Technology’s pace of change has had a profound impact across nearly every industry. These days it’s not enough to keep atop of emerging technologies but to keep ahead of them.
In the coming year, new and evolving ways of wrangling data will take center stage in the enterprise. Innovations in artificial intelligence, edge computing and software robots will increasingly be leveraged for competitive advantage as organizations look to quickly and efficiently use data to make better business decisions. Organizations that fail to anticipate these and other emerging trends risk a rapidly accelerating existential crisis.
To get a sense of where businesses should place their bets, we spoke with technology experts about what they see most likely affecting a wide variety of organizations as they undergo digital transformations. Pros in these fields gave us their top picks for what should be on your radar, as well as some insights into the implications of adopting these disruptive technologies.
Robotic process automation (RPA)
Companies are seeing major gains from a simple concept: delegating tedious business process tasks to software robots for automation. Called robotic process automation (RPA), the technology is already having an impact in streamlining workflows for early adopters — well in advance of when many have thought the technology would be put to use in the enterprise.
“The rate of advancement and functional utility of robotic process automation is shockingly good and seems to be improving by the hour,” says Matt Stevens, CEO of Boston-based AppNeta. “I really didn't expect to see this level of intelligence or capability arrive this quickly.”
According to Gartner, RPA is outpacing all other segments of the enterprise software market worldwide, with expected revenue of $1.3 billion this year. The market grew 63 percent last year to $846 million.
“RPA removes repetitive and routine tasks from an employee’s daily activities and allows them to focus on higher-valued work,” says Thomas Phelps, CIO at Laserfiche. “Organizations that use RPA are able to take the bot out of the human by enabling them to focus on tasks that help innovate for the business or enhance customer experience. It helps organizations boost operational efficiency, improve quality and enhance regulatory compliance.”
With a proven path to business value, expect even more companies to be rolling out RPA initiatives in the months ahead.
AI is helping companies attack problems that would be daunting or impossible for tech or business staff, says Tim Jobling, CTO of Imagen.
“We’re not buying into the vision that machines are going to take all human jobs just yet,” Jobling says, “but we are seeing a bit of a revolution similar to when computers first became mainstream. Today we’re seeing a wave of problems being tackled by AI and ML [machine learning] approaches and this mainly takes away some of the boring workload or enables new processing at a scale not possible when the work needs to be done by people. For example, AI enables our customers to create searchable metadata from audio that can then be used and scaled at a large volume. Without AI, this process would be done manually, or not at all.”
AI is also playing an important role in defending organizations from security threats, a trend that Vinay Sridhara, CTO of Balbix, expects will continue to gain steam in the year ahead.
“Enterprises are using AI to enable their cybersecurity teams to get an accurate idea of breach risk by analyzing up to several hundred billion time-varying signals across their network,” Sridhara says. “This enables chief information security officers to continuously analyze high-volume, high-velocity cybersecurity data and gain real-time visibility into their company’s breach risk. AI-powered platforms even provide prioritized steps to remediate issues to drive cyber-risk reduction throughout the enterprise, enabling them to better protect their customers’ information.”
Adopting an agile-like approach to managing data with AI and machine learning can help give companies an edge in 2020, says Hitachi Vantara’s Renee Lahti. This collaborative, cross-functional approach to analytics, known as DataOps, could prove highly disruptive where adopted.
“Companies are just figuring this out,” Lahti says. “It’s more about the people rather than the adoption of process. According to Gartner, the current adoption rate of DataOps at less than 1 percent of the addressable market — but that 1 percent is going to have a huge competitive advantage.”
Chris Bergh, CEO of DataKitchen, says the concept melds agile development, DevOps, and lessons learned from manufacturing.
“It’s a methodology that enables data science teams to thrive despite increasing levels of complexity required to deploy and maintain analytics in the field,” Bergh says. “Without the burden of technical debt and unplanned work, data science teams can focus on their area of expertise — creating new AI models and analytics that help enterprises realize their mission.”
The approach, which unifies workflows related to data analytics, can have intangible ripple effects on an organization’s ability to extract value from its data, Bergh says. “This improves teamwork and reduces manual processes that drag down productivity. DataOps transforms data organizations from chaotic and slow to high-performance teams.”
Video and unified communication
Employee experience is becoming a crucial factor for organizational success — not just in terms of productivity but as a key draw for bringing highly sought after talent through the door. In a survey of nearly 300 companies to determine what makes a great employee experience, researchers at MIT found a surprise at the top of the list: video. Investments in video technology lead to innovation, as well as improved collaboration and productivity, researchers found.
“We see firms investing significantly in interactive video technologies particularly as they spread the use of agile methodology beyond their software development teams to the rest of the business,” says Kristine Dery, a research scientist at MIT’s Sloan Center for Information Systems Research. “This highly interactive agile method of project delivery — with daily stand-ups — requires teams to either be face to face, or to have the technologies that replicate those more intimate situations as closely as possible.”
Dery predicts that video tech will continue to simulate and improve face-to-face communication with new features, such as virtual reality (VR) and other immersive tech, especially as organizations work to fill the skills gap with distributed teams.
Similarly, AppNeta’s Stevens sees unified communications (UC) making a comeback in the years ahead.
“Chronic friction and reliability issues made early UC solutions a gamble for businesses,” Stevens says. But current tools have solved these drawbacks, he says. “The latest UC tools add critical visual and content-sharing capabilities. They can actually improve meeting efficiency, having an impact above and beyond what face-to-face interactions can deliver by allowing broader inclusion and active participation in today's highly-distributed work environments.”
5G hype tends to overlook the fact that a nationwide rollout of the technology will take years to accomplish. But that’s not stopping firms for shaping up their plans for high-speed, low-latency wireless service.
“Organizations are advancing their 5G strategies even before widespread network availability,” says Jason Hayman, a market research manager at TEKsystems.
VoltDB CTO Dheeraj Remella also sees promise in 5G but warns that expectations around the technology could lead to problems.
“If both wireless carriers and enterprises are unable to handle the data onslaught that comes with 5G, latency in processes where either employees or customers expect the real-time feedback now available with faster network speeds has the potential to incite revolts against certain brands or technologies,” Remella says.
To combat this Remella says companies should implement scalable, real-time data architectures that go “beyond simply ingesting data and ultimately driving actions by making intelligent, dynamic decisions across multiple data streams.”
Plus, Remella sees 5G having a ripple effect. “The promise of 5G is forcing organizations to identify current processes that are ripe for change and ensure the existing IT stack can meet the demands of the new networks,” he says. “For this reason, 5G is driving the adoption of other influential technologies, from edge computing to VR and stream processing.”
“It’s interesting to see IoT projects pulling in many vogue technologies like edge computing, serverless and containers, along with organizational structures around DevOps and microservices,” says Todd Loeppke, lead CTO architect at Sungard AS.
A number of our experts pointed to the widespread adoption of Kubernetes, an open source container orchestration system that automates container deployment, scaling, and management. “It’s enabling entirely new architectures that can scale up quickly,” says Tom Petrocelli, a research fellow at Amalgam Insights. “So much vendor attention is focused on Kubernetes, that it’s hurting other technology platforms. Kubernetes has also helped spawn or amplified a host of other technologies such as service mesh and container-based CI/CD pipeline automation products.”
“Kubernetes is the most popular way of dealing with containerized applications and services running across on-premises and cloud environments as well as devices of all sizes,” says Jeff Reser, global product and marketing manager at SUSE. “With more and more things to manage, automating the deployment and orchestration of infrastructure and applications is integral to software-defined infrastructures.”
Immersive experiences (AR, VR, mixed reality)
Immersive experiences have been well hyped but somewhat slow to deliver. Still, the promise is enticing, and Bill Bodin, CTO of Kony, an enterprise app maker, sees augmented reality (AR) in particular providing business benefits to a mix of industries, from brick-and-mortal retailers to industrial applications and training.
With AR, he says, “we can augment store shelves and products in real-time. In maintenance, repair, and many industrial applications, we can create informational overlays on mechanical or electrical equipment, putting key instrumentation metrics directly in the hands of the people servicing the area.”
Bodin also sees examples in the travel industry, with airports providing virtual displays, personalized to the traveler. “In banks, we can use augmented reality to direct customers to key service areas and dynamically show the names and specialty areas of the branch staff,” he adds. “For those that service banking equipment, such as ATMs, we can provide views of internal peripheral failures and deliver secure repair references tailored precisely to the problem.”
Todd Maddox, a research fellow at Amalgam Insights with a focus on brain science, also sees potential uses for immersive experiences in training programs.
“VR has great potential for training and soft skills,” he says, in particular for people skills training, such as empathy, communication, and the like. “VR and AR are very effective because they are grounded in experiential learning and because they broadly engage multiple learning and performance centers in the brain in synchrony, including cognitive, behavioral, emotional and experiential systems."
IoT and edge computing
A 2019 CompTIA research report found that about a third of U.S. firms believe IoT strategies can help drive revenue by boosting production, monetizing data or by helping to sell services as a product.
Sungard’s Loeppke is seeing advances in IoT edge computing but also sees a need for AI and ML tools to handle the data generated in a way that’s more accessible for businesses.
“Big data has been around for about 10 years now, but the real challenge with big data is finding a way to make sense of it and figuring out how to use it for business purposes,” Loeppke says.
“Traditional tools have been used with limited success in my opinion. With ML technology being made more accessible, more companies will be able to provide improved customer experience and will be more likely to monetize the data they have accumulated over the years.”
Several of the pros we talked with mentioned the benefits of smart processing — including paring down data — at the edge before it’s uploaded to the cloud.
“What humans really care about is interaction with the real world, which requires intelligence at the endpoint,” says Sumir Karayi, founder and CEO of 1E. “That’s why I think edge computing will supersede IoT. People think of IoT as this dumb entity that connects to the cloud, and so effectively provides the cloud with intelligence rather than being intelligent itself. And they’re right to think that, because connected devices spawn a whole mass of data that you have no control over. Edge computing, on the other hand, offers local decision-making capabilities and more control of personal data.”