How do you become a manager of artificial intelligence?

From my experience, to promote AI usage by enterprise customers, one needs to apply the same principles of enterprise software as in the last two decades. AI is not about coding, mathematics, or hardware, but how do you build the product market fit so that AI will be used where it brings the most value to the customer. Moreover, it needs to be better than other tools that serve the same purpose. People won't use AI just because it's AI, but because it gives them more functionality, more safety (as in my company's domain of autonomous driving), or it is cheaper or more scalable. Then, there is the fundamental benefit of AI being self-improving over time, 'learning by imitation,' for example, using supervised learning.

The core essential to successfully deploying and using AI tools is to understand that they are exactly that; tools. AI will still require proper guardrails to ensure that appropriate decisions are still being made. AI can process massive amounts of data, identify trends and red flags, and propose strategic solutions and responses, but in most situations it cannot be left to make the ultimate decision or take any final action. In other words, artificial intelligence still requires real intelligence in order to maximize its contributions and potential. After all, any fool with a tool is still a fool. AI does not change that reality.

Acquiring foundational understanding of AI entails not only grasping the basic technology and its recent advancements but also distinguishing between various concepts such as rule-based automation, pattern recognition, machine learning, and neural networks. An understanding of the relevant data context is crucial in terms of importance, relevance, and quality. It's imperative to recognize the potential of AI, including the associated risks, regulations, and ethical considerations to ensure equitable business outcomes. The use of AI varies significantly. Aligning initiatives with strategic business objectives like revenue growth, enhanced efficiency, improved customer experience, or risk management constitutes a solid foundation for success.

AI can be managed effectively in business by understanding its fundamentals, regularly monitoring system outputs, prioritizing transparency, reviewing AI chatbot responses, and mandating explanations for significant decisions. Proactive AI management requires a balance between using its capabilities and maintaining transparency, oversight, and understanding of its limits. Using AI judiciously in sales strategies can lead to a competitive advantage. For example, AI-driven chatbots can be used to identify potential customers, provide personalized offers, and automate customer service tasks, freeing up (well' to a c certain degree, at least..) more time for salespeople to focus on higher-value activities.

From experience, to effectively manage AI, it's crucial to integrate domain-specific knowledge with AI basics. This means understanding not only how AI works but also how it can be applied in your specific industry. For instance, AI in healthcare requires different considerations than AI in finance. By combining AI understanding with sector-specific insights, you can more effectively guide AI projects to meet industry-specific challenges and opportunities.

Having spent a few years in this space, I always remember I don’t know enough. Being literate is not a milestone but a journey in the evolving field of AI. Even if you are an expert in ML or NLP or Gen AI - the field is much larger and changing at a rapid pace. Embracing the learning as a journey means identifying a few different sources of knowledge and setting aside enough time to learn. Great amount of free content is available from Microsoft, Google, AWS and also from Coursera, OpenX, MIT, Stanford etc and on YouTube. Key guidelines that have helped me is to get a general understanding of the field, then a focused approach on either ML, Deep Learning or Gen AI or any other AI field. Depth is critical to have a meaningful impact.

There is no shortage of educational and informational resources on the topic of AI. And you certainly have to study those resources: watch videos, read papers, try popular frameworks. At the same time you have to spend as much time as possible using Generative AI tools - at least 20-30 hours in total. It is very important to develop strong intuition for those systems.

Do we need a AI ? Where we need and how long ? What type AI will be beneficial ? With AI without AI where we are ? what is best ? Lots of Questions all helps to identify and keep collaboration's between company ,stakeholders, users. AI Solution will be beneficial to cover all. You cant recruit or build a environment without requirements are not planned and sign off to be ready.

AI literacy should extend beyond technical aspects to include an understanding of ethical AI. This involves being conversant with concepts like algorithmic bias, ethical data usage, and the social impact of AI decisions. Managers should be able to navigate these ethical dimensions and guide their teams in creating AI solutions that are not only effective but also ethically responsible and socially beneficial.

AI literacy involves the capacity to effectively interact and collaborate with AI specialists, stakeholders, and users. As a manager, your role includes converting business objectives and challenges into AI needs and solutions, and the reverse. It's also crucial to articulate the advantages and limitations of AI to your team, clients, and partners, addressing any queries or apprehensions they might have.

When leading AI projects, emphasize creating a culture that values experimentation and iterative learning. AI projects often involve exploring uncharted territories and require a mindset that embraces trial and error. Encourage teams to innovate and experiment, understanding that not all projects will succeed, but each provides valuable learning opportunities.

Not all projects are necessarily needs to be run Agile It can be Hybrid or other methods with it . Leading and Managing Projects successful if manager have SDLC background .Who is able to choice best method to run projects due to her/him past experience how to use SDLC. If AI used mostly software base applications knowing manually E2E will bring successful automation transformations .

Leading AI projects requires a balance of technical understanding and project management skills. As an AI manager, you must oversee the project lifecycle, ensuring that AI solutions align with business objectives and deliver value. This involves setting clear goals, managing timelines, coordinating with various stakeholders, and maintaining an agile approach to adapt to changing needs or challenges.

Leading an AI Project requires an experimental mindset and setting stakeholder expectations appropriately. I do get concerned that OpenAi has set expectations that this is "easy" and guaranteed. AI implementation is expensive (compute and skills) and as with all projects needs top be clear on desired outcomes and risk.

In the process of building effective AI teams, it is crucial not only to identify skilled individuals but to find those with the ability to connect the dots accurately. While a vast amount of AI knowledge is dispersed across the internet, the true challenge lies in the analysis and utilization of this information to achieve specific goals. A successful AI team requires individuals who can navigate the wealth of available knowledge, discern relevant insights, and strategically apply them to meet the desired objectives.

AI team needs talents who understand the pragmatic use of AI in business world to improve efficiency and effectiveness and can be explained in terms of business gains

Building successful AI teams involves integrating a mix of skills, including not only technical expertise but also domain experts, UX designers, and ethical compliance officers. A cross-disciplinary team ensures a holistic approach to AI development, considering user experience, practical applicability, and ethical implications alongside technical feasibility.

In the 'Human Resources Strategy' section: Building Effective AI Teams: 1. Identifying Roles and Skills: - Understand roles: data scientists, engineers, ML engineers, AI researchers, designers. 2. Fostering Diversity and Inclusion: - Diverse team for varied perspectives in AI solutions. 3. Providing Tools and Resources: - Equip the team with necessary technology and support. 4. Encouraging Continuous Learning: - Promote ongoing skill development. Key for creating a collaborative, innovative AI team driving forward-thinking solutions.

As a Manager I need to know Scope, budget, milestones than I make sure If project appreciated to to build a team .If I am able to deliver after spendings simply . Than build a team around it with to deliver.

Risks might include: -Potential to automate tasks traditionally performed by humans, which may lead to job displacement. It is crucial to invest in retraining & upskilling programs. -Require access to large amounts of personal data, raising concerns about privacy & security. Organizations should implement robust data protection measures, including encryption, anonymization, & secure storage protocols. -May exhibit unintended behaviors or make decisions that have unforeseen consequences. Managing AI risks, it is crucial to involve multidisciplinary teams, including experts in AI, ethics, law, & policy. Also, fostering transparency, accountability, & open dialogue with stakeholders is essential in addressing & mitigating risks

Standard practice for any project, identify the risks. If you are consuming vast quantities of data for Generative AI project, do you have the rights to do so? Is there a risk you might get sued for doing so? I see legal risk and a key risk. Additionally AI experience staff are in HIGH demand and a risk that all AI projects should have is salary inflation and potential loss of key staff to the next bog thing. Just 2 examples but I dont see AI project risks as anything different to other projects if well managed.

Managing AI risks involves recognizing and mitigating potential issues and challenges associated with AI deployment in your business. As a manager, it’s imperative to be cognizant of various risks affecting your AI solutions, including technical, operational, and strategic aspects. These encompass data quality, security, privacy, bias, reliability, scalability, and compatibility. Additionally, you must be prepared to address ethical, social, and legal risks related to AI, such as accountability, transparency, fairness, trust, and compliance with regulations. Establishing a robust framework and process for evaluating and managing AI risks is crucial.

Managing AI risks involves identifying and mitigating potential issues related to data privacy, bias, and ethical implications. As an AI manager, you should establish guidelines and practices to ensure that AI solutions are transparent, accountable, and fair. This includes staying informed about regulatory changes and industry standards, and implementing robust data governance policies.

AI seems to have the answer to everything and while it is very often right, it will also shamelessly give wrong or misleading answers with the most assured tone of confidence (hallucinations). While working with AI, it is important to consider that risk and to anticipate potentially problematic situations in order to handle them as gracefully as possible. AI still needs a great deal of human supervision. Data protection is also a risk. One needs to understand the difference between an open training environment and a closed one.

Embracing AI opportunities is essential for AI managers to maintain a competitive edge. It involves being proactive in identifying AI's potential to enhance products and services, and courageous in implementing innovative solutions. This adaptability ensures that the organization stays ahead in the evolving tech landscape, fostering a culture of continuous learning. Successfully leveraging AI not only improves operational efficiency but also drives business growth and sustainability in a rapidly advancing AI-centric world. Therefore, for AI managers, embracing these opportunities is key to achieving long-term success and keeping their organizations at the forefront of technological progress.

We have set up an AI task force in our company. 4 people who are actively involved with AI to be able to distinguish the good tools from the bad. We have to seize the opportunities that AI brings us. Companies that resist this will be overtaken by companies that use AI in the future.

Embrace AI opportunities by forming strategic partnerships with academic institutions, research labs, and industry consortia. These collaborations can provide access to cutting-edge AI research, talent, and tools, enabling your organization to stay at the forefront of AI innovation and apply the latest advancements to business challenges.

AI like all the previous innovations will need a very high level of willingness to embrace and adapt. It is estimated, the AI of 2040 will be a million times more powerful compared to the AI of 2021. And it will be all around us like omnipresent. AI as we know it today has the biggest strength, which is humanly impossible to replicate -its ability to grasp all information available in the public domain, and its ability to learn and improve all the time, which have been possible because of the computing power of the neural networks and thousands of specialists creating new AI models all the time. Be open to learning the new always and you will be fine through this journey.

AI Automation to Automate Business Processes Utilizing AI to automate business processes offers numerous advantages. It streamlines operations, enhances efficiency, and reduces costs. AI-powered automation tools can handle repetitive and time-consuming tasks, allowing employees to focus on more strategic and creative endeavors. By leveraging AI, businesses can automate tasks such as data entry, customer service, scheduling, and inventory management. AI can analyze large data volumes to identify patterns and trends to aid in making data-driven decisions and optimize their processes. AI-driven automation increases productivity, improves accuracy and consistency, leading to better outcomes and customer satisfaction.

Organisations need to think of AI as an Employee in order to get the basics right first. Thinking of each AI as an employee on the team will create transparency, accountibility, and results. For each AI (aka BOT) within the organisation there needs to be a job description (What exactly do we want the AI to do?), an assigned manager (who will train the AI, allocate the AI its tasks, and manage its output), and ongoing reviews of the AI's performance, with optimisation (what tweaks will the algorithm need, why, and how often?).

For becoming an AI manager, you’ll need a blend of technical knowledge, business acumen, and excellent communication skills. You need to have knowledge in areas like computer vision, natural language processing, or recommendation systems. Also Pursue relevant degrees or certifications, gain hands-on experience, and enhance your soft skills. Effective communication is crucial as you bridge the gap between technology and business strategy for your clients.

There are different types of AI and needing different approaches. Algorithmic decision via proven casual models like in optimisation are routine products. Predictive AI using data and Machine learning are a bit of Research and mostly development. Generative AI is mostly research and applied science and needs less 'management' and more venturing. Take options on different ways and prune and focus on 'winners' but always have the outliers as history shows the outliers win far more then managers like

To enhance AI skills as a business leader, focus on identifying and prioritizing business needs where AI can address inefficiencies and innovate. Set measurable objectives for AI initiatives and ensure AI complements human expertise. Immerse yourself in AI trends, analyze successful case studies, and start with pilot programs. Secure stakeholder support, establish governance and ethical guidelines, and invest in workforce upskilling. Comply with data privacy regulations, ensure transparency in AI use, and proactively address ethical implications. Continuous learning and adaptation are key to leading effectively in the AI era.

Understanding the business challenges you're trying to solve goes hand in hand with a deep understanding of the data. In ten years of projects we never had a client that really knew what his data looked like, both in quality and especially what was not there. It takes training to see the data as the machine does, and architect the right amount of context in the same way a human learns. Finally, adding just the right amount of human expertise in the right place of the application can save months, rather than working to a 100% AI magical solution.