Here's how you can manage AI projects differently from traditional projects.

Regular software delivery can be pretty "unknowable", but AI is both fast moving in terms of innovation and new services, alongside operating in a world where the data used for the AI is moving at its own pace. This requires an adaptive and iterative approach to design and implementation. As AI is also experimental, you'll need to build in regular inspect and adapt points as you learn more about the nature of your service. Attempting to manage an AI project in a waterfall fashion will likely lead to under-delivery on expectations and a souring of AI usage in the business. You'll need to embrace failure as learning, and have back-up plans when it comes to implementation, or agree what your sunk-cost cutoff point is.

Embracing flexibility is key to managing AI projects differently from traditional ones. As an AI engineer, I’ve found that AI projects often involve more experimentation and iteration due to their innovative nature. Unlike traditional projects with fixed outcomes, AI projects require adapting to new data, refining algorithms, and continuous testing. Embracing this flexibility allows the team to pivot when necessary, incorporate new insights, and improve models progressively. This approach ensures that the AI solutions we develop are robust and capable of adapting to evolving requirements and challenges.

- AI projects really depend on data—it's at the heart of everything. ???? - Good data means good results. So, getting the right data is super important! ??? - You need a team who knows their stuff about collecting and cleaning up data. ?????? - They'll make sure the data is top-notch before any AI training starts. ???? - Remember, if you start with bad data, you'll end up with bad results. ???????? - Making data quality a big deal from the start is key for successful AI stuff. ????

Here are 5 pointers to consider:- 1. Data Focus: Emphasize data quality and quantity, as AI models rely heavily on diverse and accurate datasets. 2.Iterative Development: AI projects often need iterative cycles for model training, testing, and refinement, unlike the linear approach in traditional projects. 3. Cross-Functional Teams: Integrate data scientists, domain experts, and engineers to address the multifaceted nature of AI projects. 4. Uncertainty Management: Prepare for unpredictability in AI outcomes and performance, requiring flexible planning and adaptive strategies. 5. Ethics and Compliance: Address ethical considerations and regulatory compliance, ensuring responsible AI use and data handling.

Given the unpredictable nature of AI projects, part of the required flexibility is to always include failure as a scenario. Always. You may not get the right data. You may learn that the problem is too difficult and that your model remains inaccurate. You may find out that there are some issues with compliance. Make sure you find these things out as soon as possible in your project and then either pivot, or abort.

AI projects differ from traditional projects primarily due to their data-centric nature. The quality of data directly impacts AI outcomes, making it essential to prioritize high-quality, relevant data. This involves ensuring your team includes experts in data engineering and data science, responsible for data collection, cleaning, and preprocessing. In AI, the adage "garbage in equals garbage out" holds significant weight; poor data quality can lead to unreliable AI models and misguided decisions. Therefore, managing AI projects effectively means placing a strong emphasis on data quality right from the start.

Investing in high-quality data is essential for successful AI projects. Here are the critical considerations: Data Quality Matters: According to Gartner, Organizations lose $15 million yearly due to poor data quality. Quality outweighs quantity in data. Biases and Representation: Lack of diversity in training data may affect AI model performance. Seek diverse data sources to reduce bias. Data Architecture: Define standards for data collection, storage, & integration. Continuous Monitoring: Let go of irrelevant or inaccurate data. Poor data can lead to unexpected AI model outputs. Review Frequency: For critical AI/ML operations, monitor daily/weekly. For monthly reporting, evaluate quarterly.

?? Data quality is key in AI projects. To ensure success, prioritize these key steps: - Access to high-quality, relevant data is essential - Include experts in data engineering & data science on your team - Data collection, cleaning, and preprocessing are crucial tasks - Prioritize data quality to avoid "garbage in, garbage out" scenarios Let's keep the discussion going on how data centricity impacts AI project outcomes! ??

There are a few targeted strategies which could help: 1. Implementation of Data Governance Frameworks (from planning to de-commission). Use relevant standards (e.g. ISO 5259 series, ISO 8183) as a good practice. 2. Leverage automated data cleaning tools. 3. Implement a process of continuous data quality monitoring. 4. Integrate cross-functional training to boost domain expertise and technical data handling.

AI projects are fundamentally data-driven, making data centricity crucial. This involves not only collecting vast amounts of data but ensuring its quality, relevance, and accessibility. Effective data management practices, including data cleaning, normalization, and enrichment, are essential. Additionally, fostering a data-centric culture within the organization can lead to better decision-making and more accurate AI models. Prioritizing data integrity and utilizing advanced data analytics tools can significantly enhance the outcomes of AI initiatives.

In my experience as an AI engineer, It's evident that AI projects benefit greatly from the diverse expertise of professionals from various fields such as data science, software engineering, domain-specific experts, and UX design. This diversity fosters creativity and comprehensive problem-solving, ensuring that AI solutions are well-rounded and practical. Collaboration among these disciplines helps integrate different perspectives and skills, leading to more innovative solutions and successful project outcomes. We have been following this cross-disciplinary approach and it has been vital in addressing the multifaceted challenges inherent in AI development.

?? Building a cross-disciplinary team for AI projects is key! Here are some tips to ensure success: - Include a mix of skills like data scientists, ML engineers, domain experts, and project managers. - Effective communication is essential to align AI solutions with business objectives. - Collaboration among diverse skill sets fosters innovation and drives project success. Let's continue the conversation and learn from each other's experiences!

Imagine assembling a team of superheroes for your AI project! Just like the Avengers, you need a diverse group to succeed. Data scientists are the Iron Man of the team, wielding data and algorithms to extract insights. AI architects are like Captain America, providing vision and strategy. Machine learning engineers are your Black Widow, turning ideas into reality. Finally, business stakeholders like Nick Fury ensure everything aligns with the mission. But even the best heroes need a well-rounded team. Diversity in skills and perspectives leads to better solutions, considering everything from technical aspects to ethics & user experience. This dream team approach ensures your AI project is not just powerful but responsible & successful.

AI projects require a unique blend of expertise. Unlike traditional projects where the focus might be on general software skills, AI projects necessitate specialized knowledge in AI technologies. For instance, Quality Assurance (QA) personnel need to be proficient in testing outputs from large language models (LLMs), understanding nuances such as the model's propensity for 'hallucination' or bias. Developers, on the other hand, should be skilled in prompt engineering to effectively interact with LLMs, and have knowledge of techniques like Retrieval-Augmented Generation (RAG) for enhancing responses, as well as model fine-tuning methods to tailor AI behaviors to specific needs.

TeamAI projects benefit immensely from cross-disciplinary teams that bring together diverse expertise. Combining skills from data science, machine learning, software development, domain-specific knowledge, and even ethics helps address the multifaceted challenges of AI. This collaborative approach fosters innovation and ensures that AI solutions are practical, ethical, and aligned with business goals. Encouraging open communication and mutual learning within these teams can lead to more comprehensive and effective AI implementations.

To add continuous learning to a GenAI tool, you need a system to review and improve responses. This can be done manually, automatically, or using both methods. A helpful tactic is to let users give thumbs up or down to responses. By collecting these ratings, you can find common patterns in the feedback. Avoid making real-time changes based on just one thumbs up or down, as this can make the model less accurate. Different users have different preferences. Adjustments should be made using overall feedback to ensure the model improves for everyone.

In the rapidly evolving field of AI, continuous learning is especially crucial. Project Managers (PMs) and Product Owners must also stay abreast of the latest AI technologies and methodologies. Understanding AI-specific project management challenges, such as those associated with the iterative nature of AI model training and deployment, is vital. They should be familiar with the technical aspects that drive AI projects, including data dependency, model accuracy, and the ethical implications of AI solutions.

The AI landscape is rapidly evolving, making continuous learning a necessity for AI project teams. This involves staying updated with the latest research, tools, and best practices in AI and machine learning. Encouraging a culture of learning through workshops, seminars, online courses, and collaboration with academic institutions can keep the team’s skills sharp. Continuous learning also implies iterating on AI models based on new data and feedback, ensuring they remain effective and relevant over time.

Leadership should actively participate in learning activities to set an example. When leaders highlight their own learning, it motivates others to follow suit. Ensure that your team has access to state-of-the-art tools and platforms that can aid in their learning and development. Recognize and reward efforts and achievements in learning and development.

Risk assessment in AI projects should involve whether the solution is rule-based analysis, ML, or Gen AI. It should also include a thorough evaluation of the data used for the model training (e.g. if sensitive data is used) and, of course, if the solution will have potential reputational risk for the organization. This will help in determining the likelihood and consequences of the risk.

Risk assessment in AI projects involves unique challenges, such as algorithm unpredictability and ethical considerations. Implement robust testing and validation frameworks to anticipate and mitigate these risks. Ensure transparency in AI decision-making processes to build trust and comply with regulations. Addressing these aspects proactively will help manage potential pitfalls and ensure the reliability and fairness of AI solutions.

Risk assessment for AI is part of the larger AI governance that every AI solution should receive before being rolled out. A few pieces of that are (not an exclusive list): - Data validation: Where the best/most practical sources used in the right way? - Model validation: Did the modelers/engineers make sensible decisions along the entire dev process? - Bias testing (especially for consumer or employee facing models) - Legal checks: on variable level, data level and use case level. - Ethical check: Does this meet our AI Ethics guidelines and procedures? - Risk check: Model risk vs. benefit. Are we comfortable? - Government affairs check: Are we likely to be compliant with future legislation and regulation we can reasonably anticipate?

In addressing the risks associated with AI projects, it's important to have an established process: 1. Main phases in the continuous loop: planning, identification, analysis, establishing and implementing acceptability matrix, evaluation of risks, treatment and mitigation, re-evaluation after mitigation, and monitoring. 2. Transparent validation framework. 3. Integrate standards (e.g. ISO 23894, ISO 14971 etc) and best practices to make it robust and linked to the regulatory frameworks. 4. Involve stakeholders from multiple disciplines to have a holistic view of system, data and business-related risks.

AI projects are amazing, but like any powerful tool, they come with risks. To fight these risks, we need to understand where they hide! Data collection, model training, and even deployment can introduce issues. For instance, a model might be super accurate in testing but flop in the real world. We can also face legal troubles if our AI isn't fair or secure. The good news is there are ways to mitigate these risks. By understanding regulations, facilitating a culture of awareness, and having diverse teams, we can ensure our AI is responsible and avoids potential pitfalls. So, let's identify the villain's lair (the risk) and then call in the heroes (mitigation strategies) to save the day!

Don't build an AI in a vacuum! Unlike traditional software, AI systems thrive on real user input from the get-go. Imagine building a customer service chatbot that talks gibberish because it wasn't trained on real user questions. By incorporating feedback loops throughout development, you can continuously improve your AI. Think of it like teaching a language student - the more they interact with native speakers, the better they become. This user input ensures your AI isn't just a technical marvel but a helpful and effective tool that meets the needs of the people who will use it.

Integrating users into the AI project lifecycle is vital for ensuring the developed solutions meet real-world needs. Engaging users early on for feedback and testing can help in refining AI models and improving user experience. Understanding user requirements and incorporating their insights can lead to more intuitive and user-friendly AI applications. Regular interaction with end-users and stakeholders ensures that the AI solutions are not only technically sound but also practically valuable and widely accepted.

Don't just integrate user feedback because it's common practice and you want to follow your user testing learning objectives; use active listening throughout the entire AI project to make sure your project meets the actual, underlying user needs. Apply model refinement/finetuning based not just on generalization and use case-level but install a memory bank per user to gain more insight into their individual preferences.

Conduct interviews with potential users at the outset to understand their needs, expectations, and the problems they need solved. Create detailed user stories or personas to represent the different types of users who will interact with the AI system. This helps in designing features tailored to specific needs and preferences.

The key principles around project management apply to AI projects as much as they do any other type of project. Yes, data analysis, development of algorithms and continuous learning are particularly important, but they're important in other types of project too. It can be argued that the complexity of some AI projects make it more important than ever that they're managed effectively, according to established project management principles.

Use automated evaluation tools to run your tool multiple times and observe the outputs. This is similar to automated code testing, though the results are less binary. It's crucial to define what constitutes a good versus a poor answer. Create a rubric and feed this to the evaluation tool. The tool will then score the responses based on your criteria. This method allows you to take a data-centric approach to evaluating your tool, ensuring progress rather than regression.

start with a well-defined use case that delivers measurable business value. Rather than pursuing AI for its own sake, focus on a specific problem where AI can drive efficiency, reduce costs, or improve decision-making. By demonstrating the tangible impact of AI, you can build momentum, secure funding for future initiatives, and establish AI as a strategic capability within your organization.

To manage AI projects differently from traditional projects: 1. Iterative Approach: Emphasize agile methodologies with frequent iterations and feedback loops. 2. Data Focus: Prioritize data collection, preprocessing, and quality as foundational steps. 3. Experimentation: Allow room for experimentation and adjustments as models are trained and refined. 4. Cross-functional Teams: Involve diverse expertise including data scientists, engineers, and domain experts. 5. Ethical Considerations: Incorporate ethical guidelines and monitor for biases throughout the project. These strategies help tailor project management to the unique demands of AI initiatives. #DhandheKaFunda

If you use your creative thinking skills to their full potential, you can broaden the scope of the entire AI project, leading to more opportunity for excellence. Broaden the scope by using AI to define the project, set the objectives and understand when, how and why to use AI application on the different flight levels. Often, AI is only used for operational tasks and project management. Rethinking that habit and having AI engage with you on the strategic flight level is what sets your project apart from competing projects by clever usage of AI insights. Formulas: (innate curiosity x creative thinking) = more AI experimenting. (AI experiment insights x critical thinking) = better field overview + decisions for your AI project.