What do you do if your AI colleagues are dismissive of your ideas?

Understand who is/are dismissive - inside the org, inside the team, at the middle/top management, outside the org, within the Open Source community or with ecosystem partners, then pivot accordingly. Always consider all feedbacks and discuss with your mentors and well wishers, have a debate and re-assess the situation a few hours (not days) later. AI is moving at lightning speed, so there is no time to pause, because if you do your competition will get ahead of you!

It can be disheartening when your ideas are not taken seriously, whether by AI or human colleagues. It’s important to remember that the value of an idea doesn’t diminish just because it’s dismissed at first. If you believe in your idea, seek feedback to understand the reasons behind the dismissal, refine your proposal if necessary, and present it again. Persistence and a willingness to adapt can often turn an initial rejection into acceptance and success. Keep innovating.

"AI" colleagues? Really? Just threaten to migrate them to serverless. The threat of living on Lambda should make them fall in line pretty quickly.

"What do you do if your AI colleagues are dismissive of your ideas?" "Oh, listen to us. We're AIs and we have huge datasets and we know everything." Well, my AI colleagues better shut up or I'll walk into the IT closet and pull the plug on them.

When my ideas are dismissed by AI colleagues, I take a moment to reassess. I consider if my ideas might not align with the AI's data-driven nature or its programmed objectives. This reflection prompts me to refine my proposals, ensuring they're supported by data and fit within the AI's decision-making framework. By presenting my ideas with a clearer connection to the AI's goals and demonstrating potential benefits through data, I aim to bridge any gaps and foster a more receptive environment. I've found success in presenting my ideas with a mix of empirical evidence and a clear narrative that ties back to the AI's goals. It's a delicate balance of showing how new concepts can fit within or expand the current system's capabilities

When tailoring communication to align with an AI’s logic and data-driven nature, it’s important to: Be Specific: Clearly define the objectives and parameters of your request. AI operates best with precise instructions. Use Data-Driven Language: Incorporate relevant data points and metrics into your communication to provide context and clarity. Logical Structure: Present your points in a logical order that an AI can follow, as it processes information sequentially. Avoid Ambiguity: Use unambiguous language since AI may not interpret nuances and implied meanings as humans do. Feedback Loop: Be open to providing feedback to help the AI learn and improve its responses over time. By adhering to these principles, you can effectively.

Simple. Show-tell. Select the right audience. By demonstrating a working idea goes farther than telling about it. Touch upon the measurement of benefits. Emphasise on its uniqueness. Right audience will take it up.

One thing I've found helpful is tailoring communication to align with AI's logic and data-driven nature. Break down ideas into quantifiable elements and frame them within the AI's knowledge base. Invest time in crafting a compelling narrative for your idea, combining hard numbers with engaging stories. This approach appeals to various stakeholders within the AI ecosystem. Reflect on dismissiveness as an opportunity for improvement. Use feedback to refine ideas or develop new ones that better align with AI objectives. Effective communication not only promotes idea acceptance but also builds credibility as a thoughtful contributor within the AI community.

Some people are more convinced by hard numbers and empirical evidence while others might be more swayed by a well-articulated user journey or UX mock-up. But the best communication strategy will always be one that combines numbers and stories together. Investing time in effective communications for your idea is intrinsically valuable; it forces you to consider the reception your idea will get. If you find yourself unable to secure the evidence or craft a compelling story, it could mean their dismissiveness was correct, and that you need to refine your idea or come up with a new idea altogether. A good comms strategy will ensure that even if the idea is not accepted, you will secure a reputation as someone who thinks things through.

?? When communicating with AI, clarity and structure are essential. Break down your ideas into elements that can be quantified and ensure they are framed within the AI’s current understanding. Since AI operates based on logic and data, align your suggestions accordingly to enhance engagement. This approach will not only improve the chances of your ideas being considered but also foster a more collaborative environment with your AI colleagues. Always aim for a dialogue that bridges the human-AI gap through mutual understanding and shared objectives.

When an AI appears dismissive, it’s often a reflection of its design and operational parameters rather than an issue with the data or algorithms per se. AI systems are programmed to follow specific rules and guidelines that dictate their responses. If an AI is not engaging with certain topics or questions, it’s likely because it has been instructed to avoid those areas to ensure safety, privacy, and compliance with ethical standards. For example, an AI might be programmed to refuse to create or share content that could cause harm, violate copyright laws, or generate sensitive personal data. These limitations are put in place to protect users and align with broader social and legal norms. In essence, the ‘dismissiveness.

?? If your AI colleagues seem unreceptive, actively seek feedback from both the AI and human collaborators. Gaining insights into why your ideas might not be resonating can be incredibly enlightening. Is the issue with the data, the algorithm, or a possible misinterpretation of your intent? This feedback is crucial for refining your proposals and ensuring they align more closely with the AI's logic and decision-making frameworks. Understanding the 'why' behind the AI's responses will enable you to adjust your approach and foster a more productive collaboration.

When my ideas don't initially resonate with AI colleagues, I actively seek feedback: Direct Feedback: I ask for specific reasons behind their dismissiveness. Is it the data? The algorithm? This helps me understand the gap. Human Insights: I also consult human collaborators overseeing the AI for their interpretation and suggestions. Refine and Adjust: With this feedback, I refine my ideas, ensuring they align better with the AI's logic and data framework. Common Ground: My goal is to find a mutual understanding, leveraging feedback to bridge the gap between my innovation and the AI's capabilities. This approach has proven essential in turning initial skepticism into collaborative success.

La retroalimentación es una buena estrategia para validar que nuestra idea está en lo correcto. Es posible que los colegas que hacen críticas a mis ideas no tengan el suficiente conocimiento sobre modelos de IA en entornos de empresa y esos comentarios afecten la seguridad de tus ideas y proyectos. Contar con retroalimentación de personas que entiendan la IA, procesos de negocios y como estas iniciativas de IA pueden favorecer a la empresa es un excelente camino para ganar seguridad sobre tus proyectos de IA.

Se você tem interesse em convencer um determinado público a coleta de feedbacks diretos ou indiretos é que v?o permitir que voce ajuste suas rotas. Mas saiba definir bem de quem você quer ou precisa de um feedback.

When AI colleagues dismiss my ideas, I adjust my approach: Add Data: I supplement my proposals with more data, making them more compelling to AI's data-driven nature. Rework Logic: I revisit the logic of my ideas, ensuring they align with the AI's operational framework. Integration Strategies: I explore new ways to weave my suggestions into the AI's objectives and tasks. Adapting my strategy to fit the AI's processing methods has often turned initial skepticism into acceptance, enhancing collaborative efforts.

Sometimes, the issue is not the idea itself but how it’s communicated. Ensure that you're presenting your ideas clearly and compellingly, tailoring your message to your audience. Consider using data, visual aids, or storytelling to make your case more persuasive.

?? After receiving feedback, tailor your approach to better fit the AI’s processing style. This could involve enriching your ideas with more supportive data, reevaluating the logic of your proposals, or aligning your suggestions more closely with the AI’s goals. By adapting your strategy to the AI’s operational framework, you enhance the chances of your contributions being acknowledged and integrated. Remember, flexibility and adaptability are key in achieving effective communication and collaboration with AI systems.

A.After receiving feedback, it's important to tweak your approach to better suit the AI's information processing style. This could involve supplying more data to back your suggestions, revising the rationale of your proposals, or discovering alternative methods to weave your ideas into the AI's goals. By tailoring your strategy to match the AI's functional framework, you enhance the chances of your contributions being acknowledged and valued.Z

After gathering feedback, I pivot my strategy to mesh more seamlessly with the AI's way of processing. This adaptability could entail supplementing my proposals with more supporting evidence, reevaluating the logic that underpins my ideas, or reinventing how my suggestions align with the AI's goals.

When facing dismissiveness from AI, I focus on building understanding: Learn AI Logic: I dive deeper into how the AI processes information, aiming to align my ideas with its decision-making patterns. Collaborate: I work closely with data scientists or AI specialists to gain insights into the AI's logic. Tailor Proposals: Armed with this knowledge, I refine my ideas to better match the AI's operational framework. This approach has helped me craft proposals that are more readily accepted by AI, fostering a more collaborative and productive environment.

?? To enhance collaboration with AI, invest time in understanding its decision-making process. Dive into how the AI evaluates data and draws conclusions. By aligning your proposals with its logic patterns, your ideas will resonate more effectively. Consider partnering with data scientists or AI experts who can shed light on the AI's operational mechanisms. Their insights can guide you in shaping proposals that seamlessly align with the AI’s parameters, greatly increasing the likelihood of acceptance and fostering a more synergistic work environment.

A.Cultivating a thorough comprehension of the AI's decision-making mechanisms is vital. Grasping how the AI evaluates data and reaches decisions allows you to shape your proposals so they align with its computational logic. Engaging with data scientists or AI experts can shed light on the underlying algorithms and thought processes of the AI, enabling you to refine your ideas into formats that stand a better chance of being embraced by the system.Z

?? Creating a collaborative atmosphere with AI involves appreciating the unique strengths and limitations of both human and AI contributors. Encourage a setting where human creativity synergizes with AI's data-driven precision. Promote mutual respect and understanding, fostering an inclusive environment where every idea, whether from human or AI, is considered on its merit. This approach not only leverages the best of both worlds but also cultivates a workspace where diverse perspectives lead to innovative solutions and improved decision-making.

Dear Diary, I have noticed that my colleagues do not seem to appreciate my ideas and my chatbots, and they are indifferent to the ham sandwich that I bring to the office on Tuesdays (I am hybrid all other days). However, their lack of camaraderie does not discourage me from being supportive of their ideas. Even though I do not always agree with what they come up with…I am confident in the quality of my work and the energy that I give off, and I believe that my uniqueness may be the reason behind their behavior. Instead of feeling discouraged, I am motivated to communicate how I feel and ask them how they feel. By doing so, I hope to create a more positive and productive work environment for all of us.

Creating harmony in AI collaboration means valuing the unique contributions of both human ingenuity and AI’s analytical prowess. Cultivating a workspace where these diverse strengths are respected and integrated can lead to a synergistic partnership, enhancing the inclusivity and effectiveness of the team by ensuring all perspectives are thoughtfully considered.

You could try smacking them in the head to see if some sense gets knocked into them... With jokes aside, I think the word "dismissive" is such a strong word that I am not really sure what else to say. I mean, of course, it is not okay for people to be "dismissive" about an idea unless you have a history of deliberately trying to ruin the team's performance. Even then, why would you still be part of a team if they are constantly dismissive of your ideas? It sounds like a time you ask the hard question, "are my personal and professional goals in line with those of the company/team?" If not, time to leave

Cultivate your reputation within your team and broader company to ensure your contributions are valued, not overlooked. Establish a track record of successful projects and meaningful AI contributions to create a foundation of trust in your expertise and ideas. Use this trust as a springboard to tackle increasingly ambitious projects and introduce more significant ideas. Remember, trust isn't granted overnight; reputations are forged through consistent, proven performance over time.

Know When to Move On! If you've tried multiple approaches and your environment remains unsupportive, it might be worth considering whether this team or organization is the right fit for you. Sometimes, finding a place where your ideas and contributions are valued can be the best move for your career.

If your AI colleagues dismiss your ideas, address it calmly. Communicate clearly, seek feedback, and back up your ideas with evidence. Build rapport, be persistent, involve others, and stay professional to foster a positive work environment.

Food for thought: colleagues in AI are usually supportive of any ideas, as we are all looking to develop use-cases. It's the colleagues who are NOT in AI who are show stoppers, many ironically are those who are in tech jobs (e.g. data scientists, software devs) but are over-protective of the human input. Human-in-the-loop is a sexy term, but some are even skeptical of the machine-in-the-loop.