Here's how you can measure your performance as an AI professional against industry standards.

The most important programming language is "English" (or your native natural language). This is not a new insight because of Generative AI. It has always been true. Both developers and business people always had a challenge figuring out exactly what the business people wanted. To tackle this problem, business analysts brought a plethora of tools: object diagrams, interaction diagrams, state transition diagrams, functional specifications, stories, epics, function points, etc. However, multiple layers introduced multiple opportunities for misinterpretations. Generative AI tossed the ball back where it belongs: coming up with well-specified requirements. Now, everyone has the opportunity to learn to express their needs using Generative AI.

1. Evaluate your technical prowess. 2. Master programming languages like Python. 3. Learn frameworks such as TensorFlow or PyTorch. 4. Construct, train, and deploy machine learning models. 5. Understand algorithms and their complexities. 6. Meet industry expectations for technical skills. 7. Challenge yourself with new projects. 8. Compare your solutions with peers. 9. Gain insights into your technical skill level. 10. Continuously improve through practice and feedback.

Stay ahead by regularly updating your skills through certifications, courses, and hands-on projects. Track your progress against industry benchmarks like Kaggle rankings, GitHub contributions, or published research to ensure you're meeting or exceeding the standard. Measure your success by the real-world impact of your AI solutions. Evaluate how your models improve efficiency, reduce costs, or drive innovation in your organization, using KPIs aligned with industry standards. Engage with the AI community through conferences, publications, and online forums. Peer recognition, invitations to speak, and collaboration requests are strong indicators of your standing within the industry.

AI Professional Journey is a bit like a spiral, hard to evaluate against industry standards but you do need to evolve continuously. Like any SDLC, first gather requirements, plan and build a version based on both your/team's current skillsets and what should be acquired rapidly for the specific project. Test, Deploy and Review. Review against eveolving tools and when you identify changes that would make a huge impact to the product, end-to-end, then repeat the cycle.

As an AI professional, I measure my performance against industry standards through several key metrics. Firstly, I evaluate cost reduction achieved through automation and efficiency improvements, comparing it to industry benchmarks. Revenue generation is assessed by analyzing how AI solutions drive business growth, such as through enhanced customer experiences or new service offerings. Performance improvements are gauged by measuring the accuracy, speed, and scalability of AI models. Additionally, I consider the innovation and adoption rate of my solutions, ensuring they lead or match industry trends. Continuous learning and obtaining relevant certifications also help align my skills with top industry standards.

In pharmaceutical development, the real-world impact of AI is evident in its ability to streamline the drug development process. Few examples of key metrics highlighting this impact include: Reduction in Trial and Error: AI significantly cuts down the time, resources, and costs associated with lab experiments for in vitro and in vivo testing. For example, we reduced the duration of traditional safety testing from months to just days. Optimization of Patient Inclusion and Exclusion Criteria: By refining these criteria, we can identify the most suitable patient population for demonstrating safety and efficacy in clinical trials. This approach has led to a reduction of up to 70% in traditional patient recruitment costs for our clients.

1. Definition of Objectives: Define the desired, clear objectives before project initiation. 2. Definition of KPIs: Define quantitative and qualitative KPIs for measuring successes. 3. Measuring Success: KPIs should be tracked and measures updated at regular intervals within the lifecycle to track progress and make necessary adjustments. 4. Results Analysis: Analyze outcomes versus set goals and KPIs after finalizing the project. 5. Areas of Improvement: Identify areas that need to be improved through analyzing results and feedback.

Creating impact with AI in real life is the only way forward. It needs to be measured in the same way as your organization evaluated business performance. For instance, if customer conversion rates are a key metric, it should be also a key metric to evaluate the success of your AI product. So, it is important to identify the key metrics widely accepted in your organization that are most relevant to the outcomes of your AI product. A helpful approach is to apply product management best practices to data and AI.

As an AI professional, measuring performance against industry standards involves several key metrics. First, track engagement with new literature, courses, and seminars to ensure continuous learning. Second, benchmark project outcomes against state-of-the-art models and techniques, assessing accuracy, efficiency, and innovation. Third, seek feedback from peers and mentors, participating in industry conferences and publishing research. Fourth, evaluate the successful integration of novel ideas into practical applications, and monitor the impact on business metrics. Lastly, pursue relevant certifications and participate in competitions to gauge skills against the broader AI community.

Stay current by following industry trends, educating yourself through the latest research papers, courses, and seminars, and finding the right community to support your achievements. Engage with this community, seek their feedback, and recognize their contributions to foster mutual growth and advancement. Measure your progress by how effectively you integrate new ideas into your work and how well you adapt to industry advancements.

In the rapidly evolving field of AI, the ability to learn and adapt quickly is paramount. Learning agility refers to your capacity to pick up new concepts, tools, and technologies efficiently and apply them in practical scenarios. This could involve mastering a new programming language, understanding the latest advancements in AI research, or adapting to new industry standards. Demonstrating learning agility might involve taking on challenging projects outside your comfort zone, attending workshops and seminars, or enrolling in continuous education programs. Staying updated with the latest publications in leading AI journals, participating in webinars, and being active in AI communities can also enhance your learning agility.

AI evolves and grows at a dramatic speed. Demonstrate your ability to quickly adapt to new tools, techniques, and industry trends. Your commitment to success and professionalism, combined with a strong dedication to continuous learning, ensures you remain relevant and can effectively tackle emerging challenges. Engage with new literature, courses, and seminars to stay updated. Benchmark your projects against state-of-the-art models, assessing accuracy, efficiency, and innovation. Seek feedback from peers, participate in industry conferences, and publish your research. This proactive approach not only enhances your skills but also solidifies your reputation as a forward-thinking AI professional.

AI is a field characterized by rapid advancements and frequent updates. Staying current requires a commitment to lifelong learning. Obtain relevant certifications and compare your skillset with industry-recognized standards. Compare your performance and career progression with peers in similar roles.

I think achieving a professional Accreditation in Public Relations (APR) or its equivalent is an important part of professional development, marking that you are a strategic practitioner. I also highly recommend a specialized master’s degree in public relations or communications management. I teach in the McMaster University Master of Communications Management (MCM - mcm.mcmaster.ca) which has been a ticket to executive success or entrepreneurship for most of our 200 alumni. We are unique in Canada, but there are similar programs available in the United Kingdom, USA and Europe. Just make sure that the master’s degree you choose has a strong business administration component. Your key to success: strategic communications + business admin!

Measuring performance as an AI professional against industry standards involves a multi-faceted approach. Key indicators include peer recognition, such as invitations to speak at conferences & citations in academic papers, which validate your expertise. Active contributions to open-source projects & participation in forums showcase your commitment to knowledge sharing & community engagement. Collaborating on innovative projects & receiving positive feedback from peers & stakeholders highlight your practical impact. Certifications & continuous learning also reflect adherence to industry standards & staying updated with the latest advancements. Together, these metrics provide a comprehensive view of your professional standing in the AI field.

Being cited by peers or invited to speak at industry events is a strong indicator of respect and value placed on your expertise. Such recognition not only reflects your significant contributions to the field but also underscores your adherence to high professional standards.

Gather endorsements and recommendations from colleagues, mentors, and industry peers.Actively participate in professional networks, conferences, and workshops to build your reputation.Share your knowledge and expertise through presentations, webinars, or writing articles.

Ethics is a complex field and unfortunately I think we have created education systems and societies that have allowed individuals to essentially remain myopic in their understanding of the world. In some cases this is even highly incentivised (all the way back to specialisation as part of the industrial revolution). I am concerned about the well-roundedness of anyone working in AI. Given the potential of the technology we need people that have a full range / diversity of life experiences, not just a specialised slice of it. As AI professionals I agree it is important to demonstrate that - although I don't think it's a formalised training course to complete. It's more a matter of getting out there and having rich and diverse experiences.

Ethics in AI is indeed a CRITICAL aspect of our field. It’s essential to implement frameworks that ensure transparency, accountability, and fairness. As AI systems increasingly impact various sectors, it’s our responsibility to mitigate biases and protect user privacy. Continuous education and interdisciplinary collaboration can foster ethical practices. Moreover, involving diverse perspectives in AI development can enhance fairness and inclusivity. Ethical AI isn’t just about compliance; it’s about building TRUST and ensuring that technology benefits society as a whole. Implementing robust ethical guidelines is key to sustainable AI innovation.

Most people without a technical background still don't see AI as something you can get along with. There is a lot of prejudice against AI and AI-related products. One must explain their new AI product's compatibility with personal data protection laws and its safety measures to prevent harm and misuse. Even though showing the capability of your AI product is important for reaching out to potential customers, sometimes it is more important to assure them your product is secure and built with ethical considerations in mind.

I started learning and posting on AI in healthcare since last Nov, 2023. This is because, I knew we are building a healthcare product with use cases on AI (even though on GenAI) but it was important to know and make a journey of learnings on AI impacting on healthcare industry, especially patient centric approach. I can proudly say that I have come a long way and most of those learnings are just not incorporated as part of the product also helped in defining the AI roadmap of our product, midoc. Ex: why shall we still rely on NC4 based 32 core VMs when Azure is providing health insights as a service. well, the answer is that to try other llms with likes of Claude, medpalm, whisper package on hugging face, you would need VM to test.

To see how well you're doing as an AI professional, think about these things. Make sure you know the latest AI tech, like machine learning. Check if your projects work well and meet their goals. Think about how often you come up with new ideas. Look at how much your work helps your company or clients. Keep learning new stuff by taking courses. Compare yourself to others in similar jobs by talking to them. Finally, ask your coworkers and clients how you're doing. This will help you know if you're on track with industry standards.

Lastly, examine your career trajectory. Are you progressing through roles with increasing responsibility and complexity? Career progression is a tangible sign of performance, reflecting your ability to tackle more challenging problems and lead projects or teams. Consider the milestones you've achieved in your career against industry standards for similar roles and experiences.

Are you growing your skills? Can you solve increasing more challenging problems? Then you are making progress. You may need to switch jobs to be compensated appropriately for your skills as you increase in capabilities.

Career in ai is the Dream of every professional and student working for the Industry but having the right skill set and knowledge can land you at your dream company.

It's crucial to handle data processing well. High-quality data is essential for effective AI models, so being comfortable with tools for databases and for data management is key. Understanding software engineering basics can greatly improve efficiency and teamwork. Staying updated on the latest AI and machine learning advancements is important. Practical experience through internships, hackathons, or contributing to open-source projects offers valuable hands-on learning. Don’t overlook the importance of soft skills like problem-solving, critical thinking, and communication. Balancing continuous learning with practical application and community involvement will help you maintain and grow your technical skills in AI.

Communication is the key. AI professionals should break down complex technical concepts into simple, easy-to-understand terms for managers and stakeholders. Clear and concise communication is essential to gain their support and buy-in for AI initiatives.

In today’s rapidly evolving technological landscape, measuring performance as an AI professional requires a comprehensive approach that goes beyond traditional metrics. Innovation & creativity in solving complex problems, effective collaboration with cross-functional teams, and positive stakeholder feedback are essential. Strong communication skills, efficient time management, and project delivery within budget are important. Continual learning & adaptation to new tools & technologies are vital. Contributions through research publications & conference presentations highlight expertise. Mentorship & leadership demonstrate the ability to guide junior team members. Commitment to ethical AI development ensures alignment with industry standards.

Based on my evolving experience here, I have realized that a successful approach towards AI is going even deeper with problem statements and link it to Cx at every step. This is stemming from the fact that technology is being evolved for curating better experiences, making it available to all, cheaper and faster. Since AI has made everyone a customer, as an AI professional one needs to have a 360 degree view of the experience and outcome that the solution is going to bring about. This includes wearing multiple hats, some of which are discussed above, to eventually provide a wholesome result.

In the ever-evolving field of AI, strong problem-solving skills are crucial. Measure your performance by focusing on: 1. Identifying Use Cases: Assess your ability to recognize complex problems suitable for AI solutions. 2. Developing Solutions: Evaluate your proficiency in devising innovative and effective approaches to overcome challenges using AI. 3. Implementing and Iterating: Measure your success in implementing AI-based solutions and iterating based on feedback. Effective problem-solving includes refining your strategies for better outcomes. By honing these skills, you ensure you can navigate and thrive in the dynamic AI landscape. #AI #ProblemSolving #Innovation