Analytics has been changing the bottom line for businesses for quite some time. Now that more companies are mastering their use of analytics, they are delving deeper into their data to increase efficiency, gain a greater competitive advantage, and boost their bottom lines even more. That’s why companies are looking to implement machine learning (ML) and artificial intelligence (AI); they want a more comprehensive analytics strategy to achieve these business goals. Learning how to incorporate modern machine learning techniques into their data infrastructure is the first step. For this many are looking to companies that already have begun the implementation process successfully. There are already plenty of speech and engagement analytics platforms that help leverage AI and machine learning to capture better insights.
What are machine learning and AI? We begin with AI because it is the broader capability of machines carrying out tasks in an intelligent way. Machine learning is an application of AI that involves a particular type of data analysis to automatically build analytical models. Machine learning operates on the premise that computers learn from data; the computers and their models adapt independently as more data are fed to them. Machine learning is not a new capability, but it is evolving as data mining becomes more critical to businesses who need to make sense of Big Data. Thanks to AI and machine learning, companies can analyze more complex data and get more accurate results. Specifically, companies in the customer engagement space utilize AI and machine learning to analyze conversations, both those that end in a sale and those that don’t, and to automatically identify the language that typically leads to a sale or that predicts when a sale will occur.
To help your company understand how machine learning and AI in data analysis can benefit your business, we have rounded up examples of smart implementation, insights from the experts, and business use cases to give you the information you need to start using these types of advanced data analysis yourself. While it’s nice to hear about machine learning and AI in data analysis in theory, we want you to know about it in practice from those who are getting it right. If you feel that you need to keep up with the latest developments and trends, consider taking a course or earning a certificate in data analytics. Please note, we have listed our 50 examples of smart implementation of machine learning and AI in data analysis in alphabetical order to simplify your search process; thus, they are not ranked or rated in any way.
WittySparks is a blog run by creative minds who practice in a host of fields and write about hot topics in digital marketing, content marketing, business, and technology, among other fields. In this article, they highlight 10 companies that have made strides in using machine learning and gaining a competitive edge. Among the companies highlighted by Nastel for smart implementation of machine learning and AI in data analysis are HubSpot, Iris AI, and Descartes Labs.
Three key details we like from 10 Companies Transforming How Machine Learning is Used:
- HubSpot uses Kemvi’s DeepGraph machine learning and natural language processing technology in its internal content management system to better identify trigger events and pitch prospective clients and serve existing customers
- TrademarkVision uses machine learning in image-recognition tools to determine whether a new company logo is acceptable or violates existing trademarks
- Pinterest acquired Kosei, a machine learning company specializing in the commercial applications of machine learning, and now uses machine learning in nearly all of their business operations, including spam moderation, content delivery, advertising monetization, and churn reduction
WordStream provides search marketing management software and services as well as tools for PPC, SEO, and social. In this marketing strategy article, Dan Shewan shares 10 examples of companies using machine learning in innovative ways, including image curation at scale, improved content discovery, and to leverage chatbots.
Three key details we like from 10 Companies Using Machine Learning in Cool Ways:
- Twitter uses machine learning technology and AI to evaluate tweets in real time and score them using various metrics to display tweets that have the potential to drive the most engagement
- Google is researching nearly every aspect of machine learning and is making developments in “classical algorithms” and other applications like natural language processing, speech translation, and search ranking and prediction systems
- Edgecase uses machine learning to analyze customer behaviors and actions to provide a better experience for shoppers who may not know what they want to buy, in an effort to make casual online browsing more similar to a traditional retail experience
ThoughtSpot delivers search and AI-driven analytics for people. In his ThoughtSpot article, chief data Evangelist Doug Bordonaro explains that you don’t really need to understand machine learning, artificial intelligence, and deep learning to take advantage of them for your business. Doug shares tips for getting started with implementing this technology, such as hiring statisticians or data scientists.
Three key details we like from 5 Ways Machine Learning Can Make Your BI Better:
- Your BI strategy should include hiring data scientists who build models and find initial insights to make data available for making better decisions
- Machine learning with software such as IBM Streams and DataTorrent helps businesses discover anomalies so you can take immediate action for fraud analysis or gain better insight into online buying behaviors
- Machine learning and AI supports bots who bring data to existing workflows in a low-impact way
In his Medium article, investor and technologist Nathan Benaich uses his expertise in AI and emerging technology to encourage readers to delve into machine learning. He also provides a thorough description of AI and considers ways in which the technology will impact the economy. The focus of the article, however, is on the six areas of AI that will impact digital products and services.
Three key details we like from 6 Areas of AI and Machine Learning to Watch Closely:
- AI drives reinforcement learning, which has implications for learning to navigate 3D environments for autonomous driving
- AI is critical to generative models, which are used to create natural language in conversational interfaces, such as those with bots
- Networks with memory are making it possible to create learning agents that can generalize to new environments, robotic arm control tasks, autonomous vehicles, time series prediction, and natural language understanding and next work prediction
Professionals who want to know exactly how AI impacts their industry look to TechEmergence because they provide original market research and media on AI. In his TechEmergence article, Joao-Pierre Ruth explores six examples of AI platform providers that are holistic solutions for better business intelligence and analytics automation.
Three key details we like from 6 Examples of AI in Business Intelligence Applications:
- SAP’s HANA is a cloud platform companies use to manage databases of information they have collected; Walmart uses HANA to process its high volume of transaction records within seconds
- Domo’s AI for Business Dashboard scales with the size of a company and pulls data from applications like Salesforce, Square, Facebook, and Shopify to gain insight into customers, sales, and product inventory
- The Apptus eSales solution automates merchandising based on a predictive understanding of consumers and combines Big Data and machine learning to determine the products that may appeal to a potential customer searching online or looking for recommendations
Built In Chicago shares everything you need to know about startups and tech companies in Chicago. Andreas Rekdal’s Built In Chicago article looks at nine Chicago-based companies that are leading the way in AI.
Three key details we like from 9 Companies Establishing Chicago as a Leader in AI:
- Civis Analytics’ platform utilizes machine learning technology and powerful visualization tools to deliver actionable insights to people when they need them; they’re also “doing really interesting stuff” with natural language processing
- Narrative Science is one of the most advanced natural language startups specializing in analyzing data from disparate sources, identifying critical insights from data, and automatically generating natural language narratives to explain them for companies like MasterCard and Deloitte
- Catalytic is building a platform to improve business processes via machine learning, conversational user interfaces, and cloud computing
CRM Factory joins marketing and IT to create a branded customer experience throughout the customer lifecycle. Edison Leon’s CRM Factory article provides an overview of AI, machine learning, deep learning, Big Data, and IoT. It also reflects on the Big Data & Analytics Innovation Summit Shanghai 2017 and shares the ways in which parent company MCON implements AI applications for clients.
Three key details we like from AI and Machine Learning, Beyond the Buzzwords: What’s in for Retail Business:
- Machine learning models calculate credit scoring and predict the outcomes of giving loans to automotive customers
- Retailers can use machine learning to adjust pricing of products in real time using predictive model applications
- Machine learning is useful for car dealers who want to predict when a part or car equipment will fail so they can offer after-sales service at the right time
Sigmoidal is an award-winning data science and deep learning team of consultants. Their AI Business Use Cases share detailed glimpses into how machine learning makes it possible to automate common data workflow, detect objects by image, and understand text.
Three key details we like from AI Business Use Cases:
- Businesses use recommender systems that utilize Big Data to suggest products to consumers based on a variety of reasons including past purchases, demographic information, and search history
- Machine learning and AI systems are helpful tools for navigating the decision-making process involved in investments and risk assessment
- The financial services industry is using AI to save time and money and add value, such as using AI capabilities to analyze and gain a better understanding of how account holders spend, invest, and make financial decisions to customize the advice they give customers
Business News Daily aims to help entrepreneurs build their business and make smart decisions about products, services, and ideas. BND B2B staff writer Adam C. Uzialko explores exactly why AI has become a necessity for businesses that want to have and maintain a competitive edge.
Three key details we like from AI Comes to Work: How Artificial Intelligence Will Transform Business:
- AI is an ideal tool for finding holes in computer network defenses
- Applying AI to a customer relationship management (CRM) system creates a self-updating, auto-correcting system that automates relationship management
- Machine learning analyzes data to identify patterns in systems capturing vast amounts of data, such as smart energy management systems collecting data from sensors on a variety of assets
Pegasystems Inc. is a leader in software that streamlines businesses and enhances customer engagement. Their article on AI and customer engagement explores how the technology improves operations, drives engagement, and optimizes customer experience. It also examines how businesses have implemented AI and gotten a substantial return on investment (ROI).
Three key details we like from Artificial Intelligence (AI) Applications for Customer Engagement:
- Pega’s Adaptive Decision Manager applies machine learning to continuously improve Next-Best-Action recommendations by capturing a feedback loop so that previous recommendations inform future recommendations
- Companies utilize Pega Customer Decision Hub because its always-on brain constantly uses new insights to engage customers more effectively
- Machine learning and AI empower organizations to make highly-relevant recommendations, add intelligence to digital marketing, predict the likelihood of a sales lead conversion, provide intelligent guidance to agents, optimize a customer’s lifetime value, and automate and streamline efficiency
The Harvard Business Review is the leading destination for smart management thinking. Erik Brynjolfsson and Andrew McAfee’s HBR article examines exactly what AI can and cannot do for organizations. They refer to AI and machine learning as “the most important general-purpose technology of our era,” because the machine continually improves its performance without humans needing to explain how to accomplish all its tasks.
Three key details we like from The Business of Artificial Intelligence:
- Supervised learning systems have replaced memory-based filtering algorithms used to make personalized recommendations to customers
- More efficient, robust systems based on machine learning have replaced classic algorithms for setting inventory levels and optimizing supply chains
- Robots using reinforcement learning identify and sort objects they had never encountered before and speed the pick and place process in distribution centers for consumer goods
ZDNet operates at the intersection of technology and business. George Anadiotis explores how organizations still struggling with the digital transformation and becoming data-driven are going to transition to artificial intelligence. Anadiotis also explains that organizations who want to tackle AI should start with analytics to evolve to the advanced technology of machine learning.
Three key details we like from Data to Analytics to AI: From Descriptive to Predictive Analytics:
- Businesses should follow the chain of evolution in analytics as put forth by Gartner, from descriptive and diagnostic to predictive and finally to prescriptive
- Companies with a handle on analytics should use existing data to train predictive machine learning models to use predictive analytics
- Tools such as Machine Learning Canvas help businesses working on ML projects gain a clear shared understanding of the project and its goals
- Denmark’s Largest Bank is Using Machine Learning to ‘Tear Everything Apart’ – and It’s Starting to Pay Off
Business Insider Nordic reminds readers that Danske Bank, Denmark’s largest bank, has been creating disruptive innovations since introducing MobilePay in 2013. Now, they are shaking up the banking world by focusing on customer behavior and analytics with its in-house startup, Advanced Analytics, which features leading-edge AI and machine learning technology.
Three key details we like from Denmark’s Largest Bank is Using Machine Learning to ‘Tear Everything Apart’ – and It’s Starting to Pay Off:
- Danske Ban uses predictive models to assess customer behavior and preferences on a personal level
- The Bank analyzes customer data to identify their preferred means of communication, and then improve marketing campaigns using the valuable information
- Be prepared to encounter skepticism and resistance to change when implementing AI and machine learning; also, be prepared to deal with unrealistic expectations for instant ROI
Infogain, a global business-oriented IT consulting provider of front-end, customer-facing technologies, processes, and applications, presents CTO Ramesh Subramanian’s article, Enterprises Approach to Machine Learning. Subramanian reminds readers that machine learning provides enterprises with the framework, insights, and algorithms needed to ensure better predictive ability.
Three key details we like from Enterprises Approach to Machine Learning:
- 62% of enterprises will deploy AI technologies by 2018, as machine learning and AI become vital for industries seeking deeper insights than those for enhancing decision-making processes
- Enterprises adopt ML algorithms to increase flexibility of shop floors, supply chains, collaborative partnerships, and detect the prices that customers will prefer
- ML controls applications like real-time speech translation, biometric identification system, gene mapping, web-content curation, and others
MIT Sloan Management Review leads the way for academic researchers, business executives, and other influencers and thought leaders about advances in management practice, especially those shaped by technology. In her MIT SMR article, Jeanne Ross warns companies to be cautious about implementing AI because companies that do not insert value-adding AI algorithms into their processes correctly suffer.
Three key details we like from The Fatal Flaw of AI Implementation:
- Keep in mind that machine learning applications augment human efforts rather than replace them
- When implementing AI, be prepared for addressing the skilled tasks requiring sound judgment and domain expertise that replace nonspecialized tasks
- To generate competitive advantage from machine learning applications, you will need to upgrade your people’s skills, redesign their accountabilities, and ensure your workforce is prepared to consume business intelligence and take action
eeNews Europe delivers news, analysis, product, and design information to the electronics engineering community. Alexander Khaytin’s eeNews Europe article examines how companies are implementing Big Data analytics, machine learning, and AI technologies so that others can learn from those experiences.
Three key details we like from The Five Stages of Machine Learning Implementation:
- Machine learning technologies successfully are used in predictive and recommendation services
- When choosing the task of applying machine learning technologies, choose one with measurable results and economic effect
- The process of creating a predictive or recommendation project consists of several stages; begin by determining the objectives and constraints used in the modeling process
Health Affairs is at the intersection of health, health care, and policy. In their Health Affairs blog, writers Ernest Sohn, Joachim Roski, Steven Escaravage, and Kevin Maloy consider how ML and AI systems can improve health care. Specifically, they look at four ways to generate value from EHR systems and machine learning applications.
Three key details we like from Four Lessons in the Adoption of Machine Learning in Health Care:
- EHR vendors still do not offer robust machine learning, natural language processing, cognitive computing, or Ai solutions for processing health data
- While machine learning solutions are not yet mature or sophisticated enough to support complex clinical decisions, it can be used to reduce routine, time-consuming, resource-intensive tasks to allow personnel who typically attend to those tasks to focus on higher-end work instead
- Machine learning, in conjunction with natural language processing, can assist clinicians in going through a patient’s entire medical history and provide recommendations on what is most critical based on the patient’s presenting symptoms
MIT Technology Review gives audiences the intelligence needed to understand the world as it becomes increasingly shaped by technology. Elizabeth Woyke’s MIT Technology Review article explores the ways in which GE, one of the companies on its list of smartest companies, weaves AI throughout its operations.
Three key details we like from General Electric Builds an AI Workforce:
- Employees who are able to combine data science with their traditional roles are critical components of companies leading the way in AI and incorporating it into its machines and industrial processes
- GE hopes that creating smarter models via AI will give them an advantage over their rivals
- Begin implementing AI by training the technical brains, certifying employees in data analytics, and moving existing employees into digital analytics jobs
CIO UK is the UK’s leading community of CIOs, IT directors, and business technology executives. In his CIO UK article, Thomas Macaulay looks at how machine learning and forward-thinking CIOs are driving innovation so that others can learn from their example.
Three key details we like from How 11 CIOs are Using Machine Learning to Boost Innovation:
- Developing AI to assist with reviewing documents is more accurate and efficient than relying on human review – it’s also less expensive
- Travis Perkins is implementing machine learning to assist with product identification and workflow management
- Companies looking to predict supply and demand benefit from automating the process with machine learning because completing the task manually does not complete the process with as much detail
After Erik Brynjolfsson and Andrew McAfee published their HBR article arguing AI and machine learning will become “general-purpose technologies,” HBR senior editor Walter Frick sat down with Hilary Mason, the founder of Fast Forward Labs, to discuss how companies can put these technologies into practice and how to take advantage of them.
Three key details we like from How AI Fits into Your Data Science Team:
- You can’t do AI without machine learning, and you can’t do machine learning without analytics, and you can’t do analytics without data infrastructure
- Deep learning makes data accessible that previously was inaccessible to analysis, allows companies to create predictive models at a level of quality and sophistication that previously was impossible, and enhances the product function of data science because it generates new product opportunities
- Get your infrastructure and analytics solid before jumping into R&D
InnovationEnterprise is the leading global voice in enterprise innovation, providing access to cutting-edge content across nine distinct channels. Jake Hissitt’s IE article explores how three companies, Airbnb, Huawei, and Microsoft, are building data-driven products using AI and machine learning.
Three key details we like from How Airbnb, Huawei, and Microsoft are Using AI and Machine Learning:
- Airbnb is revolutionizing the way the industry uses machine learning techniques to create a dynamic pricing capability and learn from the analysis of historical data
- Huawei creates impressive data science and machine learning facilities called Noah’s Ark Laboratory that concentrate on deep learning for natural language processing, intelligent banking solutions, app recommendations and search engines, intelligent help, learning to match, and much more
- Microsoft’s Azure is constantly improving and getting closer to IBM’s Watson and has utilized medical data to predict the chances of success and improvements from surgeries
- How are Businesses Using Artificial Intelligence? 16 Uses for AI and Machine Learning in the Enterprise
Computerworld UK offers the latest technology features, analysis, and expert advice for IT professionals. Scott Carey’s Computerworld UK article takes a look at how machine learning and AI are being used by 16 different companies, including Ocado, Tesco, and Bloomberg.
Three key details we like from How are Businesses Using Artificial Intelligence? 16 Uses for AI and Machine Learning in the Enterprise:
- Ocado combines Google’s open source TensorFlow machine learning tools and cloud APIs to support internal AI projects, such as automating management of customer service-related emails
- EDF Energy uses AI to perform character recognition to pick out and process the figures on meter readings reported by energy customers; they then use machine learning to do pattern recognition to spot trends in usage data that is collected
- The NHS is testing an AI-powered chatbot on the UK’s 111 non-emergency helpline in London
In her Business Insider article, Mai-Hanh Nguyen considers the evolution of chatbots and the AI at work within them. She also takes a look at the machine learning, natural language processing that drive chatbots and enable them to work for business.
Three key details we like from How Artificial Intelligence & Machine Learning Produced Robots We Can Talk To:
- Chatbots operate through a number of channels such as web, within apps, and on messaging platforms
- Chatbots assist with digital commerce, banking, research, lead generation, and brand awareness
- Companies are experimenting with using chatbots for ecommerce, customer service, and content delivery
MindK Ltd. is a full-cycle web and mobile development company. In his MindK article, researcher Pavlo Zinchenko looks at the future business implications of AI and machine learning. Dubbing AI “the most important tech of the 21st century,” Zinchenko explores the extent to which the technology will change the face of various industries.
Three key details we like from How Artificial Intelligence and Machine Learning Will Change Your Business:
- AI is a disruptive technology, and chances are that the first companies to integrate it will reap rewards and those late to integrate it risk losing their business
- Finance, retail, and media stand to have the greatest impact of AI and machine learning
- AI will help retailers make personalized offers, predicting future trends to optimize stock, and improve logistics
SMB Group is a technology industry research, analysis, and consulting firm for small and medium businesses. In their perspectives report, that provide a comprehensive overview of AI and machine learning and examine how smart apps are impacting small businesses and the implications of the technology on small businesses.
Three key details we like from How Artificial Intelligence and Machine Learning Will Reshape Small Businesses:
- Small businesses benefit from smart apps built on AI platforms that use the company’s internal data and draw upon critical external data to learn and deliver insights
- Solutions are available that apply Ai and machine learning to applications for sales, marketing, customer service, chatbots, and more
- AI and machine learning solutions give small businesses the ability to put machines to work on mundane tasks so their people can focus on creative, value-added activities to make their companies grow
Capterra helps businesses find the right software so they can focus on the tasks that matter most. In her Capterra IT Management Blog post, Cathy Reisenwitz looks at the ways in which the AI market has grown in recent years. In fact, the Accenture Artificial Intelligence Report predicts that AI may cause annual economic growth rates to double and boost productivity by nearly 40% by 2035.
Three key details we like from How Businesses are Using Machine Learning and AI in 2017:
- To start using machine learning today, you need large volumes of historical data and a business case for it, in addition to a plan for making it pay for itself before you start
- AI helps companies make deeper, more meaningful customer relationships to engage customers at unparalleled personalized levels
- AI and machine learning are projected to have a market size of $100 billion by 2025, including deep learning, natural language processing, and cognitive computing
The Energy Conference Network organizes and executes the most timely, insightful, and respected conferences in the energy sector. Their blog, Talking IoT in Energy, focuses on the IoT and its impact on the energy sector. Editor Jared Haube explores how Halliburton, a leader in Big Data analytics and emerging technologies, utilizes machine learning.
Three key details we like from How Halliburton Addresses Machine Learning & Big Data Analytics:
- The exploration and production (E&P) industry in oil and gas is complex, which makes harnessing the capabilities of machine learning a challenge
- To implement machine learning algorithms, E&P companies need to connect datasets together to create a holistic picture of their operations and needs
- The predictive capabilities of machine learning enable E&P companies to save money and improve efficiency
HealthITAnalytics delivers real-world news and tips for everyone involved in healthcare analytics. This featured article considers how AI and machine learning will impact the healthcare industry and how providers can begin to prepare for it.
Three key details we like from How Healthcare Can Prep for Artificial Intelligence, Machine Learning:
- AI technologies in healthcare may include pursuing projects like personalized medicine based on genomics and clinical decision support for complex conditions such as cancer
- The most promising use cases for AI tools include predictive analytics, precision medicine, and clinical decision support, and development is underway in all of these areas
- IBM Watson Health uses natural language processing and semantic computing abilities to train in clinical decision support at top organizations across the country
Maruit Techlabs is a professional team delivering end-to-end software solutions related to chatbots, mobile platforms, application development, and web analytics. Their machine learning article explores how the technology boosts predictive analytics, yet only 60% of business leaders who cite growth as a key source of value from analytics have predictive analytics capabilities.
Three key details we like from How Machine Learning Can Boost Your Predictive Analytics?:
- One major hurdle in achieving predictive analytics capabilities is applying the right set of tools that can pull powerful insights from your stockpile of data
- Machine learning and AI algorithms give businesses the ability to optimize and uncover new statistical patterns that are the basis of predictive analytics
- Before adopting AI and machine learning, ensure predictive analytics will fulfill your business goals
TechCrunch is on the pulse of the tech industry and shares breaking technology news, analysis, and opinions. Lukas Biewald’s TechCrunch article asserts that machine learning is forcing massive changes in company operations and explores how businesses use machine learning every day.
Three key details we like from How Real Businesses are Using Machine Learning:
- Machine learning models identify the best user-generated content; Pinterest uses the technology to show more interesting content, Yelp uses it to sort through user-uploaded photos, and NextDoor uses machine learning to sort through content on message boards
- Ecommerce startups like Lyst and Trunk Archive use machine learning to show high-quality content to users, and Rich Relevance and Edgecase use ML strategies to give their customers the benefits of machine learning when users browse for products
- Companies invest in machine learning because it yields positive ROI
Daniel Faggella’s TechEmergence article investigates why companies at times have difficulty applying machine learning to their business problems. He also explores categories of business problems that ML can address in addition to actionable advice for starting a ML initiative with the proper approach and perspective.
Three key details we like from How to Apply Machine Learning to Business Problems:
- Companies such as Amazon, Netflix, and Spotify use machine learning to predict what users might buy, watch, or listen to next
- ML identifies patterns of speech and convert speech to text
- ML helps identify patterns in user behavior and determine which ads are most likely to be relevant to individual users
Technology decision makers rely on InfoWorld for expert, in-depth analysis of enterprise technology. Matt Asay’s InfoWorld article explores how companies can get started with machine learning using TensorFlow.
Three key details we like from How to Get Started with Machine Learning:
- Because Gartner estimates that fewer than 15% of enterprises put machine learning into production, companies need to start experimenting with the technology
- Companies need to be prepared for the complexities machine learning adds to your life cycle and the way in which it impacts your business
- Tools such as TensorFlow give company leaders the opportunity to use complex models even without mathematical training
Clickatell, a leader in mobile messaging and transactions, explores using machine learning in business. The article also examines the ways in which businesses benefit from the technology.
Three key details we like from How to Use Machine Learning in Business:
- Machine learning boosts a company’s ability to find patterns and automate value extraction in several areas
- As consumers expect nearly immediate responses to their needs, companies need to adopt machine learning to sift through enormous amounts of data faster than any human analyst could do
- Companies already using machine learning include Pinterest, Disqus, Yelp, and NextDoor
Barnard Marr, bestselling author, keynote speaker, strategic performance consultant, and analytics, KPI, and Big Data expert, shares how Walmart uses machine learning, AI, IoT, and Big Data to improve performance. The company uses the technology to enhance the shopping experience and create a seamless experience between online and in-store shopping.
Three key details we like from How Walmart Is Using Machine Learning AI, IoT and Big Data to Boost Retail Performance:
- Walmart uses machine learning to optimize delivery routes of their associate home deliveries
- Soon, facial recognition technology could identify unhappy or frustrated shoppers to trigger opening additional checkouts and analyze trends over time in shoppers’ purchasing behavior
- Walmart has applied for a patent to integrate IoT tags to products to monitor their usage, automatically replace products when needed, and monitor expiration dates and product recalls
Edgy Labs is comprised of a group of technologists and successful tech entrepreneurs who specialize in growth hacking, SEO, artificial intelligence, virtual reality, augmented reality, and the Internet of Things. Zayan Guedim’s Edgy Labs article points out everyday uses of machine learning and the ways in which it already is and soon will be impacting business.
Three key details we like from How You Use Machine Learning Everyday and Business Will, Too:
- AI technology affects the experience of customers and employees
- Accenture Research conducted a study showing that AI boosts business profitability that already is declining and more significantly increases the business’ potential to grow as it is integrated into business processes more
- AI already impacts the workplace via virtual assistants and chatbots
Deloitte is a global network of member firms that helps clients achieve their goals, solve complex problems, and make meaningful progress. In Intelligent Automation Entering the Business World, Patrick Laurent, Thibault Chollet, and Elsa Herzberg exploring how AI drives automation that could change the game for process efficiency in the financial industry.
Three key details we like from Intelligent Automation Entering the Business World:
- Robotic process automation combines AI, such as natural language processing, machine learning, autonomics, and machine vision with automation
- Machine learning systems can predict fraud and has gotten sophisticated enough to detect when behavior deviates from typical consumer habits
- The financial industry has started to consider natural language processing as a way to automate trading strategies
IoT For All is written by the engineers at Leverege and provides readers with everything you need to know about the Internet of Things. Product engineer Narin Luangrath presents questions companies should ask before implementing machine learning to determine whether the technology is appropriate for you.
Three key details we like from Is Machine Learning Right for Your Business?:
- Machine learning in business includes email filters marking messages as spam or not, Netflix recommending movies or shows you likely will enjoy, Google maps predicting the difficulty of parking at a particular destination, Facebook’s facial recognition identifying people in photos, and anomaly detection algorithms to identify fraudulent purchases
- To do machine learning at a high level, pick a ML model or algorithm, train your model by feeding it data, and use the trained model to make decisions or predictions
- To determine whether machine learning is right for your company, ask whether you’ve tried traditional data analytics and statistics before, if you have data that is relevant to solving your problem, and if you have lots of data
Boston Consulting Group is on a mission to unlock deep insight and challenge traditional thinking in order to drive transformation. Here, Philipp Gerbert, Martin Reeves, Sebastian Steinhäuser, and Patrick Ruwolt share the findings of a report BCG conducted with MIT Sloan Management Review to determine exactly how businesses use AI and establish a baseline to help companies compare their efforts and goals with the technology and to offer guidance for future initiatives
Three key details we like from Is Your Business Ready for Artificial Intelligence?:
- Nearly any company has the ability to establish competitive advantage and shape value creation from Ai in your sector
- It may take a few years before the effects of AI on your offerings and processes meets your expectations
- You must have an AI strategy in place
- Machine Learning and Artificial Intelligence (AI) in Biotechnology – We Are in a Golden Age of Medical Research
Denodo senior product marketing manager Saptarshi Sengupta wrote this article BioStorage Technologies to examine the ways in which machine learning and AI impact medical research. These technologies especially apply to biomedical research because the data is spread across a vast array of entities around the world.
Three key details we like from Machine Learning and Artificial Intelligence (AI) in Biotechnology – We Are in a Golden Age of Medical Research:
- Data virtualization is a critical component of a platform for biomedical research
- In terms of medical research, companies utilizing machine learning technologies must make privacy, data protection, and data governance a priority
- To use machine learning most effectively for biomedical research, collaboration and innovative partnership models are a must
TG Daily operates at the intersection of hardcore technology and social marketing, digital advertising, and the enterprise. Craig Boyle’s TG Daily article considers the limits of AI and machine learning in regard to its impact on business and consumers.
Three key details we like from Machine Learning, AI and Digital Intelligence’s Effect on Business:
- Companies need to use the knowledge gained from machine learning appropriately, or they can upset customers by using information about them in irresponsible ways, such as Target did when the company predicted which customers were pregnant and marketed to them as such
- Companies run the risk of algorithms containing information that they are unaware of, as the technology advances and becomes more complex
- Companies run the risk of consumers viewing them as being intrusive if they use their data to create an experience that is too personalized
The Financial Brand shares ideas, insights, and inspiration for financial marketing executives. They also share this article by Scott Hackl, global head of sales for Finacle at EdgeVerve, which presents his argument that banks and credit unions should use Ai and the power of advanced analytics in order to become agile and remain relevant.
Three key details we like from Machine Learning, AI and the Future of Data Analytics in Banking:
- Advanced data analytics, by way of machine learning and AI, gives traditional financial institutions insight into customer behaviors
- Increase customer loyalty with digital assistance to manage routine inquiries and provide personalized advice
- Modern AI platforms are more accessible to banks and credit unions that previously would have needed R&D and in-house resources to pursue AI technologies; as long as you are prepared to do away with your legacy system and have a unified goal for your use of AI
Amanda Bowman’s crowdspring article provides an overview of AI and machine learning and the ways in which it impacts everyday life today. She also addresses the ways in which the advanced technology can work for small businesses and investigates several services and products that make AI and machine learning accessible for those businesses.
Three key details we like from Make Your Small Business Smarter with Artificial Intelligence and Machine Learning:
- One of the best ways to apply AI and machine learning to your small business is to use it to uncover patterns and trends in your existing customer data
- Companies integrate chatbots, driven by AI, in a range of areas, from taking on simpler customer service tasks, to using previous conversations with a customer to respond as effectively as possible to current requests
- Cloud-based SAAS systems make AI more accessible to small businesses
Esri users create maps that are used around the world. Mansour Raad’s Esri article explains how a team of engineers created an AI system capable of making predictions even when no data existed. For companies looking to implement AI from scratch, this article serves as a case study.
Three key details we like from A New Business Intelligence Emerges: Geo.AI:
- Collect data and information from any source you can as a precursor to implementing AI technology
- Using AI makes it possible for you to stop worrying about changing algorithms to account for every exception
- AI has the ability to enhance precision farming, disease prediction, and predictive policing, in addition to predicting demand spikes, identifying high-margin prospects, improving supply chain efficiency, and optimizing service delivery
Mary Branscombe’s CIO.com article offers a realistic look at the ways in which machine learning can impact business and the ways in which you can use it today in your company. As she points out, machine learning is the most likely form of AI that you will use in your business, such as in chatbots, product recommendations, and spam filters.
Three key details we like from A Practical Guide to Machine Learning in Business:
- Keep in mind that the information gleaned from machine learning is in terms of probabilities rather than exact results
- Many AI technologies allow you to customize them using your data
- You don’t need to set up your own infrastructure; machine learning services in the cloud assist in building data models, and they include more of the expertise you need to get the results you want
Adobe Marketing Cloud delivers a complete set of integrated digital marketing solutions. Their digital marketing blog article, by the Adobe Retail Team, urges retailers who want to deliver personalized, relevant experiences to adopt AI now.
Three key details we like from Retailers: Adopt Artificial Intelligence Now for Personalized and Relevant Experiences:
- Machine learning and predictive analytics deliver personalized experiences at scale
- Retailers should take a phased approach to implementing AI and then applying the insights they gain from it
- Begin implementing AI slowly and on a small scale to rise to the top of your industry
The Marketing Artificial Intelligence Institute connects modern marketers with marketing AI news, trends, resources, and technology. Paul Roetzer’s Marketing AI Institute article centers on an interview with Beehive CEO Adi Dagan, who discusses the limitations and current applications of AI in business, including using it to boost customer loyalty.
Three key details we like from This Personalization Platform Uses Machine Learning to Stop Customer Churn for Gaming Companies:
- Before implementing AI, clearly define the problem you hope to solve with the technology
- Solutions such as Beehive automate the cycle of segmenting the customer base and creating relevant marketing campaigns
- Don’t settle for the first AI solution you come across: you may purchase a more robust, comprehensive solution than you need
NextWorld Capital invests in early-revenue stage enterprise tech startups and helps them become global leaders. In his article, NextWorld Capital general partner and SaaS, AI, and IoT expert Tom Rickert summarizes the conversation he had about AI in business with Textio co-founder and CTO Jensen Harris, Osaro CEO Derik Pridmore, Clarifai CEO Matt Zeiler, and msg.ai CEO Puneet Mehta.
Three key details we like from Using AI and Machine Learning to Beat the Competition:
- AI and machine learning are being built into platforms and services often enough that companies no longer can market on algorithms alone
- Capitalizing on AI means adopting a data-centric approach
- AI and ML make data available for daily coaching and feedback; thus, AI is not an employee replacement, but it is a tool that helps employees have more significant impact and better performance
BusinessTech, delivers the business technology community in South Africa with news and discussion. In this article, they explore deep learning and machine learning and the ways in which Gartner predicts deep learning will be a critical component of demand, fraud, and failure predictions by 2019.
Three key details we like from Using Machine Learning and AI to Add Value to Business:
- Companies use deep learning to solve complex business problems that are rich in data, such as interpreting medical images to diagnose cancer earlier
- PayPal uses deep learning as part of its best-in-class approach to stopping fraudulent payments and has cut its false-alarm rate in half as a result
- To be as effective as possible, machine learning and AI initiatives must include a blend of skills, infrastructure, and business buy-in
- Why AI, Machine Learning and Big Data Really Matter to B2B Companies
KPI and Big Data expert Bernard Marr’s Forbes article examines the significance of AI, machine learning, and Big Data for B2B companies. As Marr points out, customers have the same expectations whether they are interacting with a B2C or a B2B, and B2B companies need to be prepared to use advanced technology to meet and exceed those expectations.
Three key details we like from Why AI, Machine Learning and Big Data Really Matter to B2B Companies:
- Machine learning and AI enhance the gathering of information for lead generation and analyze unstructured data in interactions with prospects, which also assists in making marketing campaigns even more effective
- Machine learning streamlines lead scoring and helps companies prioritize where to focus sales efforts; machines facilitate employees in making account-based marketing decisions
- Machine learning delivers actionable insights into customer behavior because the technology efficiently consolidates data points and analyzes data for meaning
SalesforceIQ delivers relationship intelligence technology to help companies save time and close more deals via smarter selling and better relationships. In this SalesforceIQ article, Rahka Srivatsan explores how small businesses can take advantage of advanced technologies and should embrace them to stay ahead of the competition.
Three key details we like from Why Machine Learning Isn’t Out of Reach for Small Businesses:
- Machine learning optimizes the next best offer for customers and reduces the abundance of analytics for companies
- Small businesses no longer need to build ML tools from scratch; less expensive specialized systems and support are available for use by these businesses today
- When adopting machine learning, be prepared to make a fundamental change in your approach to challenges concerning data and experimentation
Is your business staying ahead of the curve by implementing machine learning and AI?