What do you do if non-technical stakeholders struggle to understand complex AI concepts?

Here's some I have used. Algorithms: Consider algorithms like a recipe. Providing the computer with instructions to solve a problem by providing step by step instructions to achieve a desired dish. Neural Network: A network of roads connecting cities (neurons). As you would learn the best route, neural networks learn the best connections between data points to achieve the outcome. Machine Learning: Like teaching a child to ride a bike. Initially, you guide them closely, they learn from each ride (data) and gradually ride smoothly (make accurate predictions) without help. NLP: Like a skilled translator who knows many languages, cultural nuances, slang, and context, NLP allows computers to understand and generate human-like text.

Non-technical stakeholders aren't going to dive into the topic enough to understand the nuances unless they have a personal interest in the technology. Concepts like neural networks and vector databases are very deep in the weeds. The best thing to do is to abstract it out - for example, an LLM is a bot that has been trained on vast swathes of the internet. Obviously it is far more complex than that, but for them it doesn't need to be. What is most important to them is actually seeing the capabilities, i.e. put together a demo. It doesn't have to be fancy, but when properly built it will show them just how powerful the technology can be and more importantly, what it can do. Remember the old saying: a picture is worth a thousand words.

In a business environment, the majority of stakeholders and end users are non-technical. A good product should make people feel seamless in using it. When people use iPhone, they don’t think about the backend mechanism. If understanding the concept requires effort, then the language isn’t simple enough.

Start with "Why?". I've found too often that people get too early into the technology discussion/solution, and don't spend enough time up front discussing the business problem or opportunity, what job they are seeking to do (or improve), and for whom. Once that is clear, it becomes easier to talk about AI as a tool to help achieve a well-identified outcome. The more people can connect with the specific goal or outcome, the easier I have found it is to understand the technology when it comes to that layer of discussion.

In bridging the gap between technical jargon and non-technical stakeholders, employ storytelling techniques to simplify complex AI concepts. Utilize visual aids and analogies to make abstract ideas tangible. For instance, during a project presentation, you could liken AI algorithms to recipe ingredients, making intricate concepts accessible and engaging for diverse stakeholders, fostering collaborative understanding and buy-in.

Use analogies they relate to. AI is like teaching a Martian to speak Earthling—it requires a lot of examples and corrections. Break down complex concepts into bite-sized, relatable pieces. Visualize the invisible. Charts, graphs, and visual aids can bridge understanding faster than jargon ever will. Show them AI’s impact with real-world outcomes

If non-technical folks find AI concepts baffling, turn to visual aids. Diagrams, flowcharts, and infographics work wonders in breaking down complex ideas. Imagine using a flowchart to trace data's journey through an AI system, or an infographic that lays out step by step how AI learns from data to decide. These visuals can often convey what words take paragraphs to explain, making them super handy for getting your point across clearly and quickly. Whimsical AI is a great AI tool to use for this.

Craft your technical concepts around a visually appealing story. Make it a real world scenario that the non technical audience is sure to understand.For example, use a common scenario like a work happy hour to explain the complex and technical concept of transformers. "Your walking around the Happy hour listening to snippets of conversation.You don't hear every word but you use the context to infer what is being said.This is similar to how transformers take in parts of a data, understand the context and relationships between the data, and generate or interpret information based on that understanding." Accompany this concept with enticing visuals (graphs,flowcharts,images) and you are sure to keep your audience both engaged and following.

Visualizations including charts, graphs, and diagrams can be powerful tools for conveying complex AI concepts visually and intuitively. Create visual representations of data, algorithms, and processes to illustrate key concepts and facilitate understanding.

I often use visual aids, such as diagrams, flowcharts, and infographics, to illustrate how AI systems work. These tools help in making abstract concepts more concrete, providing a visual narrative that guides stakeholders through the intricacies of AI in a more digestible manner.

Sharing real-world examples can be a game-changer. Mention how AI powers recommendation engines in streaming apps or makes virtual assistants on phones smarter. It's about connecting AI to things they use daily, showing its role in making life easier and more efficient. This approach not only demystifies AI but also underscores its value and presence in everyday life, making the concept less abstract and more relatable.

When it comes to understanding AI for non-technical stakeholders, real-world examples are key. Take the use of AI in customer service chatbots, like those used by Sephora, to streamline inquiries and enhance the customer experience. Or consider how AI algorithms in predictive analytics help healthcare organizations like IBM forecast patient admissions and improve outcomes. These practical applications show the tangible benefits of AI in everyday business operations, making the technology more accessible and relevant.

To bridge the gap further, I present real-world examples and case studies that highlight the practical applications and benefits of AI. Connecting complex AI concepts to tangible outcomes demystifies the technology and showcases its value in solving real business challenges, making it easier for stakeholders to grasp and support AI initiatives

Using real-world examples and case studies can help contextualize complex AI concepts and demonstrate their practical applications and benefits. Explain how AI is revolutionizing and helping industries solve complex use cases.

A demo is often the most effective method for illustrating how an AI or ML concept can be applied in real-life scenarios. When users/stakeholders witness firsthand how the technology can benefit their specific use case or daily tasks, they are more likely to appreciate its potential value and understand the logic. Help stakeholders understand the broader context of the AI concept by explaining how it fits into larger trends or industry developments. This can help them see the relevance and importance of the technology.

Show, don't tell. Seeing is believing. The stakeholders do not necessarily even HAVE TO understand complex tech stuff, they just need to realize what it can do for them. You also do not need to know how a combustion engine works in order to drive a car, right?

A picture is worth a thousand words! I came up with an idea for a user-controlled RAG system for our product. I told everyone about it for weeks, and everyone agreed it sounded like a good idea. But there was no excitement for it. It wasn't like they disagreed with me, but they couldn't share my vision until they actually saw it for themselves. So I put together a proof-of-concept that actively demonstrated the idea along with a naive implementation in our existing UI. Once they saw it, then they were able to share the same excitement I had. I didn't need an hour long meeting to explain it in words, I showed them with a two minute Loom recording. People need to feel the experience to understand the technology.

WIIFM. I would align on the business challenge first. This builds trust and creates a foundation. Next, have them imagine how their work or results would be different if that challenge was reduced or eliminated. Only then would I educate about the techniques and in layman’s terms, with visuals wherever possible, how this project will solve the challenge. Finally I’d share the metrics that will help demonstrate that the chosen technique does in fact resolve the use case.

In my journey as an AI/ML Consultant, I've found that the essence of conveying complex AI concepts to non-technical stakeholders lies not in the intricacies of algorithms or data structures, but in the tangible impacts these technologies have on the real world. My journey in AI/ML has crystallized one philosophy: elucidate the consequences of AI rather than its constructs. This pragmatic pivot from 'how it works' to 'what it achieves' demystifies AI, making its value palpable. This approach demystifies AI, turning abstract notions into relatable narratives that resonate on a human level, fostering a deeper, intuitive understanding and appreciation of the technology's potential.

Don't start with talking about the technology - it's almost never ideal to do that. Start with the business level goals/outcomes sought, and explain the purpose of the technology in those terms. Explain the business level risks as well - again, this doesn't have to dive deep into technology. If it does, perhaps that means the business outcomes are not defined clearly enough.

To get non-technical folks on board with AI, zoom in on the impact rather than the nuts and bolts. Talk about how AI can tackle challenges, boost efficiency, or open up new chances, all within the scope of their interests or business aims. This way, you align the discussion with what matters to them, helping them grasp AI's value in reaching their goals. It's about showing the "why" and "what" of AI benefits, leaving the "how" for another time.

In my experience people respond best to relevant interactive learning models. A good example are photography or cooking courses . The determination to learn the new skill is interest led & then step by step hands on demonstrations with the opportunity to ask “ your question” means people get to understand the basics and you can then walk them up the ladder of knowledge . This is very inspiring as people can literally feel themselves grow with new skills & this opens the mind to a real debate on the wider issues that AI will bring to society & work culture. As each team member grows in understanding they are brilliant in involving others to engage with this new science & new learning mindset

When talking AI with non-techies, promoting ongoing learning is key. Recommend easy-to-digest resources like other LinkedIn articles, some of the AI workshops and bootcamps we host, or webinars that match their expertise. Create a culture that invites questions and integrates AI learning into daily routines. As AI tech grows, keeping everyone's understanding fresh ensures that all stay in the loop and engaged.

In my experience people respond best to relevant interactive learning models. A good example are photography or cooking courses . The determination to learn the new skill is interest led & then step by step hands on demonstrations with the opportunity to ask “ your question” means people get to understand the basics and you can then walk them up the ladder of knowledge . This is very inspiring as people can literally feel themselves grow with new skills & this opens the mind to a real debate on the wider issues that AI will bring to society & work culture. As each team member grows in understanding they are brilliant in involving others to engage with this new science & new learning mindset

When explaining AI to non-technical stakeholders, we usually begin with the familiar—specifically, the economics of AI—which is key. We've found that highlighting AI's role in driving innovation, especially efficiency innovation, through its predictive power makes the topic more interesting and engaging. Then, that's when we delve into the "how"; this is also where clear visualizations of the process become truly useful. Finally, to solidify understanding, we incorporate relevant use cases, sharing real-world applications and the ROI, going back to what's important for the stakeholders.

There's only one right answer to this. It's to learn how to tie what you're doing to what problems they need to solve. They need to come up with creative content? Your AI helps them do that by taking your data and using it to generate articles. They need to get the latest data? Your AI helps them do that by automatically getting the right data from the database and putting it onto their dashboard. They need to see the latest market updates? Your AI collects all the latest news and summarizes it to make it digestible. etc.

I've found a combination of storytelling and real-world examples to be incredibly effective in explaining these concepts accessible to non-technical stakeholders. By weaving AI into relatable narratives, we can illustrate its practical applications and impact in a way that resonates with everyone. For instance, envisioning how a small bakery uses AI to optimize production not only demonstrates AI’s capabilities but also its direct benefits to everyday life. These strategies help to demystify AI and turn abstract concepts into understandable stories. It’s not just about simplifying technology but also about bringing everyone into the conversation.

Understand what your non-technical stakeholders are really looking for, when they struggle to understand complex AI concepts. Most likely, they're responsible for owning the outcome of the model and they probably want to better understand the cause and effect that different flavors of input (data) has on the model's outcome. Think about explaining the 'why' rather than focusing on the 'how'. This is analogous to driving a car - we don't think about how the engine, transmission or the fuel injection system works. Rather we focus on steering the wheel (stakeholder's feedback to fine tune the model), dashboard indicators (model metrics), maintenance (model updates) etc.

Do not even try to explain the complex AI concepts to non technical business leaders. Why should they spend time on that. Focus on explaining how would your AI solution solve their business problem and what business outcome you will deliver. Use layman language to explain your solution at a high level with risks and dependencies though.