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Understanding How to Apply AI in the Enterprise

Joanne Lennon Senior Manager, Product Marketing Published 25 Mar 2019

“Artificial Intelligence is a discipline that applies advanced analysis and logic-based techniques, including machine learning, to interpret events, support and automate decisions, and take actions.” – Gartner, A Framework for Applying AI in the Enterprise

Defining AI for IT Leaders and Enterprises

When we think about a framework that can support IT leaders in the quest to understand and successfully apply AI, it’s important to first understand its definition in the context of the enterprise. After all, it’s a broad, yet rapidly evolving technology practice area.

One thing’s for certain when it comes to AI: it will be led to more disruption and innovation than most technology practice areas in the next decade. Areas of study and capabilities include image processing, voice recognition, and NLP, and many more. Continued advances in analytical methods like machine and deep learning, as well as computation power, promise rich possibilities for the future.

To reap value from AI, it’s critical to define the use cases relevant to your business and prioritize accordingly.

A look into the future:

  • Customer experience is the main source of AI business value
  • Cost reduction is the second source of AI business value
  • In 2021, the main primary source of AI business value is predicted to be new revenue

Download the Gartner Report A Framework for Applying AI in the Enterprise to learn more!

Leading AI and Secondary Technologies of Today  

In order for IT decision makers to successfully create AI strategies, it’s important to understand the core AI and supporting technologies of today.

1. Machine learning and compute infrastructure

“Formally defined, machine learning (ML) is a technical discipline that aims to extract knowledge or patterns from a series of observations.” – Gartner, A Framework for Applying AI in the Enterprise

Per Gartner report, “Deep learning (DL) is a variation on ML: Business problems are solved through the extraction of knowledge from data. DL expands standard ML by allowing intermediate representations to be discovered. These intermediate representations allow more-complex problems to be tackled and others to be potentially solved with higher accuracy, fewer observations and less-cumbersome manual fine. At present, the most common type of DL is feedforward deep neural network (DNN). DNN uses multiple layers of interconnected processing units to find intermediate representations within raw input data and offers a framework that can be applied to a myriad of business challenges.”

2. Natural language processing

Natural language processing isn’t new; some capabilities have been available for over a decade. However, recent DL advancements have improved its performance and accuracy, bringing opportunities to improve both services and operations.

Per Gartner report, “The most useful applications for NLP at present are aimed at improving customer service and employee support”. Enterprises looking to harness the capabilities of NLP should initiate pilot projects with reasonable goals to effectively show the success. Projects can evolve in scope and complexity as experience is established.

3. Computer vision

Per Gartner report, “Computer vision technologies encompass capturing, processing, and analyzing digital images for meaning and context.” There are numerous practice areas, including:

    • Machine vision
    • Optical character recognition
    • Image recognition
    • Pattern recognition
    • Facial recognition
    • Edge detection
    • Motion detection

4. Data science and analytics

“Data Science and ML Platform is a cohesive software application that offers a mixture of basic building blocks essential for both creating many kinds of data science solution and incorporating such solutions into business processes, surrounding infrastructure and products.” – Gartner, A Framework for Applying AI in the Enterprise

Amidst the competitive business environment of today, many organizations are focused on making analytics accessible to everyone across business units. This has effectively changed the traditional model for business intelligence.

Meanwhile, data scientists are using data science and ML platforms to create and deploy solutions. Per Gartner report, “Data science platforms support data scientists throughout the entire data and analytics pipeline, including:

    • Performance engineering
    • Deployment
    • Training
    • Testing
    • Feature engineering
    • Interactive exploration and visualization
    • Data preparation
    • Data access and ingestion
    • Advanced modeling”

Modern BI platforms enable nontechnical users to perform complete analytics workflows, while traditional BI platforms support IT-produced analytics content.

5. Robots and sensors

“These technologies deal with automated machines that can take the place of humans in dangerous environments or manufacturing processes, or that resemble humans in appearance, behavior and/or cognition. Many of today’s robots are inspired by nature, contributing to the field of bio-inspired robotics.” – Gartner, A Framework for Applying AI in the Enterprise

Per Gartner report, “RPA tools are software applications designed to replace or assist in manual tasks, mimicking the “manual” path taken through applications by a human. Generally, there is no AI in RPA. Most RPA provides functional graphical user interfaces (UIs) on top of scripting tools that gather data (often using screen scraping) from one application and enter it (via rekeying) into another application….”

Key Takeaways for Technology Leaders Looking to Harness the Power of AI

Technology leaders who are looking to reap the benefits of AI in business would do well to begin by getting familiar with AI and how it can be applied within your specific vertical and enterprise. From there, seek to take advantage of the practice areas that offer the most significant opportunities; for instance, using deep learning to mine data efficiently in order to reveal insights that would take far longer to discover manually. Finally, share knowledge with leadership and throughout the business with respect to the innovation opportunities that AI brings. Identify how different business units can take advantage of advances.

Learn everything you need to know about applying AI in the enterprise by downloading Gartner’s report!

*Gartner, A Framework for Applying AI in the Enterprise, 29 January 2019, Bern Elliot, Whit Andrews, Jim Hare

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