You're working on an AI project with a team. How do you ensure everyone is on the same page?

At the heart of every successful AI project lies the trifecta of leadership, culture, and communication: 1. Set Clear Goals: Define success with SMART goals to align the team. 2. Open Communication: Use meetings and tools like Slack/Trello for transparency. 3. Build a Unified Culture: The project's success is deeply intertwined with the team's culture. 4. Implement Continuous Feedback: Create a structure for regular feedback, encompassing both project milestones and individual contributions. 5. Lead by Example: Leadership is action, not position. Demonstrate your commitment to the project's goals, show openness to feedback, and navigate challenges with resilience. How do you keep your team on the same page?

Additional two items (see previous answer for first three points) 4. Plan to have quick data quality checks otherwise the project can be at risk. Have a plan on how will you handle the cases when additional data sources may be needed that are not planned initially (change request can be an answer ). 5. Most times, you will not know whether that F-measure or that false negative number can be reached or not. That is the risk and how you put that in scope and manage will be the key. Many more items of the same nature are needed to be taken care of while defining scope...

RACI matrixes are great to help leaders determine roles and responsibilities. You can also put people into categories as to what their natural propensities are e.g. helpers, doers, ideas people, structurers, Networkers, and Protagonists. Running a team is like a small family, so the leader is really the Patriarch or Matriarch of the whole situation. Each person should contribute to what the scope is from the very beginning and the project does not start until everyone agrees on scope. #ai #artificialintelligence

Lead by example, with enthusiastic commitment to and accountability for precise project objectives. Use SMART goals to align the team and hold off-site open communication sessions to bond a unified culture and agree on the project scope. Regularly assess milestone progress, proactively address challenges, and celebrate progressive success and final individual and team achievements.

We have customers, who want us to help in selecting the right problem to solve using AI. And even after selecting the right problem, there are many unknowns for AI project (more than a web/mobile project). Therefore, I suggest the following key and critical things to care : 1. Spend time in understanding what the as-is process and what the to-be process will be. 2. Spend time in benchmarking the processes today otherwise you will never be able to prove value later. (Recording of processes/interviews etc will be recommended). 3. Have the AI Governance (AI Risk/AI Adoption) plan in place. Tame the fear of failure or losing job initially within the client organization in case if it is their first AI project.

In some situations, the vision is the easiest thing to agree on - the key to real success is harnessing that shared future as a motivator - for each member of an #empowered team to identify potential obstacles and work to eliminate them. At times the ultimate outcome is clear and mutually beneficial - but can still be blocked if any critical function hasn’t been adequately managed along the way. Driving accountability can boil down to connecting back to that outcome - working from “We all want to achieve X…” to identify the variable that may be standing in the way.

The vision should come with mission statement that actually means something and has some substance to it. That is what will inspire. What can you think of that relates to your project that has alturistic meaning to fulfillment to your team? The more inspired your team is, the better they are likely to perform. #ai #artificialintelligence

Sharing the vision of an AI project goes beyond the mere formulation of goals; it is like a beacon in the fog of uncertainty. This revelation is more than just information - it ignites the spirit of collaboration and turns disparate individuals into guardians of a shared dream. Like a lighthouse, it guides the team through the storm of challenges to the shore of innovation. In this way, a foundation of mutual accountability is created, and a network of goals goes beyond the project's boundaries and touches the lives of users, stakeholders, and even society itself. By sharing the vision, we are not just delegating tasks; we are handing over a piece of the future and inviting others to dream, dare, and act with us.

The vision encapsulates not only the overarching goal and intrinsic value of the project but also its potential impact on the broader landscape. Additionally, it should candidly outline the challenges and opportunities that lie ahead, setting the stage for a transparent and engaged team dynamic. Articulating the projects vision is a critical step in galvanizing your team, nurturing their commitment, and instilling a deep-rooted sense of ownership and accountability towards the project's success. Furthermore, a well-conveyed vision serves as a powerful narrative when engaging with external stakeholders, be they clients, users, or managers, enabling them to grasp the significance and potential of your AI endeavor.

1. Clearly Define the Vision: Start with a clear and compelling vision statement for the AI project. It should be ambitious yet achievable, outlining the impact of the project on the organization or society. 2. Communicate the Why: Explain the rationale behind the project. Help team members understand why the project matters and how it aligns with the broader goals of the organization. 3. Use Engaging Formats: Share the vision through multiple formats - presentations, meetings, written documents. Visual aids like infographics or videos can make the vision more tangible and engaging. 4. Encourage Ownership: Invite team members to contribute their ideas on how to achieve the vision, fostering a sense of ownership and collaboration.

Create a forum to accept, observe, analyse feedback from every team, methodically. While the project / release could be focused on specific outcomes, the perspectives help you identify and fulfill the gaps from different stakeholders - obseravbility - performance - cost-to-value - manageability/ serviceability - time-to-market - flexibility to fine tune - hooks /interfaces for automation - security and regulatory compliances.

Since AI is an extension of human intel collective group insights are really powerful way to leverage more capability of AI. Depending on the project, if possible add more minds to the collaboration I think the users are the most important part here not to miss. We tend to get hung up on the data and algorithms and all the technical jargon and infrastructure that it takes to deploy an AI solution, but data is really at the center of all that and at the center of data are the users. Whether they are customers, employees, or stakeholders, all user types need to be involved in design, development and deployment of AI, so front, middle and back and continuous because we can learn from them all #ai #artificialintelligence #aicollaboration

Imagine you have a team working on building a chatbot that assists users with customer support inquiries. Each team member brings different expertise to the table: one specializes in ML algorithms, another in linguistics, and another, an UX expert. The ML expert can share insights into the latest NLP models and techniques for improving the chatbot's understanding of user intents and responses. The linguistics specialist can provide valuable knowledge about language nuances, helping to fine-tune the chatbot's language understanding capabilities and ensure it communicates effectively with users. The UX designer can contribute ideas for crafting a seamless conversational experience, incorporating a seamless UX.

Incorporate technical tools like version control systems (e.g., Git) and project management platforms (e.g., Jira) to streamline collaboration. Establish coding standards and documentation practices to ensure consistency and clarity. Utilize code reviews and pair programming sessions to exchange technical expertise and catch potential issues early on. Leveraging collaborative coding environments like GitHub or GitLab fosters real-time collaboration, enabling team members to learn from each other's code and practices.

Working on any project the objective is the same what are we solving for. How will it help the general population or the working group that needs the information? listening to those who have expertise, but also listening to those who have an objective opinion that’s not part of the overall project. Streamline the information as much as possible, but establish how this will affect the overall group. To make the project successful I have to believe in what you’re doing and why you’re doing it you’re people will probably feel the same they may not say it but it’s important.

Some great tools for innovation collaboration include miro boards if your team is remote For management of projects, I recommend Teamwork Project Management Software And for onsite teams, you can never really go wrong with early, mid and late day sprints, or if too much then you can just run them daily #ai #artificialintelligence

In a recent project focused on predictive analytics, I documented our utilization of a time-series dataset from financial markets, clarifying its extraction and preprocessing, which involved normalization and segmentation. I detailed our adoption of an LSTM model, specifying hyperparameters like the number of layers and dropout rate. This documentation also covered our evaluation process, where I explained the use of RMSE for performance assessment. By documenting each step, from data sourcing to model evaluation, I ensured that people could easily understand, replicate, and build upon our work, fostering a collaborative and productive environment.

One of the cool things AI can do is generate content. If you make sure you give it the right prompts, it will help create most of the documentation. AI is adept at the entire content creation lifecycle, from articulating initial product concepts to generating epics and user stories, coding, testing, and crafting operational runbooks, through to continuous logging and reporting—all with precision and clarity when guided by accurate prompts. By leveraging AI across these stages, organizations can streamline their development and operational processes, ensuring that products are not only brought to market more quickly but are also more reliable and easier to maintain.

Document project objectives: Clearly define the project objectives, goals, and milestones in a project roadmap or document that all team members can access.

Communication is key in AI projects. Regular team meetings, documentation, and collaborative tools help ensure everyone understands project goals, progress, and challenges.

AI projects are not a linear process but you also want to eliminate as much back and forth as possible between team members. 1. Setting regular standups and check-ins 2. Establish a decision making process; it is not a dictatorship but there needs to be some sort of process 3. And of course use the proper collaboration methods #ai #artificialintelligence #aicollaboration

Review and iteration in AI projects are especially important considering the fact that some times in inner parts some models or configurations might cost much more than we would expect. It especially happens in pay-as-you-go style systems. To make sure that our project and production is perfectly trackable and not the subject to single point of failure, we have to iterate even faster than other software development projects. And review processes should be designed in very careful and straightforward manner to make sure that it perfectly aligns with AI ethical rules, industry standards (especially considering AI tools are often subject to get updated).

The project work is best organised in a Scrum mode with constant review and iterations of user stories. I have made the experience, that neither users nor stakeholders are easy to convince to attend reviews, so it is always also worth the effort to assign business counterparts as "key users" or "collaborators" with the task to collect input and give feedback in reviews. Extra reviews as a form of outcome presentation for stakehodlers are also a good idea.

AI projects are often complex and unpredictable. Regularly evaluating progress, testing assumptions, and being ready to pivot based on feedback will help ensure that your project stays on track and delivers the expected results.

Sometimes you might be going down a path due to decisions already made knowing that it isn't going to get you to the outcomes you are looking for. So, be prepared to scrap the project and start over - sometimes that's better than going down the wrong path.

Ensuring everyone is on the same page in an AI project necessitates clear roles and responsibilities to avoid confusion, regular communication to discuss progress and challenges, setting expectations regarding timelines and quality standards, implementing feedback mechanisms for continuous improvement, ensuring alignment with organizational goals to maintain focus, and celebrating milestones to boost morale. By incorporating these elements into project management practices, teams can foster collaboration, alignment, and collective understanding, ultimately leading to more successful outcomes in AI projects.

Ensuring the success of any endeavor hinges upon the presence of a capable quarterback. A skilled leader is paramount to keeping the team aligned, possessing an extensive understanding of the overarching vision, technological nuances, and business dynamics in motion. This individual excels at breaking down the program into manageable components and aligning tasks with team skills and interests. Furthermore, they exhibit the insight to empower the team, steer through pivotal decisions, handle executive expectations, and commemorate incremental victories, thus nurturing enduring team commitment.

I think upskilling of the impacted functions or departments is often underestimated although direly needed. It doesn’t mean to have everyone trained to be a data scientist or architect, but to have a baseline knowledge and understanding of the definition and role of data along with the different types of AI. This also possibly unlocks bottom up idea collections from the operational levels who are in this way also empowered to develop their working environment.

In the landscape of AI projects, the intertwining of technical prowess with human ingenuity often dictates the path to success. Stories of overcoming obstacles through creativity, harnessing diverse perspectives to address AI bias, and the commitment to ethical considerations highlight the multifaceted nature of these endeavors. The essence of AI development transcends algorithms and data models, deeply rooted in the collaborative spirit, ethical foresight, and continuous learning of the teams behind the technology. This underscores a fundamental truth: the journey of AI innovation is as much about advancing human values and collaboration as it is about technological breakthroughs.

Não só em projetos de IA, mas em qualquer outro projeto, vale a máxima de planeje 2 vezes e execute 1, se errar, erre rápido e corrija mais rápido ainda. Mas uma peça muito importante para que isso aconteça é a velha e boa comunicação, todos devem estar na mesma página e cientes do papel que cada exerce neste processo.