Enable javascript in your browser for better experience. Need to know to enable it? Go here.
Blogs Banner

Hyperagility: Being a Smart Agile Enterprise

Gartner’s hype cycle methodology shows the evolution of technology trends over time so businesses can use this to consider their innovation strategies for achieving business goals. One such top strategic technological trend from Gartner is “Hyperautomation” - or the augmentation of intelligence to automation of tasks. Extending on this principle of hyper automation, I would further define “Hyperagility” as the appending of smart intelligent automation to agile practices in an organisation, enabling it to become a smart agile enterprise. By exploiting the trends that are on the upcurve of the hype cycle, businesses can future-proof their strategies for the next five years, to stay ahead of competition. 

Figure: Gartner hype cycle, the evolution of technology trends over time. 
In my opinion, hyperagility can be explored in three different ways. Firstly, Augmenting machine bots to the initial discovery/inception phase of the agile project lifecycle; Secondly, Autogrooming suggestions for product backlog based on data analytics; and thirdly, real-time mining by integrating Devops with customer data insights to identify business value delivered. We will explore each of these ideas for achieving hyperagility. 

Augmenting machine bots to the initial discovery/inception phase

Today, the workshops for discovery/inception are largely conducted manually, where user research is gathered, and user stories are written and categorized in a user journey map. Hyperagility will involve the intelligent automation of these tasks, wherein crawlers and bots make this possible. For example, given an industry space to a bot, it can automatically come up with the user story card suggestions in the interested industry area, crawling the net for a similar industry value chain, and delivering a well written initial user story card. It can also suggest priorities based on usability insights where possible. During the workshop, these autogenerated user stories can further be picked, enhanced or new ones created, to supercharge the process. Today, digital assistants are already across major websites and they know the user story categories across their websites to their customers. Having an auto-user-story suggestion bot can exploit this feature across any industry. It can crawl the net or tap into other intelligent bots for user story scenarios, allowing hyperagility in a smart agile enterprise. 

While some may say automation may make humans redundant, on the contrary, augmented intelligence is a reality that is seeping into many job descriptions today.  The use cases show that they enhance human productivity by intelligent and informed decision making.

Autogrooming suggestions for product backlog based on data analytics

Another important aspect of stories is their prioritization. Hyperagility allows for automated suggestions on priority based on user research insights. This then assists in product backlog grooming so the highest impact goes first. Based on the prioritization model, a choice of parameters may be customised into the bot. For example, if high-impact/high-value is a prioritization parameter, stories may be suggested based on these settings for data insights. Business stakeholders can then make use of these autogrooming suggestions to make informed choices based on value. This assumes availability of an opensource data jungle, where customer behaviour insight data are easily accessible. For example, behaviour data from Big Tech such as Amazon, Google, Face book etc may be shared in an opensource platform, adding to the “Internet of behaviours”, that may be leveraged for building open intelligent bots. 

Real-time mining by integrating Devops with customer data insights

Building on the idea of open source data of behaviours is the integration of the Devops value chain with the data insight analytics for hyperagility. Agile practices of continuous integration and delivery may be extended with real-time feedback of how features performed with end customers. Hyperagility allows various developer and product dashboards to be enhanced to showcase end-customer insights throughout the devops agile delivery lifecycle. Bots can hence aid with intelligent data insights to development and prioritisation processes. These Intelligent crawlers that obtain customer insights information to integrate with the daily dashboard will aid in supercharging the smart agile enterprise devops to hyperagility. 

While three examples of hyperagility have been explored in this article they can be many more applications possible depending on the use cases. As organisations embrace artificial intelligence in the mainstream rather than on the side, applying hyperautomation will make hyperagility a reality. By combining automation and intelligence with agile practices, an enterprise can be empowered to be smarter and more efficient in their agile delivery lifecycles. Businesses can explore what this trend means to their strategic business goals and how they might leverage hyperautomation and hyperagility to make the right investments in their agile and technology practices to stay ahead of the competition in the next five years. 

Disclaimer: The statements and opinions expressed in this article are those of the author(s) and do not necessarily reflect the positions of Thoughtworks.

Keep up to date with our latest insights