AI projects often involve working with team members who have different backgrounds, skills, and perspectives. How do you leverage their expertise and knowledge to achieve your goals and deliver value to your stakeholders? In this article, we will share some tips and best practices on how to use AI team members' expertise and knowledge effectively and efficiently.
The first step is to understand what each AI team member does and what they are accountable for. This will help you to assign tasks, communicate expectations, and monitor progress. For example, a data scientist may be responsible for developing and testing algorithms, a data engineer may be responsible for collecting and processing data, and a business analyst may be responsible for translating business requirements and evaluating outcomes. By clarifying their roles and responsibilities, you can avoid confusion, duplication, and conflict.
A couple of areas where we have found AI especially generative AI particularly helpful is in 1) Idea generation and brainstorming. Generative AI tools are especially useful in providing a lot of ideas at the beginning stages of a project that opens one's minds to areas not hitherto covered. Also some of these broad ideas while not useful as they are, help trigger our thoughts in directions that lead to clutter-breaking ideas 2) Development and detailing. Generative AI convert vague ideas into some form, image, shape or flow that can then be debated upon without misunderstanding. These tools also can expand on short briefs and eliminate the grunt-work for example writing presentations
With the importance of AI in day-to-day work, your teams are no longer pure human centric teams - The team composition should be hybrid human-AI teams with AI systems being treated as a co-equal partner. The roles of that of humans and the AI- systems should be clearly defined. Based on various studies available online, typical roles are - The Specialists, The Driver, The Creatoer and The Doer. Technology leaders should design the new workplace configuration where AI- systems can work autonomously within a team by handling independent as well as interdependent tasks. AI system should no longer be perceived only as an assistant where Humans can delegate tasks to AI or take help in making certain decisions. All team members.
The second step is to appreciate what each AI team member can bring to the table and what they may struggle with. This will help you to recognize their contributions, provide feedback, and offer support. For example, a data scientist may have strong analytical and problem-solving skills, but may need guidance on how to present and communicate their findings. A data engineer may have strong technical and operational skills, but may need help on how to collaborate and integrate with other systems. A business analyst may have strong domain and stakeholder knowledge, but may need training on how to use and interpret AI tools and methods. By appreciating their strengths and challenges, you can foster a culture of learning, growth, and trust.
Effective collaboration is critical to achieve meaningful and valuable outcomes. Don't simply give the data scientist a laundry list of requirements and leave them alone for a month! As a simple example: business stakeholders understand the key pain points to focus on, while data scientists understand technical methods to build models of the world. Data scientists should be curious and willing to ask questions - enabling a two-way dialogue to tease out implicit assumptions, appropriately consider cost/benefit tradeoffs, and ensure the system designed will solve the problem. Business stakeholders should allow data scientists to demonstrate the 'art of the possible', and be guided by their suggestions, rather than prescribing a solution.
The third step is to encourage each AI team member to share their input and feedback on the project. This will help you to leverage their expertise and knowledge, identify risks and opportunities, and improve quality and performance. For example, a data scientist may have insights on how to optimize and validate an algorithm, a data engineer may have suggestions on how to streamline and automate a data pipeline, and a business analyst may have feedback on how to align and measure an AI solution with business objectives and user needs. By encouraging their input and feedback, you can create a collaborative and innovative environment.
The fourth step is to align the goals and expectations of each AI team member with the project vision and scope. This will help you to ensure that everyone is on the same page, working towards the same outcome, and following the same standards and processes. For example, a data scientist may have a goal of developing the most accurate and robust algorithm, a data engineer may have a goal of building the most scalable and reliable data infrastructure, and a business analyst may have a goal of delivering the most valuable and usable AI solution. By aligning their goals and expectations, you can balance the trade-offs and dependencies between them.
The fifth step is to adapt your communication and collaboration styles to suit each AI team member's preferences and needs. This will help you to build rapport, respect, and rapport with them, as well as to avoid misunderstandings, conflicts, and delays. For example, a data scientist may prefer to communicate through code, charts, and metrics, a data engineer may prefer to communicate through diagrams, documents, and logs, and a business analyst may prefer to communicate through stories, scenarios, and mockups. By adapting your communication and collaboration styles, you can facilitate effective and efficient information exchange and decision making.
The sixth step is to celebrate the achievements and successes of each AI team member, as well as the project as a whole. This will help you to acknowledge their efforts, recognize their impact, and motivate them to continue and improve. For example, you can celebrate a data scientist's breakthrough in developing a novel algorithm, a data engineer's accomplishment in deploying a robust data platform, or a business analyst's success in delivering a satisfied customer. By celebrating their achievements and successes, you can foster a sense of pride, gratitude, and satisfaction.
Whether team members are technical or non-technical, it’s essential to build AI Literacy skills across the entire team. This includes: 1. AI Basics: Understanding AI principles and key terms such as AI, ML, neural nets. 2. Data Literacy: Recognizing the importance of data in AI, being proficient in understanding and handling data. 3. AI Ethics: Being aware of ethical considerations, such as bias, data privacy, and even societal impacts. 4. AI Intuition: Knowing what problems can and cannot be solved with AI. 5. AI Lifecycle: Learning the different phases of an AI project, including evaluating AI performance, model monitoring, observability, etc. This will help teams collaborate effectively, make informed AI decisions & use AI responsibly.