What are the best practices for eliciting requirements for AI and blockchain technologies?

Identifying the problem is key to coming up with a solution and understanding the scope of work. A very close next step is understanding the various user personas and how they will be using and interacting with AI and blockchain technologies.

I follow 5 point approach to scope the project including the problem and what we are trying to solve:I use a 5-point approach to project scoping which includes defining the problem, setting SMART objectives, determining scope, understanding stakeholder requirements, and analyzing feasibility and risks.

Your priority should always be the people you serve. Listen attentively, empathize, and put yourself in their shoes. Let their perspective guide you in understanding their needs. While technology is an essential tool, it shouldn't be the main driver in problem-solving. AI and blockchain are powerful tools involving multi-disciplinary teams and skills, all of which need to understand the scope of the problem. I am running out of space, so I'll refer you to a poem, the first four lines of which offer great insight into business analysis. I Keep Six Honest Serving Men - Rudyard Kipling (The Elephant's Child) I keep six honest serving-men (They taught me all I knew); Their names are What and Why and When And How and Where and Who.

The best way to ensure the success of a project, regardless of the framework, is to have the scope of the problem well defined. Basically the objectives must be based on this, in turn, pushes to generate the definition of the requirements and an efficient care plan.

Taking inspiration from Design thinking, a PBO first approach where you try to identify Positive Business Outcome against the stated scope or use case. This will further help prioritise the elements in the Scope during later phases of Prioritisation, Feedback etc.. For B2B sector scoping wrt AI and block chain technologies, it is imperative to identify the positive outcomes that any change/requirement can bring in a ratio which aligns with the business goals of the Stakeholders with a budget plan in hand. The scope should also have a MVP at its core for the power users of the end UX to be impacted by AI and block chain tech deployment and an iterative hook to arrive at the ICP in due cycles.

This is the case with any new capability or tool. It's crucial to capture good business requirements and ideal state. Using that as your launchpad, try to marry functionality, feasibility with the desired business outcome. Look to collaborate with strong sme's who understand capabilities and experience. They can help you bridge the gaps.

Here’s how you can capture requirements for a system that uses AI/Blockchain technologies: 1. Understand the technology impacts on business processes and communicate it to all relevant stakeholders 2. Collaborate extensively with data scientists, developers, and compliance experts to explore various use cases. 3. Create a risk assessment and mitigation plan for the use cases identified 4. Design feedback mechanisms to capture user behavior and preferences, which can inform AI model training and improve blockchain governance 5. Prioritize data security throughout the requirements gathering process 6. Incorporate flexibility to accommodate future changes in regulations, data availability, and user needs

Start identifying the stakeholders. Look up for the Subject Matter Experts and reach out to them with questions and listen on what they have to offer about the subject you are into? Collaborate with various teams, expecially the tech teams through KT sessions, workshops, peer reviews etc. While the field of data science in inter-twined with a lot of disciplines, the idea behind is knowing your data right. Dig into the details to gather both the Functional requirements and Non-functional requirements .

"Alone we can do so little; together we can do so much." - This fits well when we think about collaboration and collaborating with experts is crucial when dealing with complex technologies like AI and blockchain. These domains require diverse expertise in data science, cryptography, programming, security etc areas where a business analyst may not have all the required skills! therefore engaging with experts through interviews, workshops, and prototyping can help elicit their input and ensure that requirements are well-understood and accurately captured.

In the intricate dance of AI and blockchain innovation, the business analyst is the choreographer, not the sole performer. True brilliance emerges when diverse minds converge—data scientists, cryptographers, ethicists, each bringing their unique rhythm. This collaboration isn't just helpful; it's essential. It transforms the impossible into the achievable, turning technical jargon into a symphony of possibilities. In this realm, the most powerful tool isn't knowledge, but the humility to seek it. The future isn't built by lone geniuses, but by orchestras of expertise.

One way I find helpful is to run focus groups especially when you need more information about specific aspects of the project or use case and when time is short. It's an effective way for stakeholders to be more forthcoming and articulate solutions and to obtain domain specific information at once to validate assumptions and to test biases.

In the ever-shifting landscape of AI and blockchain, requirements aren't carved in stone—they're written in sand at the edge of a digital ocean. Validation isn't just a step; it's a constant dance with uncertainty. Each requirement is a hypothesis, each test a revelation. We're not just building systems; we're crafting adaptive organisms that evolve with every byte of data, every regulatory ripple, every user interaction. True innovation lies not in perfect foresight, but in creating frameworks flexible enough to embrace the unknown.

Validating requirements is a required step in our approach at OmiSoft when dealing with AI and blockchain technologies. These areas are inherently prone to uncertainty and change. To ensure clarity, consistency, and alignment with project goals, we use rigorous validation methods. It's crucial to employ various validation techniques, including reviews, inspections, and testing, to ensure that the requirements are not only clear and complete but also testable and aligned with project objectives. This meticulous validation process not only enhances the quality of deliverables but also instils confidence in stakeholders regarding the project’s integrity and suitability for deployment.

Um dos aspectos chave da análise de negócio é garantir que os requisitos sejam claros e entendíveis por todos os stakeholders do projeto, além descrever de forma consistente a solução proposta. Precisa-se que tenha em mente que a tecnologia, como blockchain, é um meio para solução de um problema de negócio e não a solução em si. Portanto, uma análise de negócio não deve ser orientada a uma determinada solução técnica, mas sim pode identificar em uma determinada solução um potencial para resolver um determinado problema de negócio.

Thoroughly validating requirements is a fundamental aspect of every project, involving not only collaboration with stakeholders but also engagement with the developers or engineers responsible for implementation. This ongoing process deserves the necessary time and attention to ensure precision and accuracy from the outset.

Eu uso muito uma matriz de esforço e geração de valor! Aquela que tem menos esforço e gera um valor maior deve ser a primeira a iniciada! Se não gera valor, não deve ser olhado e priorizado!!

Priorizar os requisitos considerados minimamente necessários para alcançar os objetivos definidos pela organização gerando valor para os interessados.

In the AI and blockchain arena, not all requirements are created equal. Prioritization isn't just sorting—it's the art of seeing the future. Each requirement is a seed; we must choose which to nurture first. It's a high-stakes game of chess, where every move shapes the endgame. We're not just building features; we're crafting a technological ecosystem where each element must earn its place. True innovation lies not in doing everything, but in doing the right things at the right time. In this world, the ability to say 'not yet' is as crucial as the power to say 'yes'.

In a particular instance at work, managing the expectations surrounding the implementation of AI features, we faced a multitude of requirements from various stakeholders, each emphasizing their perceived importance. It became evident that without a structured prioritization approach, the project would struggle to deliver value in a timely and efficient manner. A particularly beneficial approach I've discovered is the use of a prioritization matrix, incorporating criteria such as urgency, importance, dependency, cost, and value. This matrix enables the systematic ranking of requirements based on their relative significance.

To effectively prioritize requirements for AI and blockchain technologies, consider criteria like urgency, importance, dependency, cost, and value. This ensures you deliver the most valuable features first while managing resources and risks efficiently.

In the fast-evolving fields of AI and blockchain, maintaining a clear trace of requirements through changes is critical. Utilize a requirements traceability matrix, management software, and version control systems to link requirements with project artifacts like design and testing. This approach ensures coherence across the project lifecycle, enabling effective tracking of changes and their impacts, essential for adapting to new data and feedback.

Based on my experiences, utilizing tools like the requirements traceability matrix, requirements management software, and version control systems has proven effective in maintaining linkages between requirements and other project artifacts. This systematic approach is essential for adapting to the dynamic nature of these technologies and ensuring the successful development and deployment of AI and blockchain projects.

In the dynamic realm of AI and blockchain, requirements aren't static endpoints—they're living, breathing entities in a complex ecosystem. Tracing isn't just record-keeping; it's mapping the DNA of innovation. Each requirement is a thread in an intricate tapestry, woven through design, development, and deployment. We're not just building systems; we're cultivating digital organisms that evolve with every iteration. True mastery lies not in controlling change, but in creating frameworks that thrive on it. In this world, adaptability isn't just an asset—it's survival.

1st understand the requirement set priority explore how and what solutions will fit in to your model and move forward forecast or force see the results

Establish a robust traceability mechanism to link requirements back to the business objectives. This ensures that every implemented feature aligns with the original goals of the project. For example, in an AI-powered healthcare diagnosis system, trace requirements back to specific medical conditions to ensure comprehensive coverage.

Clearly and consistently communicate requirements across the development team and stakeholders. Utilize concise documentation and facilitate regular meetings to address any ambiguities or changes in requirements. In deploying an AI-driven predictive maintenance system, ensure that the development team understands the specific thresholds for equipment failure prediction communicated in the requirements.

To effectively communicate requirements for AI and blockchain technologies, utilize clear, concise, and accurate formats such as user stories, use cases, diagrams, models, and mockups. Tailor your communication to address stakeholders' varying levels of technical expertise, ensuring understanding and buy-in across the board.

AI and blockchain technologies requires a careful approach to ensure that all key considerations are addressed. Few of best practices can be engaging relevant stakeholders, define use cases, conduct workshops and interviews to gather insights and analyze and document functional and non-functional requirements. Developing prototypes and proof of concepts can be valuable by allowing stakeholders to visualise and interact with the proposed solutions. This can help leading to the successful implementation and deployment of these advanced solutions.

Documentation while gathering and eliciting the requriements, recording the MOMs during meetings, thoroughly making note of each and every proposed solution through mocks/prototyping and maintaining change control request forms with record keeping practices for Knowledge sharing purposes and effective communication amongst the teams, assuring the data accuracy, version-tracking while utilizing data analytics and business analysis tools for predicting the data behavior based on the metrics identified.

In my opinion Clear communication of requirements is vital for AI and blockchain projects. Key practices include engaging stakeholders, understanding domain specifics, prototyping, defining user stories, iterating, addressing data needs, ensuring compliance, prioritizing security, scalability, thorough documentation, fostering cross-functional collaboration, and implementing feedback mechanisms. Effective communication ensures alignment between technical capabilities and business goals, driving successful AI and blockchain implementations

For blockchain projects, clearly articulate the blockchain application purpose, choose an appropriate consensus mechanism, define smart contract logic and functions, adhere to regulatory compliance, ensure interoperability, and prioritize data security and privacy considerations. No matter the project, involve all relevant stakeholders early and often, adopt an agile approach, and keep detailed records of requirements, decisions, and justifications. By adhering to these practices, you can effectively establish and manage requirements for successful A

-Involve all relevant stakeholders early in the requirement elicitation process. This includes business users, subject matter experts, IT personnel, and end-users. Understanding their perspectives and requirements is crucial for the success of AI and blockchain projects. -Gain a deep understanding of the organization's business objectives, challenges, and opportunities. Align AI and blockchain initiatives with these goals to ensure that the resulting solutions deliver tangible value. -Organize workshops, focus groups, and one-on-one interviews to gather requirements from stakeholders. Use techniques like brainstorming, mind mapping, and storyboarding to explore ideas and capture essential features and functionalities.

Based on my experience, it is wise to anticipate user adoption by implementing the project alongside a change management strategy that includes effective communication and user training.

As a member of the GenAI Advisory Council for one of the largest technology companies in the world, I've had the privilege of seeing firsthand some of the best examples of GenAI at work, improving efficiencies for companies. If you want to explore how GenAI can make your company even better, please connect with me. I'd love to share examples we've seen work for so many great companies.

Continuous monitoring & optimization - regularly monitor network performance & scalability metrics to identify bottlenecks & areas for improvement. Continuously optimize the blockchain infrastructure & protocols to maintain scalability as the network grows.