How vendors help generating value with generative AI
The hype around generative AI, in particular ChatGPT is still at a fever pitch. It created thousands of start-ups and at the moment attracts lots of venture capital. Basically, everyone – and their dog – jumps on the bandwagon, with the Gartner Group predicting that it is getting worse, before it is going to be better. According to them, generative AI is yet to cross the peak of inflated expectations. Gartner Hype Cycle for Artificial Intelligence, 2022; source Gartner There are a few notable exceptions, though. So far, I haven’t heard major announcements by players like SAP, Oracle, SugarCRM, Zoho, or Freshworks. Before being accused of vendor bashing … I take this is a good sign. Why? Because it shows that vendors like these have understood that it is worthwhile thinking about valuable scenarios before jumping the gun and coming out with announcements just to stay top of the mind of potential customers. I dare say that these vendors (as well as some unmentioned others) are doing exactly the former, as all of them are highly innovative. Don’t get me wrong, though. It is important to announce new capabilities. It is probably just not a good style to do so too much in advance, just to potentially freeze a market. This only leads to disappointments on the customer side and ultimately does not serve a vendor’s reputation. For business vendors, it is important to understand and articulate the value that they generate by implementing any technology. Sometimes, it is better to use existing technology instead of shifting to the shiny new toy. The potential benefits in these cases simply do not outweigh the disadvantages, starting...
Salesforce brings its Field Service solution forward by a notch or two
The News On September 1, 2020 Salesforce announced its next round of updates to its Field Service Management solution. Eric Jacobson, Salesforce VP Product Management, Field Service and Gary Brandeleer, Senior Director Product Management, Field Service gave me an interesting briefing and demo beforehand. The new releases are all about efficient processing of the engagement throughout the whole process. In detail they are about Dynamic job scheduling Using Einstein Recommendation Builder to ensure that service technicians have the right spare parts available Asset management capabilities that allow companies a detailed view into the installed base at their customers. This is developed in cooperation with ServiceMax Improvements to the appointment assistant to provide as accurate as possible information to the customer about the arrival time of the service technician. The various features shall be made generally available through the next 6 months; as this is a forward looking statement, this may be subject to change. For your convenience, find the complete announcement below. Introducing the Next Generation of Field Service at Salesforce: AI-Powered Tools for Trusted, Mission-Critical Field Service By Mark Cattini, SVP of Field Service Management Today we are announcing the next generation of Salesforce Field Service, equipping teams across industries with AI-powered tools to deliver trusted, mission-critical field service. Built on the world’s #1 CRM, Salesforce Field Service includes new appointment scheduling and optimization capabilities, AI-driven guidance for dispatchers, asset performance insights and automated customer communications, all of which help ensure jobs are completed the first time, on time, every time. When the pandemic first hit, many industries that send employees out to complete jobs in the field...
Salesforce Einstein Search – The Formula for Customer Success?
The News Last week Salesforce announced Einstein Search, an enhancement of the search mechanisms that are already available in its applications. As usual you can read the announcement online or below. Salesforce wants to release three main issues with Einstein Search: The diverse interests and objective of users of enterprise search make it hard to be as good as a consumer search as delivered by Google or Bing, or the other consumer search engines, especially if in an ecommerce environment. In an enterprise setting, objectives can vary between closing a deal or solving a case, or creating new campaigns. This creates hidden complexities. There are no safe assumptions. Data is residing in different silos and frequently not linked. Further, there is no one size fits it all as Salesforce as an application normally is customized to suit an individual customer’s needs Third, the data simply does not belong to Salesforce, with the consequence that Salesforce cannot look into the data, even not with the objective of improving search. This makes it impossible to use traditional machine learning approaches. As per now Einstein Search is in a private beta stadium with only a few customers using it. General availability is planned for 2020 but limited to customers on Unlimited, Enterprise, or Performance Edition plans with 150 or more active licenses for the Sales or Service Cloud. So far the implementation of Einstein Search covers the top 5 searched objects: accounts, opportunities, contacts, cases and leads, but is intended to support further objects. According to Will Breetz, VP of product management for Einstein Search at Salesforce, Einstein Search is...