What is the best way to get your AI team to collaborate and innovate?

While consulting a logistics major, I noticed that their AI team was exceptionally skilled but fragmented in their efforts. They were exploring chatbots, recommendation engines, and predictive analytics - but in silos. A lack of alignment was hurting the organizational outcomes. One must take a step back to see the bigger picture. When you define clear objectives and pick only those projects that are aligned with this larger narrative, the AI initiatives start building momentum.

AI Team Collaboration Today’s companies need humans and AI working together. AI project teams require alignment and integration of skills and abilities. Successful AI projects require a strong team culture focused on exceeding customer expectations. Without that culture, the AI project will fail. Culture is enabled by close and continuous communication and collaboration, along with continuous learning, re-skilling, and up-skilling in the rapid development of AI itself. Overall, you need a well-considered approach to assembling the best AI team to meet company project needs. And innovation must be a 24/7 non-stop process.

In my view, innovation should be integrated into the everyday workflow of teams. I appreciate the concept that those closest to a problem should be the ones to devise solutions and drive innovation. The management process should allocate time for contemplation and innovation. In terms of collaboration, it's crucial to establish clear responsibilities to prevent conflicts and redundancy in work. To achieve this, tools such as RACI can be highly beneficial.

AI is an exciting field in early stages, which means the pool of talent available for innovation is quite limited. To build a successful team, it's crucial to have a hiring strategy that attracts the best minds with strong research backgrounds - individuals who are truly passionate about solving complex problems, whether it's in video, music, or any other area where AI can have an impact. Research also plays a massive role. It's a constant process of ideation, innovating, and refining strategies to achieve goals. The journey can be long and challenging, but when everyone on the team shares the same passion and vision, collaboration becomes much more efficient. So by fostering a culture of curiosity, we can accelerate our progress.

In my experience of aviation then setting guidance on how to secure a fit for purpose and safe aircraft function when novel as in AI Machine Learning then we need experience in the field. Unfortunately, the aviation industry partners and the key Regulators (FAA and EASA) are all learning together and we cannot wait to gain ad hoc experience to lean on, so what’s the solution? Well the regulators with over 600 members are collaborating in producing the AI ML standard from SAE G-34 and Eurocae WG-114 members. ARP 6983 is coming soon and will cover best practice objectives for supervised off-line solutions like classification and linear regression. It’s a new approach but it’s a safe approach as we will learn together traveling on the road.

More important than agile project management tools is to make sure the team working on the project is comfortable with an agile work environment. Not everyone is comfortable with changing timelines and priorities. AI technology is changing quickly and the team working on building solutions should also be strategic in their approach. ie is AI the best way to solve the problem or is there another way to do it? Is the AI solution being presented the best experience for the customer/end user of the product? Is the AI solution adding value to the organization? You want a team of thinkers not only builders.

There are tool for value measurement as well. Don't forget the likes of product analytics to track, analyze the performance of your product experience (once shipped). It's not just about delivery.

AI project management can be tricky, to say the least. Resources like "The Cognitive Project Management for AI" (CPMAI) method outline a structured approach to AI project management, involving phases like Business Understanding, Data Understanding, Data Preparation, Model Development, Model Evaluation, and Model Operationalization. This approach emphasizes the importance of a data-centric perspective, recognizing that AI projects are fundamentally data projects. By following these phases, teams can ensure they address the correct business problems with appropriate AI solutions, thereby enhancing efficiency.

To explain with an example: Agile methodologies for AI projects are like scientific experiments. First we form a hypotheses, conduct experiments, gather data (feedback), and adjust our approach based on the results. Scrum and Kanban serve as our experimental frameworks, breaking down the AI project into testable components. Tools like Jira, Trello, or Asana serve as our lab notebooks, where we record observations and collaborate with the team. This scientific approach allows the AI project to continuously improve, just like refining an experiment for more accurate results.

For AI projects, the dynamic nature of research and development can be daunting. Utilising agile methodologies, like Scrum or Kanban, facilitates adaptive planning and promotes a flexible response to change. Breaking projects into smaller tasks allows for continuous revision and improvement, making it easier to address unexpected challenges. Tools such as Jira, Trello, and Asana offer platforms to manage these tasks, enabling seamless team collaboration. They provide a central hub for tracking progress, ensuring everyone stays informed. This adaptability not only enhances productivity but also ensures the end product aligns more closely with user needs and the ever-evolving AI landscape.

Diverse teams bring various perspectives, which can lead to more innovative solutions. It's important to ensure diversity in terms of ethnicities, skills, backgrounds, and experiences to produce an AI solution that serves the world's population. It's also important to encourage cross-functional collaboration. For instance, involve data engineers, data scientists, software developers, ML engineers, and domain experts in brainstorming and design sessions.

"Diversification is a protection against ignorance," said Warren Buffett. Diversity in AI teams is quite like diversifying a stock portfolio. Just as a diverse portfolio is resilient against market volatility and poised to capture a broad range of growth opportunities, a heterogeneous AI team is better equipped to prevent challenges like bias and innovate with well-rounded perspectives.

Embracing diversity and inclusion is not just a moral imperative but a strategic advantage for AI projects. The fusion of diverse perspectives, backgrounds, and skills injects a wealth of fresh ideas and innovative solutions. To foster such a culture, recruit talent from various genders, ethnicities, disciplines, and backgrounds, ensuring representation across the spectrum. Creating a safe and respectful environment is paramount, allowing every team member to voice their opinions, ask questions, and learn collaboratively. Leverage digital tools like Slack, Zoom, or Miro to bridge geographical divides, facilitating seamless communication and collaboration, even among remote or globally dispersed teams.

Consider these examples: 🔹In a ML project, a diverse team might include data scientists, software engineers, & domain experts. The software engineers can use Git and GitHub for version control & collaboration on code, while data scientists can use Jupyter notebooks to share & experiment with ML models. This diverse collaboration and toolset can lead to innovative AI solutions. 🔹In a NLP project, a diverse team may include linguists, data engineers, & machine learning specialists. The linguists can use specialized linguistic analysis tools to preprocess text data, while data engineers can employ Apache Spark for scalable data processing. This diverse skill set enhances the project’s capability to handle complex language tasks effectively.

Diversity and inclusion in AI teams can drive innovation, providing varied insights that stem from different backgrounds and experiences. A team reflecting various genders, ethnicities, and disciplines is more likely to approach problems holistically. Ensuring a respectful environment is pivotal, where individuals can freely express opinions and contribute without apprehension. This not only fosters creativity but also mitigates biases that can inadvertently creep into AI systems. Tools like Slack, Zoom, and Miro play a vital role in bridging communication gaps, especially in remote setups, promoting seamless collaboration and unity in diverse teams, ultimately enriching AI project outcomes.

I learned a long time ago—almost 28 years ago—that when I signed up to be in tech, it meant I would be a lifelong learner. Continuous learning is a mindset that I instill in every team that I lead so that no one is left behind. As leaders, we must encourage team members to stay updated with the latest advancements, especially in AI and subsets like Generative AI. There's always an opportunity to provide resources for training and development, such as online courses, workshops, and conferences. Organizing internal knowledge-sharing sessions where team members can present new techniques or findings increases engagement and excitement for a given technology.

We must encourage culture of learning and participation/contributing to papers, talks etc. Team members should submit top conference papers and attend at least 1 or 2 during the year - this helps them keep up with latest research in the field and also encourages them to look at original contributions of their own.

To remain at the forefront of AI advancements, fostering a culture of continuous learning and innovation is essential. Offering resources like workshops and access to platforms such as Coursera or Udemy can boost skill development. It's also beneficial to encourage knowledge sharing within teams, which can lead to collaborative problem-solving. Platforms like Medium and GitHub provide opportunities to showcase team innovations, while resources like Stack Overflow offer troubleshooting support. Maintaining an adaptive approach not only enhances project outcomes but also ensures that the team is always ready to tackle new challenges in the ever-evolving realm of artificial intelligence.

Also establish a collaborative, knowledge sharing environment. Team members should feel empowered to collaborate and learn with each other.

To foster learning and innovation in your AI team, offer opportunities and resources for skill development, domain exploration, and experimentation. Encourage knowledge sharing, feedback-seeking, and mentorship within the team. Leverage online platforms like Coursera, Udemy, and Kaggle for courses and datasets, and use outlets like Medium, GitHub, and Stack Overflow to publish work and engage with the AI community. Cultivating a culture of continuous learning and sharing drives AI project success and innovation.

Innovation future-proofs an organization by creating new products/services that address the needs of its customers, and AI teams are no exception. To get your AI team to collectively innovate, it is super important to create a culture where people feel comfortable experimenting and taking risks, even if it means failing sometimes. If an experiment is not going as expected, I guide my team to pivot, learn from their failures, and explore a different approach to test/build/validate the problem that can be productized and help generate great ROI. Also, acknowledging team's successes when presenting the MVP and celebrating those small wins during the POC build will cement the trust within the team and ensure successful collaboration.

Acknowledging both successes and setbacks is crucial for a resilient AI team. Recognising efforts fosters a positive environment, while addressing failures provides growth opportunities. Regularly highlight team contributions and ensure feedback is both constructive and actionable. Tools like Kudos or Bonusly can facilitate peer recognition, boosting morale. However, it's equally vital to reflect on challenges, as these offer invaluable learning moments. Using platforms such as Google Forms or SurveyMonkey can garner feedback, aiding in continuous improvement. By embracing both triumphs and obstacles, you cultivate a balanced, growth-oriented team ready to tackle the complexities of AI projects.

Recognize both successes and failures within your AI team. Acknowledge contributions, offer constructive feedback, and celebrate milestones. Tools like Kudos, Bonusly, and Giphy can help send praise and recognition, while platforms like Google Forms, SurveyMonkey, and Retrospective facilitate feedback collection and improvement identification. Valuing efforts fosters team morale and growth.

AI projects, while fulfilling, can also be strenuous. It's essential to recognize both the successes and setbacks of your team members. Celebrate achievements by acknowledging contributions, offering constructive feedback, rewarding performance, and commemorating milestones. Tools like Kudos, Bonusly, and Giphy can facilitate praise and lighthearted interactions. For feedback collection and performance assessment, platforms like Google Forms, SurveyMonkey, or Retrospective are valuable in pinpointing areas of growth.

It’s all about leadership and not just management 🙂 Constructive feedback, celebrating achievements, & learning from failure are very important in AI/ML or any other project. Recognizing team’s efforts & achievements can boost morale & build a positive professional culture. For example, when the team achieves a significant accuracy improvement in a natural language processing model, we can celebrate it by organizing a party where team members share their experiences & challenges, turning a small achievement into a rewarding celebration of teamwork. This encourages continuous effort and collaboration among team members.

Working as a Product Lead in anomaly detection at a B2B startup and other AI products, my learnings are to foster AI team collaboration and innovation 1. Clarify vision and goals. 2. Establish process for cross-functional collaboration for product development. 3. Maintain regular communication. 4. Allow innovation time. 5. Encourage skill development. 6. Promote an experimentation culture. 7. Organize hackathons and challenges. 8. Collaborate externally in events. 9. Establish a feedback loop. 10. Recognize and reward contributions. 11. Embrace agile development. 12. Prioritize a user-centric approach. 13. Ensure data quality and privacy. 14. Continuously evaluate and monitor model health. 15. Encourage patience and persistence.

Having my team roleplay and map the customer journey for users that will be impacted by an AI product has led to improvements in model performance, reduction in model risks, and leaner more cost-effective models.

Take time to test the system at the end. It isn't always clear what patterns AI might be picking up on. In order to pick up on possible problems and biases in your AI-project test the final product with a diverse group of users and see how they feel about the product and whether it meets their needs.

AI projects often face ethical dilemmas. For example, an autonomous vehicle AI might need to make split-second decisions in a dangerous situation. Balancing safety and fairness can be challenging. These ethical considerations require open discussions, careful design, and ongoing reflection to ensure responsible AI development. AI projects can be like exploring uncharted territory. Just like early explorers needed teamwork, innovation, and learning to navigate unknown landscapes, AI teams must collaborate, innovate, and adapt to discover new possibilities in the world of artificial intelligence.

My Learning While leading team for "AI based revenue/Credit cohort prediction" model- Make your AI teams to sit on separate tables and to write down- 1. what you think other team does? 2. What you think your own team does? Ask them to write separately. Make all teams present Answer 1 first (the mirror part)... Then ask teams to reveal answer 2. This is a fun experiment to teach alignment between all teams working in silos!