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Agile Analysis and delivery in the age of (1) (1)

Agile Analysis and delivery in the age

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Agile Analysis and delivery in the age of (1) (1)

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  1. Agile Analysis and Delivery in the Age of AI/Automation- Research Perspective Coffee table conversation between Dr. Nirmala Joshi and Samruddhi Shetty

  2. The Flow of discussion • Present Practices • Trends and Future Expectations • Research Done so far • Our Proposed Research The Focus Area • Agile Analysis and Delivery in the Age of AI/Automation- from Business Analysts’ Perspective

  3. Present Practices

  4. The Agile Methodology and BA Principles

  5. Agile Process from BA Perspective

  6. The Agile Scrum Framework “Be Agile, instead of being fragile” In simple words, Agile means the ability to move quickly and easily, i.e. making a small change quickly, learning from it, making the required adjustments according to the requirement, and repeating the process as many times as required. 

  7. Business Analysis Competency Model- IIBA

  8. The Business Analysis Tools and Techniques, Process Framework – IIBA website Analysis & Communication Techniques Both are Key Sets of Business Analyst Skills This is not going to change even with ML and AI

  9. BA’s Role in Agile -Prioritize Delivery, Minimize Waste, Create Better Business Outcomes and Increase Value -BABOK Guide

  10. Challenges in Requirements Elicitation What will you do? Can AI help you in this? Your experience is knight in shining armor

  11. Where can we use AI

  12. The Agile Enterprise Big Picture- Scaled Agile

  13. Trends and Future Expectations

  14. The Agenda for discussion from Research Perspective What is happening presently In Agile framework ? We have discussed What will happen with AI Intervention? What would be the role of BA s in that? How Research can help to explore the phenomena? Business Analysts and Data Scientist

  15. “Time to combine agile programming and Agile data science” “Data Scientists are from Mars and Software Engineers are from Venus” Anand S Rao https://towardsdatascience.com/time-to-combine-agile-programming-and-agile-data-science-26df7532b0e9 Andrej Karpathy, the senior director of artificial intelligence at Tesla, introduced the termSoftware 2.0 and contrasted it with traditional programming Agile Data Science Manifesto Agile Data Science in 2013 and a revised version called Agile Data Science 2.0 in 2017, Russell Jurney

  16. Agile 2.0Developing software powered by (AI/ML) models by facilitating continuous integration (of data, software, and models), delivery, and (machine) learning Agile Software Development 1.0 Manifesto • Individuals and interactions over processes and tools • Working software over comprehensive documentation • Customer collaboration over contract negotiation • Responding to change over following a plan Agile Software Development 2.0 Manifesto • Multi-disciplinary teams AND individuals & interactions • Insightful actions and decisions AND working software • Data & model exploration AND customer collaboration • Being innovative & disruptive AND responding to change In contrast, Software 2.0 can be written in much more abstract, human unfriendly language, such as the weights of a neural network. No human is involved in writing this code because there are a lot of weights (typical networks might have millions), and coding directly in weights is kind of hard (I tried). The “classical stack” of Software 1.0 is what we’re all familiar with — it is written in languages such as Python, C++, etc. It consists of explicit instructions to the computer written by a programmer with some desirable behavior.

  17. Integrated Agile Software 2.0 Process Interleaving the sprint cycles and having a separate clock for software sprints and model sprints. • Product Start/Backlog: Value Delivery Loop • Sprint Backlog • Finished Work and Value Delivery:

  18. Can Agile be disrupted by artificial Intelligence?

  19. Towards effective AI-powered agile project management- Research Paper by Profs of Australian University • The rise of Artificial intelligence (AI) has the potential to significantly transform the practice of project management. Project management has a large socio-technical element with many uncertainties arising from variability in human aspects e.g., customers’ needs, developers’ performance and team dynamics. AI can assist project managers and team members by automating repetitive, high-volume tasks to enable project analytics for estimation and risk prediction, providing actionable recommendations, and even making decisions. AI is potentially a game changer for project management in helping to accelerate productivity and increase project success rates. In this paper, we propose a framework where AI technologies can be leveraged to offer support for managing agile projects, which have become increasingly popular in the industry

  20. Research/ Experiment Done-Use case – Virtual Scrum Master

  21. Building guided assistance for sprint planning and retrospective sessions “Virtual Scrum Master – Application of AI” As an example, sourcing Agile Lifecycle Management (ALM) tool data, the virtual Scrum Master has the capability to build co-relation between user story quality with various engineering and testing activities that the team is performing. It can monitor Jenkins logs over time and warn team members if it sees decreasing or increasing trends which could potentially harm the team’s outcome.

  22. Virtual Scrum Master – Sprint Planning Assistance • Reaction from the team on the Sprint Planning Assistance •  Assessment of Retrospective Session & Outcome • Challenges in adopting the Virtual Scrum Master- • People • Data • Technology

  23. Aspects of Project Management which could use AI Support – Very helpful to BA’s • A Study on the Impact of Artificial Intelligence on Project Management https://www.researchgate.net/publication/343049623_A_Study_on_the_Impact_of_Artificial_Intelligence_on_Project_Management

  24. Application of AI Patterns in Agile PM AI can be used in Agile - 1. Chat bots 2. Predictive Modeling 3. Goal Driven Systems 4. Patterns and Anomalies

  25. Anticipated Evolution of AI in Project Management – PwC Report 2018

  26. “3 ways AI will change Project Management for the Better”– Scott Middleton Depending of the requirement of the project, the quantum of repetition of tasks

  27. Can Agile be disrupted by artificial Intelligence? • AI may not replace human judgment in the foreseeable future, but it is highly needed to support humans. • Unlike traditional project management tools, AI can predict more accurately future issues based on previous data thereby minimizing risk. This includes risks related to people, vendors, entities etc on the project. Cost assumptions and time constraints can be examined by combining current project data with historic data to run multiple scenarios and generate, assess and rank viable outcomes • Gartner anticipates that AI will eliminate 80% of present manual project management tasks by 2030 in transport industry

  28. The Proposed Research Study on“Agile Analysis and Delivery in the Age of AI/Automation”

  29. Is it only IT or Management ? Is it feasible?What will be the out come?Who will benefit ? When we talk about Agile and AI, it sounds all technical, Its IT? Where is the management thought process or how it will be helpful to management professionals?

  30. Towards effective AI-powered agile project management- 6Professors of Australian Universities studied and proposed a framework Use of AI in Prioritization AND Sprint Planning Dam Hoa, Tran Truyen, Grundy John, GhoseAditya, Kamei Yasutaka, 2018/12/26

  31. The Architecture of an AI Powered Agile Project Management Assistant • A detailed Algorithm was used to test • Further research is going on to improve it.

  32. Research questions from BA’s perspective- • What would be the acceptance of the AI backed project in Agile framework? What would be value addition? What would be impact on Productivity ,Risk, Customers and other stakeholders? • Need to study Adoption & Perception of Teams involved • Creating and Testing Simple AI based model and taking feedback for research

  33. Evaluating the Adoption of AI in Agile framework – TAM • Data collection from Agile Team members /BA’s for their perception and readiness to build and test the proposed hypotheses

  34. Unified Theory of Acceptance and Use of Technology (UTAUT) UTAUT integrates 8 models and gives better results

  35. Stanford Report on AI 2021 • If you think Artificial Intelligence (AI) is a short-lived hype, think again. Stanfords 2021 AI Index shows that global corporate AI investment amounted to a record-high $67 bn. • Higher Investment in AI shows strong adoption in various fields • More students in AI /ML • More Ph.Ds heading Industry as AI experts • Higher Focus on Drugs due to pandemic • Canada published the world’s first national AI strategy in 2017, more than 30 other countries and regions have published similar documents as of December 2020. • https://hai.stanford.edu/news/state-ai-10-charts

  36. AI is a wonderful asset for project managers. Memo, wiki that’s always up-to-date is an assistant for helping technical teams manage notes and instructions. Predictions become more reliable, easier and appropriate because AI notes the changes in tasks performed. The quality, metrics and the graph of the project becomes precise and clear in every next step when the assistants expand their understanding, increasing the performance and decreasing the effort. Black Swan, a data- mining startup that uses artificial intelligence had Disney as their first clients. They were able to predict DVD sales, which goes down to the supply chain and marketing. Algorithms devised by Black Swan changed the way retailers used the products and supply chain. AI chatbot assistants in software applications is another major example of developing AI system that uses natural language processing to map a spoken or written input, they are rapidly entering the workplace.

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