What do you do if you want to lead AI initiatives but lack AI literacy and understanding?

Don't worry, most vocal AI enthusiasts you'll come across these days have limited knowledge of the subject matter and are learning on the fly. In fact, as you spend more time in this field, you'll learn to distinguish between AI experts that have been working in this space for a long time, from short-term evangelists that are leading teams but don't really have a clue. How to avoid becoming yet another AI grifter that uses generic AI buzzwords? Focus on the fundamentals, learn about the different approaches, the history of AI and how we got here, the strengths and limitations of AI models (in particular LLMs). By engaging with the science and technology behind what people these days call "AI" you'll become more effective as an AI leader.

When you lead an initiative such as AI, remember that it is more about business needs rather than the technology that drives it. Therefore, the primary focus should be on understanding how to solve the problem. Then bring in (hire or partner) the right AI expert to help you with that. Educate yourself to understand AI terminology, and the limitations of AI. Attend webinars and take online courses—there are many for business leaders with no technical depth.?Stay informed to know the latest developments, emerging trends, regulatory changes, and ethical and societal considerations, that may impact your initiatives.

The Most Important Aspect is Data Literacy and Data Analytics ( Data-Analytics-Machine learning- AI) and Develop Critical Reasoning and thinking Secondly - Domain or functional Area Knowledge Thirdly - Understanding the Objective of AI Implementation whether it's improving the experience, increasing the revenue, reducing the cost or enabling new services is critical Fourth - How the AI Integration into the existing business processes and systems

No worries, today AI is everywhere! Start with building some knowledge. I would suggest to focus on "applied AI". A lot of FREE, yes FREE, trainings are available. Go and look for trainings from Microsoft and IBM through coursera. That's how I started my journey. Next step should be to build your network and be part of expert groups!

Simple: Go to coursera, udemy, or my personal favorite, MIT OpenCourseware and start digging into the foundational concepts of AI. It's largely a lot of statistics, some computer science, and an overwhelming amount of noise.

As mentioned above, step 2 is to build your network and join expert groups. LinkedIn is the place to be for that. Follow groups line GenerativeAI, Deeplearning.AI. Be part of the discussions, connect with expert and from there on, just build and learn. I follow that path personally. It helped me to grow my network, increase my knowledge and gain momentum/ hands on understanding on AI

There are many trusted experts in this space, including Andrew Ng. You can find tutorials on youtube or on sites like Deeplearning.Ai where you learn directly from such experts. Fast.AI is another good place to find experts and learn as a part of a community. Joining Discord servers for trusted tools such as Langchain and LlamaIndex will also help you find a community of experts to communicate with. Do your own writing as you learn because you can position yourself as a subject matter expert in that way, and also by writing what you have learned, you help to solidify your knowledge.

Don't hire Jimmy from accounting to run your AI strategy, find the right 3rd party partner and team up with them to build a digital transformation approach leveraging AI tools in the market, and make sure you select relevant use cases when evaluating your approach.

Their deep understanding bridges knowledge gaps, driving your projects forward. Engaging with AI specialists enhances technical insight and innovation. Through collaboration, experts can help navigate challenges, ensuring ethical and impactful AI applications. This strategy doesn't just solve immediate problems; it fosters a culture of continuous learning and innovation within your team. Remember, the success of AI initiatives lies not in individual knowledge but in collective expertise and collaborative effort. Embrace the power of partnership to lead effectively in the AI space.

Find people who can serve as mentors and work together with AI teams so that you can gain practical experience and learn from their expertise.

This skill - fostering collaboration - is universally necessary, irrespective of AI project or not. Having said that, some unique challenges with AI projects where you will need to lean in on these collaboration skills include (a) managing expectations (AI is not "magic"); (b) managing concerns (AI can be risky, so explain how you plan to mitigate those risks); (c) managing understanding (Generative AI is new, and AI is quite technical - what are some of the basics that everyone needs to understand before they can engage in productive conversation?)

Collaboration is key in AI initiatives, as Steve Jobs once highlighted, "Great things in business are never done by one person. They're done by a team of people." Take Google's approach to project management, where cross-functional collaboration ensures AI projects meet both technical excellence and user needs. Such synergy between departments, blending technical expertise and market insight, leads to innovative solutions that drive success. Let's champion this model of teamwork for impactful AI advancements.

Fostering collaboration is about creating an environment where ideas flow freely, and diverse perspectives are not only welcomed but valued. It's the art of bringing together unique talents and viewpoints to forge solutions that are greater than the sum of their parts. True collaboration requires open communication, mutual respect, and a shared commitment to common goals. By nurturing these elements, we can unlock the full potential of our teams and drive innovation that makes a real difference.

A powerful way to foster collaboration is to crowdsource ideas for AI usage across your organisation. This can be via large workshops (where you can take the time to educate them on the possibilities and capabilities of your current AI stack), via surveys, or strategic tools like SilkFlo.com. Just be sure to assess every idea to see whether they're feasible; the data needed is of good quality; the total cost of ownership is understood; and there are no other technologies that could better solve said use cases. Never use technology for technology's sake.

Through the formation of cross-functional teams and the promotion of knowledge sharing, a culture of innovation and the implementation of AI can be fostered.

This is really important. You want to do "just enough" to gain momentum. So, it should be small enough that it's achievable within a reasonable period of time (this is subjective, but a good rule of thumb is less than one quarter from start to finish for the initial phase), but big enough that it's valuable. Have this first phase be part of a larger vision that you articulate. Make sure that larger vision denotes the value and also the risks so that everyone's expectations are set, and so that you can pivot if the first phase doesn't meet whatever goals or success criteria you set forth.

Starting small with AI implementation is a strategic move, as Jeff Bezos aptly said, "We are stubborn on vision. We are flexible on details." This approach mitigates risks and aligns with business goals. For example, Netflix's use of AI for personalized recommendations showcases how pilot projects can deliver significant benefits, fostering confidence and setting a stage for wider application. Let's leverage these insights to navigate the AI landscape effectively and transform challenges into opportunities for growth.

The best way to ensure successful implementations of artificial intelligence is to begin with smaller projects, measure the results, and iterate based on feedback.

Encourage involvement in your organisation's AI journey by educating and empowering your workforce. Teach them the art of the possible, but ground them into the real capabilities of your current AI tech stack and what it can and can't do. Align on company goals but also remember what's in it for them. Will learning AI improve their roles? Will it help their prospects? Or will it negatively affect them. You can't have change without change management. You can't encourage AI without showcasing the benefits.

Just lead anyway, there’s no shortage of uninformed and incompetent wannabes leading all sorts of things without any talent or understanding whatsoever. The market will hopefully sort things out, eventually.

If you are leading AI initiatives, there is a lot to start from scratch. In such scenarios, accept that there will a lot of change that you will experience in future. The earlier you realise and embrace this though process, the more you will be ready towards learning and applying.

In order to successfully navigate the ever-changing landscape of artificial intelligence, it is essential to maintain awareness of the latest trends in AI, adapt to emerging technologies, and adopt a growth mindset.

If you're aiming to lead AI initiatives but find yourself lacking in AI literacy and understanding, the key is to embrace continuous learning. Cleo Academy is an excellent resource for bridging this gap. It offers a comprehensive selection of online programs designed to elevate your tech knowledge from the ground up. Whether you're starting with the basics or looking to delve deeper into specific AI domains, Cleo's expert-led courses provide the foundation and advanced insights you need to confidently spearhead AI projects and contribute meaningfully to discussions and decisions in the AI space.

Have you considered funding your team for upskilling and certifications? The learning process is seldom focused on when adopting a transformation strategy for AI, and this should be included in your annual budget forecasts.

AI is revolutionizing industries. Business leaders must stay informed to compete. Here's your AI strategy: Build Your AI Foundation ???: Grasp core AI concepts (like machine learning) through online courses or industry resources. This empowers you to ask the right questions and explore AI applications within your organization. Embrace Continuous Learning ??: Stay informed by following AI news, attending conferences, and networking with AI leaders. This keeps you ahead of trends and allows you to make data-driven decisions for your AI initiatives. Focus on Business Value ??: Align AI with clear business goals, like improving efficiency or customer experience. Let AI directly contribute to measurable business outcomes.

In a lightning fast evolving space as AI, at the moment the only constant is continuous learing. There are advancements and new information getting disseminated every week. To keep track, you must be spending time to read, think and understand.

You can deepen your understanding of artificial intelligence by attending industry events, reading research papers, and experimenting with AI tools. This will help you stay current with the advancements in AI.

A parallel step to building your knowledge and your network is to GAIN MOMENTUM. Sounds like a buzz word but in reality is just: test and try. Said differently, implement AI tools such as Chat GPT, DALLE, etc... in your daily work. Move your mindset to a hands-on mindset and learn while using tools. This will broader your understanding of the AI and how to use this broad technology.

Don't overlook ethics! Addressing ethical considerations early grounds your organization's strategic direction, instills confidence in your workforce and customers, and prepares you for evolving governance and regulatory requirements. Ethical considerations are critical in AI initiatives, impacting privacy, fairness, transparency, and accountability. Integrating ethics from the start demonstrates commitment to responsible AI development, mitigates risks, and fosters trust with stakeholders. Proactively considering ethics ensures alignment with legal frameworks, avoiding compliance issues and reputational harm. Embedding ethics is both a moral and strategic imperative, enabling sustainable and responsible AI innovation.

Utilize Advisory Boards: Set up an advisory board that includes AI experts who can provide strategic insights and guidance on AI initiatives. Leverage AI Tools: Make use of AI tools and platforms that are designed for non-experts. Many modern AI applications offer user-friendly interfaces and robust support resources. Implement Training Programs: Organize training programs for yourself and your team to build a stronger base of AI knowledge within your organization. Focus on Ethical AI Use: As you learn more about AI, ensure to incorporate discussions and policies around the ethical use of AI. Understanding the ethical implications is crucial for responsible leadership in AI.