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How Salesforce Is Packaging AI into Their Product

January 28, 2021

Software Creates Demand for Data Labeling Services

When done right, this approach lets users more easily develop AI models and integrate the models into the software platform’s existing workflows and applications. Of course, the software vendor will claim the benefits provided by AI as part of the value provided by their software product.

When dealing with a software provider like Salesforce, who has a huge installed base, this becomes a fast growing and non-traditional channel for effectively deploying even more AI.  The effectiveness is less in question because the AI is deployed as part of the predictions and decisions provided by the vendors current workflows and applications. In the case of Salesforce, the workflows and applications are related to CRM.

Salesforce calls their bundle of AI capabilities “Einstein” and bundles these AI capabilities right into the CRM. 

“Einstein is a turnkey intelligence solution that accelerates decision-making and productivity at each stage of the sales cycle from prospect to close”. 

The idea is that Einstein provides custom predictions and recommendations that can be embedded into any Salesforce record or application.  Einstein then operationalizes the predictions by integrating them with Salesforce workflows and business processes.

Einstein customers can build AI models with less code because the model building can be done by clicks in the provided interface. They can develop, test and deploy AI models without leaving Salesforce.

Salesforce’s Einstein Uses AI to Discover, Predict and Automatically Deliver Recommendations

Let’s continue to use Salesforce Einstein as an example of AI capabilities packaged into a big software vendor’s product for creating further differentiation. 

Einstein has foundational AI capabilities like Natural Language Processing (NLP) and Computer Vision (CV).  NLP extracts meaning from every piece of text to find linguistic patterns. In Einstein’s world, these patterns are often associated with products and brands. By building NLP into CRM applications, Einstein can classify a body of text’s underlying intent and sentiment.

CV includes visual pattern identification and data processing to do things like track products and brands – and even to recognize text in images. Einstein Vision can train deep learning models to recognize text and images. Of course, these models can then be embedded into Salesforce applications.

With AI capabilities like NLP and CV, Einstein enables discovery, prediction and the automatic taking of the “next best action”. Einstein Discovery finds relevant patterns in data – whether the data resides inside or outside of Salesforce.  Einstein Prediction Builder creates AI models on any set of Salesforce fields with clicks and less writing of code. Einstein Next Best Action delivers proven recommendations right into the apps where people work.

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Accelerating AI Model Creation Through labeled Data

Going back to our example of Salesforce Einstein, the ability to create AI models with more clicking and less coding increases productivity. The ability to within Einstein test and deploy AI models that have been developed also increases productivity.

Based on my experience helping people with Salesforce use Einstein, data issues tend to be the limitation. Even when exclusively using data in Salesforce, the development of AI models that use the data differently can result in data issues. This problem can get bigger when data from outside of Salesforce also has to be used.

Integrating AI capabilities into software platforms accelerates a user’s AI model creation …but many of the AI models created will need to be trained and run with labeled data.

“The true advancements will be found in tapping into the unstructured data and through labeling, it’s made actionable and differentiated for their models.”

Utilizing Different Types of Data labeling is Essential

As we discussed in earlier blogs, the productivity and financial returns of your AI program can greatly depend on a data labeling partner who efficiently and reliably provides high quality labeling. The same is true for the part of your AI program using the packaged AI capabilities in your vendor provided software platforms.

There is a large ecosystem of very specialized labeling tools and capabilities for Natural Language Processing (NLP), Computer Vision (CV) and other AI-driven areas. Choosing a data labeling partner who regularly works with all the tools and technology for highly specialized labeling applications is key to efficiently getting the quality labeling that your AI program needs. 

“Choosing the right partner and tool for the job greatly impacts the speed, efficiency, cost and quality of the labeling being done.”

In earlier blogs, we talked about how iMerit has hired, trained and retained its employees AND developed its project management workflows with these realities in mind.  iMerit built itself around the need to efficiently provide its customers with high quality data labeling.

As part of this, iMerit everyday works with the ecosystem of companies with highly specialized technical data labeling capabilities. iMerit understands how the specialized data labeling technologies work AND which technical capabilities to use in a particular customer situation.  This allows the customer to stay focused on improving their AI models and achieving the benefits of the models they’re developing.

A Practical Recommendation

You’ll benefit from iMerit’s ability to help you avoid or solve the data labeling challenges that can be a limiting factor to your AI program’s productivity and financial success.

  • Pull in an experienced partner: When it comes time to take advantage of the packaged AI capabilities in your vendor-provided software platforms, bring a partner such as iMerit into your planning discussions. 
  • Leverage expertise: iMerit’s Solution Architects will draw upon their deep expertise in all forms of data labeling and annotation to help define the requirements, training, workflow, feedback loops and data validation required.  Choosing the right tool for the annotation and labeling is critical to the success of the project, both in terms of process, efficiency, cost and data quality.
  • Actively work towards performance improvement: Once the annotation and delivery process has begun, iMerit will continue to use a dynamic improvement model by incorporating feedback through constant monitoring, quality scoring and edge case analysis to gain insights, and further ensure data quality and accuracy.
  • Select a partner, not a transaction: You want value beyond a single data labeling project. Focusing on building a partnership, where mutual knowledge and expertise is shared and leveraged, will provide dividends in the long-term success of your project.
a practical reconmmendation