3 Technical Writing Trends to Improve Documentation in 2023

3 Technical Writing Trends to Improve Documentation in 2023

With origins dating back to the time of Aristotle, technical writing is a practice that seeks to enable technology adoption through documentation and communication. From step-by-step instruction manuals to API guides on developer portals, technical writers produce content that is essential to business-technology success. 

In the 2000s, technical writing in the software & hi-tech industry has evolved to become a must-have for all of an organization’s products and tools to manage complexity and increase usage. Looking forward to 2023, as technology continues to evolve, so too will technical writing. 

In this blog, three leading experts from Wizeline’s Technical Writing practice share their predictions of the industry’s trends that will define 2023. Let’s get started!

Interactive Diagramming Tools to Document Software Architectures and Processes

Software architectures have been getting more complex over time, and cloud technologies have opened the door for more and more services to be involved in software development and information management. This is making managing and understanding the interaction between these services increasingly complex.

Previously, with a simple diagram, you could cover the entire infrastructure of a solution. Now, you need to generate different layers of information to go deeper into each architecture module. It becomes very complex when you want to analyze how two services in different layers or modules are interrelated, and interactive diagramming tools present an elegant solution.

Isaí Cortés

Wizeline Associate Technology Director, Technical Writing

In addition, it is no longer necessary to graphically display only the services involved in a solution. It is now also needed to describe the interactions of these same services from different perspectives. For example:

  • Classes and functions in code
  • Information flows
  • Processes
  • Networking

New diagramming tools have been developed to provide multiple functionalities and views to architectures, focused on easing access to information and directly interacting with the items in the diagrams. They have also improved the process and time to create the diagrams by using Diagrams as Code.

Some examples of these tools are:

Hyperautomation, Robotic Process Automation, and Documentation

Over the next 2 to 5 years, hyper-automation is expected to become a trillion-dollar behemoth. Driving the core of the hyper-automation evolution is Robotic Process Automation (RPA). And while over the past few years, the industry has witnessed a range of RPA and related technologies coming to the forefront, an important step in the process to implement has been, is, and for the foreseeable future, will be documentation.

Traditional documentation models won’t suffice in this new era. As automation enables projects to be completed in months instead of years, documentation practices must also evolve.

Arunabh Nag

Wizeline Technology Director, Documentation

Ranging from automated processes, instant scoping, and reusable content to looking into natural Language processing (NLP), machine learning (ML), and artificial intelligence (AI) themselves to help create core content faster, content experts will be hard-pressed not to use these fantastic tools to deliver optimal-quality documentation in the required time.

Documentation Analytics and Data-Driven Culture

The software industry has been using analytics to understand how users interact with digital products for several years now. However, the type of metrics needed to analyze product consumption are not necessarily the same ones for documentation. Collecting the correct information has typically been a challenge for documentation.

In software documentation, the analytics need to focus on the main goals of the organization. To make documentation analytics successful, gathering data is as important as its analysis. With this in mind, we should foster a data-driven culture for documentation.

Angela Bauche

Wizeline Associate Technology Director, Technical Writing

The following are some examples of goals that we could focus on and the type of data that we may want to analyze:

  • Communicate technical concepts to an intended audience in a clear, precise, and concise manner. The data we need to collect to measure this goal must focus on how useful the content is to the audience. Also, on whether they learned what we expected them to learn. (Example, rating tools or using a hands-up or hands-down tool).
  • Onboard someone to a company faster. The following are examples of analytics for this goal: 
    • How long did someone take to complete a ticket from when they joined?
    • How many times did someone contact an experienced member to ask for more information? 
  • Reduce the number of customer support calls. In this case, the measurement should be the number of customer support calls before and after documentation.
  • Help someone complete a task. The measurement for this goal can be how long users spend on a page. Contrary to other products, in this case, it is better when users spend less time because it means you helped them achieve their goals faster.
  • Create relevant documentation. We could measure this goal by assessing which sections the users have more interest in through their navigation patterns.


Judging by these trends, the future of technical writing will be centered around increasing the efficiency, usefulness, and overall business impact of documentation. There is little doubt that interactive diagramming, hyper-automation, and documentation analytics all will have a large role to play in doing so for both practitioners and users.

If you’re interested in learning more about any of these trends or getting in touch with an expert about your technical writing needs, visit our landing page or contact to start the conversation today!

Tajma Brown

Posted by Tajma Brown on November 28, 2022