Here's how you can offer feedback as an AI expert to foster innovative solutions.

In my experience, "Listen First" emphasizes strategic empathy and inclusivity, fostering innovation. 🌟 Leveraging AI and advanced psychological techniques, we engage deeply—utilizing tools like eye contact and open-ended queries for profound understanding and cultural enhancements. 🤖👥 Aim for continuous feedback and ethical integration to ensure that all voices are heard, promoting a supportive and innovative environment. 📈🔍 This approach not only respects diverse perspectives but also catalyzes creative solutions, making every interaction meaningful and productive.

An AI colleague and AI itself can effectively boost innovation by exploring and leveraging external ideas, use cases, and future innovation trends for your team to enhance its innovation abilities. AI tools can also automate tedious tasks and match employees based on necessary traits, experience, and aspirations. In this regard, AI can be used to provide inputs and feedback to help resolve differences of opinion. This can boost innovation while promoting employee well-being, learning, and career development. This overall knowledge and management can then be used to develop a project roadmap and to measure performance as the innovation project unfolds, thereby suggesting corrections and improvements as the team proceeds.

I cannot emphasize enough the importance of listening. Listen impartially, non-judgmentally, and with curiosity. Never jump into solutioning and problem solving, without fully understanding the "Why" behind the problem. You never know what you might learn and the new perspectives you'll gain. As an AI expert and leader, I've always listened to my customers, team members, and peers. This approach has expanded my knowledge and enabled me to design & build the best AI solutions. Listening is a valuable skill not just in AI but in any field. It fosters innovation and ensures your feedback is relevant and impactful, enhancing projects without overshadowing the team's existing work.

I encourage you to adopt the following mindset: -every person in the organization needs to become an expert in using AI -whatever business you have been in so far, you are now in the AI business -AI is never a silver bullet. Check everything frequently -The underlying foundation of AI is humanity. Do not forget about that! You do those things and you will retain top talent and leverage AI as your competitive advantage long-term.

We all have two ears and one mouth! So - before providing feedback, actively listen to the AI team's ideas, challenges, and objectives. Arrange a meeting or call to gain a thorough understanding of their project, target users, and desired outcomes. Ask open-ended questions to encourage them to share their thoughts and concerns. Take notes and summarise key points to ensure you have a clear grasp of their situation. Increasingly there are good tools for transcribing meetings automatically (Teams with Copilot for example), and Notion for collaborative note-taking can also help with effective listening and understanding.

AI is not a magic wand; it operates within certain constraints and requires a well-defined problem to solve. While setting goals, it's essential to consider the feasibility of achieving them within the AI's capabilities and the available resources. Additionally, iterative refinement is a key aspect of AI development, so goals should be flexible and adaptable.

Work with the team to make sure there are clear goals that relate to outcomes of importance to the product or solution. Define specific areas where your expertise, and the capabilities of current AI technologies, can help refine their solutions based on those goals. Break down broad objectives into smaller, actionable targets. Use project management tools like Trello or Asana to create a shared roadmap with well-defined milestones and deadlines.

To give helpful feedback for innovation, set clear goals. Be specific about what needs to be improved and what's already good. Encourage creative thinking by praising unique ideas. Explain how the solution fits into the big picture.

As an organisation starts on its AI journey they would like to start off by selecting projects which offer business value but at the same time not overtly complex. For the success on the AI journey a clear goal settting is essential as you would like to draw out the objectives you have set for your project. Ensure you track the KPI s of your project and the AI algorithms are built accordingly and devoid of AI bias. It is essential that the feedback loop that goes back to the AI model is fed back and makes the model more cognitive. Understand the degree of adoption and user interaction with the AI model which can be used to fine tune the model. Always aim for measurable improvements which will pave way for stakeholder and user buy in.

AI works on data and human intelligence. It is imperative to define what we want to achieve from the AI solution, how it is quantitatively and qualitatively making significant changes in business operations and what data we have which impacts. Not only there is alot of noise in data, but at most of the times our Goals are not clearly defined when it comes to AI. Measuring the success of AI solution will only happen when we know the what and why(s) we are solving through AI. Setting clear goals and defining clear expected output helps reduce time invested in designing model, cost of computation & achieve better efficiency

A constructive tone is important and, with this, it is not just down to tone but also how you deliver it. You want your feedback to be remembered and properly considered by the people you want to influence. In terms of stle, that has impact, think of yourself as a TV news anchor. *Tell them what you are going to tell them* ("We need to consider how non-techie people will interface with the new LLM). *Tell them* ("Users will understand the true benefit of the LLM through both training and 1-on-1 follow up sessions"). *Tell them what you've told them* ("By providing both training and follow on sessions, we will not only give them new skills but also change their behaviours")

A constructive tone turns feedback from critique into a catalyst for innovation. Start with Appreciation: Acknowledge the effort and recognize strengths in the work. This builds trust and makes individuals more receptive to suggestions. Specificity is Key: Provide concrete examples of what's working well and what could be improved. Offer actionable suggestions rather than vague generalizations. Focus on Potential: Frame feedback around the possibilities inherent in the work: "This idea has real potential; let's explore how we can enhance it..." Collaboration Mindset: Use "we" language to emphasize joint problem-solving ("How can we work together to make this even better?").

Listening plays a crucial role in enabling constructive tone. Popping open the notes from the listening session with a thorough understanding of the customer base, and what they are feeling, thinking paves the path offering for a constructive tone. Offer encouragement for the work well done and then provide feedback.

It always helps to contribute in a constructive, supportive manner that encourages the team to embrace improvement. Use positive language and frame critiques as opportunities for growth or for improvement in other ways. Begin by acknowledging the team's strengths and accomplishments. When addressing areas for improvement, provide specific examples and explain the potential benefits of implementing changes. Maintain a collaborative tone, using phrases like "let's explore" or "we could consider" or "how might we?" to emphasise partnership. Tools like Grammarly can help ensure your written feedback, if it's needed, conveys the right tone.

Mantener un tono constructivo es vital para fomentar una colaboración positiva y productiva. Al ofrecer comentarios, enfócate en ser específico y objetivo, destacando tanto las fortalezas como las áreas de mejora. Evita críticas destructivas y, en su lugar, proporciona sugerencias prácticas y realistas para superar desafíos. Un tono positivo y alentador motiva al equipo, mejora la moral y facilita una comunicación abierta. La retroalimentación constructiva no solo resuelve problemas inmediatos, sino que también impulsa el desarrollo continuo de habilidades y la innovación dentro del equipo.

Absolutely! Harness cutting-edge AI infrastructure like Azure's custom silicon and AI accelerators for robust, scalable feedback solutions💡. You now know, but how can we leverage generative AI for real-time solution modeling in AI feedback systems? 🌎🤖 Integrate ethical AI practices and advanced cybersecurity to safeguard innovations🛡️. Employ generative AI for dynamic solution crafting, ensuring adaptability and forward-thinking strategies. This approach merges technical sophistication with ethical foresight, promoting sustainable and innovative solutions🚀.

Whatever solution you offer, make sure it’s: → Addressing the right problem → Is understandable for the team → Is feasible to implement

Once you have identify potential solutions for your business, it’s crucial to act promptly and offer solution. Implement proof of concept (POC) swiftly, get stakeholder feedback, and iterate(just Agile). Delaying could mean competitors or others catch up the same idea.

If you are able to identify the problem and understand the resources available, it is time to propose a tangible solution that fits your client's objectives and contribute to their business. Keep in mind, it is not just about suggesting solutions, you should find sustainable ones! Consult BI specialists when needed! Remember, only 𝟲% of those millions of solutions in the market are successful (inc.com) 𝓐𝓵𝓮𝔁𝓼𝓪𝓲

Give me an A/B test all day and all night! Ha. Really, in tech product marketing - especially in this day and age - it's all about testing and gathering data. Never spend the entire budget in the first round. Slow roll with learnings so you can pivot is a universal principle in business. BUT to do this, you need to build in time on the front end to have proper time to test all accepts of the project. Remember that - this step actually is made possible by the project manager!

Encourage AI experts to take risks and try new approaches. Let them know that it's okay to fail, as long as they learn from their mistakes and continue to innovate.

Testing requires one of the major parts to keep in mind which is: - Data Governance ( figure a first, is data relatable to what you are building around, if you got late with this, trust me it's a big loss in terms of team, money & time). I used to call Scrutny before you commit, understand the use case, focus on benefits over features, and look a the data available before taking calls to build things around which in turn will help to streamline your commitment.

En el ámbito de la IA, la experimentación y las pruebas son fundamentales para el éxito. Fomentar un enfoque de prueba y error permite identificar rápidamente qué soluciones funcionan y cuáles no. Anima al equipo a desarrollar prototipos y realizar pruebas A/B para validar hipótesis y ajustar modelos. Este enfoque iterativo permite mejorar continuamente las soluciones basadas en datos empíricos. Además, facilita la adaptación a cambios y la respuesta a nuevos desafíos, asegurando que las soluciones finales sean robustas, efectivas y alineadas con los objetivos del proyecto.

Ermutige dein Team, neue Ideen und Ansätze zu testen. Experimentieren ist ein Schlüssel zur Innovation, besonders in der schnelllebigen Welt der Künstlichen Intelligenz.

You now better than me but, how can "Continuous Learning" effectively utilize AI technology to maximize team innovation? 🚀🤖 Continuous Learning is crucial in AI, blending real-time data learning and knowledge transfer to enhance model accuracy and adaptability. It encourages a culture where calculated risks and mistakes become learning opportunities, fostering innovation and adaptability 🌟. By incorporating feedback loops, AI models evolve, ensuring robustness and relevance. This approach not only nurtures technical skill growth but also empowers teams to embrace challenges as opportunities, driving groundbreaking solutions in an ever-evolving landscape 🌐✨.

One thing I found very useful is to make sure that we continuously learn and improve. This is especially crucial as AI landscape is changing at a very fast pace. Almost every week, there are new AI models being released which are reported to be more effective & efficient than their predecessors. Similarly, there is a constant updates to the regulations, policies as well as frameworks & methodologies across the industries and geographies. This paradigm shift which current generation of AI tools (catapulted by LLMs & high compute capacity) have created; allows the leaders to innovate uniquely. Accordingly, in order to leverage the latest AI trends, continuous learning for both the leader & their teams can be an essential force multiplier!

I believe feedback is crucial for fostering innovation. It's not just about pointing out flaws, it's about nurturing growth. When offering feedback, focus on constructive criticism, highlighting strengths and areas for improvement. Emphasize that feedback is a chance to learn and grow, encouraging others to embrace challenges and acquire new skills. In the ever evolving AI domain, feedback becomes a catalyst for continuous learning, it gives us an opportunity to adapt and innovate. So, embrace feedback - think of it as that tiny nudge in the right direction , that makes you wanna learn and pick up on new skills . Because in this ever evolving AI space , learning everyday is what’s necessary, to say the least ! Happy Learning :)

Given how fast things are unfolding, it’s imperative you find real time and peer to peer type learning so you’re not only staying ahead but you’re multiplying your learning by the amount of experience your peers in the same room have. Getting diverse perspectives and use cases from others can accelerate and amplify your learning.

Innovative solutions are born in environments where continuous learning is encouraged. As an AI expert, your feedback should foster a culture of learning. Encourage team members to stay updated with the latest AI research and developments. Recommend resources, workshops, courses, and seminars that can enhance their skills and knowledge. This not only helps in solving immediate project challenges but also prepares the team for future advancements

Few people claiming to be “AI experts” actually are… Because knowing 1,000 examples use cases, reposting flawless demo videos, making GPT do tricks, or reading tons of white papers is simply not the same as building AI platforms designed to deliver tangible results for customers at scale. And anyone who is doing THAT would likely tell you that the space is evolving and advancing every day. So I wouldn’t look for “experts” as much as I’d look for people with experience building something tangible.

Here are some feedback approaches I used in my work that might help: - be specific and objective, focus on actions, not personal attributes - use the sandwich method: start with positive feedback, insert constructive criticism, end positively - encourage two-way communication, listen actively and encourage questions - focus on improvement, suggest actionable steps and future growth - provide timely feedback by addressing issues as they arise, not weeks later - adjust based on the recipient's preferences and sensitivities - illustrate points with specific, relevant instances and examples - discuss sensitive issues discreetly to maintain respect and dignity

You need to look for what value AI would bring to the organisation. what are the benefits in implementing such a solution. based on that you need to calculate the ROI for such a solution. Your ROI should include Qualitative and quantitative evaluations.

IMO there's no magic bullet to innovation, but there are a few accepted lifehacks. For example, design thinking as a model is a good way to unearth pains that go unnoticed in an organization. Another approach is to do competitive innovation such as hackathons - as a way of quick prototyping. If you are an expert, your immediate value is imparting knowledge via original content - which can be complimentary to these methods. Traditional research and consulting also have a place, but these avenues could take too long to be practical for an organization. In any case, the fact is expertise is not enough, you need an effective venue to impart it.

Innovation blossoms with varied perspectives from AI professionals. Data-driven insights, combined with cross-functional team collaboration, and technical expertise from AI professionals, not only pave the path for the rapid development of innovative solutions but also emphasize the responsible use of solution building to address use cases. AI professionals can help guide the team in predicting risks and offering potential mitigation strategies.