Reviving Insights: The Evolution of AI Applications in Agile Methodologies Since 2018
- Luba Sakharuk
- Jul 13
- 15 min read
Artificial intelligence (AI) has transformed many industries over the past decade, but its impact on Agile methodologies stands out as particularly significant. Back in 2018, after completing a course at MIT on Business Applications of AI, I wrote a blog post exploring how AI could enhance Agile practices. That post, originally published for Cengage Learning, has since become difficult to find. I recently updated and refined that content to reflect the advances and lessons learned since then. This post revisits those early insights and explores how AI’s role in Agile has evolved over the last several years.
AI-powered Agile dashboard showing project progress and predictive analytics
How AI Supported Agile Teams in 2018
In 2018, AI was beginning to show promise in supporting Agile teams by automating routine tasks and providing data-driven insights. Some of the early applications included:
Automated backlog prioritization: AI tools analyzed user stories and historical data to suggest which features or fixes should be tackled first.
Sprint planning assistance: Machine learning models helped estimate task complexity and team velocity, improving sprint planning accuracy.
Defect prediction: AI algorithms identified code areas likely to contain bugs, allowing teams to focus testing efforts more efficiently.
Sentiment analysis: Natural language processing (NLP) was used to gauge team morale and stakeholder feedback from communication channels.
These applications aimed to reduce manual effort and improve decision-making speed. However, AI adoption was still limited by technology maturity and integration challenges.
Advances in AI Technologies Enhancing Agile Since 2018
Since then, AI technologies have advanced rapidly, enabling more sophisticated Agile applications:
Improved natural language understanding: Modern NLP models better interpret user stories, requirements, and feedback, enabling more accurate backlog refinement.
Predictive analytics at scale: AI now forecasts project risks, delivery timelines, and resource needs with higher precision using larger datasets.
Intelligent automation: Robotic process automation (RPA) combined with AI automates repetitive tasks like test case generation, deployment, and reporting.
Adaptive learning systems: AI tools continuously learn from team performance and adjust recommendations dynamically, supporting continuous improvement.
These advances have made AI a more integral part of Agile workflows, helping teams respond faster to change and deliver higher quality software.
Practical Examples of AI in Agile Today
Many organizations have integrated AI into their Agile processes with tangible benefits. Here are some examples:
Sprint backlog optimization: A software company uses AI to analyze past sprint data and customer priorities, automatically suggesting an optimized backlog that balances value and effort.
Automated code reviews: AI-powered tools scan code commits in real time, flagging potential issues and enforcing coding standards before human review.
Risk management: AI models predict which features or modules are most likely to cause delays or defects, allowing teams to allocate resources proactively.
Team collaboration insights: AI analyzes communication patterns in chat and email to identify bottlenecks or collaboration breakdowns, helping Scrum Masters intervene early.
These examples show how AI supports both technical and human aspects of Agile, improving efficiency and team dynamics.
Challenges and Considerations When Using AI in Agile
Despite the benefits, integrating AI into Agile is not without challenges:
Data quality and availability: AI depends on clean, relevant data. Many teams struggle to collect and maintain the data needed for accurate AI predictions.
Tool integration: Agile teams use diverse tools for project management, code repositories, and communication. Seamless AI integration across these platforms remains complex.
Trust and transparency: Teams may hesitate to rely on AI recommendations without clear explanations of how decisions are made.
Human factors: AI should support, not replace, human judgment. Balancing automation with team autonomy is critical.
Addressing these challenges requires careful planning, ongoing training, and a culture open to experimentation.
The Future of AI in Agile Methodologies
Looking ahead, AI will likely become more embedded in Agile practices, with several emerging trends:
Personalized coaching: AI agents will provide tailored guidance to individual team members based on their skills and work patterns.
Real-time adaptive planning: Agile plans will continuously adjust in response to AI-driven insights about progress, risks, and market changes.
Cross-team coordination: AI will help synchronize work across multiple Agile teams, identifying dependencies and optimizing delivery pipelines.
Ethical AI use: Teams will adopt frameworks to ensure AI supports fairness, privacy, and inclusivity in Agile decision-making.
These developments promise to make Agile even more responsive and effective in complex environments.
AI’s role in Agile has grown from simple automation to a strategic partner that enhances planning, quality, and collaboration. Revisiting the insights from 2018 highlights how far the technology has come and points to exciting possibilities ahead. Teams that embrace AI thoughtfully can unlock new levels of agility and innovation.
If you are involved in Agile development, consider exploring AI tools that fit your team’s needs and data maturity. Start small, measure impact, and build trust in AI’s capabilities. The journey to smarter Agile practices is underway, and the next few years will bring even more powerful ways to work.
This post provides informational content based on industry trends and experience. It does not offer specific legal, financial, or professional advice.
>>>BELOE IS THE ORIGINAL BLOG RAN THROUGH CLAUDE TO REMOVE DEAD LINKS<<<
Artificial Intelligence and Machine Learning in the world of Agile
By: Luba S., Director of Agile Transformation Originally published August 5, 2018, on the Cengage News/Perspectives blog
A good friend and mentor of mine mentioned casually over lunch the topic of Artificial Intelligence (AI). While he was searching for a new opportunity and had some free time, he took a six week course on this popular topic. This made a huge impact on him and he spoke excitedly about the future of AI. This is a person who had a huge impact on my career choices, and I made a mental note to look into it. Little did I know that within a few weeks, I would be reading Artificial Intelligence articles the same way many read Mysteries of Agatha Christie (you know, staying up late because you don't want to put the book away?). It is a truly fascinating topic and I believe Artificial Intelligence must become part of strategic planning for all organizations.
How it all began
That same night after a nice lunch with my friend, my Facebook feed showed an advertisement for MIT Management Executive Education. They were offering a six week course on Artificial Intelligence. Was it a coincidence? Was it a sign? Was it magic? Did Facebook get so sophisticated that it could actually read your mind? Scary! Well, the common sense and the little knowledge that I had about AI took over, and I was pretty sure it had something to do with me searching the topic on Google earlier. Maybe there was no connection between the two, but long story short, the timing of the course turned out to be perfect, and after a little more research, I signed up and my journey began!
1 week into the course…
I was excited. Week one of the course was pretty simple: write a quick intro, meet people in your class, set up your profile, and make sure your Online Campus works as expected. Week two got real. It turned philosophical. "What does intelligence mean to you?" was the first question. After many postings of opinions, we got the official definition, along with hours of additional material on the topic. Part of the first week's homework was to write a quick essay about your organization and the current use of Artificial Intelligence. A few weeks into the course, one of my co-workers posted a very interesting and relevant blog on this topic, but at the point of the first assignment, I knew very little and so I turned to my friends at work.
Jerry C., Software Director of Engineering on the Analytics team, and Tyler L., Director of Application Architecture, allowed me to pick their brain, and I was able to submit the following:
About the Organization: The organization I work for, Cengage, provides technology and content for higher education, K-12, library markets, as well as career schools. It is a book publishing company going through a digital transformation.
Recently, we announced a new model for students, where they can take advantage of all the content for a low subscription cost. Although we are trying to disrupt the industry and provide affordable access to unlimited content, we don't rely on cost leadership (which means no frills). Regardless of the fact that we sell solutions to instructors and institutions, we focus primarily on our students, their learning objectives, and their achievements.
Current state: We have a team that collects unique data points and generates reports answering questions such as "What content is accessed more frequently?", "How many users login?" or "How many users login more than once per week?" This is called Descriptive Analytics.
There are a few other types of analytics, such as Predictive and Prescriptive. As you go through the initiatives below, think about whether they are descriptive, predictive, or prescriptive.
A major challenge organizations face is to identify and collect data consistently. Some teams at Cengage work on different hypotheses, e.g. "Is content X effective?", "Do students engage with multiple pieces of courseware per session?" Having multiple platforms and erratic data collection streams from numerous sources isn't consistent. The team that gathers all the data also handles the data clean up. We do still have a long way to go before we are able to test out our hypotheses, determine probable outcomes, and predict measurements, but we are definitely on the right track!
The maturity of the organization will come in phases. We have to first implement consistent event tracking (logins, learning path customization, content launches, assessment submittals, assessment scores…). When we create content and assessments, we need to have analytics top of mind, just like accessibility. Building a profile on every student, so we can understand their interests and study habits, would be essential in applying AI, but we need to pay close attention to how we handle it to make sure we are compliant with the General Data Protection Regulation (GDPR), which is designed to enable individuals to better control their personal data.
Side Note: It was mentioned in some of the reading material that due to the GDPR regulations in Europe and non-existing regulation in China, China might soon get much further in its development of AI. Not to deviate too much, but it is worth mentioning that one article on AI regulation argued that for AI regulation to succeed, it would need to be coordinated across countries, rather than leaving a costly, complicated patchwork of differing national rules for AI developers to navigate.
Back to AI at Cengage. As mentioned earlier in this blog and in detail in my co-worker's blog, a lot of focus at Cengage right now is on the Student. As part of the course, the final project is to come up with a roadmap of different AI initiatives that would be closely aligned to the business and/or IT overall strategy. As I was learning about different aspects of AI and different applications of it, I was thinking about what initiatives I should propose. We had to answer questions such as "who from the organization would we need to involve?", "what technical challenges might we face?", etc.
After a lot of consideration, I decided to primarily focus on the initiatives that are within my area of expertise, which is the Scrum Framework, as well as Agile Values and Principles. It is worth mentioning that, to my surprise, it turned out there isn't that much information out there combining AI and Agile. When I asked a question on LinkedIn (to a large community of Agile Coaches) about who had experimented with AI, I received a bunch of likes but no actual comments. Agile and Artificial Intelligence are two very popular topics on their own these days. Digging into combining them both seemed challenging and intriguing, so I decided to go for it!
2 weeks into the course… first set of proposed AI initiatives
Improving facilitation of Scrum Ceremonies using Cogito's real-time emotional intelligence software — not just for facilitators, who are expected to have very high emotional intelligence, but the rest of the participants too. If every participant had software to coach them through conversations, we would improve the effectiveness and efficiency of our meetings tremendously.
Using AI to encourage team members to pick up tasks that build new skills needed on the team, rather than always working on tickets they're already comfortable with. A team member in Scrum ideally should be capable of picking up any work item from the top (most valued) of the backlog.
Using AI and ML to predict the success of a Sprint in Scrum. Not an easy task, but we do have data, such as the status of work items in Jira, total work left to do, people's capacity, and available skills. We would need to categorize skills and tag each work item, as well as skills on the team. We could take it a step further and predict the success of longer releases.
Along the same lines, during a Retrospective, a lot of feedback is given regarding process improvement. Often (although facilitators try to handle it), there is finger-pointing as to why something hasn't gone well. If we could capture what is being said and pay attention to phrases like "waiting for QA" or "Jira was down," we might be able to spot patterns.
Optional… only if the VP of Customer Support decides to lead this effort…
Customer Support is an area of high interest to me because the success, happiness, and satisfaction of our students heavily depend on our customer support agents. I would also propose using Cogito's real-time emotional intelligence software, especially since this is the area it was specifically designed for.
WARNING: These were just random ideas that would require additional investigation, feedback from experts, as well as experimentation.
3 weeks into the course… second set of proposed AI initiatives
I work with a team of Agile Coaches, and we organize a lot of meetings, sometimes with over a hundred attendees. I would experiment with x.ai (a scheduling-assistant tool popular at the time).
My company periodically goes through an acquisition process, and I believe using software pre-trained with a set of clauses to watch out for in contracts could be very beneficial to our legal team.
Quill software seemed totally applicable in our world — the tool generated natural-language feedback for students on how they were doing and what they should study to improve. (Original source: a Vimeo demo video, no longer available.)
Along similar lines, there was a natural language processing (NLP) demo where you could run text through a machine learning model and it would return a sentiment score, along with categorized emotions. I was wondering if something like it could be used to scan a team Slack channel to assess happiness and job satisfaction. (Original source: an IBM Watson/Bluemix "Natural Language Understanding" demo page, no longer available.)
Along the same lines, we collect a lot of survey data. Running a quick sentiment analysis on it would allow us to focus on negative or angry sentiment versus positive. It allows you to quickly decide which of 10,000 surveys to read first.
I would love to use AWS DeepLens to capture faces during a meeting to gauge how well the meeting went and what the level of engagement/boredom/excitement was. (Original source: an AWS DeepLens community projects page, link no longer resolves — the "DEAR" project may still be searchable by name.)
From the list above, items #1, #4, #5, and #6 fall within my direct responsibility, so I would primarily focus on those. I would learn more about #2 and #3 so I could recommend them to others, but I wouldn't be directly involved.
4 weeks into the course… third set of proposed AI initiatives
To help with onboarding, we could use a robot programmed with frequently asked questions about the company. Over time, it would learn more and more. It's a repeatable task, so it would work.
The ability to get a self-driving cart to pick you up from your current location, drive you to your conference room, and then go back to recharge until the next call could be really cool and useful. It is easy to get lost in our current building.
Often people are either busy in meetings or too absorbed in their work when hunger takes over. Being able to ask a robot to bring you food (called via an app) from a pre-packed vending machine would come in handy.
As a facilitator of certain recurring meetings that are part of the Scrum Framework, you often find yourself reminding folks that it's time to join. We have Slack reminders too, but it would help to have a pre-programmed robot announce it in a friendly manner, so you as a facilitator don't come across as nagging.
Data Science
During week 4 of the MIT course, General Assembly happened to be hosting a one-hour course on "Data Science." Not only did they give a very interesting overview of what it takes to be a Data Scientist, they covered a set of questions that are helpful to ask yourself when given a data-related task:
Identify the problem:
What are you trying to answer?
Acquire the data:
Is the data you need available?
How is it stored?
Is it public or proprietary?
Can it be supplemented?
What tools will you need to import it?
What tools will you need to work with it?
Parsing:
What documentation is available?
Were you able to import the data?
What did you learn from initial exploratory analysis?
Is the data, in fact, sufficient?
How much munging will it require?
Mine (Wrangling/munging/mining — different names, ~80% of the work happens here):
What sampling methodology will you use?
What secondary features need to be derived?
Refine:
What new trends appear?
Any notable outliers?
Notable results from applying derived features?
Are you documenting your thought process and findings?
Model:
What model or models are most appropriate for the data and the problem?
Is the data in the appropriate format for the desired models?
How did the initial model perform?
Any symptoms of overfitting?
Present:
What narrative do I want to tell?
What assumptions are we making?
What inherent limitations should be disclosed?
Was the success criteria met?
The instructor made a very interesting comment about defining success criteria. He said that unless you write it down and can refer back to it, it's very easy to deviate from the original question you were trying to answer, and end up justifying answering something else as if it were success. What you answered instead could also be extremely useful, but staying true to the success criteria as originally defined takes discipline. I made sure to keep that in mind when drafting the set of initiatives for the future of AI at Cengage Learning!
5 weeks into the course… fourth set of proposed AI initiatives
A lot of the discussion this week was about Ethics.
Most of the initiatives I proposed over the course of this class had to do with having AI as a peer to an Agile Coach or Engineering Manager, and in the case of a robot helping with onboarding, a peer to HR. I don't see any risk of unemployment there. I wouldn't trust a robot (just yet) doing performance reviews or deciding whether to give someone a bonus or a raise. I can see, though, how — if done thoughtfully — a robot could conduct exit interviews, gathering insights on different reasons why people are leaving.
Another example of where a human cannot be completely replaced is in the analysis itself. Here's a great line from our Data Scientist, Doug Needham, who lives in the trenches of Data Science, Artificial Intelligence, and Machine Learning: AI and ML are great tools, but a human still needs to actually look at the data, at least initially, and then work to refine the statistical model driving the analysis — and that human effort needs to be scheduled and treated as a first-class part of the process, because analytics is not a silver bullet and the industry is still maturing.
6 weeks into the course – The Future of AI at Cengage
My organization had been going through a digital transformation for at least three years at that point. There had been a lot of focus on behaviors, values, and principles that would allow people to work together more effectively and efficiently: "We are one team," meaning we don't throw things over the wall — your problem is our problem, and you're not done until the solution is done, not just your part. We have a team of 12 Agile Coaches in the company whose primary focus is on how people work together and communicate with each other. Respect and empowerment are valued a lot more in my organization than command and control. There has been a lot of investment in leadership skills among managers, directors, and VPs to ensure the desired behaviors don't fall by the wayside the moment something doesn't go according to plan. I think we were in good shape when it came to soft skills in my organization, although there was room for improvement for most individuals, including myself :).
We have a lot of Data Science, Artificial Intelligence, and Machine Learning enthusiasts, beyond the few people whose primary job is to improve student outcomes through DS, AI, and ML. What we need to do to achieve a competitive advantage is start tagging and collecting data in a consistent manner. It's already on many of the right people's radars, so we were heading in the right direction in the AI space. When I asked Doug Needham (whose recent project was developing a method for classifying both students and courses that consistently showed a strong relationship between patterns of platform feature usage and final scores) to review this blog, one of the comments he made was worth quoting: it's easy for people to think AI/ML/DS is a magic bullet, but the "magic" is really just hard work — research, study, and plenty of late nights. It's important to keep that in mind when selecting initiatives to implement in an organization.
As the final homework for this course, we had to aggregate everything we'd learned and proposed in the previous assignments and come up with a roadmap of AI initiatives. Although many of the AI initiatives are super cool, we always have to think about return on investment. Do we REALLY need a robot to fetch us a beer or bring us lunch from a vending machine? I don't think so :). In fact, I'd argue it's good for anyone who's been sitting for a long time to get up and take a walk. On the other hand, having a Slack bot remind an employee that it might be a good time to get up and take a break could go a long way!
In Conclusion
One of the main takeaways of this course was: if you're afraid of AI and think it will soon take over the world, calm down — it's not happening :). I am definitely not afraid of robots, and I love the promise of more interesting jobs being added while boring tasks get taken care of by computers. Taking this course allowed me not only to learn about many aspects of AI, including ethics, but also to have some great conversations with my co-workers, learn about the current state of my organization, and come up with initiatives on how AI could be implemented in my organization as a peer to an Agile Coach or Engineering Manager — NOT a replacement for that role. It also confirmed my belief that soft skills and human touch cannot be easily replaced by a machine.
Originally published on the Cengage News/Perspectives blog, August 5, 2018. Tags: tech.
Note on links: Several links in the original post (the Vimeo Quill demo, the IBM Watson/Bluemix NLU demo, and the AWS DeepLens community project page) no longer resolve, as the underlying services and demo pages have since been retired or moved. They've been described in place rather than linked, since I could not verify a working replacement URL for each.
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