Software applications are essential in operating any business. Enabling predictable processes, automating some tasks and managing the access to information all help companies do business more effectively and in some cases can give a competitive advantage. The situation, however, isn’t all positive—many current applications create a high productivity “tax” in their ongoing operation. Manual tasks, particularly data entry, can take a large investment of time and effort.
The next wave of applications has the opportunity to significantly reduce this tax by leveraging newer technologies to become “intelligent applications.”
Intelligent applications incorporate artificial intelligence technologies like machine learning, natural language processing (NLP), predictive analytics and deep learning. By using AI, these applications can take two basic approaches: 1) Providing relevant data to the person or team that needs it, when it is needed and with the proper context, and 2) Automating simple routine daily tasks that take time away from more value-adding activities. Both of these approaches take advantage of the rapidly-growing volume of structured and unstructured data that companies can access.
Recently Salesforce, the cloud-based customer relationship management company, announced a new program, Einstein, that fits the definition of an intelligent applications platform and set of applications. The Einstein project is comprised of several acquisitions including MetaMind, PredictionIQ, RelateIQ, Tempo and a few others; and a lot of data science work in a new AI research lab.
The approach is similar to Salesforce’s other broad technology initiatives like social collaboration tool Chatter, embedded into the Salesforce Application Cloud Platform, which makes it available for use by developers building any other application on the platform. Applications built with Einstein embedded can use the technology to support decisions like next best offer, or when to take a specific sales action like providing relevant content. But perhaps even more importantly, they can automate labor-intensive functions like scheduling meetings or updating forecasts.
Or course, AI isn’t new. Einstein takes advantage of a lot of the consumer-focused work like Amazon’s predictive recommendations, Apple’s Siri which uses NLP, Google’s deep learning and Facebook’s application of machine learning. At a high level, Salesforce Einstein is built to capture real-time data; learn through predictive analytics, NLP and machine and deep learning; and to connect with customers by providing a “better” customer experience (CX). Embedded inside the Salesforce App Cloud platform, Einstein has access to a broad set of customer data, both transactional and sourced through online listening, data integrations and partnerships. Using that broad dataset Einstein can discover insights, predict outcomes to support better decision making, recommend the next best actions to maximize interactions, and automate tasks to allow employees to focus on other more productive activities.
To capture the diverse dataset needed to support the embedded AI, Einstein connects to many other data sources internal to the Salesforce products and a variety of external platforms. Those sources include:
- CRM: account, contact, lead, opportunity and custom objects
- Calendar: Salesforce Calendar, Google Calendar, iCal
- Email: Salesforce inbox, Gmail, Yahoo Mail, Apple Mail
- Social: Twitter, Facebook, LinkedIn, Google+ and others
There are many ways to apply Einstein inside the current portfolio of Salesforce Clouds beyond the use by developers on the App Cloud Platform. For example, it can be used:
- Inside the Sales Cloud to guide sales personnel to the best leads and opportunities
- Inside the Service Cloud to enable proactive service by helping customers find their own answers and recommending the correct content to customer service agents
- Inside the Marketing Cloud to help marketers build predictive journeys, offering up the right content at the right time based on observed online behaviors
- Inside the Commerce Cloud to personalized shopping experiences by recommending products and offers that are relevant to the specific customer
- Inside the Community Cloud to personalize experiences by recommending content and people to answer specific questions
- Inside the Analytics Cloud to automate and prioritize insights
- Inside the IoT Cloud to automate recommendations from the “best” sensors for predicting events
Salesforce Einstein is a clear example of the next generation of intelligent applications. It will be interesting to see the many benefits it will bring to Salesforce CRM users and the independent solution vendors that build applications on the Salesforce App Cloud Platform.
Contributed by: Michael Fauscette, Chief Research Officer at G2 Crowd, an online platform and community where people can connect and share experiences about business software, and gain user experience-based insight to support business software purchase decisions. Prior to joining G2 Crowd, Michael spent almost ten years as an executive and senior analyst at technology market research firm IDC. At IDC, he led the Worldwide Business Application Software Group as the group vice president. He also held senior consulting and services roles with software vendors ranging from large enterprise companies to startups, including Autodesk, Inc. and PeopleSoft, Inc.
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