What do you do if your AI feedback isn't motivating or inspiring your colleagues?

You give the LinkedIn algorithm that asks you to contribute to an article to gain a badge as an example of AI uninspiring feedback.

É importante revisar a abordagem e o conteúdo do feedback. Certifique-se de que o feedback seja específico, construtivo e direcionado para o crescimento pessoal e profissional dos colegas. Além disso, considere oferecer exemplos práticos e sugestões claras para melhorias, e esteja aberto a ouvir o feedback dos colegas sobre como o processo de feedback pode ser aprimorado para melhor atender às suas necessidades e expectativas.

Demandez des retours à vos collègues sur pourquoi les commentaires actuels ne résonnent pas avec eux. Cela peut vous aider à identifier des lacunes spécifiques ou des domaines d'incompréhension. Le mieux est de le faire en format "brainstorming" avec toute l'équipe autour de la table.

I believe AI should be taken as a technological evolution that will help us to improve ourselves as human beings, and not precisely as a setback for society, this because it will allow us to deal with issues and topics that are more relevant to human beings. As a humans we will stop carrying out automated and operational tasks, allowing the skills improvements of many people to evolve and develop, personally and professionally speaking, as a consequence, this technological evolution of AI allows us to develop our creativity as human beings.

Muchas veces ponemos demasiado énfasis en generar un Efecto WoW o algo emocional en nuestra audiencia con nuestros comentarios de IA. Y nos solemos olvidar de aquellas personas que ya están evangelizadas. Por eso es importante poner contenidos del estilo "Tutorial" que resulten interesantes a estos otros usuarios.

In my experience as an AI expert, personalization is indeed a game-changer in AI feedback systems. I once worked on a project where we integrated hyoer personalization into an AI system for a global client. This project involved 3D printed orthotic devices custom fitted to each unique foot. We used complex computer vision algorithms to construct 3D models of each foot, and then fed that 3D model into a 3D printer. This significantly improved user engagement and satisfaction. It's like giving a personal touch in a digital world, which can be highly motivating and inspiring.

Spot the Uniqueness: Let the AI discover the distinct strengths of each team member. This makes the feedback more relatable and uplifting. Adapt to Learning Preferences: Everyone learns differently. Some love visuals, others prefer text. Customizing feedback to these preferences can boost its effectiveness. Aim for Personal Milestones: Collaborate with each team member to set individual goals. The AI can then guide them on this personal journey to success. Dive Deeper with Data: Incorporate detailed metrics into the AI, like project-specific measures or soft skills like teamwork.

If AI feedback lacks motivation or inspiration for colleagues, focus on enhancing personalization. 1. Tailor feedback to individual strengths, goals, and preferences to enhance personalization. 2. Incorporate constructive encouragement and recognition of achievements. 3. Foster a positive and supportive work environment by acknowledging individual contributions and milestones.

Yes, agreeable. It would take some time for the machine to learn before the term Machine Learning can become mainstream and accepted. It will only happen with heaps, tonnes, and huge volume of data.. and that’s what the humans have to do

Motivation is the drive to action. Action and motivation must be defined prior to the utilization of AI and feedback itself. We progress towards a goal when there is resonance between the sound of our inner selves and the sound of the goal. Feedback, likewise, resonates with the tones of goals and visions.

Fostering communication is key to improving AI feedback's effectiveness. Encourage colleagues to openly discuss their reactions to AI-generated insights and share their perspectives. This dialogue helps refine the AI's approach to align more closely with the team's needs. Ultimately, it fosters a collaborative environment and makes the feedback process more inspiring and effective.

Para maximizar o impacto do feedback de IA, é crucial estabelecer canais de comunicação que permitam uma discussão aberta e construtiva. Encorajar equipes a compartilhar suas percepções e experiências com o feedback recebido fortalece o senso de comunidade e colaboração. Utilizar sessões regulares de feedback coletivo, onde tanto as conquistas quanto os desafios são discutidos, pode ajudar a ajustar a abordagem da IA para melhor atender às necessidades da equipe.

AI feedback can be based on the company's values. The company must embody its values through its products, the quality of its business, and the customers it selects. Feedback should align with the company's values, focusing not on good or bad, but on how individuals implement these values and the potential for change.

Encourage Dialogue: Promote open discussions about AI feedback. This can help identify areas for improvement in the AI’s approach. Value Individual Perspectives: Each team member’s viewpoint is unique and valuable. Their insights can help align the AI feedback with the team’s needs. Iterative Refinement: Use these discussions to iteratively refine the AI’s feedback process. This collaborative approach can lead to more effective feedback. Foster Open Communication: Cultivate an environment that values transparency and open communication. This can make team members more comfortable discussing AI feedback, leading to a more motivating and inspiring atmosphere.

The most vital factor that everyone has to internalize is that AI is working for humans and not the other way around. Humans are contributing to the machines with their inputs, thoughts, and processes to make the machines efficient enough and help the humans in a long run

What they say is that even if you give the best car in formula 1 racing, still it depends on the driver and entire team when it come to winning the race. Similarly you can install the best AI system or algorithm but at the end the human creativity and style, mood, inquisitiveness , knowledge and experience will super power the AI feedback and make it more useful and more insightful. That's why you see lot of AI content which you can easily make out that it's directly coming out of the oven in a pre programmed receipe but some content you feel happy and acknowledge it because lot of hard work and there has been unison between AI and human brain. AI is augmented intelligence and we tend to believe is that it replaced human intelligence.

Montrez comment l'IA peut libérer du temps pour des tâches à plus haute valeur ajoutée, en permettant aux membres de l'équipe de se concentrer sur des aspects créatifs ou stratégiques de leur travail. Pour cela faites par exemple des démonstrations d'outils IA en petit comité pour montrer des études de cas et comment ils peuvent concrètement s'en servir tous les jours.

Leveraging AI should be about augmenting the human element of feedback, not replacing it. AI can analyze performance data to identify trends, suggest areas for improvement, and even highlight successes, but it lacks the nuance to understand individual employee sensitivities, preferences, and the complex dynamics of human motivation. According to a Gallup poll, employees who feel their managers are invested in them as people are more likely to be engaged. This investment is shown through personalized, thoughtful feedback and recognition, underscoring the importance of human interaction in the feedback process.

Combine with Human Insight? So you're telling me that not only I'm supposed to read the feedback that ChatGPT wrote for me, but I should even add my own input? Oh man, managers can't catch a break these days.

L'IA est puissante, mais elle ne peut pas encore remplacer le jugement et l'expérience humains. Soulignez comment les outils d'IA peuvent être utilisés pour compléter et amplifier l'intelligence humaine plutôt que de la remplacer. Cela peut aider à surmonter la résistance et à inspirer une adoption plus large.

Your colleagues are tired of hearing you harp on about AGI "emerging" eventually, as soon as OpenAI and others have managed to throw in enough computational resources behind it. They understand that there is plenty of hype around AI but they also have a basic understanding of the background behind AI research to know when someone is trying to sell them a self-serving vision. Instead of parroting what you are seeing certain AI influencers say in the media, focus on practical applications and risk managed solutions that leverage AI and don't require AGI to suddenly appear out of nowhere. Your colleagues have seen waves of technologies in the past and are wise enough to know when to wait a bit and not to drink the Kool-Aid.

Long before AGI we need ai tools and processes to service today's use-cases reliably, at scale. These tools also need to be process driven, in order to be used by employees and managers(basically, upping the productivity while decreasing cost, not rip and replace people). Just my 2 cents.

I mean seriously; who has expectations that would result in questions like "What do you do if your AI feedback isn't motivating or inspiring your colleagues?" Nobody thinks AI-feedback is inspiring. Get real.

Esta sí que es una cuestión importante. Hay que mostrar los avances con sus pros y sus contras. De lo contrario podemos generar altas expectativas que en comparación con el estado actual de la tecnología no sean realistas.

Sometimes you have to adjust your expectations. A lot of people expect current generation AI to be as good as a human, but these are really first or second generation models based on current technology. We also often compare AI to a perfect human when in reality, most AI is better than the average human. Remember every time you sell a stock someone else is buying so one of you is wrong. Remember that 50% of people are below median drivers. Remember that all those ads trying to get you to buy gold are… well if gold was a good investment why are the gold people wasting money on ads instead of buying more gold? Why do we expect AI to be so smart when most people you know are not very smart?

Let's take a moment away from that shiny object. Feedback comes from observations. Motivation comes from coaching. They're not the same. Feedback informs what to stop or continue doing. Coaching enables what to start doing and why. If your AI feedback isn't sparking motivation or inspiration...perhaps your circuit is incomplete. Consider adding coaching, by a Human, into the loop.

🪴 Imagine um jardim. Cada planta, cada flor, representa um membro da equipe. O feedback da IA é como a luz do sol, essencial para o crescimento, mas às vezes pode ser muito intenso ou não suficiente. O que você faz quando as plantas não estão florescendo como deveriam? Você ajusta a quantidade de luz? 🏡 Não, você ajusta o ambiente. A melhoria contínua é como a jardinagem. Não se trata apenas de ajustar a luz (o feedback da IA), mas de cultivar um ambiente que permita que todos floresçam. 🌴 Isso pode significar fornecer mais sombra (apoio) para aqueles que estão lutando, ou talvez mudar algumas plantas para um novo local (novas funções ou desafios) onde elas possam prosperar.

The most vital factor that everyone has to internalize is that AI is working for humans and not the other way around. Humans are contributing to the machines with their inputs, thoughts, and processes to make the machines efficient enough and help the humans in a long run

Encourage continuous improvement by soliciting specific, constructive feedback, focusing on growth opportunities, and recognizing achievements. Foster a culture of collaboration and learning, where feedback is valued and used to drive positive change. Lead by example, provide ongoing support, and celebrate progress to inspire colleagues and cultivate a culture of continuous improvement.

The ideal scenario is a human-in-the-loop approach. Imagine using AI data as a springboard for human-centered conversations. We can use AI's insights to guide conversations, identify areas for growth, and personalize development plans. And finally, let's not forget to celebrate successes! Tracking progress and acknowledging milestones achieved based on both AI feedback and human coaching reinforces the positive impact of this combined approach.

Integrating AI into the feedback process should be done with the aim of enhancing the efficiency and effectiveness of human-delivered feedback, not diminishing its value. This means using AI to provide data-driven insights that humans can interpret and deliver in a meaningful way. Continuous evaluation of employee satisfaction and engagement levels, through surveys and direct feedback, can help organizations fine-tune how they use AI in feedback processes. The ultimate goal should be to use AI to create more space for meaningful human interaction, not less, ensuring that feedback continues to motivate and inspire employees.

I am speechless that this is considered a legitimate question. What kind of leader delegates something as personal as employee feedback to a machine? A lazy, uninterested, uninvolved one, perhaps. Of course, AI feedback is not going to be motivating or inspiring. Why would you expect anything else? If you ever consider using AI to provide feedback to colleagues, it is time to re-examine your motives and your suitability to your role.

Most important aspect about AI initiatives is that it needs to be put in the context of business change. I.e. one needs to factor how the people in the org are expected to change behavior as a result. This usually means a lot of selling and getting key LOB / C-suite buy-in to change the op model etc.. Also Persuading affected line staff their life can be better as it's a either no brainer to adopt the conclusions or the business case is so compelling (e.g. addresses an on fire problem like hemorrhaging millions per year) that the key Business lead sees massive value in adopting. Another aspect is AI is about small incremental accuracy improvements not absolute results which is a culture change for most and needs to be catered for.

Educate and Market It is very important that the end-users, AI team and managers all talk same language. Quite often using AI systems requires its end users to be educated and brought on-board. Another important aspect is your tool marketing. It is one essential component of selling a product. One needs to market their AI tools heavily and prove to people what happens when people use the tool vs when they don't. Classical A/B testing type of work.