Stay Ahead of the Game:
Every year, the Gartner Group presents a list of top technology trends—that they believe will influence a company’s IT plans. Gartner, as an organization, interacts with a lot of different types of businesses; nonetheless, it’s true that they consult more with large enterprises than with start-ups or companies with smaller IT budgets. That “large enterprise” bias sometimes influences the things that they view as “trends”. And sometimes, they talk about technology trends for organizations that do not bear similarity to our business and/or clients. So, it’s important to read the top ten list with an eye toward those leanings.
But having said that, each year, I find it fruitful to review Gartner’s technology trends list and assess how relevant those trends are to anticipated work in the coming year or even for the foreseeable horizon.
In some years, the list is somewhat like the prior year’s top 10 list, but with a few replacements and refinements. 2023’s top ten list is a significant overhaul compared to 2022.
Gartner has assembled their tech trends into four themes:
- Optimize: this theme describes technologies used to improve the reliability of IT systems, but not just in the Operations sense. It’s not just about keeping the servers running; this category also includes optimizing business processes—especially data-driven business decisions. As AI plays a greater role in business processes, maintaining the models that underly those AI-based processes are also a key concern of this category.
- Scale: this theme speaks less to “scale” in the “add more servers” sense, but rather about “doing more with the same effort.” It’s not the server bank that is being scaled, but rather scaling the IT delivery processes. Items in this category speak to technologies that speed up the time to market and reduce IT efforts.
- Pioneer: this theme talks about embracing new technologies that are disruptive, or which represent, for example, new types of virtual markets.
- Sustainable Technology: in some ways, this last category undergirds the other It’s also a theme with only one trend assigned to it, a trend also called “Sustainable Technology”. Over the last several years, we’ve seen, for example, large IT players such as Apple and Google open significant new buildings and data centres with sustainability as an end goal. Apple’s new headquarters is powered by 100% renewable energy and is pushing its suppliers to do likewise. Gartner argues that in 2023, more IT organizations will explore the need for ensuring their IT infrastructures are greener—not just as environmental lip service, but in significant, meaningful terms.
That fourth theme is self-explanatory, but let’s take a bit of time to look at the first three themes in greater detail.
Theme 1: Optimize
As mentioned above, the scope of this theme is broader than just operational optimization: it also includes improved business process optimization. There are three trends that make up this theme:
- Digital Immune System;
- Applied Observability;
- And AI TRiSM
Digital Immune System
For the last few years, Gartner has included trends related to improved security for business applications: the details of these trends evolve as approaches to securing systems evolve. This year, the focus is on crucial practices and technology in the application development and deployment lifecycle. They cite 6 key prerequisites to achieve a digital immune system:
- Observability: it is essential that systems provide visibility into both the normal and exceptional functioning of those systems.
- AI-augmented testing: while automated testing has proven a boon for regression and functional testing, AI-augmented testing can fill some gaps in other testing objectives, such as vulnerability testing
- Chaos engineering: much like how Netflix tests the reliability of its server network by randomly turning instances oﬀ, more organizations need to use experimental testing approaches to determine how well their applications recover from significant failures.
- Auto-remediation: while Chaos Engineering is a practice related to understanding how the application behaves when parts of the application start to fail, implementers need to build in Auto-remediation capabilities to allow the system to recover from such failures. The goal is to have systems that recover without the intervention of operations staﬀ.
- Site reliability engineering: This is a term that originates from Google and relates to practices employed in Application Development/Management processes to ensure that the application meets its service level agreements.
- Software supply chain security: over the last few years, there have been high-profile instances of “supply chain” software attacks. Typically, a hacker creates a new version of a software library that embeds some malware and uploads that new version to common software library dependency management sites such as npm or a Maven repository. To do so, the hackers must additionally compromise credentials on those download sites. The net eﬀect of these types of attacks is that software projects that dynamically fetch software libraries can unknowingly embed the corrupted code into their applications. Preventing that requires new practices around securing the import of software libraries.
These practices, alone, cannot guarantee the security of a software system, but Gartner is reporting that this trend is receiving increased attention in 2023.
A motivating example that Gartner uses regarding this trend is Tesla Insurance. Tesla oﬀers insurance to Tesla drivers that is rated based on actual driving data: Tesla cars are engineered to gather data about driver behaviour as the cars are used. Using this driving data, Tesla can categorize good drivers and bad drivers, and price insurance accordingly. Good drivers benefit because they can enjoy reduced rates; bad drivers end up bearing an appropriately greater financial burden.
This is the essence of Applied Observability: making decisions based on evidence-based sources. These decisions can be about business processes or infrastructure/operations or even about application design.
Gartner argues that by deploying applied observability solutions, 70% of organizations will realize faster decision making and that will make those organizations more competitive and will decrease risk. If there’s a useful feedback mechanism between customer behaviour and rewards (discounts, better terms, etc.), that will express improved customer loyalty.
AI TRiSM is an acronym for AI Trust, Risk and Security Management, and it’s a term coined by Gartner to describe a framework for ensuring safeguards and governance of the use of AI.
Gartner additionally defines 5 key pillars of this trend:
- Adversarial Attack Resistance;
- Data Anomaly Detection;
- Data Protection;
- Explainability; and
- ModelOps or Model
The AI TRiSM model aims for greater transparency about how AI models arrive at their decisions, and that transparency can help organizations navigate emerging AI regulations and assuage customer concerns about AI-based tools and systems.
Theme 2: Scale
The theme of “Scale” relates more to scaling up the delivery capability of the organization than about scaling the throughput or number of concurrent users of individual applications.
Industry Cloud Platforms
Cloud Platform adoption has been in full swing for over a decade. Even the most technologically conservative organizations have Cloud adoption plans that are well underway. We are well-familiar with the idea of picking and choosing infrastructural Cloud Services such as database products or virtual server hosting. We’re even familiar with cross-functional business services such as authentication management systems.
What’s new in this space is the availability of industry-specific cloud platforms and services: payment gateways, fraud detection systems, order management services, etc. Unless a business function is a distinguisher for your organization, it’s better to acquire an existing implementation of that function than to build it oneself. That’s conventional wisdom.
Now that composable Cloud Services are so well understood, application development can be boosted using cloud platforms that are tailored for specific industries, which can make available industry-specific services that an application might need.
Platform Engineering is an application development discipline that seeks to streamline application development processes inside an organization by creating easy-to-select-and-use tooling that is pre-tailored to the organization’s standards and development preferences. In such an environment, a new project might have fast access to pre-made DevOps scripts, standard cloud configurations, components that handle authentication and other types of platform tools that have been pre-curated by the organization’s technology specialists.
Wireless Value Realization
This item is primarily an Internet of Things consideration recognizing that many of the barriers to ubiquitous wireless access are disappearing and systems can often rely on more than just connectivity: that we can also rely on wireless location services, information about power levels, and other value-added services.
Theme 3: Pioneer
This theme relates to technical trends involving change and the evolution of the business in the face of new technologies or environments. As we’ve seen for many years now, technological advances are opening new behaviours (among both customers and employees) and new markets. As a result, organizational business models need to change and adapt to meet those new opportunities.
Gartner has identified three trends in this area:
- Adaptive AI; and
I’m going to talk about these trends in reverse order.
Much like smartphones became a new, important channel in business interaction with users, Gartner argues the metaverse will similarly become a new channel for customer interaction, sales, and other new business opportunities.
Gartner does not reduce “the metaverse” to any one implementation: they’re not talking specifically about Facebook’s Meta Platforms, or any other VR environment. Rather, they talk about this trend as comprising “multiple emerging technologies.”
There are groups of people—often gamers, but others as well—who enjoy immersive virtual worlds and are willing to buy digital assets in those worlds. As more people participate in these metaverse worlds, the demand for financial services in the metaverse grows. One could expect that, as we see in the online gaming world, people can buy digital things using credit cards. But I expect that model will face competitive challenges.
Digital metaverse assets are increasingly being built on blockchain technology, and it stands to reason that new types of blockchain-based financial instruments will become the norm.
Organizations that ignore the shifting technological landscape of the metaverse are in danger of missing out, or at the very least, lagging in the myriad ways that those new ways of transaction will change the business landscape even beyond the metaverse.
In 2022, Gartner included a trend called AI Engineering which appears to have evolved into this year’s Adaptive AI. As with last year’s trend, Adaptive AI is concerned with the lifecycle of AI models and ensuring that organizations have the engineering maturity to manage their AI models. This includes such things as retraining the AI models as the driving data changes over time.
Gartner argues that the key difference between AI Engineering and Adaptive AI involves “significantly strengthening the change management aspect of AI engineering efforts.”
Obviously, AI models aren’t static. In many cases, as a system is used, it often creates data that should feed back into the AI model that is at the heart of the system. In an adaptive AI organization, this kind of retraining should not be treated like “the next release of the system” but should be a more organic process.
Gartner describes Superapps as a “Swiss army knife”—apps that surface several selectable functions that are composable and configurable, often by end-users. The examples in this space are apps such as WeChat, PayTM and Kakao. These apps, which often originate as messaging apps, provide access to many services, including food delivery, ride shares and payments.
While these Superapps have been most popular in south and south-east Asia, the Superapp model is showing up in Latin America and Africa as well. I’ve even heard suggestions that tools such as Slack and even Microsoft Teams could evolve into Superapps; I confess that I’m more optimistic about the former than the latter.
While a Superapp builder might be especially interested in this trend, other types of organizations might also be especially interested in providing their services in the context of these Superapps: payment providers (especially non-traditional types of payment providers) are an obvious type of organization that might seek to integrate with Superapps. But other services—loyalty services, travel booking, or scheduling services such as babysitting, massages, or yard work—might discover their ability to get customer traction works best in these “app malls” that are becoming increasingly popular.
Another interesting feature of the Superapp trend is Gartner’s assessment of some of the technology that they view as key to the Superapp trend (and which we can predict will grow in adoption along with this trend): mini-app front-end frameworks, multi-experience development platforms, and low-code application platforms.
It’s true that a non-trivial number of projects that we are asked to join are wrestling with more foundational concerns: they might be struggling with productivity or desperately needing to re-platform a system before a crucial environment has been sunset. In those environments, strategic trends such as those Gartner assembles can seem like future pipe dreams. But, in my experience, there are no organizations that aren’t at least wanting to “look left and right” before crossing the street. All our clients want to be able to talk about industry concerns of the day, and the annual Gartner Strategic Trends report is one source we consider in shaping those conversations.