all sizes to help them adopt and be successful with AWS Cloud. He has over 20 years of experience in developing large scale systems for telecommunications, media, automotive and financial services industries. Prior to AWS, he built the fleet management system called TATA Fleetman for automotive original equipment manufacturer TATA Motors, as well as the third-party application programming interface gateway for enterprise smart payment systems at Ezetap. Shekar graduated from the University of Madras.
(2:43) You speak with developers and business leaders, encouraging them to adopt some of Amazon’s machine learning (ML) capabilities. What ML capabilities do you see making their way into enterprises?
Madhu Shekar: When we look at the challenges that enterprises face, whether it is customer service, sales and marketing, quality control—these are the areas I see ML in. For example, we look at companies using machine learning in customer service or manufacturing companies looking at sales and retail execution. These are the pain points where they can use machine learning.
Machine learning could be the most powerful tool that can take companies to the next step. I always focus on identifying areas where they have a competitive advantage. What is the next level? That’s the area where you have the deepest strength. Because machine learning requires a huge volume of data, determine the areas where you have the best—not the most—data and start building your competencies.
Once you have achieved that, you can start expanding that capability into other areas of the business. If you are a fast-moving consumer goods company with a great amount of data on retail distribution or consumer buying behavior, focus the machine learning in that space.
(5:20) I’ve interviewed someone that’s said prioritizing efficiencies may not be the best way to apply ML, and here you are saying “think about competitive advantage first.” Not merely starting small by automating some processes. You are saying, “let’s see where the business is going and let’s take ML to make that leap forward.”
MS: If you have an advantage over your competitors in the same area, applying machine learning could potentially give you a generational advantage. To do that requires a lot of clean data.
(6:40) When people come up with ideas for their business, what advice do you give them to help them achieve these advantage areas? How do you prompt them to look in the right direction?
MS: When I talk to business leaders at the C-level, I ask them to understand why they think they have an advantage. If you are building a new business opportunity or a new model, what is it that you want to do with this? How are you collecting data? What kind of data are you collecting? Then I take them to the next step and start building machine learning models.
Some of these conversations can go differently. For example, one executive asked “do you want to get started with machine learning because you want to achieve advantages in, for instance, customer services or sales where they want to use ready-made solutions such as Amazon Polly or the Amazon Lex chatbot engine. These are ready-made services that you can consume and quickly deploy to get the results that you want.
Another way is to look at areas that offer competitive advantages. What reliable data set do you already have? Is it your manufacturing data? Is it customer data that you are confident can bring results? How do we take advantage and support that in the machine learning process?
(9:45) Understanding what kind of data is currently available seems to be an important first step in ML and to finding the best application. Do the exercises you conduct with business leaders emphasize the importance of organizing data because it is the foundation of their advantage?
MS: When a business faces challenges in data organization, we start looking at the data lake to determine what is clean and unclean data. Once the data is cleansed, the business now has the ability to process it. Put the data in a single space and define schema as you start building and reporting from that.
Machine learning, for all the coolness it is known for, first is about data. Eighty percent of the work in machine learning is getting the data organized, structured, trustworthy. Because the machine is now going to make decisions for you.
(12:39) Three years from today, what kind of themes do you see around treating and handling data? What other things do you hope to see more of so enterprises can adopt machine learning faster?
MS: I think there will be more data lakes. Businesses will start to structure data to make it as less expensive as possible. That’s what Amazon Business is all about: being able to store data at $27 per terabyte. The goal is to be the most viable per dollar. Once this is set, the next step is to build the data pipelines to process and cleanse the data, and make it available across the entire organization. Democratizing the data gives the business the capability to use the data as quickly as possible and benefit from machine learning every day.
Each one is going to make that data better for their respective business use case. Does the quality person need access to some of the customer data? That’s the kind of feature you want to build as well to avoid data leakage. You also want to have data security.
One of the things we recognize in Amazon is data patterns. If someone is accessing the data too frequently when they should not, the organization needs to be alerted. Amazon Macie looks at the packets of data, analyze it in the back end to determine patterns and raise alerts. Your data lake should have these capabilities that democratize on one end and control on the other.
After this comes the next step. You will have layers of data: raw customer data, first-level cleansed data. A product manager will build a particular cohort and market segmentation of the data suitable for a specific use case. Someone will build another off that cohort, build hierarchies of these dependencies, evolving through a natural progression. That is the organization’s data platform. Now you start building machine learning on top of this.
Simple binary classifiers, multiclassifiers can solve a lot of issues. A lot of things can be solved using traditional algorithms. Build models on top of that and take it to the next step. Specific business use cases have tremendous returns. Having the wrong data set and processes can be more expensive than the investment itself.
Header Image Credit: Smartdatacollective.com […]
In recent years, artificial intelligence has enabled pricing solutions to track buying trends and determine more competitive product prices. While static pricing keeps prices absolute, dynamic pricing adjusts prices to offer customers different prices based on external factors and their individual buying habits. While there are some dynamic pricing options on the market, automatic dynamic […]
Episode Summary: At TechEmergence, we often talk about the software capabilities of AI and the tangible return on investment (ROI) of recommendation engines, fraud detection, and different kinds of AI applications. We rarely talk about the hardware side of the equation, and that will be our focus today. For hardware companies like Nvidia, stock prices […]
Episode Summary: Facebook and Google’s advertising complex is founded on machine learning, allowing people to self-serve their data needs across a broad audience. India-based InMobi is a company in the advertising technology space that delivers 10 billion ad requests daily. Today, we speak with Avi Patchava, Vice-President of Data Sciences and Machine Learning at InMobi, which […]
In providing this industry application report, we aim to paint a clearer picture of the landscape of solutions that detect and prevent credit card fraud. By shortlisting potential applications below, we hope this report helps business leaders explore whether deploying a preventive solution is right for their business. […]
eMarketer estimates that 62% of global internet users accessed digital video in 2017, and that number is expected to rise to 63.4% by 2020. This may be marginal growth, but, essentially, consumers are expected to spend more time watching video content. As more and more of the world’s internet users gain access to faster internet, […]
Episode Summary: Companies with wells of data at their disposal may find themselves asking how they can use them in meaningful ways. Generally speaking, a clean set of data is the foundation for AI applications, but business owners may not know how exactly to organize their data in a way that allows them to best leverage […]
From the soaring stock price of NVIDIA, to the cutting-edge developments at Facebook and Google, AI hardware is a hot topic. We set out to learn more about what executives should know about the coming developments in AI hardware – and how it might impact different industries and sectors. With the help of Kisaco Research, […]
Robo-advisors are digital platforms that provide automated, algorithm-based financial planning services with little to no human supervision. In the past decade, financial technology companies have elevated robo-advisors to now handle more complex tasks such as tax-loss harvesting, investment selection and retirement planning. To begin using a robo-advisor, most of these services first request customers to […]
In early 2017, Amazon unveiled a new application of AI outside its online domain: Amazon Go, an artificial intelligence (AI)-powered grocery where there are no cashiers and checkout lines. Shoppers activate the store’s smartphone app upon entering the store, grab the items they need, and leave through the same “gates” they enter without pausing to […]
Developments in artificial intelligence have had global banks recently integrate online chatbots into their websites and mobile apps. Smaller, newer banks are following this example. The gaining popularity of chatbots could be considered surprising for an industry that handles other people’s wealth and perceives security as top priority. However, today’s tech-savvy banking customers have begun […]
Despite the emergence of chat apps and social media over the past decades, email has continued to thrive. A 2017 study from Radicati marketing research claimed that email accounts were expected to reach 4,920 million by the end of 2017, a number that has not changed much since 2013, and that about 100 billion business emails […]
According to the World Health Organization (WHO), the array of mental disorders include depression, bipolar affective disorder, schizophrenia and other psychoses, dementia, intellectual disabilities and developmental disorders including autism. These conditions manifest in different ways such as abnormal thoughts, perceptions, emotions, behavior, and relationships with others. Depression alone affects more than 300 million people according to […]