If you’re leading an enterprise software company, you’re probably juggling plenty of responsibilities. It might seem wasteful to spend time following tech trends – after all, you’re intimately living with your own technology. But this is an incredibly valuable endeavor for multiple reasons:
- Tech trends drive corporate strategy. If you build enterprise software, your clients and prospects undoubtedly see technology as a tool for business growth. They follow tech trends to understand emerging opportunities to drive efficiency, profitability, and savings through technology. They will also collect jargon along the way, and it is important to speak the language and be ready with a perspective.
- Trends can point to new markets. Technology trends basically act as a shortcut to helping you understand what customers will be looking for a few years down the road. You can use them to anticipate which products will be in demand later, and develop them now, before your less – aware competitors jump on the bandwagon.
- Trends introduce emerging methodologies. Consider, for example, the shift to containerized code in the past several years. That new method dramatically shortened time to market and became a new best practice for competitive software companies. Those who were fastest to adopt this approach were much better positioned to scale and grow.
- Tech trends can influence pricing models. The advent of SaaS brought a new, exciting pricing model to the software world; the subscription model is now much more common than licensing fees. But even SaaS companies should already be thinking about the trend toward composable applications (see below). Analysts predict that business leaders will soon expect to pay for the individual software features and functionalities that they need.
Gartner’s Top Technology Trends for 2022 and Beyond
Gartner is by far the most reliable source for technology information. Their list of top trends for 2022 provides great food for thought. Here’s what made the cut:
- Data fabric: “A flexible, resilient integration of data sources across platforms and business users,” data fabric essentially makes data accessible from anywhere, regardless of where the data resides. A well-designed data fabric can significantly reduce data management efforts.
- Cybersecurity mesh: Cybersecurity needs constantly shift as new threats–and best practices–emerge. Cybersecurity mesh allows you to use standalone security solutions together. This approach can improve overall security and move control points closer to the relevant assets. It’s important to note that cybersecurity mesh can work across both cloud and non-cloud environments.
- Privacy-enhancing computation: Consumer concerns about data privacy have grown steadily over the past decade (and rightfully so). Privacy-enhancing computation uses diverse privacy-protection strategies to ensure that value can still be extracted from data, without violating compliance requirements or data protection laws.
- Cloud-native platforms: We’ve left behind the days of the software “lift and shift.” Today’s most successful new technologies are often built on cloud-native platforms, allowing quicker response to digital change, providing a more resilient application architecture, and simplifying maintenance.
- Composable applications: This is a new riff on the concept of containerization (popularized back in 2013, thanks to Docker), where a single package or “container” of software can be transferred to use on any platform or application. Composable applications are built from these modular components, so they can go to market much faster than custom-built applications. They are also a more flexible pricing model, since customers can buy only the modules they need for their business operations.
- Decision intelligence: Using augmented analytics, simulations, and AI, decision intelligence can support and enhance human decision-making. But more importantly, decision intelligence represents a more disciplined, process-based approach to organizational decision-making.
- Hyperautomation: Also Gartner’s #1 trend for 2020, hyperautomation isn’t really new. Back then, Gartner focused on how it could contribute to building a digital twin of an organization (DTO). Today, it’s about using a business-driven approach to strategically automate as many business and IT processes as possible.
- AI engineering: Implemented alongside a strong AI governance, AI engineering helps operationalize the delivery of AI. It automatically updates data, models, and applications.
- Distributed enterprises: Organizations like banks and hospitals have long operated as distributed enterprises. But the Covid-19 pandemic accelerated this trend across industries (in turn accelerating the preference for cloud-native platforms). Distributed enterprises better serve remote employees and customers, but they also present unique technology challenges.
- Total experience: A business strategy that integrates employee, customer, and user experiences, the total experience approach relies on holistic management of stakeholder experience. Technology obviously plays a critical role in effectively delivering a total experience because it requires actionable data from many disparate sources.
- Autonomic systems: An autonomic system can self-manage and respond to its environment, for instance by updating its own algorithms in real time. They’re especially valuable in complicated ecosystems where new requirements and situations frequently emerge. Autonomic systems generally rely on various AI systems and tools to operate.
- Generative AI: Using data artifacts, generative AI creates new content that’s similar to the original. It has potential in multiple fields: think generating new video content for digital marketing, or accelerating R&D cycles for new pharmaceutical products. Molecule.One is an interesting startup doing work in this area.
Which of these trends will stick and which ones are destined to become buzz-words for a concept that didn’t come to be? Let us know what you think in the comments.
