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Identifying the Best Technology Sources for Enabling AI in the Enterprise

Kendra Luciano Managing Editor, Content Marketing Published 29 Aug 2019

Evaluating the Primary Resources for Launching AI Initiatives in the Enterprise 

AI is a fast-growing technology, and IT leaders are on the lookout for support in implementing AI capabilities in the enterprise. We believe in their recent report, “4 Technology Sources for an AI-Enabled Enterprise,” Gartner has identified the primary sources businesses should be aware as their exploring extending AI capabilities within the business. Advancements in technology are eliminating mundane tasks and giving IT admins the opportunity to focus on innovation. Consequently, it’s important to understand where to garner support and expertise, as well as parse out the advantages and disadvantages of AI and automation on the journey to create a smart, autonomous enterprise.

As AI is increasingly becoming an area of interest among the enterprise, it’s worthwhile for application decision-makers to gain a clear picture of the options available in prototyping solutions. Before we dive into the details, let’s take a look at the state of the market today.

  • “Over half of Gartner end-user clients are adopting artificial intelligence (AI) technology using cloud-based development services.”
  • “AI capabilities and features are increasingly being integrated into customer relationship, supply chain, resource planning, and knowledge management software via enterprise application suites.”
  • “A wide range of AI-infused products from data science and machine learning (ML) platform vendors is driving AI to be the second fastest growing segment of the analytics and BI market.”
  • “More IT leaders are turning to consulting partners and seeking support to augment their enterprises’ AI capabilities across the entire data and analytics value chain.”

 – Gartner, 4 Technology Sources for an AI-Enabled Enterprise

The Four Main Technology Sources to Build and Deploy AI in the Enterprise

Upon setting out to launch AI initiatives, it’s easy to experience confusion when evaluating the tools and technologies in the marketplace. When preparing to deploy AI, application leaders must be able to identify the right tools for their projects, as well as discern the differences between available options.

AI deployments typically start with the analytics team. In most cases, there’s not a great deal of internal AI expertise. Additionally, budgets are limited. Thus, application leaders will do well to generate proof of concepts and begin with pilot projects. This allows for greater clarity with respect to available vendor selections.

Below you’ll find a general overview of the main AI technology resources decision-makers who are working on prototyping need to understand. 

Cloud Providers

With the growth of AI initiatives over the last few years, major CSPs are extending AI-based hosting services into the market. Such service options support machine learning, language processing, and computer vision.

It’s recommended that application leaders working toward prototyping AI projects look further into how CSPs can help. This is an especially beneficial option if the organization has already established a relationship with the vendor and utilizes other services. During testing, there’s no need to worry about infrastructure, resources, or licensing.


  •         Fast deployment
  •         Faster compute as a result of hardware-optimized frameworks
  •         Full development suite
  •         Access to helpful user community


  •         CSP AI capabilities may require compromises on behalf of the enterprise
  •         Limited model portability
  •         Multicloud ecosystems don’t have good support
  •         Algorithms can’t be used across vendors

Showcase AI Capabilities Already Available in Everyday Enterprise Apps

Frequently overlooked in the enterprise are the AI capabilities that are already available in the applications we use every day. CRM, HCM, ERP, and SCM apps have begun integrating AI-based features—they’re just not easy to spot for the average user because they’re embedded in the apps. Showcasing the new, emerging efficiencies and capabilities within the tools businesses use every day can be a great foray into more advanced use cases for AI.

A few examples include, “recommended email replies, auto layout of graphic elements in presentations, grammar and spelling recommendations, suggested analytic insights from ERP or CRM applications.”

-Gartner, 4 Technology Sources for an AI-Enabled Enterprise


  • AI benefits are obvious to personnel
  • Little to no need to learn new tools
  • Quick wins lessen fear and lower the barrier to entry into AI
  • Better analytics, leading to further experiments
  • Pre-integrated with data


  • Supported use cases are limited to those within business apps
  • Personnel who learn the realistic capabilities of AI may be disillusioned
  • These use cases do not offer the full scope of AI’s potential
  • There’s no ability to use data outside the app

Investing in a Machine Learning/Data Science Platform

“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.”

-Gartner, 4 Technology Sources for an AI-Enabled Enterprise

Data science and ML platforms (some of which are listed in “2019 Magic Quadrant for Data Science and Machine Learning Platforms”) are increasingly available for a broad spectrum of users.

Another worthwhile resource for AI initiatives machine learning/data science platforms. More useful options are cropping up on the market, built to accommodate numerous types of users. This includes: operational workers, engineers, and data scientists focused on deploying models and designing experiments.


  • Customization
  • Establish a solid foundation for developing solutions
  • Encourage collaboration between varying roles
  • Gain visibility for analytics leaders
  • Create data science solutions rather than pretrained models


  • Requires more involved work
  • Need experts
  • Costly
  • Risk of provider acquisition/going out of business
  • May require data curation

Lean on a System Integrator to Extend Talent and Gain Prebuilt AI Assets

Businesses who already work with a system integrator may want to consider a discussion on AI prototypes. Sis usually offer AI tools and technologies to customers. In fact, according to Gartner “Over 80% of the SIs interviewed by Gartner during the last two years had developed a platform that was being utilized to anchor their offering.”

It’s worth noting: application decision-makers should be aware that AI options among Sis may not necessarily offer a precisely applicable solution. However, most Sis have established processes for helping customers create AI solutions using their platforms, even without pretrained solutions.


  • One-stop shop
  • SI is accountable for platform and services
  • Sis have processes for supporting customers in their ideations


  • Higher risk of lock-in
  • Platforms sometimes only available with services
  • Relying on an SI may limit the ability to build upon internal AI skills

Embracing the Future of AI/ML Capabilities

AI is here, but it’s not taking over—and it’s here to augment humans in the workplace; not replace them. Decision-makers who are focused on integrating AI into the business should consider the following:

  • Rely on cloud vendors you’ve already established relationships within addition to in-house AI skills.
  • Don’t forget to evangelize the AI-enabled capabilities that are already available within your workforce, unbeknownst to many end-users.
  • Choose the data science platforms that align with your internal expertise.

At Extreme Networks, our vision for the future is the autonomous enterprise, of which AI is a significant component. We encourage our customer base to further explore the possibilities for initiating AI projects at a comfortable pace.

To get the full scoop on recommendations, as well as predicted future trends for leveraging these technology sources, read the complete Gartner report.


Gartner, 4 Technology Sources for an AI-Enabled Enterprise, Saniye Alaybeyi, Susan Tan, Peter Krensky, Van Baker, 9 April 2019

Gartner, Magic Quadrant for Data Science and Machine Learning Platforms, Carlie Idoine, Peter Krensky, Erick Brethenoux, Alexander Linden, 28 January 2019

Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.

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