aheadCRM

The news During the Salesforce AI Day on June 12 as well as the Salesforce AI Industry Analyst Forum on June 20, Salesforce provided a lot of interesting information on how the company addresses the challenge – or should I say problem – of trust into artificial intelligence. Salesforce sees this gap caused by hallucinations, lack of context and data security as well as toxicity and bias. According to Salesforce, this gets compounded by the need for integrating external models into business software. To address this problem, Salesforce has announced its AI Cloud that combines an “Einstein GPT Trust Layer”, Customer 360 and its CRM to offer AI-powered business processes that are built right into the system, based on an AI that can be trusted. The main vehicle is the Einstein GPT Trust Layer that takes care of secure data retrieval from business applications, dynamic grounding to reduce the risk of hallucinations and to increase response accuracy by automatically enriching prompts with ...

Everybody is talking of "creating customer experiences" - even I, although I am convinced that any experienced is created by the person on the receiving side of an engagement.  But then, how to formulate this in only a few words? Nevertheless, when it comes to engaging with customers or prospects, we are immediately also talking content, be it images, texts, videos, podcasts, 3D rendered objects, or whatever the future will bring us. We are also talking about more than just marketing processes, but also service- or employee-facing processes. Which brings us into the realms of knowledge management, product information management, web content management, enterprise content management and enterprise search. Or the question about how the same content can be used in different scenarios? How can it be found, so that it can be made available? Does it need a platform? What type of platform? Looking at individualised marketing campaigns that theoretically ask for individually tail...

It is the end of 2021 and I do not have anything better in mind than writing a last post for the year. So let’s do it. There have been some terms that were used more than others with some of them actually being quite hyped. Some of them for the right reasons, some of them for the wrong ones. My favorites of these terms include in no particular order: ·       Metaverse ·       NFT (non-fungible token) ·       RPA or robotic process automation ·       Hyper personalization ·       Hyper automation ·       Customer Journey Orchestration ·       Low code / No code ·       Artificial intelligence / machine learning Hyper personalization I already did a short video that expresses my thoughts on hyperpersonalization. You cannot really avoid the term when browsing the web with anything related to CX in mind. Back in the day, what is now called hyper personalization was called...

The word is that it takes two to Tango. It turns out that sometimes it needs three! I will not spoil the excitement by naming who they are, though. Why do we have silos in enterprise software? How to remove them? Do they even need to be removed? Does throwing more software at a software problem help? Aren't the problems a software problem after all? If more or different software helps, what type of software can or should be used? Or are corporate silos even a Wallstreet problem? Great questions and even better answers by Joshua Greenbaum of Enterprise Applications Consulting in this CRMKonvo. < This one got even bigger than I expected. But then, Josh thinks in big lines. And he has wits, a lot of wits. Apart from a lot of knowledge. He did not disappoint, really! Listen in to our CRMKonvo. I can also highly recommend to read Josh's article Death to all Silos, with Aphorisms . It is very worthwhile, promised.

During the past weeks I had a couple of observations and conversations that lead me to thinking that sometimes software vendors underestimate the power that their machine learning based systems could have to improve the lives and experiences of employees and customers.  From various vendors in various lines of business, from process mining and automation via application performance monitoring to vendors of conversational AI and pretty much everything in between I hear something like the following: “Our machine learning based system continuously analyses the process/interactions and detects anomalies. From there on it identifies the patterns and can make suggestions how these anomalies can be avoided or resolved.” Of course, this is paraphrased, but you get the meaning. Here are some examples. A real life scenario that I once encountered is as follows: A global B2B e-commerce solution using synchronous pricing is set up in a template approach. It is using one single ERP system for p...