Emphasize the rewards: Although Pegasystems' "What Consumers Really Think About AI" survey reveals that 72% of respondents are afraid of AI in some way, 68% of people want more AI if it will make their lives easier. For this reason, companies must educate consumers on how AI benefits the customer experience. Examples include getting better product offers, faster customer service, and more relevant messaging from favorite brands. Be transparent: Transparency tops the list of features valued most highly among business leaders, and for good reason: Consumer trust is the foundation for real engagement. In response, companies should disclose information on how consumer data is being used and stored, and the precautionary measures being taken to protect consumer privacy--a critical step in building trust with customers.
This capability is particularly critical for organizations in highly regulated industries to ensure they maintain compliance while also delivering intelligent, personalized customer experiences. To complicate matters, the European Union's General Data Protection Regulation (GDPR) mandates that businesses must be able to explain the logic behind AI models using European customer data to make decisions or risk massive fines up to four percent of global revenues for non-compliance. Conversely, for financial institutions under strict regulations for the kinds of loans they offer customers, marketers may require highly transparent AI models to ensure they can demonstrate the resulting product offers are appropriate for the financial needs for each individual. Pega Customer Decision Hub works in tandem with Pega's suite of CRM applications for marketing, sales, and customer service to anticipate customer needs and provide highly contextual brand experiences for each individual customer.
From back to front Such applications may be driving greater profitability at the back end of Shell Downstream's operations but with crude oil prices still sitting at around half their 2014 rates, Shell is also turning to AI to move the focus from volume to customer-centric commerce. By applying AI at the front-end of operations -- the Downstream division supplies fuel and lubricants to businesses, aviation and shipping, as well as operating 40,000 Shell-branded service stations -- the company can offer more personalized rather than generic products and services, right down to the individual. For Walker that approach is designed to build customer loyalty, with AI used to develop new levels of customer knowledge and insight that will enhance services.
When you are Robotic Automation Ready, others know that you have achieved a foundational understanding of how to build and deploy automation solutions in Pega applications. This means that you can deliver immediate positive results to Pega implementation projects. When project managers, customers or other key stakeholders see the Robotic Automation Ready badge in your transcript, they can be confident that you are ready for their Pega implementation project.
Automation platforms like Pega have capabilities that span tactical intent and strategic intent, namely Pega robotics, cognitive decision making, business function transformation, and analytics. But if you have Pega robotics as the entry point, the development lifecycle actions follows the sequence of Assess, Record, Design, Unit Test, Verify, Soft Launch, Iterate and Manage Exceptions, and Full Launch. But when you're creating cognitive automation processes, these work on data and patterns produced by RPA systems, other IT systems, or all of them. Because of the underlying risk involved in working with production data, process, and soft launch iterations, production movement, and deployment planning is the most important element of the development cycle.
This key trend from the 2017 Accenture Technology Vision highlights how AI is beginning to enhance a user's experience with technology, improving adoption and data quality by working in collaboration with users. Transavia developed the vision of a single solution, allowing gate agents, customer service representatives, cabin attendants, baggage handlers and maintenance personnel to all see flight status in real-time. To achieve this vision, Transavia and Accenture have been collaborating to develop the Accenture Aviation Experience Accelerator, based on the Pega 7 platform. By leveraging Pega's AI solution, these personalized experiences can be created at scale and provide seamless interactions across all channels and roles in the travel journey.
Simply put, Artificial Intelligence (AI) means making computers and machines capable of human-like, intelligent behavior. For businesses today, the promise of AI is to improve customer engagement through better anticipating customer needs and optimizing work to provide better, faster, and more effective customer experience. Pega has been pioneering and delivering these capabilities since our founding in 1983, from advanced business rule engines, to data-driven predictive and machine learning analytics. Recent advances in data processing speeds, big data volume, and machine learning methods and algorithms at lower costs mean the promise of AI can be even more readily extended to most any customer engagement scenario.
As the world begins to turn away from fossil fuels and depend increasingly on renewable resources, the energy sector is presented with a problem. Texas-based oil and gas company Pioneer Natural Resources has said that using AI could help ensure accurate and optimal drilling locales. Oil and gas companies are dedicating large research teams to the development of AI as for its potential to increase production without the need to hire many more workers, an attractive prospect as crude oil prices continue to be unstable. While the necessary systems needed to transform the renewable and traditional energy industries are still in development, we can expect big changes soon.
Arithmetically, the problem is a combination of collapsing productivity and insufficient capital investment. On February 19, 2017, the New York Times ran a feature story on recent changes in the United States oil industry.2 The focus was on the recent "embrace" of technological innovation in the industry after the 2014 plunge in the global oil market. This was just one of a rash of such pieces in the popular press, relying, as is typical of such writing, on a smattering of skewed, decontextualized data, a healthy serving of the anecdotal, and a host of the worst tech journalism clichés ("a few icons on a computer screen," "a click of the mouse," video game marathons as job training, a compulsory reference to drones). Zeroing in on the effects of these changes on workers in west Texas, the article's upshot is unobjectionable enough: as oil prices recover, output rises, and production becomes more capital-intensive, many workers who lost jobs in the downturn will be replaced by machines.
There is a lot of buzz around Machine Learning (the most implemented type of Artificial Intelligence) these days and its applications. The RigBasket technology team thought of offering a quick explanation of what it means and how it could help your business. To begin, let's answer whether machine learning really works. In order for us to understand how Machine Learning can help solve inventory management problems, we first need to understand what it is and how it works. To most people, Machine Learning is a black box where you throw something in, some advanced calculations are made and the computer predicts the outcome or the future.