Artificial Intelligence (AI) has been around for several decades now in different forms, yet only now is it truly being used in everyday functions. The next phase of AI is just round the corner, and marketers need to prepare for the coming dawn by adopting a few basic steps. With so much of data now generated and subsequently analyzed, marketers need to beware whether the right kind of Big Data is being assessed. They could be instead tracking the wrong sources, measuring the wrong metrics, or simply backing the wrong choice. AI needs to be at the forefront of digital marketing, directing all actions and operations from a central vantage point. For that to happen, the AI channels need to be aligned with all other such data sources. Methods need to be devised so that data from all internal sources can be directed towards the AI platforms for the analysis to take place. The kind of insights gained from AI need to be instantly put into proper use.

Source:http://www.martechadvisor.com/articles/cloudiaaspaas/how-marketers-can-prepare-for-the-new-wave-of-artificial-intelligence/

Much has been made out about Artificial Intelligence’s (AI) impact on work and human life. Science fiction has portrayed it as akin to magic while fear mongers claim it will take away human jobs. AI is already impacting several kinds of work such as e-commerce, logistics, media and advertising. Yet, as the founding lead of Google Brain and present head of Baidu’s AI wing testifies, the kind of functions it is performing in these areas is largely confined to only one type- supervised learning. This basically implies A kind of data being fed for B kind of output to be processed. There is one major limitation though. It is, that enormous quantity of data warehousing needs to be done, often to the tune of hundreds of thousands of units to process meaningful insights. Whether it is in photo tagging, loan approvals, speech recognition or language translation, this limitation lasts. Yet once all this is done, there is enormous amount of automation that AI can facilitate.

Source:https://hbr.org/2016/11/what-artificial-intelligence-can-and-cant-do-right-now?referral=00563&cm_mmc=email-_-newsletter-_-daily_alert-_-alert_date&utm_source=newsletter_daily_alert&utm_medium=email&utm_campaign=alert_date&spMailingID=15858721&spUserID=OTY0OTMwNTk5NwS2&spJobID=902915771&spReportId=OTAyOTE1NzcxS0

Facebook founder Mark Zuckerberg has imbibed some new learnings about Artificial Intelligence (AI) that he has picked open over the last year while working on the virtual butler Jarvis. First of all, any home based AI network has to be able to connect everything just as Apple does with Home Kit and Alphabet with “Works with Nest”. In fact Jarvis will have to be able to do such tasks at multiple home platforms available. Just as Apple’s Siri and Amazon’s Alexa, it has been realized that digital assistants are truly native to the smartphone. The success of Amazon’s Echo has spurred a lot of research on voice based interfaces. However, text based inputs will play a big part in AI devices decoding meaningful business intelligence and putting it into play. Microsoft is on the verge of creating the perfect combination through its Cortana offering.

Source:http://time.com/4606721/mark-zuckerberg-ai-butler-jarvis-2016/

2016 was a big year for Artificial Intelligence (AI). While AI has been around for a while now, it was this year that it truly became a tool for easing business processes. That is why AI is being best used for the part of the business which actually drives home the revenue – sales. Even vendors are recognizing this aspect. There exists an information overload, but to derive meaningful business intelligence out of that is the challenge. Conversica has developed a digital sales assistant to ease the work of sales executives. In the field of CRM, several organizations have started using AI in a big way. Examples include large firms such as Oracle and Salesforce as well as startups like Base and Tact. CRM has historically been a platform to record transactions, but now data warehousing techniques are being applied using AI to actually predict moves and help the sales personnel to close down deals combining the insights supplied by technology with innate human intelligence.

Source:https://techcrunch.com/2016/12/17/artificial-intelligence-finds-its-way-into-business-through-sales/

Some Frequently Asked Questions (FAQs) have emerged about the fields of Artificial Intelligence (AI) and related customer service. The first one of them is the definitions of these terms to which one may simply answer that AI is the process to make machines imitate human intelligence. Machine learning though is more implicit as it involves a set of algorithms which the system uses to naturally learn from interactions. Some specific examples may then be asked and one can commit speech recognition, computer vision and dialogue management. As for the use of AI in customer service, one can point out digital assistants such as Siri, Cortana or Google Now that tabulate enormous amounts of data to generate meaningful business intelligence for people to use. However, beyond these digital assistants also AI serves many purposes such as connecting to rather than replacing customer service agents. New techniques are now possible such as applying deep learning and providing predictive analytics to customer care staff. Automation can be used to facilitate ease of work. There is a lot of fear that AI may replace call centres, however that is not the case right now. Some changes need to be made in work patterns, but human decision making and empathy will always be valued.

Source:http://customerthink.com/8-faqs-about-artificial-intelligence-and-customer-service/

With Artificial Intelligence (AI) becoming increasingly important to business operations, some technologies have been acknowledged that are making substantial impact at present. The first one of them is market intelligence. Beyond Google Alerts, companies are now using AI to sieve through millions of online pages to get latest news or trends on topics of their relevance. Bitvore is one such California based AI firm engaged in the same. The next aspect is network management. It is a form that combines digital assistants to update all communications using multiple channels. It also helps in accounting through platforms such as SMACC. And then there is sales which can be best optimized using platforms such as Conversica or Salesforce’s Einstein. Both use AI empowered digital assistants who sieve through huge chunks of data and deliver genuine business intelligence so that clutter can be removed in the selling process.

Source:http://m.newstimes.com/news/article/4-AI-Technologies-Impacting-Business-Operations-10779750.php

Artificial Intelligence (AI) is set to alter different aspects of the economy. The priority and focus of businesses will shift. COOs and CMOs can now get pinpointed business intelligence on which aspects they need greater resources in. Operational inefficiencies may be eliminated so that business leaders can focus more time on creative aspects. Greater organizational collaboration can be fostered. Reminders about whom to meet up, with whom to share data and phone calls to attend are getting outsourced to AI outfits. Client servicing should also experience an improvement as machine learning will enable historical data to be understood and trends captured. The enormous amount of data captured and its subsequent business analytics performed will provide insights on individual customers as well as segments so that tailored services may be provided.

Source:http://venturebeat.com/2016/12/03/3-ways-ai-will-alter-the-enterprise/

Contrary to popular perception on Artificial Intelligence (AI), it is not so remote any more. AI is not about robots replacing humans, taking away jobs or communicating through bots. Instead it is much closer to home with a clothing company Stitch Fix exemplifying that trend. Stitch Fix does not produce or sell clothes, instead they conduct surveys and engage with customers through social media to derive valuable business intelligence about customer preferences. It collects enormous amounts of data which it then processes using business analytics and distributes it to clients in the fashion industry. Formal data is collected using survey method, while informal data is collected using social media platforms such as Pinterest boards. The fashion industry has always been geared up for constant change, and this data is used for the quick changes reflecting on customer trends. Exact analysis on which items are in demand and which aren’t can be identified using such studies.

Source:https://hbr.org/2016/11/how-one-clothing-company-blends-ai-and-human-expertise