AI and the Data-driven Future of Sprint Retrospectives
There's change on the horizon. Across industries worldwide, workflows are becoming leaner, faster, and more efficient thanks to emerging technologies like AI, machine learning, and other data-driven analytics. In the world of agile project management, also called the scrum sprint, lean and efficient operations are already the name of the game. Together, artificial intelligence and the sprint are propelling the incremental development cycle forward at a rapid pace.
We're going to take a look at how emergent technology like AI and machine learning stand to affect and ultimately improve, the sprint retrospective process that takes place after a sprint cycle
AI and the Sprint: a Metaphor
Let's say you are the coach of the LA Lakers and you're in the NBA championship. You've just lost game one of the series. What do you do? Do you stumble ahead into game two using the same game plan, or do you stop and analyze what happened together as a team?
Of course, any good coach is going to analyze what happened on the court. How they do it is what matters. Imagine if you relied only on your memory and your subjective opinions about the game? It's valuable to deal with the surface emotions surrounding failure and success, but they only tell part of the story.
A good coach is going to re-watch the game tapes and take a look at the stats. They're going to come at the problem from a quantitative, data-driven angle as well as an emotional one.
Artificial intelligence acts like the coach, combining deep stats and data with the human side of the retrospective.
Sprint Retrospectives: a Recap
The goal of a sprint is to continually push progress, incremental improvements, and complex deliverables. A sprint is not a step-by-step method or how-to explanation; it's a lean and adaptable framework that allows small teams to put out high-quality work quickly and by a method of their own choosing.
We all know the three roles on a sprint team:
- Scrum master
- A project manager
- The development team
However, with the emerging power of data-driven analytics, one could add a fourth designation to the sprint team: artificial intelligence.
The Need for Your Retrospectives to Evolve
New and disruptive innovations have changed the game at a fundamental level. The way that we talk about our work processes has to change with it. The sprint retrospective needs to embrace a similar change on both the local and global level for businesses to stay progressive in the current environment.
On the local level, many retrospectives fall into a particular trap: it's far easier for participants to speak on a qualitative level instead of including necessary quantitative metrics. Far too many retrospectives begin with an analysis that involves the participants’ own subjective opinion about the preceding scrum activity without reinforcing perception using dispassionate data.
On the global level, a multitude of businesses, regardless of industry, have been forced to adopt a work from home (WFH) model. Agile project development is still a perfectly usable framework under WFH, but the process requires an added level of attention and scrutiny to achieve the same results. Companies that use an agile framework need to embrace retrospective methods that are data-driven rather than predicated on subjective opinion.
How AI Benefits the Retrospective
Artificial intelligence and machine learning are the answer. By embracing data-driven solutions, businesses that participate in agile project management can bring their retrospective process up to speed, aligning it with current technology. AI stands to impact the retrospective process through these four key benefits.
1. Helps balance project management
Data-driven solutions such as chatbots provide the perfect business assistant to help key players balance their workload. This is especially critical in roles like the project manager or even scrum master.
On the surface level, an AI assistant can help busy project managers tasked with overseeing multiple projects to keep track of their various duties. If your particular project manager is dedicated to a single team, AI can help them analyze and manage their product backlog more effectively.
Additionally, an AI assistant helps the scrum master to more accurately delegate the development team's responsibilities using a data-driven approach in applying their skill set.
2. Provides deeper retrospective insights
It is the job of artificial intelligence to analyze and learn from its human counterparts. Each progressive sprint serves as the perfect test case for an AI to garner more actionable information about the team, their process, and the overall project itself. From that data, the AI can engage in further analytics and quantitative modeling in order to predict future outcomes.
3. Helps mitigate project risk
One of the benefits of AI is constant project oversight that updates in real-time as the project unfolds. This helps your development team to better understand and mitigate risk factors using the predictive analytics that artificial intelligence is capable of.
AI can systematically and dispassionately pull apart the friction points of each sprint and use them to develop a preemptive risk management protocol that can identify and eradicate the root causes of your team's inefficiencies.
4. Streamlines your processes
The overall goal of a sprint is to produce completed units in a tight timeframe while focusing on the team's incremental improvement. It is already a streamlined process with efficiency at its heart. Data-driven solutions only help to further refine your team's developmental process by helping you keep track of a wider set of information and variables than you would otherwise be able to.
As the AI engages in machine learning, it will develop predictive models for your workflow, giving you a pre cognizant view of the possible outcomes. This makes for an invaluable tool during your team's retrospectives, one that is based on data rather than opinion.
What AI Looks Like During the Retro
It's easy to see the big picture and envision how big data analytics are going to integrate into the retrospective, but what about the minutiae? Where might AI show up during a retro? The scrum master might use the following AI-driven reporting techniques:
- Exporting work logs for comparative purposes
- Generating throughput reports
- Quality assurance analysis
- Velocity analysis
The magic of AI is that it thinks globally. It's "big" data. AI can take metrics like an exported work log and compare it historically across similar tasks or situations within your company. AI has no trouble picking out anomalies and friction points as it goes. But why stop there? Like our fictional coach watching game tapes, AI can take complex metrics and compare it to data from other companies, letting you set well-researched, data-driven benchmarks to improve your process.
How AI Integrates Into Retrospective Formats
Infusing your sprint retrospectives with artificial intelligence is all about analytics and augmenting your data capture in order to grow. There are, however, a number of different retrospective formats that you can use at the end of a sprint:
- Lean coffee
- Oscar Academy Awards
Methods like Mad-sad-glad, Remote retrospective, Lean coffee, and Oscar Academy Awards are more subjective, qualitative, and opinion-based formats by default. AI oversight acts as a counterpart to them. AI integrates directly with the Iteration and Sailboat models more effectively and serves as a complement to those particular formats as they are more closely aligned with quantitative data.
Regardless of the retrospective format you use, there's change on the horizon. AI, machine learning, and data analytics promise to streamline an already lean and efficient framework. Those who adopt these incoming technologies stand to experience deeper and more fruitful retrospectives.
Becoming Better Through AI Retros
If the sprint retrospective is about making incremental change, then AI is all about accelerating the process. It's about changing the outcome of game two so there will never be a game seven. Using big data analytics as part of your scrum process you'll be able to single out the real problems facing your team, and you'll be able to reconcile your team members' memories, attitudes, and opinions with hard data so that they grow. All while doing it with the utmost efficiency.
Are we there yet? AI is growing in across ever conceivable industry worldwide. There's a long way to go, but many companies are already doing their best, most introspective work using big data analytics. And while there will always be a need for subjective opinions during the agile development process, it helps to have another player on your team, one with the power of incredible insight.