When IBM's Watson system defeated the two highest-earning Jeopardy! champions in 2011, it helped propel artificial intelligence further into the mainstream consciousness. Until then, AI was largely a scientific and research pursuit -- particularly after an effort to commercialize AI systems flamed out in the 1980s.
Watson itself went somewhat undercover after its quiz show victory, but the cognitive computing platform got a $1 billion push from IBM in 2014, leading to the development of Watson-based analytics offerings for healthcare, financial services, customer engagement and other enterprise uses.
IBM isn't alone: Microsoft, Google, Amazon Web Services, Salesforce and a growing horde of other vendors now offer enterprise-focused AI technologies, from chatbot tools to machine learning and deep learning platforms.
As AI in business applications moves from possibility to reality, organizations are learning how to use such technologies to better serve both internal users and external customers. For example, chatbots handle routine customer service inquiries, freeing up staff members to attend to higher-level issues. Advanced analytics programs incorporate AI to segment customers for targeted marketing, to score sales leads, to identify potential problems in internet of things (IoT) devices and more.
In this guide, we cover the current state of AI technology in the enterprise and the role of platforms like IBM Watson in enabling new types of analytics uses. Learn how AI-powered advanced analytics and cognitive computing systems are furthering corporate initiatives, and get advice from industry experts and IT professionals who've been tasked with setting and implementing strategies for using AI in business operations at their companies.
1AI technology roadmap -
New functionality sets the stage for AI in business
Artificial intelligence applications often require a shift in the approach that organizations take for analytics initiatives. But they also require the right software and systems. The articles in this section take a look at key features and technology developments designed to help users create effective AI applications that enable their companies to fully capitalize on the insights produced by machine learning algorithms and other forms of AI.
Industry watchers credit IBM's analytics and AI software for strong functionality. But the company needs to better explain how it all fits together, they say. Continue Reading
While AI chatbots are adept at providing scripted responses, they need to get better at really chatting with people to maximize their business value. Continue Reading
Vendors are embedding machine learning functionality into applications so users other than skilled data scientists can take advantage of the technology. Continue Reading
IBM's director of accelerated cognitive infrastructure discusses how the company improved deep learning scalability via faster communication between GPUs. Continue Reading
See how machine learning tools from Amazon, Google, IBM and Microsoft that are used in the cloud stack up on features and pricing in this comparison chart. Continue Reading
Expert Scott Robinson takes a look at the partnership between IBM Watson and Salesforce Einstein and what it means for the two AI platforms. Continue Reading
Big-picture views of advanced analytics and enterprise AI
AI in business applications is changing the use of data in the enterprise -- and creating new technology needs and management challenges for organizations. In this section, learn about AI-related trends and issues to be aware of before jumping into deployments, including what's involved in building and maintaining machine learning platforms, how AI tools could affect jobs, and what AI's inherent analytical ambiguity means for analytics teams.
Wikibon analyst James Kobielus discusses the growing adoption of AI tools and the need to continually re-evaluate machine learning models to keep them valid. Continue Reading
The use of AI and other advanced analytics applications by businesses is expected to grow, along with a need to better manage the algorithms that power them. Continue Reading
While there's fear that AI tools will take jobs from people, the real story might be how workers will need to adapt to changing job duties as AI use grows. Continue Reading
There's a lot of potential for AI in business, but organizations may need to rethink how they utilize analytics tools to compensate for AI's uncertain nature. Continue Reading
Analytics managers weigh in on the issue of deep learning vs. traditional machine learning, which involves data -- and, potentially, business value, they say. Continue Reading
Today's uses for AI in business -- and at home -- are just the tip of the iceberg. But there are challenges, and some ground rules may be needed. Continue Reading
Machine learning models are becoming an essential element in enterprise software of all stripes. Find out why so many vendors are embracing the technology. Continue Reading
Cognitive computing applications can address business challenges -- but they can also create some for companies looking to take advantage of the technology. Continue Reading
3Advanced analytics processes-
Methods and techniques for making sense of data
Business applications, big data systems and other data sources can produce a great deal of data on customers, products, machinery and more -- but how can organizations take advantage of all that information? In this section, you'll find advice on how to implement predictive modeling, machine learning and deep learning techniques, plus other aspects of managing advanced analytics and AI applications.
There are some important processes that analytics teams should be sure to include when developing machine learning models for predictive analytics uses. Continue Reading
Siloed data can be a roadblock for advanced analytics applications, especially in big data environments -- but organizations have found ways to break silos down. Continue Reading
Today's data science teams need people who can explain machine learning and predictive analytics results to business decision-makers, according to experts. Continue Reading
Deep learning is generating a lot of buzz, but users say traditional analytics concerns, including solid design and testing of analytical models, still apply. Continue Reading
Consultant David Loshin explains how automated algorithms associated with three popular machine learning techniques work and what can be done with them. Continue Reading
Predictive modeling is about more than just rolling out a system. Experienced users detail best practices that can strengthen advanced analytics efforts. Continue Reading
The Talking Data podcast team discusses deployment and management advice shared by some early adopters of IBM's Watson cognitive computing platform. Continue Reading
4Business use cases for AI-
Putting AI technologies to work in business applications
Rolling out AI in business environments is easier said than done, but business use cases are emerging for AI tools and related advanced analytics technologies. In this section, learn more about those uses and the challenges that some organizations have faced -- plus lessons they've learned, such as the need to tightly tie AI efforts to business objectives and to be aware of technology limitations.
To really benefit from AI tools and techniques, analytics projects must be grounded by a tight focus on meeting business goals, according to experienced users. Continue Reading
AI-based tools that generate text are gaining a foothold in businesses as users look to add descriptions and contextual info to dashboards, web applications and more. Continue Reading
The use of AI chatbots in customer service operations is paying off for organizations -- but in combination with human agents, not as a full replacement for them. Continue Reading
To help boost user engagement, Facebook uses deep learning models that analyze images, videos and text to tailor what people see on its site to their interests. Continue Reading
For the most part, the notion of cognitive businesses built around artificial intelligence tools is still a goal, despite technology advances in recent years. Continue Reading
Advanced analytics and cognitive computing systems are helping to make internet of things devices more interactive for customer service and other applications. Continue Reading
5The user view-
Real-world advice on advanced analytics and AI in business
As technology vendors try to turn all the AI hype into reality, IT professionals are learning how to take advantage of AI, machine learning and cognitive computing platforms. In this section, get advice and points of view from technology and analytics managers who've led the charge in their companies to modernize analytics approaches and deploy AI in business applications.
The vice president of analytics at HiRoad Assurance Co. details how the auto insurer is using machine learning to promote better driving habits in customers. Continue Reading
The VP of engineering at online real estate listings provider Trulia LLC thinks more companies will adopt AI tools because the barriers to doing so are low. Continue Reading
Businesses may be tempted to accelerate deployments of AI applications, but LendingTree's analytics chief says not to rush it on data governance and management. Continue Reading
The data science manager at sports consultancy STATS LLC says there's no real limit to the amount of data his team wants to analyze in deep learning applications. Continue Reading
Scott Porad, the CTO at Rover.com, explains how machine learning algorithms underpin the company's website, which helps users find dog sitters and walkers. Continue Reading