Gartner's recently published Hype Cycle for Emerging Technologies, 2018 identified five distinct emerging technology trends which, the report says, will blur the lines between humans and machines. The Hype Cycle for Emerging Technologies report (fee charged) is the longest-running annual Gartner Hype Cycle and offers perspective on the technologies and trends that enterprises should consider deploying in the years to come. Dozens of technologies are at many different points along the hype cycle, but the research firm predicts five emerging technologies will have a huge impact on the digital workplace. They are:
- Democratized AI.
- Digitalized Ecosystems.
- Do-It-Yourself Biohacking.
- Transparently Immersive Experiences.
- Ubiquitous Infrastructure.
Of all these trends, artificial intelligence (AI) is already playing an important role in the workplace, with Gartner predicting that it will be virtually everywhere over the next 10 years. However, according to the report, AI will not only be used in the workplace by a small group of early adopters, it will be used by just about everyone. AI, the report reads, will be democratized. A number of different technologies also found at different stages in the hype cycle will support this democratization, including:
- AI Platform as a Service (PaaS).
- Artificial General Intelligence.
- Autonomous Driving.
- Autonomous Mobile Robots.
- Conversational AI Platform.
- Deep Neural Nets.
- Flying Autonomous Vehicles.
- Smart Robots.
- Virtual Assistants.
“Technologies representing democratized AI populate three out of five sections on the Hype Cycle, and some of them, such as deep neural nets and virtual assistants, will reach mainstream adoption in the next two to five years. Other emerging technologies of that category, such as smart robots or AI PaaS, are also moving rapidly through the Hype Cycle approaching the peak and will soon have crossed it,” Gartner said in a statement about the report.
Wide Range of AI Tools Creates Broad Adoption Span
It also seems that fears about AI replacing humans in the workplace are starting to subside. The Walker Sands 2018 State of MarTech Report (registration required) found 61 percent of marketers felt the advancement of MarTech will not threaten their jobs. Fifty-six percent of marketers said an equal mix of creativity and technology will drive marketing strategy five years from now. The report also revealed 69 percent of marketers don’t think the perfect marketing stack exists, and they’re not sure it ever will. In fact, marketers aren’t even completely sold on the technologies said to be on the rise (e.g. chatbots, blockchain, AI).
Tom Smith, research analyst and business strategist at Cary, NC-based DZone, said that while AI is clearly on the rise in the enterprise, the many different subsets of AI make it difficult to track what will become popular immediately and what will take longer to develop. "AI has many subsets: machine learning, deep learning, natural language processing, robotic process automation. Given the ubiquity of AI in our lives today with recommendation engines (Amazon, Netflix), search (Google), smart speakers, and the number of AI services cloud providers (AWS, Azure, GCP) are adding to their services, I have no question AI will be available to everyone in 10 years and to the moderately digital savvy in five,” he said. He noted that in the B2B world, Salesforce, SAP, SAS, CA and others are already offering AI/ML as part of their solutions.
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AI Is Already Here
In a sense, AI is already in everyone's hands. "Many people are already accustomed to receiving a weather notification every morning or even a traffic alert depending on their location. It's this type of predictive technology which is laying the foundation for something much more advanced," Jon Hayes, marketer at Pixel Privacy, said. The problem is, however, that for meaningful implementation, there will need to be an incentive for developers.
While there is certainly passion among developers, taking AI to the level where it can improve people's lives will require a lot more than passion to get things off the ground. The type of infrastructure and databases needed to sustain a deep learning platform would be phenomenal. “There is good chance that AI will be commercially driven,” he said. “This isn't necessarily a bad thing, however. As consumers become more privy to the way in which large businesses approach them, any sensible company will be looking to provide a truly personal and useful AI implementation. Since there will inevitably be money to be made through these personalized, AI-driven experiences, however, there is certainly a future on the horizon where AI is placed in everyone's hands."
Today, the AI tail — machine learning — is wagging the PR dog. AI is already available to everyone with a typical smartphone. From speech recognition to translation to navigation apps to enterprise apps that help an account manager decide which brochure to send to a prospect, a lot of people use AI every day. So almost everyone can use AI today without even realizing it.
Related Article: What Is Deep Learning and How Does it Relate to AI?
The Rise of 'Citizen Data Scientists'
However, if the question is whether everyone will be able to create new, personalized solutions using AI in the next 10 years, that’s a little more complicated, according to Adrian Bowles, VP research and lead analyst, AI at Morgan Hill, Calif.-based Aragon Research. He said we have already seen great advances in tools to help people develop machine learning solutions without a detailed knowledge of machine learning itself — aka “citizen data scientists.” If you have enough good data, these tools — such as Watson Explorer from IBM or the recently announced AutoML from Google — will guide you through the analysis and help you gather insights from that data.
“It won’t take 10 years to have those tools in the hands of everyone who wants to make evidence-based decisions based on machine learning,” he said. “The big shift in the next 10 years will be towards true distributed intelligence, where the learning and reasoning tasks are performed throughout the global network on devices ranging from your smartphone to your refrigerator to your car to a military command center.” Bowles sees the timeline evolving as follows:
- In three to five years: The power to create AI-powered solutions will be available to everyone with a modicum of technical or business acumen.
- In five to 10 years: Individuals at the center of their own ecosystem, with AI-powered devices monitoring their behavior and emotions and responding with context-appropriate data and services.
Related Article: Move Beyond AI Hype: Design Your Automation Strategy
Will Network Bandwidth Crash the AI Party?
Stephen Farkouh, SVP of customer applications and portals at Little Rock, Ark.-based Windstream Enterprise, argues that with breakthrough AI tools entering the market at a brisk pace and the cost of AI coming into reach, AI is no longer emerging — it's mainstream. Currently smaller and mid-size enterprises that adopt AI are doing so for the same reasons the Amazons and credit card companies are: improving core business functions and speeding and personalizing customer experience.
AI breakthroughs depend on data, and accelerated demand for data requires advanced networking. As AI continues to move into the mainstream, adopters of all sizes will move more data than ever before. Companies of every size will need to be sure their networks are as fast and reliable as possible, to guarantee real-time data transactions to and from the cloud to deliver on the promise AI holds.
"This new generation of bandwidth-hungry customer/user experience-enhancing technologies and apps (AR, VR, etc.) are about to crash the network party. When it comes to supporting enterprise AI with network infrastructure, it's like when Chief Brody said to Captain Quint after his first up-close look at the Shark in Jaws: 'You're gonna need a bigger boat,'" he said.
Traditional networks were developed for a vanishing enterprise technology landscape. Left unaddressed, this will at best lead to annoying bottlenecks. At worst, it could bring a swift end to AI and IT digital transformation initiatives that over-promised and under-delivered.