What are some strategies for AI professionals to improve their project management skills?
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Project management is an essential skill for AI professionals who want to deliver successful solutions, collaborate effectively with stakeholders, and manage their time and resources efficiently. However, managing AI projects can pose unique challenges, such as dealing with uncertainty, complexity, and ethical issues. In this article, we will explore some strategies for AI professionals to improve their project management skills and overcome these challenges.
Before starting any AI project, you need to clearly define the problem you are trying to solve and the value proposition you are offering to your clients or users. This will help you set realistic goals, scope, and expectations, as well as align your AI solution with the business objectives and user needs. To define the problem and the value proposition, you can use tools such as problem statements, value proposition canvases, and stakeholder maps.
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Umaid Asim
CEO at SensViz | Building human-centric AI applications that truly understands and empowers you | Helping businesses and individuals leverage AI | Entrepreneur | Top AI & Data Science Voice
In the early days of SensViz, clarity on project objectives was crucial. I made sure to understand the problem thoroughly before diving into solutions. It was not just about identifying the issues, but also understanding the value solving them would bring. This approach helped in aligning the team and stakeholders. We knew what we were aiming for, and why it was important. It made discussions productive and kept us on track, saving time and resources. This simple yet effective strategy significantly sharpened my project management skills. It instilled a habit of seeking clarity which proved invaluable in guiding SensViz projects to success.
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Leonard Rodman, M.Sc.
🕊️ Top AI Voice | L&D Product Owner | Project Manager | ChatGPT Prompt Engineer | Machine Learning | MS365/Azure Admin | 10k followers & 2M views/5mo | If you're reading still, follow me or let's connect!
I think that a project management certification can be really helpful on knowing where to begin. I’ve been studying for Agile and PMP have found the training intensely helpful. Basically any AI development is going to be Agile based.
Depending on the nature and scope of your AI project, you may need to adopt different project management methodologies and tools. For example, you may use agile methods, such as Scrum or Kanban, to manage iterative and incremental development cycles, or you may use waterfall methods, such as PMBOK or PRINCE2, to manage linear and sequential phases. You may also use hybrid methods, such as agile-waterfall or agile-lean, to combine the best of both worlds. Additionally, you may use tools such as project management software, data science platforms, or AI frameworks to facilitate your workflow and communication.
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Umaid Asim
CEO at SensViz | Building human-centric AI applications that truly understands and empowers you | Helping businesses and individuals leverage AI | Entrepreneur | Top AI & Data Science Voice
Selecting the right methodology and tools is pivotal for managing AI projects efficiently. For instance, switching to Agile from Waterfall can foster faster feedback and adjustments, crucial in AI's fast-paced domain. Additionally, choosing tools that align with the team's skills and project needs can streamline workflows, enhancing collaboration and tracking. These choices, tailored to the project and team's demands, can significantly impact the project's success, making it essential to choose wisely based on project specifics.
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Arslan Ihsan
Turning Vision into Value | Senior VP Professional Services @ Addo | Leading AI & Digital Transformation | Unleashing Growth via Data-Driven Strategies & Innovative Web3 Solutions
Tools like Jira, Monday, Asana Trello, etc, are making project managers' lives much easier, including AI projects. In my opinion, practice experience matters the most, and every team, situation, and project nature is different and you have to utilize the flexibility of Agile and tools to tailor your execution accordingly. I am certified in CSM, PMP, and SAFe, covering Agile methodologies, comprehensive project management, and scaled Agile for large projects. Certifications help you to know the process but it's your experience that helps you to deliver. My favorite AI-embedded tools for automation include JIRA automation, Slack bots, Scrum Assistant bot for auto scrum ceremonies, and Grammarly to summarize and respond to emails.
One of the most critical aspects of managing AI projects is managing the data and the models that are used to train, test, and deploy your AI solution. You need to ensure that your data is reliable, relevant, and representative of the problem domain, and that your models are accurate, robust, and ethical. To manage the data and the models, you can use techniques such as data quality assessment, data preprocessing, data augmentation, model validation, model testing, model monitoring, and model explainability.
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Arslan Ihsan
Turning Vision into Value | Senior VP Professional Services @ Addo | Leading AI & Digital Transformation | Unleashing Growth via Data-Driven Strategies & Innovative Web3 Solutions
The cornerstone of AI projects is the adept management of data and models. Ensuring data quality and model integrity is non-negotiable. For me, data isn't just data—it's the foundation upon which reliable and ethical AI is built. Adopting a comprehensive data governance framework is critical, especially one that respects privacy and utility. When it comes to models, robustness and fairness are paramount. I advocate for an approach where models are not only trained to perform but also to be understood and accountable. Techniques that foster resilience to novel data inputs, coupled with explainability practices, are integral. It’s about creating AI solutions that are not only powerful but also principled and transparent.
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Raj Gupta
Vice President at Adroit Vantage
In my experience effective data and model management is the backbone of successful AI projects. Beyond technical steps, it demands a holistic approach. It necessitates not only accuracy but also data ethics, as biased data can lead to biased models. Regularly reassess your data and models to adapt to evolving real-world conditions and ensure they align with your project's goals. Ethical AI should be at the core of your strategy, and explainability is essential for transparency and trust. In an ever-changing landscape, continuous monitoring and adaptability are key to AI success.
Another key aspect of managing AI projects is communicating effectively and transparently with your team members, clients, users, and other stakeholders. You need to keep them informed of your progress, challenges, and results, as well as solicit their feedback and input. You also need to communicate the value and the limitations of your AI solution, as well as the risks and the implications of using it. To communicate effectively and transparently, you can use methods such as status reports, presentations, demos, dashboards, and documentation.
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Zachary Rattner
Co-founder and CTO at Yembo: The leader in AI-powered virtual surveys | Author | Speaker
Ever wondered what drives the AI project success? The answer lies in resource allocation – a strategic art. It's not just about meeting project goals; it's about optimizing them. Prioritizing tasks, managing time, and budgeting effectively are the essentials that may not be the focusing key factor of the project. As AI progressively grows every minute, adaptability doesn't become only a choice but a necessity. Shifting resources in response to evolving needs and unforeseen challenges is the secret sauce. I would recommend directing your focus towards strategic resource allocation, the compass guiding you to smoother AI project journeys. Embrace punctual milestones, and navigate AI projects confidently into the future of technology.
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Kapil Madan
VP Global Business Development | Venture Institute Cohort 2 @VC Lab| Ex-Adobe, Ex-IBM| Fintech, Generative AI
Communication across stakeholders is key to success of AI project. Successful deployment of AI projects often requires becoming acquainted with unfamiliar knowledge and collaborating with other professions
Finally, managing AI projects is a continuous learning process that requires you to learn from your failures and successes. You need to evaluate your performance, identify your strengths and weaknesses, and implement improvements and changes based on your lessons learned. You also need to celebrate your achievements, recognize your contributions, and share your insights and best practices with your peers and the community. To learn from your failures and successes, you can use tools such as retrospectives, post-mortems, surveys, and blogs.
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Arslan Ihsan
Turning Vision into Value | Senior VP Professional Services @ Addo | Leading AI & Digital Transformation | Unleashing Growth via Data-Driven Strategies & Innovative Web3 Solutions
Achieving success with AI projects can be challenging and requires a significant amount of data. Despite having a large data science team, I witnessed one of the largest job portal websites struggle for over a year. While with Generative AI, they suddenly achieved exceptional results and ROI within weeks. The key takeaway here is that it's not always your fault when a project fails. There could be various factors such as environmental challenges, organizational challenges, stakeholder challenges, lack of data, computation power, or absence of innovative solutions. Achieving success requires consistency and periodic retrospectives to learn from your mistakes, re-evaluate, think back, and come back stronger with renewed energy and vision.
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Brian L. Keith
Data, AI & Cloud Leader | Azure Cloud | I help government leaders to digitally transform the way they operate and deliver services.
This is why I recommend using an agile methodology to deliver your AI projects. You need to have a retrospective after each sprint and adjust accordingly. You need the flexibility to fail fast and recover.
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Heena Purohit
LinkedIn Top AI Voice | Building Next-Gen AI Products @ IBM | 3x Top 10 Women in AI Award Recipient | Keynote Speaker | Startup Advisor | Responsible AI Advocate
Additional traits of exceptional AI PMs include: - Risk Management: They anticipate and plan for potential risks, including ethical considerations of AI use, ensuring AI is developed and used responsibly. - Innovation: They recognize that AI development requires a degree of trial and error, and foster an environment that encourages creative thinking and experimentation. - Stakeholder Management: AI projects require collaboration with more diverse groups than traditional IT projects. Great AI PMs identify all the stakeholder groups early, including end users, business teams, data providers, domain experts, execs, legal, etc. And they proactively prioritize the right groups to engage throughout the project lifecycle.
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Umaid Asim
CEO at SensViz | Building human-centric AI applications that truly understands and empowers you | Helping businesses and individuals leverage AI | Entrepreneur | Top AI & Data Science Voice
In the journey of managing AI projects, continuous learning and adapting are the keys. I recall when we initiated SensViz, every project brought a new learning curve. Over time, cultivating a culture of open communication within the team and with stakeholders significantly optimized our project management processes. Also, staying updated with the latest project management tools and AI technologies helped in aligning our strategies effectively with the evolving demands. Lastly, embedding ethics in every project phase ensured that our AI solutions were not only technologically sound but also socially responsible.