How Adobe, Salesforce and Others Are Embracing Machine Learning

Compared with all the attention given to the giant AI-related investments made by tech giants, the investments being made by many enterprise software firms don’t get as much attention.

But as talks with industry execs often drive home, investments in software features that leverage AI/machine learning have also become a key R&D priority for many publicly-traded enterprise software firms — even if their spending on such work can’t compare with what tech giants are doing.

When looking at how enterprise software firms are using “AI” to strengthen their offerings, it’s worth remembering that there’s a difference between broader investments in machine learning, and investments in deep learning in particular. Machine learning (ML) covers the general use of algorithms that analyze data to make conclusions and predictions, and which get better as more data is analyzed. Deep learning (DL) is a computationally demanding subset of machine learning that involves creating artificial neural networks (ANN) that in some respects function like a human brain.

Whereas tech giants have been investing heavily in training advanced deep learning models for a variety of applications, some (though not all) of the “AI” work being done by enterprise software firms doesn’t involve deep learning, but rather fits under the broader definition of machine learning.

With that qualifier in mind, machine learning-related investments by enterprise software firms appear to have picked up considerably in recent years. Here’s a run-down of how a handful of these companies are leveraging the technology:

• Adobe (ADBE) has rolled out Sensei, a ML platform that powers a number of features for both the content-creation apps in its Creative Cloud and Document Cloud suites, as well as the marketing, advertising and e-commerce software offerings in its Experience Cloud suite.

• Salesforce.com (CRM) has been making investing heavily in its Einstein ML platform, with the goal of letting its various CRM software apps — among other things, they cover sales, marketing, customer support and e-commerce — surface insights and make recommendations based on data produced by a number of different apps. In addition, analytics software firm Tableau Software, which was just acquired by Salesforce, recently launched Ask Data, an ML-powered solution that lets users make natural-language data queries within Tableau’s software.

• ServiceNow’s (NOW) Intelligent Automation Engine, which launched in 2017, uses ML to analyze a customer’s operations data to do things such as prioritize tasks, detect potential problems and automate routine jobs.

• Along similar lines, PagerDuty (PD) , a leading provider of software used by teams of on-call IT workers, has been using ML to group related problems turned up by its software, and figure out which issues are worthy of immediate attention.

• Anaplan (PLAN) , a leading provider of business planning software, is working on using ML to provide planners with accurate predictions about what will happen if a particular decision is made.

• Dynatrace (DT) , a top provider of application performance monitoring (APM) software, has launched an ML engine known as Davis that attempts to figure out the root cause of a problem that an app is seeing. The company also wants to eventually use Davis to automatically detect anomalies within business data.

Growing investments by enterprise developers in machine learning is a tailwind for Nvidia (NVDA) . The company has long had a dominant position in the market for the accelerators used to train deep learning models, and (though this field is more competitive) has also begun to see sales ramp for accelerators used to perform deep learning inference — the running of trained models against real-world data and content. Recently, Nvidia — via its RAPIDS software platform — has also been working to grow the use of its GPUs to more broadly accelerate machine learning workloads.

In multiple ways, machine learning investments by enterprise developers also benefit public cloud giants such as Amazon (AMZN) Web Services (AWS), Microsoft (MSFT) Azure and Alphabet’s (GOOGL)  Google Cloud Platform (GCP). The cloud giants offer a host of services that developers can use to build and train machine learning models, as well as programming interfaces (APIs) that let developers leverage the powerful models the cloud giants have developed for tasks such as image analysis, text analysis and voice recognition.

In addition, with many enterprise cloud software providers relying on public cloud infrastructures to host their apps, their machine learning services often run on the cloud giants’ servers, regardless of how they were developed.