What are the steps to define AI project scope and deliverables?

Tips for defining AI project scope and deliverables: 🔹 Be as specific as possible. The more detailed the scope statement, the less likely there will be misunderstandings or disagreements down the road. 🔹 Be flexible. The scope of an AI project can change as the project progresses, so it's important to be flexible and willing to adapt. 🔹 Use an agile approach. Agile methods can help you to define the scope of an AI project iteratively and incrementally, which can help to reduce risk and improve the chances of success.

In the Agile methodology, assigning a Product Owner role to the Pod (team) is paramount. Particularly when this role is assumed by a member of the business sponsor's team, it acts as a bridge, connecting the technical aspects of the project with the overarching business goals. With their profound understanding of the business's needs and the nuances of the data landscape, the Product Owner ensures that the project scope and the vision of the analytics product remains finely attuned to business objectives. This alignment enhances the relevance and effectiveness of the AI solution.

Understand why you think AI will help you solve this problem. Be experimental and go for an agile iterative approach so you can learn as you go.

It’s very important to ensure alignment with goals. ✨Understanding the problem is the foundational step in any project. ✨Analyze the existing scenario to identify challenges & inefficiencies. ✨Recognize potential areas where AI can bring transformative improvements. ✨Define the ideal outcome you aim to achieve with the AI solution. ✨Conduct a comprehensive analysis of the problem domain. ✨Underst& the needs & expectations of stakeholders. ✨Identify relevant data sources for developing the AI solution. ✨Explore various avenues to address the identified problem. ✨Define clear criteria for determining the success of the AI solution. ✨Establish measurable Key Performance Indicators (KPIs) which will help measure the solution's effectiveness.

In addition to the impact of the AI project, would recommend to perform initial risk assessment of the use case as well. By gauging the level of risk in relation to the projected return on investment (ROI), organizations can make informed decisions on whether to prioritize or deprioritize the AI use case. This proactive approach not only safeguards against unforeseen challenges but also optimizes resource allocation, ensuring that AI initiatives align with business goals and contribute positively to the overall strategic direction.

I think below factors can be crucial to understand the problem- 💡Root Cause - It will help us identify if applying AI is really required to solve this ? 💡Constraints - What factors can limit AI based solution? 💡Ethical Consideratiins - Personally I think it important to ensure that our solution doesn’t create bigger socio-ethical issues. 💡Data - Surely, data availability and quality is very important.

Understanding a problem is pivotal in AI projects. But beyond recognizing pain points, it's vital to probe why they exist. Delving into root causes and historical trends offers invaluable context. Additionally, when examining data sources, it's crucial to assess data quality and inherent biases. Since AI thrives on data, the principle "garbage in, garbage out" remains relevant. So, a holistic view of the 'why' behind issues, combined with meticulous data assessment, ensures a robust foundation for AI solutions.

A crystal-clear plan can begin forming only with those done: Deep Dive Analysis: Before you even think about algorithms, get into the nitty-gritty of the problem. What's the pain point? Who's feeling it? Why should they care about your solution? Why even thinking that AI is the cure? Stakeholder Workshops: Organize sessions with every Tom, Dick, and Harry involved. Get their input, their reservations. Data Audit: Scrutinize your data sources. Are they biased? Incomplete? Outdated? If yes, toss 'em out. Garbage in, garbage out. Feedback Loop: Once your AI is even starting to blink, have a mechanism for users (internal or external, doesn't matter) to provide feedback, and more importantly, act on it. Only then think of KPIs and plans.

Clearly define the problem that you want to solve with AI, and the features, functions, and capabilities of your AI solution. Identify the technical, ethical, and legal constraints and risks.

Clearly defining the AI solution is vital. Ensure that the AI features and capabilities directly address the identified pain points and opportunities. Moreover, it's crucial to consider ethical and legal implications from the outset, especially in industries with strict regulations. Consult legal and compliance experts to avoid complications later in the project.

I like to think it as an AI Task, and use below framework Definition - High-level process flow: To understand the fitment of the AI task into the overall process. - The inputs and the outputs of the task - The resources it will access. E.g. database, knowledge base, etc - Does it interface with humans through chatbot? or as an API to be called from an application? Design - Model type: Prediction, Forecasting, Generative, Recommendation, Classification, etc - Training Needs: From-scratch, Fine-tuning, Off-the-shelf. - Training Data: Available data and its structure. The structure needed for training - Data Engineering Design: to collect and process data for training. - Evaluation and Testing Approach - Deployment & Operations approach

Before defining AI solutions, you need to make sure the solution meets the following factors 1. Specific use case that you are solving for 2.AI Security framework 3. AI ethical framework 4. AI Legal framework

✨Defining the AI solution shapes the blueprint, a roadmap considering all dimensions success. ✨Enumerate the specific features & functions the AI solution will encompass. ✨Clarify how the AI solution will address the problem & provide value. ✨Create a vision statement outlining the challenging objectives. ✨Define the scope, including main objectives & benefits. ✨Identify the essential requirements that the AI solution must meet. ✨Ensure alignment with stakeholders' needs & expectations. ✨Recognize technical limitations that may influence the solution. ✨Address ethical implications, ensuring responsible AI use. ✨Identify legal constraints & ensure adherence to regulations. ✨Analyze potential risks & establish mitigation strategies.

Do a feasibility study to scope what technical landscape could support a potential solution. Check legal compliance and ethical considerations especially on data and personal details. Document detailed requirements for the AI solution. This includes functional requirements (what the system should do) and non-functional requirements (performance, scalability, security, etc.).

Solutions shouldn't just be about addressing present concerns, but also foreseeing future scenarios. Think of AI as an evolving entity: it learns, adapts, and grows. As you define its features, consider how these might be scaled or adapted in the future. Anticipate the potential evolution of stakeholder needs, and ensure flexibility in the design. Furthermore, given the dynamic landscape of AI ethics and legislation, it's prudent to have a proactive stance. Instead of just navigating current regulations, forecast where ethical discussions might head and pre-emptively align your solution. Remember, in AI, foresight isn't just an advantage—it's a necessity.

Remember to consider the need and benefits of using APIs as part of your AI solution or offering your solution as APIs. Often a "point-of-solution" API offering easy access to teach the ML or use the algorithms is key to success and scaling. Remember, though, that it may be possible to steal an algorithm or model via an API, so protect your API and model. Keep the scope minimal and have a clear value prop for each APIfied AI "skill", and you and your API / AI consumers will be able to easily orchestrate new solutions based on these APIs.

AI is here to serve humanity, not to add to the problems. We should have the clarity to maximize the value for maximum stakeholders ( investors , business , government entities, end users or anyone whosoever is impacted ) . We should understand that it's a transformation phase & AI is still in learning phase , we can't directly jump to the conclusions . We need to develop a holistic view here . So we need to take a balanced approach that minimize the risk for stakeholders and think moreover as a learning curve for AI.This will definitely help mature AI in a more humane manner.

In AI-driven projects, it's key to remember that static planning can be a limitation. It's not just about following a roadmap, but also about expecting detours and welcoming them. Integrating continuous feedback loops, not only from team members but also from the AI itself, can yield richer insights. As AI models train and evolve, so should your approach. Embrace unconventional team structures; consider involving AI ethicists or anthropologists to foresee societal implications. Remember, AI isn't merely a tool; it's a partner in your journey. Craft your plan keeping in mind that AI will 'grow', and your strategies should be malleable to harness its ever-evolving potential.

Planning lays the groundwork for efficient project execution,fostering adaptability & collaboration. ✨Outline how the AI project will be structured & managed. ✨Detail the approach to executing the AI solution development. ✨Define roles, responsibilities,& skills of project team members. ✨Specify tools, methods,& standards for development. ✨Embrace an agile approach for flexibility & iterative progress. ✨Develop a comprehensive project plan with Scope Definition, Schedule, Budget, Quality Standards & Communication Plan. ✨Integrate regular feedback cycles to refine the AI solution. ✨Identify potential risks & formulate mitigation strategies. ✨Allocate resources effectively to meet project demands. ✨Engage stakeholders for alignment & support.

When determining how to organize and manage the project, take into account the existing capabilities and resources within your organization. Agile methods are indeed valuable for AI projects, allowing for flexibility and adaptation as the project progresses. Make sure to involve cross-functional teams that include both technical experts and domain specialists. Collaboration between IT and business units is key to success.

This facilitates efficient task management & effective collaboration, ensuring steady progress. ✨Divide the AI project into manageable components & modules. ✨Create a work breakdown structure (WBS) outlining hierarchy & dependencies. ✨Define tasks & sub-tasks needed for each module. ✨Prioritize tasks based on their significance & interdependencies. ✨Assign tasks to project team members based on skills & capacity. ✨Develop a product backlog with User Stories, Features & Acceptance Criteria. ✨Map out task dependencies to ensure smooth progression. ✨Align with agile principles, enabling flexibility & adaptability. ✨Plan iterations with focused goals & deliverables. ✨Regularly review & refine the work breakdown structure & backlog.

Breaking down an AI project is more than just task allocation; it's akin to creating the DNA of your solution. Recognize that unlike traditional projects, AI thrives on iteration and experimentation. Instead of a rigid hierarchy, envision a fluid ecosystem. While WBS helps in alignment, a 'learning backlog' can be introduced, where the AI's insights, anomalies, or 'surprises' are catalogued for team evaluation. Such practices make space for serendipity - sometimes the AI may find a solution pathway you hadn't thought of. Cultivate an environment where both humans and AI can refine and redefine the project's trajectory.

Breaking down the AI project into manageable units is where your extensive technology background will shine. Consider the modularity of your AI solution, making it easier to prioritize and assign tasks. Embrace the idea of continuous improvement. Encourage your team to regularly review and refine the work breakdown structure to ensure alignment with evolving project goals and emerging insights from the data.

Estimating AI projects goes beyond traditional metrics; it's about forecasting the known and preparing for the unforeseen. While time, cost, and effort are foundational, one should also estimate the 'knowledge accumulation'—how quickly the AI model matures and the iteration loops needed. Introduce the 'elasticity factor,' gauging the system's ability to adapt to new data without significant reworks. And don't just budget for development; allocate resources for post-launch AI 'tuning' sessions. Remember, AI is a bit like fine wine; it matures with time and data. So, set up a 'refinement reserve'—a dedicated space in your estimation for AI's unique evolutionary path.

It ensures realistic outlook & effective resource management. ✨Estimate duration required for each AI solution unit. ✨Determine costs for development, deployment, & maintenance. ✨Assess the effort needed from the project team & resources. ✨Utilize various techniques, such as Expert Judgment, Analogous Estimation, Parametric Estimation. ✨Utilize estimation tools & software for accuracy. ✨Account for uncertainties & project variability. ✨Formulate plans to mitigate unforeseen challenges. ✨Allocate buffers to handle unforeseen delays. ✨Factor in potential risks that could impact estimation. ✨Continuously refine estimates based on actual progress. ✨Optimize resource allocation for efficiency. ✨Establish a budget that covers all project aspects.

A couple of essential things to consider in estimation are the Uncertainty/ risk involved in this type of project and the complexity of the solutions. AI is still new and requires lots of research and experimentation before reaching out to a perfect model for the business case. Having contingency in the project gives Data scientists scope to try new things. I generally work closely with my project team and stakeholders to establish the estimations for the AI projects.

Accurate estimation is challenging in AI projects due to their inherent complexity and unpredictability. It's wise to provide stakeholders with a range of estimates to account for uncertainties. Be prepared to adjust these estimates as the project unfolds and you gain a better understanding of the data and technical challenges.

The main validation of the project deliverable is against the core objective of customer and Product vision. It needs to go through rigorous testing and code review to ensure high-quality output One of the important validations for AI projects is - Bias and Fairness Analysis: Assessing the AI solution for biases that might lead to unfair or discriminatory outcomes. Use tools and methodologies to identify and mitigate biases that could negatively impact certain groups.

✨Engage stakeholders & customers throughout the process. ✨Ensure AI solution aligns with user expectations & needs. ✨Collect feedback from stakeholders & customers; analyze & integrate as needed. ✨Employ varied techniques, such as:Usability Testing, Beta Testing & Surveys/Interviews. ✨Leverage agile principles to adapt & improve. ✨Embrace iterative cycles for ongoing enhancement. ✨Ensure the solution delivers tangible value to beneficiaries. ✨Validate solution's adherence to requirements. ✨ Confirm alignment with regulations & ethics. ✨Utilize testing tools to ensure reliability & accuracy. ✨Validate that the solution meets the defined scope. ✨Do final review of solution before deployment. ✨Continuously iterate based on validation insights.

Validating especially an AI project should include business, risk, compliance, bias and impact to people: employees, customers, citizen, policy, business environments and markets, not just technical validation.

Sounds like this whole article was written by ChatGPT. Lots of statements of motherhood and apple pie, with little real insight. So lesson #1 for managing AI projects is to use your own brain, skills and talent in your team and not use ChatGPT to generate pseudo-content.

The following measures enhance the efficacy & sustainability of AI solutions. ✨Continuous iteration helps in optimal performance. ✨Collaboration across domains enriches AI projects. ✨Solutions should be scalable without major overhaul. ✨Prioritize robust data security & user privacy measures. ✨Solutions should avoid bias & promote fair & ethical outcomes. ✨Ensure AI decisions are understandable & transparent. ✨UI/UX should prioritize user comfort & interaction. ✨Solutions that learn & adapt enhance relevance over time. ✨Utilize pre-built AI libraries & frameworks for efficiency. ✨Solutions should anticipate & accommodate emerging trends (future-proofing).

Documentation is often overlooked while developing software. It helps to ensure that the project is well-planned, well-executed, and well-documented, and that it can be easily maintained and scaled in the future. - Ensure work done in the project is repeatable and reproducible - Documentation helps to make it easier to maintain the machine learning model over time. - Documentation helps to ensure that the machine learning model is transparent and understandable - Documentation helps to ensure that the machine learning project is compliant with relevant regulations and standards

Make sure you have plenty of diverse perspectives included in the process. Many software projects could have achieved their potential if they had involved more viewpoints and challenge. Especially from the various stakeholders who will use or be the recipients of the system.

If you have a desired end state, you have set your goals, and scoped the project as best you can then you should begin looking for data sources that are available to you and the models that are within reach. Often times you may find a model you think will get you there but you don't have enough data so you need to get creative. Often times the grand vision you have set out requires a few forks in the road and machine learning models, the data to train them, and your actual training methods will take some tweaking and time. Make sure to set those expectations up front. Many times people think AI will just magically work but there is a lot of unknowns going into it more so than most work.