Agile & AI: From Theory to Implementation
According to Gartner, it is estimated that by year 2030, up to 80% of routine work — which represents the bulk of human hours expended across today’s PPM disciplines — could be eliminated as a result of collaboration between humans and smart machines. Similarly, by year 2030 Agile would have become standard delivery method adopted by organizations across the globe. It means there is very high probability of activities that Scrum Master, Product Owner and the team which are doing today will be automated or will be done managed by smart machines.
AI will play significant role to automate most of the activities involved in the areas of Agile requirement/backlog management, project management, scrum management & continuous delivery areas. Bots and AI will be extensively used to achieve this and thus enabling team to focus on other high value innovation activities.
In this proposed workshop, we will help participants to familiarize with industry standard AI maturity levels and popular Machine Learning methods and how to apply them to Agile delivery processes with real world solutions. Solutions include automating routine tasks through to providing concrete, data-driven insights to improve team performance or to identify areas which calls for Agile coach’s attention.
The workshop will adopt case study based role play approach which will guide participants to apply their newly acquired AI knowledge to identify potential areas for deployment of Automation and AI.
Disclaimer: This Workshop Proposal has been submitted for participation purposes only and is not intended to serve as advice of any nature whatsoever. This Workshop Proposal also contains certain information available in public domain, created and maintained by private and public organizations. The Workshop Proposal is the property of Accenture and its affiliates and Accenture be the holder of the copyright or any intellectual property over the news release. No part of this document may be reproduced in any manner without the written permission of Accenture. Opinions expressed herein are subject to change without notice.
Outline/structure of the Session
First half (Topic Introduction):
- Challenges Experienced While Adopting & Scaling Agile
- Top 5 Success Factors
- Understanding What AI Is?
- There Is No Single Technology Called AI
- Intelligent Business Automation Continuum
- AI Technologies And Capabilities
- AI Is Going To Help In Success Of Agile
- Automation & AI In Agile Is Already A Reality
- ML Models With Agile Examples
Second Half (Hands on Session for Participants/Workshop)
- Case Study Walk through including data sets
- Participants to identify Automation & AI Opportunities
- Categorize AI Opportunities Based on Intelligent Business Automation Continuum
Case study details
- Case study of an agile project will be shared with the participants.
- The current challenges which different scrum teams are facing will also be highlighted in the case study.
- Participants (through role play) will have to visualize and identify that how AI can help to solve the challenges.
- The conceptual knowledge and practical examples shared with them, will help them to achieve that.
Agile SMEs should be able to leverage AI to address delivery challenges and to contribute to Agile success in their organizations. Additionally
- Participants will be exposed to real agile examples to understand how AI can complement Agile
- Workshop to help participants visualize how challenges of agile projects can be resolved through AI.
Change Enablers, Scrum Masters, Product Owners, People with Academia interest
Prior Agile exposure preferably either as scrum master or as agile coach
schedule Submitted 1 year ago
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Applying Agile is a mindset change rather than a process change compared to traditional methods. Some of the typical challenges faced by teams in the Industry while practicing Agile are:
- Limited experience with agile
- Slowing down of work due to limited access to Product Owner
- Incomplete\less detailed user stories leading to high onshore dependency
- Struggle to keep momentum with continuous churn of agile events through active participation and to maintain quality of artefacts (backlog, burndown, impediment list, retrospective action log)
These challenges manifold when delivery teams within the Industry are practicing distributed agile at scale.
In the last couple of years within the IT industry we are seeing the infusion of Artificial Intelligence (AI). The questions which come up are:
- Can AI disrupt Agile with practical use cases?
- Can AI help in the mindset change?
- Can AI address the above challenges?
- Is AI in Agile a fad that will wither away with the passage of time?
At Accenture, we have been grappling with these questions and we will share our answers based on our practical experiences through the Virtual Scrum Master. We will also share on what the future holds in the field of Agile in AI.
Agile has become a standard delivery method adopted by organizations across the globe, according to VersionOne’s 11th Annual State of Agile Report. While 94 percent of survey respondents said their organizations practiced agile, 80 percent said their organization was at or below a “still maturing” level.
Multiple challenges including limited Agile experience, insufficient access to product owner and the extensive effort required to maintain momentum and quality, are intensified when practicing distributed (offshore, near-shore and onshore) Agile. Add in the automation challenge wherein IT organizations are looking at boosting the speed of delivery by adopting agile quickly and automating their daily work.
Based on experience, value stream mapping—can help the Agile delivery teams address these challenges plus identify where automation can be introduced to increase the speed of Agile development.
At Accenture, with our practical experience have introduced several intelligent virtual agents that use machine learning to collaborate with their human co-workers to overcome these challenges. One agent, in particular, our Virtual Scrum Master, monitors numerous aspects of Agile development projects—consisting of requirements, releases, metrics and resources—alerting the team of any potential issues and providing possible solutions.
Our virtual Scrum Master resides in our intelligent automation platform, Accenture myWizard®. The myWizard platform combines Accenture’s industry and technology assets and business knowledge across 40 industries with intelligent automation, including artificial intelligence (AI) at its core to enable smarter, more innovative and more efficient application services. Our virtual Scrum Master helps the Agile teams:
- Identify and automate repetitive Agile tasks
- Gain insights to effectively drive Agile ceremonies, and create quality deliverables
- Use historical information to predict the future and take corrective measures when needed.
- Analyze patterns/relations/co-relations of historical and transactional project data to diagnose root causes
- Drive standardization of Agile practices while scaling in a distributed fashion
In this session, we will walk you through a client case study, where we implemented the virtual Scrum Master to gain efficiencies across a distributed Agile team and automated nearly 40 percent of the manual Agile processes.
Disrupted Agile with artificial intelligence (AI) is no longer a trend. It’s a reality that’s being accelerated by increased automation, via artificial intelligence and cognitive computing. AI and machines can help us do Agile better. Embrace your new digital co-worker.
AI in Agile will not replace the collaboration between and within the teams but augment it to the next level where we are continuously improving.
Jeffson Dsouza - As an Associate Director, Jeffson brings with him 19+ years of software industry experience to his role as Agile Community of Practice Lead at Accenture. He has expertise in adopting Agile and Lean in various organizations.
Rajendra T Prasad – As a Managing Director, Rajendra leads Automation and Artificial Intelligence, Accenture Technology Services. In this leadership role, RP focuses on driving efficiency in of Application Services across the IT application lifecycle. He leads the team that created and currently deploys Accenture myWizard, an intelligent automation platform with Artificial Intelligence at its core.