What do you do if you're an AI professional receiving feedback and want to avoid common mistakes?

Approach it with an open mind and a willingness to learn and grow. Avoid becoming defensive or dismissive, even if the feedback is difficult to hear. Take the time to listen actively, ask clarifying questions, and seek examples to understand the feedback better. Reflect on the feedback received and consider how you can use it to improve your skills.

Egal wie offen man Feedback gegenüber ist, ist es trotzdem manchmal schwer dieses positiv anzunehmen. Deswegen ist es extrem wichtig eine entspannte Atmosph?re zu schaffen in der genügend Zeit und Raum für das Feedback und ggf. Rückfragen ist. Es sollte NICHT unter Zeitdruck kurz vor oder zwischen anderen Meetings sein. Ein m?glicher Zeitpunkt ist der letzte Termin des Tages. Danach hat man auf dem Heimweg oder bei anschlie?enden Sport eine gute Gelegenheit das bekommene Feedback zu verarbeiten und zu reflektieren!

Como profesional de IA, valoro el feedback como una herramienta clave para mejorar. Escucho activamente y analizo la retroalimentación objetivamente, separando las emociones de los aspectos prácticos. Priorizo acciones según su impacto y desarrollo un plan claro para implementar cambios. Si el feedback es crítico, busco perspectivas adicionales para asegurar una comprensión completa. Comunico mis planes a quienes me proporcionaron las observaciones, demostrando mi compromiso con la mejora. Reflexiono sobre el proceso para aprender y optimizar mi enfoque en futuros proyectos, fomentando una cultura positiva de feedback en mi entorno laboral.

AI professionals must adeptly receive feedback for a continued career growth, additional key points you may consider following : -Listen actively, without interruption or defensiveness. -Take notes of key points for active reflection. -Identify patterns to uncover hidden areas for well rounded improvements. -Seek guidance from mentors or colleagues. -Implement changes by adjusting work or approach. -Follow up periodically for progress and additional feedback. =>By consider following these key steps, AI professionals can skillfully embrace feedback, avoiding pitfalls and advancing continually.

Take time to reflect on the feedback received, considering both the content and the underlying intentions. Reflect on your actions, decisions, and performance to gain insights into areas for improvement.

Unless exactly same build AI not used as a replica you need to use feedback s and consultation s very often to build a build new AI to your Organization s .Agile to use project deliveries so Agile works with clients / vendors regular feedbacks

Seeking clarification during the feedback session or at the end of the feedback session is perfectly fine. A good practice at the end of the feedback session is to ask whether you could come back in a few days after reflecting on the feedback. This demonstrates your maturity. This will also give you time to get the emotions out of the feedback and to reflect on it objectively. After a few days, you can always seek clarifications but do not postpone this second session. Good luck!

Approach feedback with an open mind and actively listen to what is being said. Pay attention to both the content and the underlying sentiments expressed. If something isn't clear, don't hesitate to seek clarification. Ask questions to ensure you fully understand the feedback given. Take time to reflect on the feedback without becoming defensive. Consider how the feedback aligns with your own observations and experiences.

Communication, that too formal communication is very crucial for the success of AI project. The code or the draft ensuring the latest draft and the last working draft in case of the code itself is a mandate. Companies may spend a lot on code version control, in research we simply save different versions of the code, in the name of enhancement we cant offered to lose the last working file. Coming to communication, yes I have seen success whenever modular level work is specifically allotted among the members, some may be good at literature, coding, artistic works etc.,

AI implementation Strategies Strategic AI implementation starts by defining specific, measurable objectives aligned with business goals. Next, assess your data's readiness and assemble a diverse, skilled team. Select AI technologies that match your goals and pilot a small-scale project to test them

Many people get feedback but fail to implement corrective actions in time. Getting feedback is only the first step. Feedback is useful if it results in an action. Action may be to double down on the same path (depending on what the feedback was) or to change course. In any case, taking effective action based in the feedback is very important. Good luck!

Create a channel between AI Management to related stakeholders So regularly collect their feedback and filter them depends on your need as well as depends on your strategically planned AI Priority list .

Maintain open communication with the individual providing feedback to ensure ongoing dialogue and collaboration. Keep them updated on your progress and any challenges encountered, seeking their guidance and support as needed.

Receiving feedback and taking action does not necessarily lead to improvement. Even if theoretically, we can gain valuable insights from feedback and enhance our actions accordingly, this isn't always the case in practice. For this reason, it's crucial to evaluate the outcomes of our actions. Are the KPIs improving, or are they stable or even getting worse? Through careful evaluation, you can determine whether the feedback was accurate or misleading, and whether the actions you took were appropriate or if different ones would have been better. In some cases, you might realise that you need more in depth feedback. Progress cannot be consistently achieved without measurement and evaluation.

Monitor E2E all process so be aware of changes,issues to taken care of ,assignment s and stakeholder s Use road maps ,tools, communications, collaborate companies departments to research similar AI implementation s and if they come a cross any issues? How they deal what was the logic to resolve ?