As an AI professional, you know how valuable your skills are in the current market. But do you also know how to communicate your value to your manager and earn their trust? Trust is essential for getting recognition, feedback, and opportunities for career advancement. In this article, we will share some tips on how to build trust with your manager as an AI professional.
One of the best ways to gain trust is to show your results. AI projects can be complex and abstract, so it is important to demonstrate how your work contributes to the goals and outcomes of your team and organization. Use clear metrics, visualizations, and reports to showcase your achievements and impact. Explain how your solutions solve problems, improve efficiency, or create value. Highlight your role and responsibilities in the project and how you collaborated with others.
Just like with anything else - trust is built off of an honest and transparent relationship. Sharing not only results but pulling back the curtain on the process will be key to get and keep skeptical managers onboard. It will not only show them how you’ve strategically and tactically thought through the problem and executed a solution but it may educate them on AI capabilities or steps they weren’t aware of. This should strengthen your relationship and the value they place on your contribution to the team and business.
Certifications don’t really exist yet in AI, at least to any substantial extent. Given this, work product and face to face discussions are all the more critical.
AI professionals can gain trust from their managers by demonstrating reliability through consistently delivering accurate and actionable insights from their projects. Transparency in methodologies and limitations of AI models, clear communication about project timelines and potential setbacks, and ongoing education about AI capabilities and risks can build credibility. Additionally, aligning AI initiatives with organizational goals and showing tangible results in terms of ROI or efficiency gains can foster confidence. Collaborative problem solving with stakeholders and ethical considerations in AI development and deployment are also key to establishing trust.
I build trust in my company by clearly communicating the capabilities and limitations of AI systems, setting realistic expectations, and delivering consistent results. Demonstrating ethical considerations and transparency in AI's decision-making processes also fosters trust. I always ensure my team understands and explains the 'why' behind AI solutions, which has been pivotal in building trust across the company, starting with my managers.
"Actions Speak Louder than Words" When you can tangibly demonstrate the impact of your #AI work through data and metrics, you build confidence in your abilities. Managers appreciate the clarity, and it solidifies your position as a trusted asset to the team. ????
Another key factor for building trust is to communicate effectively. AI professionals need to communicate not only with their peers, but also with their managers and other stakeholders who may not have the same level of technical expertise or understanding. Use simple and concise language to explain your ideas, methods, and results. Avoid jargon, acronyms, and technical details that may confuse or overwhelm your audience. Listen to their questions, concerns, and feedback and address them respectfully and constructively. Keep your manager informed of your progress, challenges, and needs.
According to my experience, it's important to know how to program, but even more crucial is the ability to convey results and key points. Managers often handle various projects, so it's a good practice to include 1-2 project overview slides and a final one for results and next steps to ensure alignment in project execution.
Don't be the lone-wolf. Building, deploying or managing AI solutions is not a solo-journey and involves collaborating with a wide set of stakeholders in the organization. The journey to building trust that AI solutions are effective in delivering their promised outcomes is an arduous one. Communicate effectively & openly regarding challenges and do not hesitate to seek your manager's support. Help your self and help your manager to proactively address issues or risks in a transparent fashion and ensure success of your AI project.
At this point in the lifecycle of AI, professionals who are competent in AI are usually well ahead of others in their business. Those who are ahead can help their internal narrative by assisting their peers to accelerate their AI learning. Being able to tell a manager what you did is great, but being able to tell them about the scale you're creating in the business by upskilling others is really impactful.
If you are an AI professional efficient communication is the bridge to gaining your manager's trust. Clarity, conciseness, and the ability to convey complex AI concepts in simple terms demonstrate your expertise and understanding. When you can articulate your ideas effectively, you build trust and collaboration, which are essential in this dynamic field.
Communication is universal but technical understanding is not! Always consider that your managers or stakeholders may not have the same depth of knowledge in your field of work. Analogies can really work wonders here to better communicate outcomes. Here's an example to go and wow your c-suite: "You know when you open your sock draw, there's so many socks everywhere that its difficult to find the ones you want or if some are missing. Well, its the same with code. So what we have done through our code optimization is to organize our sock draw into black, blue, white and rationalize to 3 pairs of each. This makes us much faster and efficient and allows us to fault find far more effectively too." ;)
A third way to gain trust is to learn and grow. AI is a fast-changing and dynamic field, so you need to keep up with the latest trends, tools, and techniques. Show your manager that you are eager to learn new things, improve your skills, and expand your knowledge. Seek feedback from your manager and others on how you can perform better and deliver more value. Take advantage of training, mentoring, and coaching opportunities that are available to you. Share your insights, resources, and best practices with your team and manager.
Learning and growth encompasses both self-education and helping others develop. A great way to stand out and build skills is to seek opportunities to share your knowledge and insights. For instance, share lessons and reflections from recent projects or releases with your colleagues. Or take the initiative to present at industry events such as conferences or meetups. From speaking at 100+ external events, I've found that presenting helps solidify my knowledge and I also learn a ton from the questions or discussions with others. These are great ways to build essential skills such as public speaking and to establish your thought leadership. They’re also invaluable in helping you stand out in your company and the broader industry.
Learning and growth are highly valued at SensViz. We believe they are crucial for gaining a manager's trust. We promote a culture of continuous learning, encouraging team members to upskill and tackle challenging projects. This not only improves their expertise but also their confidence in presenting results to managers. Our managers notice this growth, which in turn, builds trust. Over time, this trust translates into more responsibilities and opportunities for career advancement. This culture has significantly contributed to our success and the personal growth of our team members.
As a manager, I highly value employees who demonstrate a proactive approach to learning and personal growth. It's not just about knowing the latest AI trends but about applying that knowledge to current projects. To gain trust, show eagerness to learn and improve by seeking feedback and utilising available training and mentoring opportunities. When you actively apply new techniques to solve real project problems and share your insights, you not only contribute to the project's success but also earn recognition and trust within the team.
In my opinion, trust in AI is bolstered by continuous learning. Stay on top of emerging trends and innovations in AI. Demonstrate to your manager or team lead your commitment to skill enhancement and knowledge expansion. Actively seek feedback to refine your approach and increase value delivery. In addition, look at leveraging available training, mentoring, and coaching to deepen your expertise. Share your insights and new learnings with your team and management, which in turn can foster a culture of growth and collaborative advancement. This proactive learning attitude can help showcase your dedication in keeping pace with the evolving AI landscape.
I think there is alot to be desired when it comes to the way in which we build and deliver educational & developmental content. We've revolutionized the way we innovate, but not the way we teach. Immersive adoption should drive training mentoring,coaching and growth initiatives.
A fourth way to gain trust is to be ethical and responsible. AI professionals have a great responsibility to ensure that their work is ethical, fair, and transparent. You need to follow the principles and standards of your organization and profession when developing, deploying, and using AI solutions. You need to be aware of the potential risks, biases, and impacts of your work on people, society, and the environment. You need to be honest, accountable, and respectful of the data, code, and systems that you work with.
Ethics and equity should be the cornerstone, not an afterthought when advancing AI. Fairness, accountability, transparency and social responsibility must be woven into our work and systems from the beginning, not tacked on at the end. Do not wait for risks and harms to emerge—preempt them through rigorous impact assessments and inclusive design. Center diverse voices and perspectives. Question assumptions baked into data and models. Lead with moral courage and conviction, even when difficult. This foundational mindset earns trust. Our technology shapes lives. That power warrants the utmost ethical stewardship. If we do not anchor to equity first, we anchor to nothing at all.
AI professionals should actively engage in continuous education about ethical principles and stay informed about emerging concerns and discussions in their field. It's also crucial to implement an ethics review process, where AI projects are routinely evaluated for their social, ethical, and environmental implications. Advocate for diversity and inclusion within their teams to reduce unconscious biases in AI models. Engaging with a wide range of stakeholders, including those who could be affected by AI systems, for their insights and concerns is also important. This might include public consultations or partnerships with ethicists and social scientists. Be transparent with AI strategies ensuring users can interpret and challenge decisions
Working together with your stakeholders, and customers, and setting up a clear process that embodies ethics, fairness, responsibility, diversity, the elimination or reduction of bias, respect for privacy, security, and provides as much transparency as possible earns trust. Taking this one step further and setting the goals and objectives to target what is best for the group as a whole / best for everyone (rather than the individual / project) helps to setup a win-win scenario for everyone in the group and puts the project on a successful path. Share the progress at every step and how it was measured to stakeholders, so that everyone understands how it was achieved, and that it is safe, fair, ethical, and meeting all the criteria above.
Being ethical and responsible as an AI professional not only fosters trust but also ensures that your contributions align with the highest standards of integrity and fairness. In an era where ethical considerations in AI are paramount, demonstrating a commitment to these principles will not only gain your manager's trust but also contribute to the ethical and sustainable development of AI solutions within your organization.
Ethics in AI transcends mere compliance; it embeds integrity into the fabric of technological advancement. By adhering to ethical standards, AI professionals not only mitigate risks but also fortify the trust placed in them by stakeholders. This responsibility means rigorously vetting AI solutions for fairness and societal impact, ensuring transparency in operations, and safeguarding sensitive data. Upholding these principles conveys a strong message of accountability and dedication to beneficial and responsible AI development.
A fifth way to gain trust is to align with your manager. AI professionals need to understand the expectations, priorities, and vision of their manager and align their work accordingly. You need to show your manager that you support their goals, values, and decisions. You need to respect their authority, expertise, and feedback. You need to be proactive, flexible, and adaptable to changing needs and situations. You need to be a team player, not a lone wolf.
Bring your Manager along for the ride. Let me explain: develop a line of thinking or a POV around your AI model, product, or application. Have a clearly articulated vision, as well as specific and measurable goals you want to achieve; Formulate a crisp understanding of the problem you're trying to solve with AI and paint the future end-state to define the art of the possible. Consider all of this within the Ethical AI framework or a set of constraints and guardrails to help maximize alignment. It's helpful to document this and socialize it not only with your Manager but your cross-fuctional team members, and stakeholders that need to be kept abreast. Ideally all of this should happen before designing, building or funding an AI project.
In my experience, it is crucial for all professionals to align their goals and expectations with their managers and other key stakeholders right from the beginning of a job or project. It's also essential to gather feedback regularly, ideally on a bi-weekly basis during the project's duration, and adjust your course of action accordingly. Moreover, if there are disagreements or differences of opinion, it's important to clarify and make necessary adjustments. Failure to conduct regular check-ins can result in frustration during performance review cycles. Therefore, having regular one-on-one check-ins to review goals and progress is of utmost importance and a key factor in professional success
Alignment with managerial vision is key in demonstrating that an AI professional is not only technically proficient but also strategically valuable. This involves proactive engagement with managerial goals and a willingness to adapt as project needs evolve. By resonating with their manager's priorities and upholding team values, AI experts can position themselves as indispensable allies, further cementing their trustworthiness and enhancing collaborative success.
A sixth way to gain trust is to build rapport. AI professionals need to establish a positive and respectful relationship with their manager beyond the work context. You need to show your manager that you care about them as a person, not just as a boss. You need to be friendly, courteous, and helpful. You need to express appreciation, recognition, and gratitude. You need to be open, honest, and authentic. You need to find common interests, values, and goals.
Knowing how to behave is the icing on the cake. Know-how convinces colleagues and is a necessary but not sufficient condition. Only a combination of know-how and interpersonal skills can convince a manager and board meetings. Actively seek opportunities to work together on projects, share ideas, and contribute to the team's collective success. Demonstrating a willingness to go above and beyond your individual responsibilities showcases your dedication and strengthens the bond between you and your manager.Furthermore, proactive communication about your progress and any challenges you encounter ensures transparency and allows for timely adjustments, reinforcing your reliability. A sense of adaptability and flexibility.
Building a rapport and connecting with your manager is less about AI, but more about finding common ground, actively listening, and showing genuine interest in their viewpoints and challenges. I would also try to demonstrate reliability through consistent performance, being proactive in offering solutions, and maintaining a positive attitude also foster a strong working relationship. make sure you are open and regular in your communication, and seeking feedback can further strengthen this rapport, making your interactions not just about work but also about mutual respect and understanding.
My 4 critical questions you need to ask before taking on your first artificial intelligence project: 1. Where can AI provide a quick win? 2. What does your data look like? 3. Are you creating value? 4. Do you know what your definition of success is? Lean on trusted partners to provide the necessary AI expertise your team will need as they ramp up and face those technical hurdles that are sure to come during those first projects.
Don't wait for your management to tell you what to do. Look for opportunities to contribute to the team and to make a positive impact. By taking initiative and looking for ways to contribute to the team, AI professionals can show their management that they are valuable assets and that they are committed to the success of the team. This can help to build trust and respect
The responsibility for learning and development in AI really falls on the individual. I've seen how IT teams can quickly build MVPs (Minimum Viable Products) using AI tools. Developers use AI to quickly make mockups, speeding up their work. UX designers let AI do simple coding, so they can focus on complex design tasks. This helps everyone work smarter and faster. This combination of AI and human skills helps speed up the MVP development process, allowing our teams to focus on refining and fully developing the most promising ideas.
My best piece of advice is... Make sure to show off good AI behaviors and mindsets, and not just focus on it being a tool. Getting someone who isn't naturally excited or engaged with AI to feel comfortable is much smoother if you address the mindset vs just talk about the technical capabilities. Conor Grennan has a great example talking about how to get a workforce healthy, you could build a gym (tool) that they MAY use or get them a 30-day personal trainer (mindset). One will have a MUCH greater impact... After you've set that stage, and if they are open, my second piece of advice is to be transparent about your usage. This will help them see your logic, how you aren't taking verbatim outputs, and truly increase their trust in YOU & AI!
I think examples explain better how it works. An AI professional could proactively provide detailed reports and insights on a machine learning project's progress, showcasing their ability to meet deadlines and handle complex tasks reliably, thus demonstrating their commitment and reliability to their manager.Also by effectively communicating and simplifying complex AI concepts into actionable business strategies, an AI professional can help their manager understand the potential impact and value of AI initiatives, building trust through transparency and alignment with business goals.