terça-feira, maio 4, 2021

Salesforce Arms Developers With Smarter Einstein Tools For AI-Powered CRM

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Cloud-centric Customer Relationship Management (CRM) software company Salesforce is keen to be a developer company. Well, as we know, every hardware company now wants to be a software company… every services-driven company now wants to be a software-defined company… and so, logically, pretty much every company now wants to be a developer company.

In fairness to Salesforce, the firm has always positioned its core base of software as a platform rather than ‘just’ a software product i.e. a base of technologies that, while functional in their own right, are equally expansive enough to be able to support developers creating something that either:

  • Sits on top of the platform to provide a new application (or service) that draws upon the information provided by a Salesforce CRM foundation underneath.
  • Uses an element of the Salesforce platform inside a new application (or service) with specific functions or tools emanating from the mothership.

So with a platform, the justification for attracting developers is typically validated. Plus of course, there are Salesforce developers who develop Salesforce itself too. So what is Salesforce offering the developer community today?

AI-powered CRM apps

Salesforce Einstein is described as a Artificial Intelligence (AI) for CRM. In simple terms it’s all about putting AI intellect into the applications being built on top of (and tangentially to the side of) Salesforce itself. This is AI-powered CRM apps, basically. Right now we find that Salesforce is extending its portfolio of Einstein Platform Services to allow developers to create custom deep learning models to fit specific business needs.

Einstein Sentiment will allow developers to classify the tone of any text (from prospect emails and social media posts to customer reviews and message boards) as positive, negative or neutral to gain insight into customer attitudes and then take appropriate actions.

Einstein Intent will allow developers to train a model to classify the underlying intent of customer inquiries to automatically route leads, escalate service cases and personalize marketing campaigns.

The firm’s Sarah Franklin, SVP developer relations, illustrates this tool with the following example, “A retail company could use Einstein Intent to build a custom app that automatically classifies inbound customer support queries to identify which customers are experiencing shipping problems and then proactively provide support messaging and tracking details.”

As a third element here, we also find Einstein Object Detection. This tool enables developers to train models to recognize multiple unique objects within a single image, as well as the location, size and quantities of those objects. Einstein Object Detection can be applied to improve service, inventory management and retail share-of-shelf apps. For example, a beverage company could streamline inventory management and service by automatically analyzing photos of retail shelves to count the product, and then calculate and process a new order.

Dawn of the data developer?

The question that emanates from these product enhancements, as worthy as they may be, is what does this all mean for software application developers in the first place? Do we still need software developers who are capable of working with algorithms and ‘building programs’ from a point of raw scratch nothing? Do and does the existence of expansive platforms of this kind mean that we don’t need algorithm-savvy developers in the same way? Do we now just need data developers?

Chief scientist at Salesforce Richard Socher says that, “For AI to work, three things are needed — AI algorithms, data to feed your models and integration with workflows. But without the data, the other two components are meaningless. Developers must learn to collect from the vast ocean of unstructured data created by mobile devices, social media, IoT and more in order to train predictive models and deliver the connected experiences consumers have come to expect.”  

Socher further argues that with advancements in abstraction and more powerful tools and frameworks, developers can now work on increasingly complex projects. With AI and automation, the suggestion here is that role of the developer is changing also. Because many basic tools are becoming automated, developers can shift their focus to using AI to solve more complex business problems that drive new efficiencies across their organization.

“Within a few years, every major decision (personal or business) will be made with the help of AI. But, in order to continue driving advancements in AI, we need to build the pipeline of tech talent. According to the World Economic Forumnearly two thirds of today’s children will have jobs that haven’t been invented yet. This means that as AI transforms the software development life cycle, we need to empower everyone with the ability to learn the AI skills needed for the workforce of the future.”

Low code on the road ahead

Salesforce clearly has a mission to champion low code software application development. This does of course lead us rather close to the area of citizen developers (laypeople building software from blocks)… and not everybody is convinced that that is a good thing.

Meanwhile then, Salesforce continues its developer love in and the firm now claims to have as many as four million programmer members signed up to its Trailhead online, interactive, gamified learning platform. Since launching in 2014, Trailblazers have earned more than 2.5 million badges, which directly relate to in-demand job skills.

The bottom line? Platforms won’t completely replace ‘hard core’ software application development for now, but a mutual coexistence with them is inevitable.

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