Technological advancements have seen a landmark couple of years and the pace of innovation shows no signs of slowing down. Add to those years dealing with the effects of a global pandemic, increasing political unrest, and challenges presented by climate change, and understandably business leaders are eager to get their hands on actionable strategies for thriving in this new digital world of business.
In response, Gartner has conducted extensive research to best advise executives on where to focus their tech implementation and adoption strategies for the next couple of years. The result: Gartner’s Top Strategic Technology Trends for 2022.
According to the analyst firm, these 12 trends are meant to help IT leaders meet their “CEO’s priorities to scale, adapt, and grow”, outperforming the competition even in the most uncertain of times.
Let’s dive right in.
Gartner Top Strategic Technology Trends for 2022
Forward-thinking organizations are constantly on the lookout for ways to best collect, analyze, and store data to support business optimization. However, data silos continue to be a problem across industries, leading to misguided decision-making and ineffective collaboration.
In simple terms, a data fabric is a single environment based on a unified architecture that helps organizations integrate and manage their data. A data fabric is meant to:
Make it easier for members of the organization to access and share data whenever, wherever
Help businesses use the data they have to solve complex problems
Analyze metadata to learn what data is used and how to make better data management decisions in the future
Accelerate the process of digital transformation by breaking down data silos
Over 40 billion records of personal and sensitive information were exposed in 2021 alone, so it’s no wonder cybersecurity makes it to the top of Gartner’s tech trend list.A cybersecurity mesh (CSM) is a progressive approach to setting up security infrastructure. Rather than having one traditional defensive perimeter, CSM secures each device within the perimeter.
This holistic approach means that individual technological systems like firewalls and network protection tools are secured and that attacks can be detected (and prevented) in real-time.
With data breaches resulting in millions of breached personal records constantly hitting the headlines, enterprises need to regain the public’s trust when it comes to the privacy of their data. That’s why Gartner chose to highlight the importance of privacy-enhancing computation (PEC), i.e. various techniques used to ensure that data can be safely shared across different systems without compromising confidentiality or privacy.
Zero-knowledge encryption, for example, is a well-known cryptographic PEC technique that involves authentications that don’t exchange any passwords. This means that nobody except you or anyone you authorize can access your data, not even the service providers themselves.
Cloud-native applications are usually made up of microservices and run in containers and were designed to be used in the cloud. This makes them much faster and more scalable, unlike their legacy counterparts which need to be migrated to the cloud. Among other benefits, using cloud-native platforms allows organizations to:
- Increase their speed-to-market
- Reduce infrastructure management costs
- Reduce system downtime
- Improve the customer experience
- Scale flexibly
Gartner’s report emphasizes that by 2025, more than 95% of new digital initiatives will have a cloud-native platform, a huge boost from the 40% recorded in 2021.
Development teams worldwide are under immense pressure to consistently deliver high-quality software as fast as possible, both to meet customer demands and satisfy stakeholders. Composable applications help them achieve this fast pace of delivery by enabling organizations to redevelop and redeploy already existing applications.
How? Composable applications work through reusable modules that teams can assemble to create new applications quickly, even if they don’t necessarily have the right coding skills or team members at hand to create applications from scratch. Since the approach allows organizations to reuse code, businesses can potentially achieve significant cost-cuttings and improve their speed-to-market, too.
When it comes to taking advantage of fleeting market windows, speed and efficient data-driven decisions are often the difference between sinking or swimming.
Decision intelligence is an engineering discipline that combines data science with other frameworks in order to make better modeling decisions. The discipline uses AI, ML, automation, and many other techniques to come up with actionable recommendations for which direction the business should go in.
Decision intelligence also enables organizations to analyze historic decisions to make better ones moving forward, ideally future-proofing the business as a result.
Hyperautomation is a framework that lays out best practices, tools, and processes for scaling automation across a business. In automating as many business and IT processes, as usual, companies aim to position themselves for growth and to free their team members up to do more meaningful work geared towards product innovation.
Hyperautomation is predicted to have a huge impact on improving business agility and reducing technical debt as a whole, so it doesn’t come as a surprise that the hyper-automation market size is expected to grow from USD 9.2 billion in 2022 to USD 26.0 billion by 2027 alone.
Simply adopting AI in your organization is not enough: its use must be consistently governed and improved to experience the full benefits of AI solutions. That’s where AI engineering comes in.
While AI itself is about creating intelligent models and capabilities, AI engineering optimizes those same models and capabilities in order to create fully-fledged systems to support the business. This discipline focuses on improving the performance, scalability, and reliability of AI models, using integrated data and development pipelines.
According to Gartner’s research, “by 2025, the 10% of enterprises that establish AI
engineering best practices will generate at least three times more value from their AI efforts than the 90% of enterprises that do not.”
In the past, the term distributed enterprise referred to a business that had multiple offices and branches in different locations. In Gartner’s Top Strategic Technology Trends for 2022 however, a distributed enterprise is re-interpreted in the context of a post-COVID-19 world of work.
Here the term refers to the emerging digital, hybrid, and remote-first business models which prioritize the quality of employee and customer experiences.
Some advantages of a distributed enterprise include:
- Cost savings due to lower office rental fees
- Improved employee flexibility and engagement
- Business agility and scalability
You’ve definitely heard of user experience. And you’ve probably come across a lot of material on employee experience, too. But have you heard of total experience yet?
Total experience (TX) aims to create superior experiences by combining:
- Customer experience
- User experience
- Employee experience
- And finally, multi-experience
In order to create an environment that fosters such a high-quality experience across the board, organizations need to focus on transparent communication throughout the customer and employee lifecycle, as well as staying on brand across touchpoints in line, and finally keeping business objectives in focus.
The term autonomic system or autonomic computing was coined by IBM’s Paul Horn back in 2001 and represents a key approach to dealing with increasing software complexity.
Similar to biological nervous systems, autonomic software systems have the ability to self-regulate. This means that they learn from their environments and adapt to them accordingly by modifying their own algorithms.
Right now autonomic system technology is still in its early stages, with many of its capabilities still theoretical. But the key here is that moving forward, they should be able to course-correct automatically, with little to no human intervention and no need for software updates.
Generative AI uses AI/ML algorithms in order to create new and original artifacts which look like the training data but don’t just copy it. In short, it looks at old content (like text, images, video, audio, etc) in order to create artificial, yet new content.
Gartner predicts that by 2025, 50% of drug development initiatives will use generative AI and by 2027, 30% of manufacturers will be using generative AI to increase the efficiency of their product development initiatives.