Artificial Intelligence is becoming Pervasive: What’s Holding Your Enterprise Back?

Stephen DeAngelis

May 07, 2019

The age of artificial intelligence is quickly and unrelentingly approaching. Mike Wheatley reports, “The adoption of artificial intelligence-based technologies by large enterprises has jumped by more than 270 percent in the last four years, according to a new Gartner Inc. survey.”[1] He continues, “Gartner added that in the last year alone, AI adoption has more than tripled. It says about 37 percent of enterprises have now implemented AI in one way or another. … The findings are a stark contrast to Gartner’s previous claims last summer that just 4 percent of enterprises had implemented AI in production. … Gartner says AI is becoming so pervasive that it will become an integral part of enterprises’ future strategies in virtually every industry.” The prospect of a full-blown age of artificial intelligence (AI) frightens some people; but, fear not, the type of AI systems being invested in by large enterprises will never rule the world as sentient computer overlords. The real concern for businesses, according to Chris Howard, research vice president at Gartner, is not embracing AI. He asserts, “If you are a CIO and your organization doesn’t use AI, chances are high that your competitors do and this should be a concern. We still remain far from general AI that can wholly take over complex tasks, but we have now entered the realm of AI-augmented work and decision science — what we call ‘augmented intelligence.’”[2]

Current uses of AI in business

Simply stating, “If you don’t use AI you should,” doesn’t really help corporate executives understand how best to put AI to use. Providing examples often sparks ideas about how AI can be used in business settings. Mahipal Nehra suggests nine such examples.[3] They are:

1. Load Balancing. Load balancing has numerous applications in areas as different as transportation and IT services. Nehra explains, “[Load balancing] on roads, transportation systems, servers, etc. with AI [provides] better management and user experience.”

2. Voice Assistants. Nehra writes, “AI-based voice assistants, like Siri, Google Now, Alexa, Bixby, and Cortana, listen to sentences in a particular language and then convert [them] into a computer readable vector, this vector is then read by a computer for replying in an output vector. This vector is then spoken by voice assistant into sentences of the same language [using] Natural Language Processing (NLP).”

3. Smart Assistants. Smart assistants, like Autodesk Eva, embed AI capabilities in human-like robots. Nehra explains these smart assistants use NLP to talk with customers in real time using relevant facial expressions to put customers more at ease with the technology.

4. Facial Recognition. Facial recognition is now for a number of purposes from unlocking your smartphone to identifying criminal suspects to making store purchases.

5. Language Translators. Almost every science fiction show set in space that involves characters of different species overcome communication challenges using some kind of universal translation system. AI now allows Earth-bound beings to enjoy some of these same features using their smartphones.

6. Autonomous Vehicles. Almost every automotive manufacturer on Earth is developing self-driving vehicles. Nehra notes, “Autonomous vehicles use radar, LIDAR (Light detection and ranging), GPS, and cameras for making 3D models of approaching vehicles. Then these models are merged together for locating the vehicle with very high accuracy.” These inputs are used by AI modules to follow roads, obey traffic signals, and avoid obstructions.

7. Image Search & Analysis. Nehra writes, “Image search & analysis is being used for checking plagiarism, finding websites linked to an image for SEO purposes, and finding offending content on social networks.” Image search can also be used by manufacturing to improve quality assurance or in medicine to detect diseases.

8. Data analytics. Only AI systems are capable of analyzing the massive amounts of data currently being generated (aka big data). With the emergence of the Internet of Things (IoT), the amount of data predicted to be generated every second of every day is only going to grow.

9. Optimization. AI can also be used to optimize processes. Everything from game playing to improved trade promotions to route identification can be optimized using AI systems.

A few other ways businesses can use AI, include: recruitment and support; targeted marketing; predictive modeling; and prescriptive analytics. Sohaib Khalid, an SEO Expert from FME Dubai, concludes, “The use of AI in the world of business [is] a significant advancement [and] more and more businesses are choosing to reap its benefits. With AI being democratized so rapidly, its proper application along with well-trained employees has become more vital than ever.”[4]

Implementing enterprise AI

Andrew Ng (@AndrewYNg), founder and chief executive of Landing AI, is among those advocating businesses to embrace AI sooner rather than later. “It is a big journey,” he told participants at a recent conference, “but jumping in is not hard.”[5] Additionally, “Ng advises starting small. Begin with a pilot project that requires only one or two engineers; set realistic expectations for what AI can and can’t do; and focus on scoring quick wins. Not only does this strategy help an organization quickly understand what it will take to continue investing in AI, but it will also help secure buy-in from higher-level executives. The best way to succeed in AI adoption, he said, is to have support from all levels of an organization.” At Enterra Solutions®, we call this a crawl, walk, run approach. This approach allows solutions to be tweaked and proven before being scaled. Ng elaborated on his approach to AI implementation in an interview with the Wall Street Journal.[6] During that interview he stressed the following points:

  • Make the pilot project a hit. “Instead of choosing the best, most-valuable use case for AI, pick a project that has a high chance of being successful. That way, it will convince other teams within the company that AI has value.”
  • Establish short timelines. “Projects that show initial results within six to 12 months are ideal. ‘In addition to gaining momentum, it helps the company start to feel what an AI project will look like,’ he said.”
  • AI training needs to be broad. “Engineers will do the technical work. Division leaders should learn how to manage AI projects and what timelines are realistic. Management also needs to be well-versed in the technology. ‘Executives should figure out how to think about AI and how it affects company strategy,’ he said.”
  • Strategy is the last step. “Form an AI strategy after the company has learned from its pilot tests. ‘Strategy is important, but it needs to be built on a foundation,’ he said.”

Although Ng may be correct about needing a foundation upon which to build an AI strategy, business leaders must have a good idea of what they want to achieve through AI implementation before pilot projects are initiated.

Concluding thoughts

Although some people remain suspicious of AI, it’s not going to disappear. In fact, Omer Khan, CEO and Co-founder of VividTech, Inc., asserts, “AI is the steam engine of our time and age.”[7] He explains, “Applications of artificial intelligence are in every field imaginable. That is because of the need for automation, which goes back to the core philosophy of how machines work: to help us do tasks with ease. In this day and age, we’re surrounded by automated devices that don’t even require people running them, and while the idea of computer-controlled services is a frightening one for some, it’s already part of our daily lives, without us realizing.” The reason so many pundits advise companies to jump on the AI bandwagon is because it’s applications are only bounded by the imagination. AI remains in its infancy and early adopters will have the leg up on competitors as the field matures. What’s holding your enterprise back?

Footnotes
[1] Mike Wheatley, “Enterprise adoption of AI has tripled in the last year, Gartner says,” siliconANGLE, 21 January 2019.
[2] Ibid.
[3] Mahipal Nehra, “Emerging AI trends: All About Artificial Intelligence,” WhaTech, 7 February 2019.
[4] Sohaib Khalid, “11 Artificial Intelligence Trends Every Business Must Know in 2019,” Readwrite, 1 April 2019.
[5] Karen Hao, “If you’re thinking about embracing AI: just jump in,” MIT Technology Review, 27 March 2019.
[6] Sara Castellanos, “Reporter’s Notebook: Andrew Ng’s Tips for Corporate AI Adoption,” The Wall Street Journal, 5 February 2019.
[7] Omer Khan, “Why AI Is the Steam Engine for This Time and Age,” Datafloq, 7 March 2019.