Enterprise AI employs artificial intelligence and machine learning technology to solve problems faced by large-scale companies and organizations. Common use cases for enterprise AI include process automation, supply chain analytics, marketing and customer service
Instead of following explicit, mathematical instructions, these computational systems identify patterns from analyzed data via algorithms and statistical models, imitating intelligent human behavior. They’re able to “teach” themselves by drawing inferences from the information sets in a sort of cognitive processing procedure.
What Is Enterprise AI?
Enterprise AI are solutions that apply artificial intelligence and machine learning to solve problems faced by large-scale companies and organizations. It's commonly used for process automation, supply chain analytics and customer service.
Uses for Enterprise AI
Nowadays, everyone’s an analyst. Enterprise AI solutions further distribute the power of data science, processing complex amounts of information and presenting it across simple interfaces for practical use by the people and teams running large-scale organizations. While boosting employee productivity, cost reduction and optimizing business operations are constant variables for executives to consider, enterprise AI offers an array of solutions to common operational hangups.
For example, enterprise AI is used to:
Enhance customer service
Smart personal assistants, such as Siri, Cortana and Alexa, as well as automated online customer support chatbots are some examples of enterprise AI used to benefit performance metrics like customer relationship management.
We know this all too well through online personalized shopping, which works through AI-enhanced recommendation engines that use a customer’s browsing history, preferences and engagement activity to pinpoint their interests and curate suggestions more likely to get them to checkout with a full cart.
AI applications can also be used for cybersecurity, and be as familiar as email spam filters and credit card fraud alerts, the latter which traces location and transaction history to detect and inform authorized users of potential security threats.
Optimize supply chains
Clean, visual reports summarizing behemoth amounts of information speaks directly to business logistics management. Any typical enterprise AI solution would be able to offer a clear overview of an entire supply chain at a glance, complete with predictive insights programmed to outperform human data analysts.
Automating mundane, repetitive day-to-day tasks can directly boost office morale while freeing up time now available to complete more rewarding jobs. Additionally, centralizing, unifying and simplifying information for easier access and distribution eliminates roadblocks to better streamline workflows.
Upgrade existing products
Using data and analysis, computerized systems can inspect products in a way missed by human inspection, able to detect product defects from the inside or by collecting and ranking customer feedback to better inform a product’s next generation model in the making.
What Are Enterprise AI Platforms?
Enterprise AI platforms are integrated sets of technology that enable large-scale organizations to design, develop, deploy and operate machine learning applications for practical industry use at scale. Their ability to turn data and information into actionable items are a key takeaway to their functionality.
Typically, these platforms act as smart data management solutions, making sense of structured and unstructured files from disparate sources in different languages now unified into one central database while simultaneously creating a dynamic, data-analysis hub equipped to facilitate efficient collaboration among peers and launch self-programming systems.
Via automation and computational inference, enterprise AI platforms help companies reduce costs, streamline operations and grow their business intelligence.
Applicable to any industry, enterprise AI is compatible with any type of business, at any stage.
10 Enterprise AI Platforms
Companies looking to front an AI-led digital transformation are developing accessible, beginner-friendly software services and tools inclusive of a range of skill sets. Here are the top 10 platforms leading that charge.
10 Enterprise AI Platforms to Know
- Amazon SageMaker
- Dataiku Data Science Studio
- DataRobot Enterprise AI Platform
- Google Cloud Vertex AI
- H2O Driverless AI
- HPE Ezmeral
- IBM Watson Studio
- Microsoft Azure Machine Learning
- SAS Visual Data Mining and Machine Learning
Amazon SageMaker is a fully managed machine learning workflow platform and development environment outfitted for enterprise-grade data labeling, model building, training, tuning and deployment. From one interface, developers can easily engage with notebooks, experiment management, automatic model creation, debugging and model drift detection. Per its website, Amazon reports that this software boosts team productivity tenfold, halves the amount of time spent training by way of advanced graphics processing units, decreases data labeling costs by 40 percent while casting over one trillion predictions per month.
Databricks describes itself as a data lakehouse, combining the raw repository features of data lakes and the organized analytics character of data warehouses. It’s designed to consolidate a company’s data needs into one place in order to cultivate a more dynamic workflow. Its core components cover data governance, engineering, streaming, warehousing, sharing and comes complete with a security and trust center. According to a press release, more than 7,000 organizations worldwide turn to this open-source, cloud-based data engineering tool to sort information from disparate sources into data pipelines, which is then processed and transformed to enhance analytical capabilities and deliver richer insights through machine learning and AI.
Dataiku Data Science Studio
Dataiku’s approach to better decision making is building machine learning models on top of data sets. Its standout software, Data Science Studio, converts raw data into actionable insights for enterprise-grade companies while conveniently integrating into an organization’s existing infrastructure. Dataiku’s low barrier for entry — featuring no-code data flows — is designed with “everyone” in mind. As CEO Florian Douetteau put it himself, Dataiku was built “in order to shorten the load-clean-train-test cycles without dumbing them down; it is a tool for qualified data scientists while remaining accessible for less technical business intelligence or marketing profiles.”
DataRobot Enterprise AI Platform
Another platform for users of all skill levels, DataRobot’s software turns data into value by providing end-to-end automation for building, deploying and managing machine learning models at scale. It aims to bring all key stakeholders onto the same page, democratizing data science while eliminating the need to write custom predictive code.
Google Cloud Vertex AI
Bringing the whole of Google’s cloud services under one, AI-powered platform, Vertex AI provides a user-friendly, drag-and-drop interface and a library of pre-trained machine learning models for productivity at scale. Google’s archive boasts tools for “any use case,” especially those programmed to execute common tasks such as occupancy counting, product recognition and object detection, in addition to fully customizable solutions — all of which require 80 percent less lines of coding than the average software application.
H2O Driverless AI
H2O Driverless AI implies that there’s no one at the wheel, meaning all of the typical barriers keeping companies from taking the leap into machine learning are automated. Developers of the fully open-source software studied the best practices in the data science field and had them built into the platform, essentially embedding the function of an actual data scientist operating at superhuman speeds. Its automated model validation and tuning is complemented by a network of 200,000 data scientists, according to its website.
HPE Ezmeral is a hybrid data and analytics platform that unifies data, stores it into a sole database and offers agile access across any infrastructure on site, in public clouds or at the edge of a network. The flexibility of its edge-to-cloud technology assists companies spanning various industries with data management and warehousing challenges, featuring automatic data tiering, built-in support for external intelligence applications, seamless machine learning and AI-project deployment as well as secure data replication, mirroring and snapshots.
IBM Watson Studio
IBM’s machine learning operation, known as Watson Studio, uses cognitive computing, which allows its models to learn with less information. With Watson Studio, enterprise teams are outfitted with the environment and tools necessary to analyze, visualize, cleanse, shape, ingest or stream data in addition to creating, training and deploying custom machine learning models. According to IBM’s website, Watson Studio can halve model monitoring efforts, increase model accuracy by 30 percent as well as increase overall net profits.
Microsoft Azure Machine Learning
The legacy brand offers access to its enterprise-grade machine learning operations, open-source interoperability and integration tools in a pay-as-you-go format. Compared to other cloud-based services, Azure’s unique storage infrastructure holds more delivery points and data centers to provide one of the most reliable user experiences in the industry.
SAS Visual Data Mining and Machine Learning
With SAS Visual Data Mining and Machine Learning, users can collaborate on the same project while exercising different disciplines of programming languages thanks to its modern, integrated environment. Its enterprise data mining tool helps users develop descriptive and predictive models in a rapid, streamlined process, delivering advanced results in easy-to-comprehend reports. This software digests big data into attractive analytic visuals that not only grab interest due to their aesthetic appeal, but also help businesses identify trends, spot correlations, separate outliers, realize exceptions, find root causes and spark new ideas previously buried beneath the data.