4 top technology sources for building an AI-enabled enterprise

IT leaders are overwhelmed by the sheer number of available tools and technologies to choose from. They often struggle to understand the differences between them and how each will fit their specific business needs.

Thousands of active artificial intelligence technology and service providers are developing AI features and capabilities for the enterprise. AI is now serving as the second fastest growing segment of the analytics and BI market. By 2021, AI will be pervasive in every new software product and service.

This is consistent with the recent growth of AI in end user organizations: The number of enterprises implementing AI grew 270 percent in the past four years and tripled in the past year, according to the Gartner 2019 CIO Survey. The primary reason for this big jump, is that, AI capabilities have matured significantly, and thus enterprises are more willing to implement the technology.

At the same time, IT leaders are overwhelmed by the sheer number of available tools and technologies to choose from. They often struggle to understand the differences between them and how each will fit their specific business needs.

According to Gartner research and client inquiries, the four main technology sources to build and deploy AI in the enterprise are:
  1. Leading cloud vendors
  2. Enterprise application software vendors
  3. Data science and machine learning (ML) platform providers
  4. System integrators

We recommend application leaders consider the following when prototyping solutions with AI:

Leverage your organization’s preferred cloud vendor
More than half of Gartner clients are adopting AI using cloud-based services. In many cases, IT leaders can use these cloud AI services to perform advanced analytics. Cloud based AI services do not require extensive data science experience. These services help simplify AI efforts.

AI enabled.jpgIn the next five years, major technology providers will develop features to help small and midsize businesses innovate through open-source ML frameworks, target emerging economies to expand their offerings, democratize AI, and be involved in federal and private AI ethics initiatives.

Adopt and promote AI-based features in mainstream enterprise applications
By 2021, all major enterprise application software platforms will have AI-enabled features. In the context of the workplace, the majority of these capabilities will blend in as “everyday AI,” where employees will be exposed to AI service without even knowing it. Examples include recommended email replies, increased use of chatbots, and grammar recommendations within documents.

In the next five years, enterprise application software vendors will provide better user interfaces. These software platforms will have the ability to support business workflows with AI, while easily moving data from one application to another. These software platforms will help enterprises automate mundane tasks, and improve personalization and data-driven business outcomes within all of their enterprise applications.

Choose a data science and machine learning platform for flexibility and control
Gartner defines a data science and ML platform as a cohesive software offering that provides the building blocks and environment for creating data science solutions, and supports incorporating those solutions into business process, surrounding infrastructure and products. Our research indicates that expert and citizen data scientists, data engineers and application developers require professional capabilities for building, deploying and maintaining analytical models.

By 2021, more than 50 percent of data science tasks including data curation will be automated, resulting in increased productivity and broader usage by citizen data scientists. In the next five years, data science platform vendors will continue to improve product features that make teams more productive.

Use a system integrator for talent augmentation and prebuilt AI assets
If your enterprise already has an established and successful relationship with a SI, leveraging it for AI prototypes is a great option. Platforms created by SIs typically include AI capabilities like natural language analysis, image recognition, ML and deep learning, workflow automation/RPA and more.

SIs provide a one-stop shop for platform, pre-trained AI models and services, but the risks of lock-in to an SI’s platform are more acute because software is not its main business. In the next five years, SIs will have more vertical market initiatives in place on which to concentrate their expertise.

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