Top 10 Emerging Technologies CIOs Are Investing In
Brian Hopkins, vice president of Forrester’s emerging-technology practice, stated that the list is organized according to how quickly organizations can expect positive ROI from these investments. However, the speed at which an organization sees positive ROI depends on how quickly it deploys the technology. It is more dependent on how the organization uses the technology.
Hopkins stated that “the problem is the firms achieving outsized growth” are only a fraction of all firms. “The most recent number we have is around 3%.”
These “future-fit” organizations are described by Forrester as using cutting-edge technology to be more adaptable, creative, and resilient.
Nearly one in four companies (37%) belong to what Forrester calls “modern”. These companies are involved in significant digital transformation and IT modernization, but may not be interested in cutting-edge technologies like Turing bots or zero trust edge yet. (More below).
Forrester estimates that 59 percent of organizations fall under the mainstream “traditional” category. Although these firms are not early adopters, Hopkins says they have significant ROI with mature technologies like cloud-native computing and natural language processing (NLP).
Hopkins stated that if you are a traditional company, you won’t be able to afford those technologies that can [return ROI within] five-plus years. “If you are in the 37% modern [category] or, even better, the 3% future-fit you can be more aggressive in how you pilot and experiment with the implementation of these technologies.”
No matter what category you fall into, almost all organizations will benefit from any of the 10 technologies listed below. 65% of CIOs believe their companies will invest in at least one of these technologies over the next 12 months.
Two technologies that yield short-term ROI
The report states that These emerging technologies can be used to benefit organizations of all types and maturity levels, cloud-native computing, and NLP.
“Cloud-native computing, natural-language processing. Hopkins said that many firms are now starting to notice these things.” These things deliver ROI for the mainstream company.
It is not new to design software that runs in cloud environments. Enterprises use cloud-native computing for modernizing their application portfolios, and to innovate on the network to improve customer experience and engagement.
The ROI of cloud-native computing has a fast development cycle that increases an organization’s ability to react quickly to changing market conditions and quickly roll out new functionality and features to customers. Cloud-native computing’s ROI can be realized quickly.
The report states that enterprises looking modernize core applications or customer experience with high-performance applications at their edge or modernize core software will benefit the most.
Cloud has been around for many years but it is not widely used. The report found that 65% of businesses are using cloud-native computing and 16% plan to do so soon.
NLP is another technology in rapid development and is showing positive ROI in the near future.
Forrester defines NLP to be “the broad range of technologies and practices that allow the ingestion processing, understanding, and generation of natural language” in artificial intelligence applications (AI). This includes AI applications. NLP’s most immediate beneficiaries are marketing and financial services, as NLP allows for more customized and convenient customer experiences at scale.
NLP goes beyond just serving customers. According to the report, NLP serves as a cornerstone of many digital-transformation efforts, as it bridges the gap between text mining, natural language understanding (NLU), and natural language generation (NLG).
Four technologies that yield a longer-term ROI
Forrester’s modern companies, which have a two- to four-year ROI window, should look at piloting four emerging technologies. The analyst firm stated that edge intelligence, explainable AI and intelligent agents, and privacy-preserving technology.
Edge intelligence refers to the ability to capture more data from edge applications and devices. This allows for real-time intelligence and insights.
These technologies involved include streaming analytics, edge machine learning (EdgeML), real-time data management on intelligent devices, and edge servers. Distributed data fabrics, Internet of Things networks, smart cities and homes, and digital twins are all examples of edge-intelligence applications.
Hopkins said Hopkins that Forrester sees three distinct edges. Hopkins said that each edge is unique and has its own set of characteristics. These edges exist outside of the data center and the cloud.
The engagement edge is made up of content-delivery networks as well as smart-home IoT. The enterprise edge is a data fabric that links tier-1 data centers with distributed sites in second and third-tier cities. It also hosts and runs enterprise applications like ERP and CRM. The telco edge, which is the backbone of telecommunications, allows all edge networks, applications, and devices to communicate more efficiently.
With edge computing, edge intelligence aims to localize and personalize the customer experience – especially when it comes to processing video, images, and sensor data.
Explainable AI (XAI), as Forrester defines it, is a set of techniques and collections of processes that help people to understand AI systems. XAI is a method of creating an artificial intelligence model (surrogate) that approximates the inner workings of the AI model.
Hopkins stated that the biggest problem with AI is companies’ distrust of it. “We believe that explainable AI technology as part of a responsible AI program is going to be the thing that really makes AI happen.”
The report states that XAI improves trust because it allows humans to see what’s happening behind the scenes of AI processes. This knowledge is believed to increase trust.
This technology is still not very popular at the moment. About 25% of respondents to the survey said they would be using XAI technology in the near future. However, this could change with the EU’s Artificial Intelligence Act. This will require different levels of explanation for AI use cases once it is in effect.
Intelligent Agents (IAs)
Forrester defines IAs as “software that can make decisions and perform services based on its environment. It also takes into account user inputs. IAs are similar to robots and robotic process automation (RPA) technologies. They can be deployed using RPA, digital processing automation (DPA), business rule, machine learning (ML), NLP, and conversational AI. IAs are able to support both self-service and conversational virtual assistants.
“RPA bots using machine learning to create digital employees . . Hopkins said that they are a precursor to an intelligent agent. They can work across many business areas, synthesizing data and reading free text. The models they have been trained on allow them to make decisions based not on set rules or heuristics, but on the models that they have learned.
The goal of IA is to improve customer experience (CX). It triggers events either as directed by humans (for example, Amazon’s Alexa or Apple’s Siri) or automatically. The report states that this tech is still in its infancy as many of the technologies involved, such as AI, NLP, and RPA, are still maturing.
Privacy-preserving technologies (PPTs), as described in the report, include privacy filtering to access personal data, security controls for processing and use of data, and controls to protect the environment where it happens. Advanced PPTs include homomorphic encryption and multi-party computation. Confidential computing, de-identification. anonymization. pseudonymization. Federated learning, data masking.
Near-term adoption curves are available for data masking, differential privacy, and de-identification.
The report states that PPTs are rapidly becoming popular for high-risk and high-value use cases whose core is processing strictly regulated personal information.
PPTs are on the rise! States and Countries around the world monitor and regulate how companies manage personal information. The report shows that 68% of IT decision-makers surveyed are now investing in PPTs.
Four technologies with a questionable long-term ROI
Forrester believes that four of the technologies on its list will not produce significant ROI over five years or longer. They may even never pay off. These technologies should be approached with caution by both mainstream and modern businesses.
For example, Web3 is more visionary and hype than reality. TuringBots are still in the early stages of AIs that can code (Ais That Code). AR and VR will take time to fuse into an extended reality. The report concluded that Zero Trust Edge is complex and technically difficult at this stage.
Extended reality (XR), as defined by Forrester, is “technology that overlays computer imagery on a person’s field of vision to create new understandings of physical reality.” It’s being used in real-world situations such as training factory workers and helping remote workers do their jobs more efficiently. According to the report, there are very few uses for it outside of gaming or higher education.
XR is a combination of three technologies that are similar to each other: virtual reality (VR), augmented reality (AR), and mixed realities (MR).
The ratio of real-world and digital elements is what makes AR different from VR. VR allows for 100% immersion in the digital realm. It is called MR when VR and AR are combined together with interactive technologies that enable users to interact with the digital realm and trigger events and processes.
Most users will find the differences between VR and AR, MR and XR largely semantic.
“Extended Reality (XR)” is a broad term that covers any technology that alters reality. It includes any kind of technology that adds digital elements to the real-world environment. This was explained by Laia Tremosa a senior content strategist at Interaction Design Foundation.
It all depends on how you access it (for example, via a VR headset), but something like the emerging metaverse could be considered highly immersive XR. Hopkins says that the metaverse is a more problematic proposition than XR overall.
Hopkins stated that “Because there are a lot of questions about metaverse,” Hopkins looked into it. Hopkins said that metaverse does not belong in the top 10. Hopkins said that there are several metaverse-precursor technologies such as extended reality and Web3 that are included in our top 10.
Web3 is defined by Forrester as “a concept that promises a World Wide Web without being dominated or controlled by established companies like banks” Web3’s egalitarian, the decentralized vision of the Internet is years away. However, Web3 is still in development and experimentation. Web3 games like The Sandbox and virtual worlds like Decentraland are some of the B2C companies that are exploring consumer-facing NFTs. However, there is still no killer app that can be used to drive vendor innovation and consumer adoption.
Forrester says that Web3 still faces many technical challenges, including:
- Identity and Key Management
- Privacy and confidentiality for public Blockchains
- Cross-chain interoperability is lacking
Web3’s hype is currently being created by investors who have invested more than $30 billion in Web3 startups since 2021. Forrester warns that it is not likely to deliver much in the way of ROI or real-world applications anytime soon.
The report states that “this technology is of no value today and it is not yet clear how it will evolve.”
Forrester defines Turing Bots as “AI-powered software which can help and augment developers’ intelligence and ability design, build and change, test, refactor and refactor software code or applications in an automatic and autonomous manner.”
Turning bots can take over code testing, and other repetitive work such as building software using unified modeling language code samples to wireframes. The Turing bot will create the base code and developers can add higher-level functionality. Hopkins said that although this sounds like software-creating software, it’s not Skynet.
Hopkins stated that machine-learning algorithms are capable of generating code when given a well-defined architecture and a readable language such as UML. A machine-learning algorithm could be described as a program that attempts 50 million different methods to find one set of code that works, based on a given set of parameters.
Zero-trust edge (ZTE), which extends the zero-trust approach to corporate network security to the edge, is the concept of zero-trust. ZTE and zero trust can be considered the same approach to protecting the enterprise. ZTE leverages software-defined networking (SDN), but this allows for capabilities beyond just authentication to the edge devices and networks.
Hopkins stated that software-defined networking allows you to integrate and insert software into your network’s functioning. Software-defined networking can be extended to containers that are not located in your data center. These are the edge environments. Then it’s about implementing a zero-trust security protocol.
It is likely that widespread use of these technologies, including next-generation meshed hardware and software, will take place within five years. The greatest benefits will be seen by organizations with large remote workforces, as well as distributed points of presence such as banks and retail.