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.
Cambridge, Mass.-based Pega on Sept. 29 introduced an industry-first product called T-Switch that gives organizations direct control over the transparency of their artificial intelligence customer engagement models. Included within the latest release of Pega's AI-powered Customer Decision Hub, T-Switch enables business users new levels of oversight to safely deploy AI based on their organization's transparency requirements. This capability is particularly critical for organizations in highly regulated industries to ensure they maintain compliance while also delivering intelligent, personalized customer experiences. The T-Switch, which will become generally available in late October, is designed to help companies mitigate potential risks and maintain regulatory compliance while providing differentiated experiences to their customers. Enterprises are increasingly deploying AI to gain better insights into customer needs and provide more personalized service, sales and marketing.
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. If you include the possible machine learning capabilities residing on top of such platforms, practical adoption patterns and methods vary widely. Traditional development methodologies normally called for, like Agile, need to be used with the understanding that you're customizing your approach for automation initiatives. For example, the most successful preferred method for scaled business transformation would be Distributed Agile, which is nothing but user story-based incremental development done with geographically distributed teams. 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.
Is your computer becoming smart enough to be a co-worker--your partner, instead of just a tool? Artificial intelligence (AI) is about to become a company's digital spokesperson. Moving beyond a back-end tool for the enterprise, AI is taking on more sophisticated roles within technology interfaces. From autonomous driving vehicles that use computer vision, to live translations made possible by artificial neural networks, AI is making every interface both simple and smart--and setting a high bar for how future interactions will work. It will act as the face of a company's digital brand and a key differentiator--and become a core competency demanding of C-level investment and strategy.
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. Renewables are simply not as reliable as oil and gas, as they are largely dependent on weather conditions such as sunny skies and windy days. In a world where we become fully dependent on renewables, there is concern that supply may not always be able to meet demand. This supply problem is compounded with the complications of individuals, businesses, and municipalities becoming small-scale energy producers themselves by way of solar panels and individual storage units connected to the grid. These producer-consumers, having varying and unpredictable patterns of individual production and consumption create instability on shared grids.
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. These workers, often Latino, are sure to be forced out of these semi-skilled, relatively well-paid jobs into other sectors of the labor market, where their skills and experience will serve little purpose. At first blush, the situation seems dire. We are told that some 30% of jobs in the industry were lost after the oil market crash of mid-2014, when employment in the industry was at its peak.