Learning & Innovation Research Manager| Project Management Institute (PMI)Spain
Are you utilizing any specific checklists or protocols within your projects or company to assess your readiness for working with Generative AI data? I'm curious to know what strategies or tools you've implemented to prepare for integrating Gen AI into your workflows. Please share your approaches in the comments below! Saving Changes...
Mike PortworsnickChange Leader| Change Factory by Mike PortworsnickEngelberg, Switzerland
Hi, I am pretty new to the GenAI world. I learned a lot from this thread. Thank you all for sharing your experience and insights. Mike Saving Changes...
Lydia RieschPM I| Association of Equipment ManufacturersMilwaukee WI, United States
We recently created and placed guidelines for AI use in our employee handbook. There was a small task force working on these guidelines for 1/2 year or so. We are at the very beginning of this whole process. Saving Changes...
Gen AI can be used as a nice tool in various aspects of project management:
- Depending on the industry, type of project and the current data available on similar projects, one could use it for doing options analysis, cost/benefit analysis, cash flow analysis, market analysis (PESTEL) and in general project feasibility studies while drafting business cases for proposed projects.
- One could leverage risk analysis from previous projects to enhance or exploit opportunities and mitigate or avoid risks.
- One could create initial drafts of quality requirements using Gen AI and available data, which could be refined with the team.
- On traditional or agile projects, lessons learned from previous projects could be leveraged to further minimize process or technical risks and efficiencies.
The main point here is that Gen AI could save project teams and organizations a lot of manual time and effort and help create different pre-filled project templates, which can be refined or customized. The saved time would mean the PM role evolving into more of an advisory role that brings constant gains in project efficiencies, team collaboration and continuous process improvement.
Saving Changes...
Anjana KakuAssociate Director - Program Management - Data & AI Services| Kyndryl Solutions Pvt. Ltd.Bangalore, Karnataka, India
1. We're confident that Gen AI can be used in a lot of ways and its output could be used to better our outcomes, but before we get there, we're trying to make sure the outputs of the AI systems we employ aren't skewed / biased / unethical etc. We're working towards evaluating the AI models we're going to use for the outputs they generate, and once we're convinced they match our expectations we'd start using those.
2. One other point where we're focused currently in order to ensure the output of the AI systems are accurate is data management aspects, such as data quality, compliance, variety, governance, privacy, reliability etc. There's a long way for us to get to a state where we've employed Gen AI and are dependent on the outputs of it, but we've already started on the AI journey. Saving Changes...
Anjana KakuAssociate Director - Program Management - Data & AI Services| Kyndryl Solutions Pvt. Ltd.Bangalore, Karnataka, India
1. We're confident that Gen AI can be used in a lot of ways and its output could be used to better our outcomes, but before we get there, we're trying to make sure the outputs of the AI systems we employ aren't skewed / biased / unethical etc. We're working towards evaluating the AI models we're going to use for the outputs they generate, and once we're convinced they match our expectations we'd start using those.
2. One other point where we're focused currently in order to ensure the output of the AI systems are accurate is data management aspects, such as data quality, compliance, variety, governance, privacy, reliability etc. There's a long way for us to get to a state where we've employed Gen AI and are dependent on the outputs of it, but we've already started on the AI journey. Saving Changes...
What tools can I use to measure AI acceptance throughout a project's lifecycle ? Saving Changes...
Shantesh MugatiProgramme Manager| Shell India Markets Private LimitedBangalore, Karnataka, India
A very good question indeed! We @Shell have developed a model for assistance and also leveraging AI generated meeting notes as a starter for the great meal ahead. I will share more during the later part of the year.
I would love to hear more from fellow program managers and project managers. Saving Changes...
It's nice to meet you all. So in an organisation I was working in until just recently we went through GenAI integration steps starting from August last year and it should be ongoing currently with the implementation of the integration projects themselves. Being a software solutions company, we explored GenAI integration in our products from an engineering perspective (as most software companies did or are doing) and in the way we did our work as parallel threads. Here's a high level view of how we approached the GenAI integration in the way we worked:
1. Team Preparation:
- Continuous Learning: We implemented a program of ongoing training for our team members on Generative AI (GenAI) fundamentals, prompting techniques, and the latest GenAI tools relevant to the team’s respective ways of working. This involved weekly workshops, online courses, and guest speaker sessions. - GenAI Knowledge Sharing Network: We established an internal hub of GenAI training, recommended tools, insights and team comments to facilitate collaboration and sharing of perspectives for mutual growth.
2. GenAI Opportunity Identification:
- Process Mapping: We began the integration by conducting a thorough process mapping exercise. This involved identifying all service delivery workflows for each team - Sales, Marketing, Ops, engineering, etc. We broke down each workflow into individual steps to understand the specific tasks involved.
- GenAI Applicability Assessment: For each mapped process, we assessed the potential for GenAI automation. Here, we considered factors like task repetitiveness, data availability, and the potential for improved efficiency or accuracy through GenAI integration. - Cost-Benefit Analysis: We didn't solely rely on automation potential. We performed a cost-benefit analysis to ensure GenAI integration adds value. This involves comparing the potential benefits (e.g., time savings, improved accuracy or outcome) with the costs of implementation and ongoing maintenance effort.
3. Integration Planning:
- Pilot Project Selection: Following the opportunity identification stage, we employed collaborative brainstorming sessions on team levels driven by managers to identify workflows best suited for pilot GenAI integration projects. Here, we prioritized processes with high impact potential and a lower risk profile for initial exploration. For example, internal processes were favoured over customer impacting ones. - Detailed Integration Roadmap: For each chosen pilot project, we developed a detailed integration roadmap. This roadmap outlined the stakeholders involved, identified necessary data sources and system integrations, defined implementation steps with timelines, and established a robust testing and validation plan.
4. Continuous Improvement:
- Feedback Loop: We actively solicit feedback from team members throughout the integration process. This allows us to identify and address challenges, refine our approach, and ensure successful GenAI adoption through collaboration. - Knowledge Sharing: We document our learnings and best practices from GenAI integration efforts. This knowledge base serves as a valuable resource for future projects and fosters continuous improvement within our team's GenAI capabilities.
Saving Changes...
Anonymous
Would be more interested in learning and exploring unstructured documents leveraging generative AI in healthcare compliance and reporting field. Saving Changes...
PMI Team Member
