15 most underhyped technologies in IT

Artificial intelligence continues to dominate technology discussions, as executives — and workers at all levels — seek ways to use AI to make work easier, faster, and ultimately more profitable.

Such hype is mostly justified, as AI is already delivering value in multiple ways. But it can be overblown, as the number of AI-related technologies on our most overhyped list attests, and it’s hardly the only tech to revere. Lots of technologies today are worthy of praise because they truly move the needle. Yet they get too little love.

So what are those underhyped technologies that deserve more credit. We put that question to analysts, CIOs, and researchers. Here’s what they had to say.

1. Small language models/small AI

Granted, small language models (SLMs) fall under the broad umbrella of artificial intelligence, which gets plenty of attention. But this subset doesn’t get the credit it’s due, says Stu Carlaw, chief research officer at ABI Research.

Small language models are trained on smaller data sets (as the name implies), counter to the more well-known large language models (LLMs), such as ChatGPT, that are trained on vast amounts of data. Carlaw says organizations can use small language models to learn very specific, confined information, such as internal training manuals, and then use those models to automate related processes.

“You ringfence around smaller data sets, asking more targeted questions, much more aligned to your own needs,” Carlaw explains.

That, he says, helps more quickly and easily bring productivity and efficiency gains to the organization.

2. AMRs and co-bots

Similarly, Carlaw believes autonomous mobile robots (AMRs) and co-bots also fall into the underhyped category.

AMRs are basically robots capable of moving through an environment and performing assigned tasks without an operator. Co-bots are similar, the main difference being they’re designed to work alongside humans.

Carlaw says advances in both AMRs and co-bots mean they’re capable of doing more work than ever, particularly low-skill labor-intensive tasks, such as inspections, quality assurance, parts delivery, and machine-tending. They can perform such tasks continually and more accurately. And they free humans to do higher-value, more engaging tasks that can’t be automated.

3. IoT security

The internet of things — that vast collection of endpoint devices, such as sensors, connected to the internet so they can send, receive, and act on data — has been in use for decades. But IoT has not had the level of security many would like. That fact increases the risks that organizations assume, says Brian Hopkins, vice president of emerging technology and principal analyst with Forrester Research — especially as operations technology becomes more mainstream.

“We’re putting more ‘things’ into our enterprise, and each one of those ‘things’ is a potential attack surface,” Hopkins says.

That’s why IoT security is such a big deal.

Forrester describes IoT security as technologies that “combine IoT device, application, and network security to identify, secure, manage, authenticate, and authorize IoT devices; protect data; control device access; and enable secure firmware deployment.”

The research firm named IoT security as one of its top 10 emerging technologies in 2024.

4. Zero trust edge

Former Forrester analyst John Kindervag coined the concept of zero trust back in 2009. In a nutshell, zero trust is centered on the belief that trust is a vulnerability.

Now Forrester is promoting the application of zero trust principles to the edge, a use case that Hopkins says is definitely needed yet not quite fully appreciated.

“The whole idea of zero trust is to trust nothing and inspect everything in your enterprise. Zero trust edge takes this idea from your enterprise and puts it into all the edge environments,” Hopkins says, explaining that creating zero trust edge relies on both security technologies and architecture capabilities, such as software-defined networking.

5. Quantum-safe technologies

Although quantum computing is still a ways off, Carlaw says it will be here faster than many anticipate. And organizations need to be ready for the day when it arrives. In fact, Chinese researchers recently unveiled a method by which quantum annealing systems could crack classic encryption, including RSA.

One way to do that is to be looking at quantum safe technology, he adds. Defined as technology secure against attacks from quantum computers, it includes post-quantum cryptography (PQC), public key infrastructure (PKI), and quantum key distribution (QKD).

6. Privacy-enhancing technologies (PET)

This is another class of tech that’s going unsung, says Dera Nevin, managing director of digital insights and risk management at FTI Consulting.

PETs are defined as a broad set of tools, such as end-to-end encryption, and methods built into products and functions to protect the privacy of users’ data.

Although individual users often deploy PETs such as virtual private networks (VPNs) on their own devices, organizations can adopt PETs such as data anonymizers and data minimizers within their environment to reduce risks and enhance the privacy capabilities it offers to customers with whom they transact.

7. Decentralized digital identity (DDID)

Another, related tech that’s a bit underhyped, according to Hopkins, is decentralized digital identity.

Forrester describes decentralized digital identity (DDID) as the “technologies and identity networks (blockchain-based issuance and revocation information) that provide decentralized, distributed, verifiable, and revocable credentials — and claims — based on trust between issuers, verifiers, and users. These capabilities allow users to generate and control their own digital identity without depending on a specific service provider.”

And Forrester calls DDID “the next phase in designed-for-privacy identity and access management.”

Despite its benefits, the market is just now gaining momentum. “And it’s going to be increasingly important,” Hopkins said.

8. Modern data platforms

Modern data platforms also seldom get the hype they deserve, according to Erik Brown, senior partner for technology and experience at digital services firm West Monroe.

Brown defines modern data platforms as those built in the cloud using cloud-native software and capabilities to provide scalability and flexibility. Modern data platforms leverage those cloud-native tools and technologies to manage, process, and analyze the data that they hold, and they can support both structured and unstructured data. They also support real-time processing as well as advanced analytics and intelligence.

Modern data platforms aren’t fully recognized for the foundation they provide, Brown says, adding that “modern data platforms make it easier to manage massive amounts of distributed data.”

“Without modern data platforms, doing meaningful stuff like AI isn’t possible,” he said.

9. Data management software

Although AI gets all the attention, the key components that make it work often do not. That’s the case with the data foundation.

Yet as organizations eagerly embrace AI in all its forms, many have neglected parts of their data management needs, says Laura Hemenway, president, founder, and principle of Paradigm Solutions, which supports large enterprise-wide transformations.

Even those who are on top of data management often downplay the powerful work their data management tools do. As such, Hemenway thinks data management software deserves more recognition for the important job it does, even as the work involved is often considered a tedious task that doesn’t have the pizzazz of making the most of ChatGPT.

Still, sound data management is a linchpin for AI and other analytics work, which underpins a whole host of processes deemed critical in modern business — from automated processes to personalized customer support. So it’s essential to get it right.

10. Synthetic data

“A lot has been made of synthetic data; I don’t think enough has been made of it,” Hopkins says.

Synthetic data, defined as data generated by AI rather than produced by real-world events and transactions, is proving to be a boon for training AI.

“In my opinion there’s a lot of room in front of synthetic data to transform how we train AI. But it’s not generally known the extent to which we can use synthetic data to improve AI. That’s why it’s flying under the radar,” Hopkins adds.

11. Spatial computing

There was plenty of buzz around the coming metaverse several years ago, excitement over which peaked in 2021 when Facebook announced it was changing its name to Meta — a nod to what the social media giant saw for the future of computing.

But with no big breakthroughs, interest fizzled and the metaverse found itself on plenty of overhyped tech lists. Don’t be so quick to write it off, Brown warns.

He says augmented reality and mixed reality in particular aren’t getting the attention they warrant — perhaps as a result of that early hype before the tech was ready for prime time. However, advances in both the hardware and software that enables AR and mixed reality is enabling the technology to deliver significant value for organizations — particularly for training and collaborative work.

12. IT management software

CIOs and their teams can hardly do their jobs nor build and manage the extensive tech stack required to support AI and any other newfangled technology coming to market today if they don’t have a handle on their IT environment.

IT management software helps them accomplish that task — and accomplish it to a practically perfect degree of stability and reliability.

“Anything that falls into the category of IT management tools is often cast aside, but these are the workhorses of IT,” says John Buccola, CTO of E78 Partners, which provides consulting and managed services in finance technology and other professional areas.

Tools that Buccola puts into this class of “unsung heroes” include Active Directory and access and identity management solutions. (“They really simplify environments that are heterogeneous,” notes Buccola, who is also an officer with the Southern California chapter of the Society for Information Management.)

“You don’t think about them. They all just work, and that’s what people want from IT,” he adds.

Other tools worth calling out are IT service management (ITSM) and IT Infrastructure Library (ITIL) solutions — which Buccola says are particularly critical for helping to keep IT expenses in check.

Indeed, it would be nearly impossible to find a CIO who doesn’t have to be diligent about managing IT costs — something they would be hard-pressed to do without the management tools to help them.

“This stuff doesn’t get a lot of press, but they’re such essentials for IT teams,” Buccola adds.

13. Cloud computing

Go back 15 years when cloud was the tech generating all the buzz, and analysts were trying to separate reality from the hype.

Today the model doesn’t seem like such a marvel, but when you think about it, cloud still deserves a lot of praise.

“It has been one of the most enabling technology shifts we’ve ever had, and because of the move to cloud, it enables us to do everything else we’re doing now. But it has gone completely to the background, because AI has sucked up all the air,” says Mark Taylor, CEO of the Society for Information Management (SIM).

14. Cloud-based ERPs

Cloud-based enterprise resource planning (ERP) is another behind-the-scenes technology that often gets overlooked in favor of newer, glossier tech, says Jeff Stovall, CIO of Abt Associates, who adds that cloud-based ERPs are rarely credited for how critical they are for digital transformation.

“We’ve done ERPs for so many years, we’ve been doing these ERP projects for decades, but with cloud ERPs, there’s a shift in how business can innovate,” says Stovall, who is also former City of Charlotte CIO and a SIM board member.

By moving from on-prem to the cloud, organizations can reimagine their business processes and transform how core facets of their work gets done, Stovall says. “It’s a catalyst for transformation, but it’s an overlooked catalyst, because we’ve become so comfortable with the concept of ERP that we don’t think about its transformational capabilities,” he adds.

In fact, Stovall sees some organizations stick with on-premises ERP even as they seek to transform other pieces of their IT environment and business processes — not realizing how much more they could accomplish if they would modernize this fundamental enterprise core and the processes it supports.

15. Cloud migration tools

Yugal Joshi, a partner at research and advisory firm Everest Group, lists cloud assessment tools as another tech that’s underhyped — and underused.

Cloud assessment and cloud migration tools, or cloud-enablement platforms, all help IT teams analyze and understand applications and cloud infrastructure so they have the information required for a solid cloud-deployment roadmap.

Sure, other technologies, such as IT audit software, can help here, as can manual assessments, but Joshi says cloud assessment tools have proven to boost the chances of successful cloud initiatives.

“CIOs sometimes think they don’t need this tool because moving to cloud has become so pervasive. They think migration is easy, but it’s complex and the choices of cloud vendors and offerings have increased, [adding to that complexity],” Joshi explains.