What AI and business intelligence skills do you need to succeed?

I see that a skill set blending technical and strategic proficiencies is crucial. Proficiency in programming languages like Python and R is essential, as well as the mastering of machine learning techniques and expertise in data (pre)processing. In addition to this it is good to have an understanding of cloud platforms (e.g., AWS, Azure) and how they intersect with AI - e.g. Natural Language Processing (NLP) and Computer Vision. On the other hand though, staying strategic and being able to marry this understanding with business goals, and effectively communicate them to/for non-technical stakeholders is imperative. Key is to always stay current in a rapidly evolving field through continuous learning.

Everyone in the organization need to have a T Shaped Skill. An understanding of how AI can be applied to their area of work is critical. Also education on Governance, Ethics, Trustworthiness etc is critical for all just like information security training is mandated.

While technical proficiency in AI fundamentals is key, one of the less emphasized but incredibly crucial aspects is the integration of 'domain expertise' into AI models. Engineers often overlook the value of subject matter expertise in refining AI solutions. Merging technical AI skills with in-depth knowledge of the specific application area can dramatically enhance the effectiveness and interpretability of AI systems, providing real-world solutions that are both innovative and grounded.

To excel in AI and business intelligence, grasp AI fundamentals like machine learning, deep learning, NLP, computer vision, and robotics. Comprehend ethical and legal dimensions, and master BI tools for data analysis, visualization, and decision-making. Adapt to the evolving landscape with continuous learning.

As part of an AI initiative you will often need to have some degree of knowledge regarding the possible impact as well as feasibility of adding such systems. In my experience, you should always start by understanding the business problem at hand. Sometimes, people jump straight into thinking about AI as a solution when there are other possibilities. AI models often need a careful amount of dedication and observation efforts which are rarely considered when making the initial decision to use AI. If the examination yields that no deterministic solution is suitable, then try to redefine the business problem in terms of things that can be evaluated by AI models and build solutions interactively. Small iteration cycles help you catch bugs early.

AI and BI skills are vital for business success. Understanding data sources, tools, and ETL processes is key. Proficiency in descriptive, diagnostic, predictive, and prescriptive analytics is essential. Effective data visualization and dashboard creation are crucial for conveying insights. Mastering these skills empowers data-driven decision-making and enhances business performance.

Developing business domain knowledge is as crucial as technical BI skills. → Learn core industry metrics alongside dashboard design skills.

Increasing the analysis skills by using the most tools is the key to get better data result that infect in taking right disitions... BI the most important skills you need to succeed

A strong foundation in programming and data skills is crucial. Proficiency in languages like Python, R, SQL, or Java is essential. Familiarity with AI libraries like TensorFlow, PyTorch, and data manipulation tools like Pandas is key. For BI, expertise in tools like Power BI, Tableau, and Excel is vital. Understanding data structures, algorithms, databases, and cloud computing is also necessary for effective implementation and analysis. My journey has shown that mastering these skills empowers you to drive insights and innovation in AI and BI realms.

Master one versatile language like Python for both AI modeling and BI analytics. If you are more advanced, R is a good choice as well. → Get comfortable with key libraries like Scikit-Learn, Pandas, and TensorFlow. That would be a good start.

Mathematics is the foundation of AI and BI. Without a solid diligence on that, is like building with weak steel at bottom. Don’t pay the attention that maths needs in data science and projects will perish.

I learned this the hard way when I first started learning about AI and BI. I had a good understanding of programming and computer science, also I have a strong foundation in math and statistics. This made it easy for me to understand the algorithms and models that I was learning about. I eventually realized that learning is contineusly math and statistics is part of daily job for this role, and it made a big difference in my ability to understand and apply AI and BI techniques.

Math and statistics mastery underpins effective AI and BI applications. → Combine calculus expertise with descriptive stats skills for robust modeling.

It helps also to have a good understanding of the economics of software development lifecycle, cloud computing, etc. as well as of the business/challenge at hand. This is important to appreciate where to focus - based on ROI which includes how well the domain or use case fits into business strategy and help prioritize solutions based on Pareto rule - lower effort, best results/outcomes.

Understanding the intricate interplay between business objectives and AI technology is paramount in today's landscape. The ability to bridge the gap between these two realms is the key of success in AI projects. By comprehending the nuanced needs of a specific industry, like healthcare and grasping the core goals of an organization, whether it's optimizing processes or enhancing customer experiences, one can tailor AI solutions to deliver not just data-driven insights, but actionable strategies. This synthesis of domain expertise, business acumen, and technical prowess ensures that AI projects not only decipher patterns in data but translate them into meaningful outcomes, ultimately fostering innovation, growth, and competitive advantage.

Start with aligning to the goals (your ROI will thank you for it). Understanding the business context is crucial for targeted AI/BI solutions. → Combine industry knowledge with technical skills to deliver high-value insights.

Artificial intelligence (AI) fundamentally requires a limited and specific target objective. Therefore it is necessary to clearly understand the value of this objective before investing significant resources into the technology. To identify value requires a sophisticated understanding of your business or company; its areas for process improvement, competitive advantages, and potential arbitrage opportunities. - In short When considering an AI solution, first learn everything you can about your business and where your company fits into the greater market. Once you have a wholistic understanding, then decide if a AI solution is right for you.

I firmly believe that while basic domain knowledge is a requirement, the role of the AI/BI engineer is to provide the technical skills that complement the deeper domain knowledge of their colleagues. AI and BI are not meant to replace experience and know-how, but rather to elevate it to a higher level.

All business leaders should be familiar with the “AI alignment problem.” This is the idea that we should steer AI towards humans' intended objectives, preferences, and morals. Alignment means the AI system advances intended human goals and misaligned does not. However, what humans are in charge of setting those goals and assessing alignment? Even within a particular company conflict can arise so humans need to work to communicate and align with each other as well.

I worked on an AI project for a healthcare group. The aim was to create a model predicting disease risk in patients. Though skilled in ML and DL, my healthcare unfamiliarity hindered relevant model development. To bridge this gap, I engaged with medical experts, grasping their challenges and needs. This led me to build a more accurate and valuable model by integrating domain insights.

If you cannot connect with the team, then the project is doomed. Soft skills complement technical expertise in AI/BI roles. → Hone communication and collaboration abilities to share insights widely.

AI is not purely a technical discipline; it requires deep understanding of change management. Therefore, uncovering blockers, concerns, fears and properly addressing them (objection handling) will go a long way to be successful in the AI space.

Working in collaboration with the appropriate stakeholders, such as Legal, Security, Audit, and Compliance, is a vital component throughout the entire life cycle of machine learning (ML) implementation. This proactive approach helps prevent unforeseen challenges down the road.

As businesses emphasis on developing cutting-edge AI models to fuel growth, it's essential to recognize the significant environmental impact associated with the increased storage of data and the resources required for model training

It’s also important to consider 3 main points aligned with neuroscience principles, that we need to succeed in AI and Business Intelligence: 1. Neurocognitive Understanding: Profound knowledge of how the brain processes information, learns, and decides, informing the design of AI and BI systems for optimal user engagement. 2. Human-Centered Adaptation: Ability to create AI interfaces and BI solutions that adapt to users' cognitive capacities, enhancing user experience and comprehension. 3. Emotionally Intelligent Design: Understanding of how emotions influence cognition, enabling the development of AI and BI strategies that resonate emotionally and drive meaningful actions.

Mastering AI requires an unprecedented blend of skills, mainly technical. Maybe, the next years could be the times when T-shaped professionals with managerial basics set the path for practical AI-powered accelerating, but this won't come without a radical change in the organisation's mindset.