How can you collaborate with other departments on an AI project?

To align your AI solution with the problem you are trying to solve, I recommend using the framework outlined in the book "Prediction Machines - The Simple Economics of Artificial Intelligence" (by Agrawal, Gans and Goldfarb). This will help you go beyond just finding a problem-solution fit by ensuring that you consider the necessary data, expertise required to evaluate the quality of the results, maintenance cycles, and even the final value you aim to deliver.

One thing that we have found helpfull is collaborative documentation and goal setting. Yes, interaction and communication is pivtol and understood when working cross departmentaly; but setting, dynamicly recording goals, resources and timelines eases a lot of the grunt work and low value communication of such projects! Additionally recording of projects parameters has shown to be an excellent historical analysis tool - also helping leverage native AI systems on the collaboration platforms!

In our experience at Grit, tackling an AI project is a bit like planning a road trip with friends. First, you gotta figure out where you're going and why – that's understanding your project's goals and what you're trying to achieve. It's like picking your destination and knowing what sights you want to see.

Building on the point of dynamic recording of project parameters on collaboration platforms. These platforms have intraorg, cross departmental visibilities thus enabling all stakeholders to get updates and check progress from anywhere on the world! The most notable tools such as the Atlassian ecosystem, Microsofts latest app: loop (and the extended ecosystem), Notion and much more. Surprising all the tools highlighted above and even some Micro SaaS tools are now having native GenAI integrations - truly general purpose!

It’s important to make the process digestible for all audiences and don’t assume they know what questions to ask. Instead of bombarding them with updates, establish an open line of communication or dedicated point of contact. Host open hours, and async docs for follow up Q&A. Help other departments understand what is and isn’t realistic and how much time certain asks or considerations can take when communicating. Often times when something involves high technicality or a specialty, it can be intimidating for others to confidently contribute. Make sure not to operate in silos and encourage a safe space to ask questions and get clarification. These are all important factors to collaborating and communicating effectively.

Maximizing the potential of regular meetings in multi-functional teams: Information Sharing: Keep everyone updated on progress and changes. Input Seeking: Gather diverse perspectives for a well-rounded view. Brainstorming: Encourage creative solutions and innovative ideas. Decision Making: Evaluate options and agree on actions. Progress Checks: Regularly assess goal achievement and address challenges. Each meeting type has a specific role, ensuring goals are aligned and teams work effectively together. #EffectiveMeetings #TeamAlignment #MultiFunctionalTeams #Collaboration

Effektive Kommunikation ist entscheidend für den Erfolg von KI-Projekten. Sie f?rdert nicht nur den Informationsaustausch, sondern baut auch Vertrauen und Verst?ndnis zwischen den Teams auf. Transparente Kommunikation verhindert Missverst?ndnisse und h?lt alle Beteiligten auf dem Laufenden. In einem dynamischen und komplexen Bereich wie KI ist dies besonders wichtig. Die Einbeziehung verschiedener Perspektiven durch regelm??iges Feedback und Diskussionen f?rdert Innovation und verbessert die Qualit?t des Endprodukts. Es geht um mehr als nur Informationsweitergabe; es geht um den Aufbau einer Kultur der Zusammenarbeit und des offenen Austauschs.

It's like being in a group chat. The second step is to chat a lot with everyone involved. Keep them in the loop about what's happening, any problems you're facing, and the cool stuff you've achieved. Also, listen to their ideas and answer their questions. You can use simple ways to stay in touch, like having quick meetings, sending emails, or sharing updates. By talking regularly, you'll build a good vibe with your team.

I think cross-functional alignment is crucial for collaborative success in AI projects. Begin by ensuring everyone's goals and expectations are synchronised through open dialogue. Establish shared objectives that serve overarching project goals while addressing specific departmental needs. Techniques like stakeholder mapping and goal-setting workshops clarify expectations and create a unified vision. Regularly reassess goals to adapt to evolving needs. This fosters ongoing alignment, maximising synergy, minimising conflicts, and driving collective success across departments.

Stress the importance of open communication and regular feedback loops during the project. This can help in quickly identifying and addressing any misalignments or concerns among departments.

It's not just about popping the champagne for the successes, though. Take some time to chat about the bumps along the way too. Think about what didn't go as planned and how you can do better next time. It's like sharing stories at the party about the funny mishaps and learning from them. There are fun ways to celebrate, like giving shoutouts, handing out awards, or even having a team reflection session. This helps create a cool, positive vibe where everyone feels like they're part of something great.

One thing I found helpful, is empowering non-technical team members with basic AI literacy. Having everyone communicate in common terms is transformative to fostering effective collaboration. This not only bridges the gap between departments but also sparks innovative ideas.

Making sure that everyone is on the same page and same base level of understanding of what AI can do, how AI will be used, as well as its limitations would help you work more effectively with other teams on AI projects. This will help make sure that each member involved in the project would ask the right questions and would flag issues and risks ahead of time (instead of just saying yes to all instructions given).

Don't underestimate the impact of change. Typically, the more significant the AI (or the underlying data), the more disruptive it will be to your processes. Some stakeholders who you would expect to be supportive may actually become skeptical for a moment. Don't give up and accept your fate: resistance is a sign of the relevance of your project.

OKRs (Objectives-Key Results) can be a game-changer in streamlining and enhancing AI project collaborations. 1. Set Clear Objectives: Define what problem you're solving and the value you're creating. This clarity aligns the project's purpose. 2. Align Across Departments: Use OKRs to ensure everyone's efforts contribute towards shared goals, aligning with various departmental needs. 3. Define Scope & Resources: Outline specific metrics, timelines, and resources in your key results. This ensures realistic planning and project feasibility. 4. Establish Shared Vision: A common understanding and objectives foster effective collaboration, especially crucial in complex AI projects. #OKRs #AIProjects #EffectiveCollaboration

Suggest establishing a central repository for documentation and knowledge sharing. This ensures that all departments have access to the latest information, project documentation, and resources.