Eres un experto en IA que busca ascender de rango. ¿Cómo puedes demostrar tu potencial de gestión?

As far as I can see from LinkedIn, what you need to do is a post a long list of 'hacks' or 'prompts' for various menial tasks and then undermine the confidence of your readers by telling them that if they're not already following these amazing ideas, they'll be left behind. Then get a bunch of people to leave AI-generated comments saying how 'awesome' your post is.

To prove your management potential as an AI expert, take leadership roles in projects, mentoring junior colleagues, and fostering a collaborative work environment. Establish yourself as a thought leader through publications and contributions to industry forums, demonstrating a strategic vision aligned with business objectives. Effectively communicate technical concepts to non-technical stakeholders, emphasizing results-driven outcomes and quantifiable business value delivered through AI initiatives. Continuously invest in learning and development, staying abreast of industry advancements to remain at the forefront of AI innovation.

Approach AI wearing a strategy hat, while focusing closely on risk management, cost benefit analysis, and scientific reality. Avoid jumping into discussions around AI without proper preparation around the benefits and limitations of AI technologies. Be aware of the latest trends, including regulation and law. Come prepared with opinions about where AI might be headed, informed discourse about closed versus open-source models and their applications. A thorough understanding of the value of quality data (and where it originates) is key, to allow your colleagues and executives in your organization to appreciate the risks that the organization needs to properly consider. Engage with senior professionals on an equal footing by preparing well.

To prove management potential as an AI expert, demonstrate strong leadership by mentoring junior colleagues, leading cross-functional projects, and delivering results that align with strategic goals. Develop communication and interpersonal skills, build relationships with stakeholders, and showcase your ability to effectively manage teams and navigate complex challenges. Seek opportunities to take on additional responsibilities and proactively contribute to organizational success. Ultimately, by consistently demonstrating leadership qualities and driving impactful outcomes, you can position yourself for advancement into management roles within the AI field.

Vision is key, but let's be honest, nothing speaks louder than tangible results. Here's a tip: Impress your bosses by showcasing a slick AI tool usage, like AI automation, marrying Zapier's with other software. That's the golden ticket, to me. And, though it might sting a bit to mention, ensure your hard work doesn't get swept under the rug or, worse, credited to someone else. Most importantly, share your vision with passion and excitement. With the incredible potential AI holds, getting excited should be the easiest part of your job.

Great managers are vision architects. They craft a compelling narrative that shows the team's purpose, the project's impact, and how AI acts as the bridge to achieve both. They weave a thread that connects our innovative AI solutions to the organization's mission and strategy, highlighting their real-world value.

Clearly articulate a compelling vision for how AI can transform your organization, highlighting potential initiatives and their impact on business goals. Seek out opportunities to work with cross-functional teams and mentor junior colleagues. Demonstrate your ability to guide, support, and foster knowledge-sharing. Take initiative on projects, showcase strategic decision-making, and embody the professional standards you expect of a team.

Volunteer for or seek out opportunities to take on management responsibilities, such as mentoring junior team members, leading project teams, or overseeing departmental initiatives. Demonstrate your ability to effectively manage people and resources.

Accumulate a track record of successful AI projects and initiatives that demonstrate your ability to deliver value to the organization. Highlight your achievements and contributions in performance reviews and discussions about career advancement.

Develop strong communication skills, both verbal and written, to convey complex technical concepts in a clear and understandable manner. Articulate your ideas, insights, and recommendations with confidence and clarity.

AI projects rarely succeed in silos. Show your ability to collaborate effectively with colleagues from diverse backgrounds, including data scientists, engineers, and business stakeholders

It is rare these days to achieve the impressive outcomes that will get you noticed for your management or leadership potential, without other people. Achieving things as a team, requires excellent collaboration underpinned by trust. You can build trust and collaboration by being open, delivering on your commitments, discussing and agreeing how you will work together, having an open feedback loop and not taking disagreements personally, but as an opportunity to improve.

Para destacar seu potencial de gestão, priorize a colaboração. Trabalhe de perto com colegas e partes interessadas, valorizando suas perspectivas para garantir que as soluções de IA sejam relevantes e éticas. Promova uma cultura onde aprendizado e feedback são fundamentais, e se esforce para mentorar e apoiar colegas, compartilhando conhecimento. Essa abordagem não só constrói relações de confiança mas também demonstra seu comprometimento com o sucesso coletivo e a capacidade de liderar pelo exemplo.

Key to your success in leadership is your ability to engage across teams and drive collaboration, delivering more value together than teams are by themselves. Decreasing friction in collaboration and identifying incentives for encouraging people to work together is a skill that you will need to learn. Building a narrative that inspires others to come together, share and ensure that we all rise together towards common goals.

Foster strong relationships with team members, other departments, and stakeholders. Effective collaboration shows your ability to work with diverse groups, manage team dynamics, and build consensus, which are key skills for a management role.

From experience, I can say this is extremely valuable. Not just for moving your organization forward but moving you and possibly other colleagues forward as well. Supporting team members can help them shine and reach their goals but also help you grow in how to teach, communicate, and develop human relation skills. It becomes rewarding for them and rewarding for you as well. With AI, sharing tools and innovate ways to get things done can show your expertise and innovative thinking.

As an expert in B2B sales and AI, I completely agree with the importance of collaboration in the field of artificial intelligence. Working with other experts, stakeholders, and users is crucial to ensure the relevance, ethics, and effectiveness of AI solutions. By fostering a culture of learning and feedback, we can all contribute to the success of our teams and organizations. Let's continue to share our knowledge and support each other in this rapidly evolving industry.

Working together is crucial for the success of AI projects. You'll work with various departments and teams, each with their own readiness and knowledge about AI. As a manager, adapting your communication style to fit these groups is essential. They may also have concerns about how AI will affect their jobs. To be an effective manager, you need to build trust and clearly share your vision. This can be done through kickoff workshops and hands-on training sessions. A leader should make sure their team can keep up and encourage a team effort to enhance progress.

To climb the ranks, show you're a team player. Work closely with everyone, from tech gurus to business folks, ensuring your AI projects hit the mark. For instance, if you're developing an AI tool to streamline inventory management, involve warehouse staff in the testing phase. Their insights can fine-tune the tool, making it more user-friendly and efficient.

To prove your management potential in the AI sector, actively seek out diverse experiences that stretch your capabilities. Look for projects that require cross-functional teamwork, offer leadership roles, or present complex challenges that necessitate innovative solutions. Volunteer for assignments that might be outside your comfort zone but offer learning opportunities, especially those that can show your ability to manage ambiguity and lead under uncertainty. Document your successes and the lessons learned from any setbacks, as these experiences are tangible proof of your readiness for management roles.

Elevating AI expertise requires a strategic approach. Demonstrate prowess in pioneering research areas, such as deep learning architectures or reinforcement learning paradigms. Implement agile methodologies to adapt swiftly to evolving trends. Foster interdisciplinary collaborations, bridging AI with fields like neuroscience or economics, for groundbreaking insights. Thus, showcasing not only technical acumen but also visionary leadership.

Being open to new ideas and trying out different approaches is important for showing your potential. By staying curious and willing to experiment, you can keep up with changes in AI and prove you're adaptable. Trying new things also shows you're not afraid to take risks, which is a quality leaders need. Plus, experimenting helps you learn and grow, improving your skills and problem-solving abilities.

There has been no better time than now to innovate and experiment. Taking what you know to another level if what helps make us excited about new things as it can impact our day to day. Just yesterday, I showed a team how to repurpose their content quickly and concisely to utilize as a teaching tool. They can in turn share that with their staff and impact all their processes in a positive way.

In the dynamic world of AI, leading isn't just managing, it's pioneering. Embrace a growth mindset, experiment fearlessly, and empower your team with resources to explore. Together, innovate and bridge the gap between vision and impact.

Na IA, sugiro uma abordagem de inovar e experimentar constantemente. Busque novas tecnologias e métodos, encarando cada desafio como uma oportunidade de aprendizado. Isso mostra sua mentalidade de crescimento e disposição para abraçar o risco de falhar, aspectos cruciais para a liderança eficaz. Incentive sua equipe a seguir esse mesmo caminho, provendo-os com os recursos necessários para explorar e inovar. Ao promover essa cultura de experimentação, demonstre ser um visionário e um líder capaz de conduzir mudanças significativas.

Implementing AI is a phased journey. It's best to begin with small pilot projects with clear goals to see how well they work and then make adjustments based on what you learn. The true cost of implementing AI systems goes beyond its sticker price; it also affects how happy and productive your team is. It's really important to start small to understand their impact. Once the solutions go into production, keeping up with feedback is crucial. For managers, it's smart to plan long-term but deploy in short, ongoing cycles.

If the goal is to demonstrate leadership potential, then the focus of innovation and experimentation should be closely tied to delivering business value or learning with an ultimate goal of accelerating business value. Many AI professionals will default to solving a problem with AI even if it could be solved more simply without. It is important to consider different cognitive biases, to ensure you aren't falling prey to confirmation bias and are creating teams with a wide variety of skills, experiences, cultures and backgrounds to ensure you are bringing the diversity of thought needed to come up with compelling/disruptive ideas.

Proving oneself for a managerial position in AI isn't just about technical prowess; it's about translating AI expertise into tangible business value. This means going beyond algorithms and code to tackle real-world challenges strategically. Starting with a crisp problem statement and setting clear project objectives aligns AI efforts with business goals. Defining measurable KPIs ensures outcomes are tracked and communicated effectively. Embracing data-driven methods fosters informed decision-making and drives continuous improvement. By showcasing the ability to wield AI as a tool for strategic problem-solving and value creation, one demonstrates readiness for managerial leadership in the dynamic landscape of AI-driven businesses.

The best way to solve business problems is to MAKE business problems. Remember back in the early days of computing people were like, damn, these calculators are the cat's pajamas! Everything was smooth at the calculator factory and there were no problems. So engineers who wanted to jump into management were like, hey, but can we get this calculator on the moon! And executives were like, now what?! So the Apollo program was born and a bunch of engineers moved right up into the management ranks. Learn from that true bit of history.

Don't just showcase technical skills. Demonstrate how your AI knowledge translates into solutions for real-world business challenges. Quantify the impact of your projects, highlighting cost savings, revenue increases, or efficiency gains.

Para ascender como gestor, foque em resolver problemas de negócios com IA. Isso envolve clarear o problema, definir objetivos, e estabelecer KPIs para medir sucesso. Utilize análises orientadas por dados para testar hipóteses e validar suposições. Demonstrando como aplica IA para gerar resultados tangíveis, mostre que é não apenas um especialista técnico, mas também um pensador estratégico capaz de criar valor real para a organização.

Great leaders leverage AI not for its own sake, but to tackle real-world challenges. We translate business needs into clear problems, define success with KPIs, and test rigorously. Our data-driven approach delivers measurable results, proving AI expertise and creating strategic value.

It's not an AI tool approach; it’s all about Use Cases. Not everything has to be solved with AI; identify challenges that get early executive buy-in, that can’t be solved by traditional means, and are data-rich. How to identify good opportunities to deploy AI? 01) Initiatives: What challenges does the industry you are in face? 02) Use Cases: How is the company attempting to solve such challenges? 03) Stakeholders: Which division/business unit does the challenge belong to? Who decides? Sponsors? Detractors? 04) Insights: Where do they come from? 05) Data: What data do you have available to solve the challenge? Is it validated? Do you need more data? 06) Tools: What tools does the company use to solve the challenge?

That AI should be used as a tool to solve real business problems. It is essential to define clear objectives, KPIs and use data-driven methods to demonstrate tangible results. Strategic thinking and value creation are key in solving business challenges with AI. Let's continue to learn and grow in this exciting field.

I completely agree that to demonstrate leadership/management potential you need to demonstrate business value from the work you are delivering with your team. Business value does not only have to be financial, but does need to be defined. Intangible value can be difficult to define, agree and correlate to improved business performance even though it is widely accepted as having a significant impact. In some cases, value may only be realized through use or by someone else's action, for example someone buys your product. Ideally try to identify value you can directly impact and understand how your business measures value and business performance and model metrics using the same philosophy.

Drawing from an engaging case in the tech industry, an AI professional successfully transitioned into a leadership role by demonstrating exceptional initiative in a company-wide AI project. Initially spearheading a small team, they orchestrated the development of an AI tool that significantly streamlined customer service processes, showcasing their innovative approach and the tangible benefits of AI solutions. Emphasizing collaboration, they brought together diverse departments, fostering an environment of shared knowledge and mutual respect. Their proactive communication about the project’s vision and goals, coupled with their commitment to ethical AI use, garnered widespread recognition within the company.

Peter Drucker famously said, "Culture eats strategy for breakfast," highlighting the paramount importance of organizational culture in any transformation, including AI integration. It’s essential to be sensitive to the existing culture and the changes AI implementation will bring. Leaders should lead by example, actively engaging with AI tools and demonstrating their benefits. This approach helps mitigate resistance and fear by showing confidence in and commitment to the AI-driven change. Encouraging a culture of learning and innovation, where trying new things and sometimes failing is seen as a valuable part of progress, is crucial.

Share your knowledge and expertise by mentoring junior team members or colleagues new to AI. This fosters a culture of learning and empowers your team.

To truly stand out as a leader, embody the qualities you value in your team. For instance, if you're pioneering an AI-driven project to enhance customer service, be the first to adopt and advocate for the new technology. Show enthusiasm, dive deep into the technical aspects, and share your learnings openly. T Leading by example, especially in the rapidly evolving field of AI, positions you as a forward-thinking manager capable of steering your team towards groundbreaking achievements.

Great leaders don't just preach AI, they embody it. We model professionalism, ethics, and a relentless pursuit of excellence. Embracing change, we adapt and inspire our teams to solve real-world problems with cutting-edge solutions. A passion for AI fuels our continuous learning, driving innovation to the next level.

As an expert in B2B sales specializing in artificial intelligence, I must emphasize the importance of leading by example. Demonstrating professionalism, ethics, and a passion for continuous improvement can inspire and motivate your team to reach new heights. Let's continue to learn and grow together in the exciting field of AI.

If you are an AI expert and you are required to show your management skills to move up the ranks, then you are in the wrong organization. There are enough managers in this world and too few AI experts. Find an organization that recognizes valuable expertise and challenges great experts not on people management but on thought leadership: also a form of leadership, but aiming to take others along, teach people, build a community, communicate clearly. I've seen too many great experts being unhappy in people management roles. There are (great) exceptions of course, but in general it is a wrong turn.

Management isn't for everyone. You can consider moving up the IC ladder as well if you don't want to manage people. Some companies (eg MSFT, AMZN, prob GOOGL) have these available to software engineers. You can move up to director/VP level as an IC by being a great engineer if that's what you love to do.

As an AI expert looking to take the next step, your focus should be on strong communication, project management, and people skills. Key strengths include clearly explaining complex technical concepts to diverse audiences, successfully delivering AI projects on time and budget, and motivating cross-functional teams. Strategic thinking is also critical - understanding business objectives and how AI drives growth. Ethical leadership is paramount as AI impacts more areas; I would aim to develop responsible, unbiased solutions while ensuring transparency, privacy, and accountability. Seek opportunities to lead multifaceted initiatives, strategize decisions, and broaden understanding of AI's organization-wide and societal effects.

There are very few genuine "experts" in AI. Taking a course or getting a shiny certificate doesn't count. Don't pretend to be an expert. If you want to move up the ranks, find a problem and come up with a way to solve it. If you want to be an expert, don't move up the ranks.

Transitioning from expert to management roles in AI requires expanding your knowledge and influence from technical domains to adjacent business areas. This requires the development of a broader and balanced set of skills that combine technical expertise with leadership and management functions. This positions emerging leaders not just as experts in AI but also as capable of guiding an organisation towards innovation and success. This transition is a well-developed path across most technical fields, with experience and knowledge available to transfer from various technical domains.

Be scrupulously honest about a few things (may not secure promotion, but should tell you if it's a place you want to tie your wagon to). The total cost of AI acquisition, training, tuning, and maintenance. The probability of achieving advertised uplift and time to achieve it vs alternatives. The amount of demand pushed around systems in terms of work to validate and act upon outputs. Time and cost to recruit, train, and retain people with sufficient local knowledge to inform all that. Maybe, judging by the quality of these AI content farming efforts, not relying on people with 'top voice' badges. All unlikely to secure promotion in an AI bull market, but it's the kind of integrity that should be valued more.

What made you a valuable part of AI technology development is not all you need to be a good manager. The skills and ways of thinking to lead teams, to help people realize their potential are critical to develop. The drive that lead you to learning AI technologies needs to be as strong or stronger to embrace the skills, habits and focus that are needed to face leadership. As equally as you embraced sites like Huggingface and tech blogs and discord servers, you need to dive into books, community and blogs on leadership and dealing with people. As an individual you are contributing value. As a leader you are managing a group of people to deliver more value than they can deliver individually.

Ultimately organizations will pick their next leaders and managers from those that show that they can consistently get things done and can be trusted to deliver on their commitments. In most cases leaders in organizations have many things competing for their time to identify prospective leaders, so another key skill to add to the list is communicating upwards and the value you bring currently and the potential value you could deliver in future.