What do you do if your AI clients have difficulty understanding technical jargon?

If customers using artificial intelligence (AI)-based solutions have difficulty understanding technical jargon, it is important to take a clear and communicative approach to ensure better understanding. First, avoid using complex, technical language when interacting with customers. Try to express concepts and technical terms in a simple and understandable way, using clear and accessible language. It also illustrates technical concepts using concrete examples and real-world situations that customers can easily understand. Connect technical concepts to everyday experiences or analogies that make their practical application clearer. By simplifying language and using concrete examples, you can help customers overcome difficulties.

Avoid the "tyranny of jargon" in all areas of technology, not just AI. Demonstrating the ability to explain complex terms or concepts using relatable examples will build bridges for your audience and increase their buy-in or your ideas and plans. I suggest using a concept that is highly visual and taps into an emotion - it will stick in their memory longer.

When communicating with clients and stakeholders about your AI solutions, platforms, APIs, and models, it’s important to speak in terms that resonate with them. Remember, the purpose of AI is not just to exist as a technological feat; it is a tool designed to enhance customer service and improve the experience for those engaging with your company, whether they are making a purchase, booking travel, or any other interaction. AI serves as a means to facilitate business objectives. Take the time to understand the business goals, plans, and customer service strategies of your stakeholders and clients. Tailor communications to align with their language and objectives, ensuring that the focus remains on how AI can support their business needs.

Anything can be explained if we can first demystify by breaking them into bite-size easy-to-consume narratives with the help of Audio/Video. Based on the listener's technical knowledge, you may need to tweak your message up or down to be on the same plane as your audience.

Visual aids are an excellent way to compliment your explanation so that clients can connect the visuals with the words in order for you to create a conducive learning environment.

In my experience, providing a great visual slide deck of your architecture that details how the system works is invaluable. In terms of pitching your product, it can be the difference between having a follow-up meeting with investors/prospects or not. For example, when I worked at Amelia, they used a great slide deck that would show how their AI would integrate with legacy systems, and how the process worked from the initial request of the user (and how Amelia's AI would break the meaning of the request down by using Ontologies and Knowledge graphs) to how the AI would connect with the required systems and execute the use case. This was particularly valuable in ITSM use-cases, where some of the architectural components are hard to grasp.

Leveraging visual aids like diagrams and infographics is key in AI communication, offering a bridge between complex technology and practical understanding. A well-designed visual can transform abstract concepts like neural networks into comprehensible, tangible illustrations. This approach not only fosters clarity but also enhances the engagement and learning experience, underscoring the visual nature of human cognition. In the AI space, where innovation is as much about perception as it is about technology, visuals are not just aids; they are a fundamental part of the narrative.

Introducing AI to clients requires an incremental approach, starting from the ground up to facilitate a solid understanding. It's about laying a foundation with the basics of data collection and analysis, then methodically advancing to the complexities of machine learning algorithms and decision-making processes. This educational journey not only empowers clients but also aligns with evolving educational methodologies in the tech industry, ensuring a robust grasp of AI's practical implications. Such a strategy cements our role as educational leaders in AI, underlining our commitment to comprehensive and accessible tech literacy.

Gradually introduce AI principles to your clientele so they are not overwhelmed. Start with the fundamentals and work your way up to more intricate concepts. Before discussing how machine learning algorithms use this data to generate predictions or judgments, for instance, you could describe data gathering and analysis. With this methodical approach, customers are able to establish a foundation of knowledge and make connections as they gain further knowledge.

Incremental learning is key to effectively introducing AI concepts without overwhelming your clients. Start with the basics and gradually build up to more complex ideas. For instance, you can begin by explaining data collection and analysis before delving into how machine learning algorithms, like those utilized by Cortana to assist Master Chief 117 in the Halo series, leverage this data to make predictions or decisions. This step-by-step approach allows clients to establish a foundational understanding and connect the dots as they learn more.

I think it's best to use real-life examples when explaining AI. Try to connect the AI concept with something they've experienced before where AI could have been useful. Instead of just talking about technical stuff, talk about how AI can help in practical ways. And don't forget to ask if they have any questions, so they feel comfortable getting the full picture.

Real-world examples, bridge the gap between theoretical AI and its impact on people's lives. Make AI feel tangible by showing, not just telling. Here's how real-world examples make it click: - Netflix Knows You: Explain that the "Recommended for You" feature is powered by machine learning analyzing your viewing habits. - Chatbot Helper: Describe how many websites' online support is actually an AI chatbot trained to understand common questions. This helps clients see the potential benefits and get excited about AI solutions.

Let's play a game. You are standing on a beach on a scorching day. The sand is hot, and your feet are burning. You can move forward, left, and right, but you cannot go back and out of the beach. What do you do? You will most likely run forward towards the water as fast as you can, right? Now, let's play the same game with a slightly different situation. Same beach, same rules. Only this time, someone has randomly left billions of dollars all over the beach. Now what do you do? Most likely, you will tolerate the pain for picking up those dollars right? See what happened? You acted based on the rewards you got. This is Reinforcement learning, one of the toughest subjects. But if your basics are clear, you can explain it to a 5-year-old.

Visual storytelling is close to my heart to be able to communicate effectively with impact. Real life examples definitely help in relating to the experience.

Nothing's better than showing. After several speaking gig on the topic, you can't explain it without use cases. I always finish my talks on a few slides showing how a blend of AI tools (Midjourney, ChatGPT, Photoshop) got me an entire Vogue cover. From the photos... To the resizing... To the upgrades... To the copies... Everything made by AI. You can't expect a "aha" moment without being able to simply show what you mean.

Not only does it tend to make more sense of abstract concepts, interactive demo's show your clients that it's real and that you are a trustworthy partner to work with. With Google's launch of Gemini for example, they FAKED certain aspects of the demo. Read Techcrunch's article: "Google’s best Gemini demo was faked." This does irreparable damage to the trust you have built with clients. Always be open & honest, in my experience clients actually appreciate it when the demo does not initially work as expected, you locate the problem live & then show why it now works. During these heated moments where the tech doesn't demo as intended: - Remain calm and composed - Explain what might be the problem - Fix the problem there and redo the demo

Make sure that, they try their hands on it. Make them use it so that they have a feel of what exactly you’re trying to do. This helps a lot . For example , when you build models, allow them input parameters, and let them see the output of your program, let them see the output of the model, this will save you of having to explain something they will never understand.

In my experience, an interactive demo is the best way to explain something technical to a non tehnical audience. Give them a demonstration of what the technology can do. Moreover, if you can provide a comparison with an example of how the problem is being solved at present, with existing technology, it will not only help the audience udnerstand it better but also give you a higher chance of converting a lead.

Encourage an environment where clients can freely ask questions and express doubts. Regularly schedule sessions to review what’s been learned and to clarify complex topics. This feedback loop is crucial for continuous improvement in how you communicate AI’s capabilities and ensures that learning is aligned with client needs. Don't try to demo an AI solution. Instead seek time to POC a prototype.

Established a feedback loop to ensure that explanations. Creating the questions and be prepared for further solutions. Whenever we establish a feedback loop, it should be in regional languages. For the better clarification to the regional users.

With feedback loops using both the positive feedback loop (PFL) and the negative feedback loop (NFL).PFL are used to reinforce the understanding, each iteration of a feedback loop creates more context and value for the client. NFLs allow for disadvantaged clients to catchup to the requirements of understanding AI, using the two together ensures that you are not treating the whole of the clients team as experts or beginers but allow for growth and understanding without leaving out any skill set

Most of all I would state, quit walking in the room loaded with all the latest information about all the AI capabilities in the market, and thinking if you throw a bunch of stats and explanations at your client that they will understand. No one wants to work with the person who needs everyone to know they are the smartest person in the room. Be empathetic. Listen to the problem. Try not to immediately solution with AI (or anything TBH). Seek to understand. Speak to the level of the individual you are across from. This is all intimidating and new. Try not to overwhelm your audience.

If AI clients struggle with technical jargon, simplify communication using plain language, analogies, visual aids, examples, feedback, additional resources, and personalized communication.

Whatever stage we are in our career, our ages, we all have a little child in us and like to play. GAMIFY your approach to empower your organization with AI. Set up quizzes, challenges, etc.. When we play, we tend remember, thus learn.... and while have fun :)

While it's important to provide a high-level understanding of AI concepts, I’ve found that most clients don't need an in-depth explanation of the inner workings of AI models. Instead, they are primarily concerned with how AI can address their pain points, improve efficiency, and drive business growth. If clients require a deeper technical understanding, they will often bring in a subject matter expert (SME) who can dive into the specifics. The emphasis should remain on the practical applications and value of AI. By shifting the focus from technical jargon to solution-oriented discussions, you can build stronger relationships with AI clients and position yourself as a trusted partner in their AI journey.