According to Gartner, 85% of artificial intelligence (AI) and machine learning (ML) projects fail to produce a return for the business. Some reasons for the high failure rate include: Unclear objectives, Poor scope definition, Bad training data, Organizational inertia, and Obscure R&D project management processes. If businesses spend $80-100 Billion this year, that is a very large number of AI projects that will fail. Leaders must apply a solid framework to develop AI systems/tools to support business requirements. 1. Identify the problem, 2. start small, 3 use already developed tools, 4, use small sprint cycles, 5, re-evaluate often

AI includes machine learning (ML), where algorithms learn from data to make predictions or decisions without explicit programming. Learn the basics of neural networks, natural language processing, and robotics. Understand that AI isn't infallible; it works within its design and the quality of data it receives. By recognizing its strengths and limitations, you can set realistic expectations and trust AI's role in various applications.

Start by understanding the different elements and types of AI that apply to and are important in your expertise and interest of application. Large Language Models are the more popular AI manifestation today, which are either consumed through applications tuned for functional outcomes - such as chatbots that leverage generative and conversant AI; or code creation AI. This field is fast evolving so familiarise yourself and embrace learning about new developments. Beyond removing ethical opacity and biases, there are issues of hallucinations in today's AI applications. So, develop and demonstrate a solid understanding of machine learning and retrieval-augmented generation that will shape future accuracy, provenance and explainability of AI.

To build confidence in AI, begin by developing a solid understanding of its principles and functionalities. AI includes machine learning (ML), where algorithms learn from data to make predictions or decisions autonomously. Familiarize yourself with fundamental concepts such as neural networks, natural language processing (NLP), and robotics, which are integral to AI systems.

The ethical aspects of AI are paramount, as the technology has a profound impact on society. AI systems deployed in critical domains such as healthcare, transportation, and finance must adhere to stringent safety standards to mitigate risks to human lives and societal stability. Similarly, ensuring the security of AI systems against cyber threats and malicious manipulation is essential to prevent harm and maintain trust.

Things to consider when evaluating AI platforms and applications: 1. Security and privacy 2. Fairness 3. Transparency 4. Accountability

I think a really important and pragmatic point is that we cannot assume that artificial intelligence is free of errors and that generative artificial intelligence in particular always delivers relevant and, above all, correct results. In the end, humans have created the foundations and specific solutions that are used today as artificial intelligence or with artificial intelligence.

In my experience, building confidence in AI requires a solid grasp of the fundamentals. Before diving into high-level frameworks, invest time researching and practicing the core concepts of your specific AI domain. For me, as a programmer, that meant working directly with LLM APIs to build libraries, in addition to using existing abstraction frameworks. This hands-on experience helped me understand the underlying technology at a deeper level. By engaging with the nuts and bolts of AI systems, you'll develop an intuitive sense of their capabilities and limitations. This foundational knowledge is key to using AI effectively and confidently in real-world applications.

You'd think from the plethora of AI classes and influencers out there that "continuous learning" would be easy. There's certainly no shortage of thing to learn and places to learn them, but knowing where to maximize your time — that's what will separate you from the pack. Don't fall for "the biggest is the best" or flashy marketing when it comes to choosing a class or learning package on AI. Instead, look for 3 things: 1) Good word-of-mouth about the instructor or class; 2) Alignment of the material with your field or interest (the narrower the focus, the better); 3) That it costs *some* money. It really doesn't matter how much, but unless you're just starting your AI journey, free classes probbly won't give you a lot.

There's no set-and-forget it. We need to be in constant learning mode, always exploring and experimenting. Make AI part of your daily routine and encourage your team to do the same. Connect with industry and academic experts, and follow like-minded professionals on LinkedIn or other platforms. There are amazing courses out there from major ed-tech companies and plenty of free resources for hands-on learning. Keep up with thought leaders, join webinars, and catch LinkedIn Live sessions. Some great people to follow are Nicole Leffer, Shelley Palmer, Allie Miller, and Pascal Bornet.

Collaboration is indeed crucial for building confidence in AI. Engaging with a community of AI professionals, including data scientists, ethicists, and policy makers, allows you to share insights and discuss the implications of AI from diverse perspectives. This collaborative approach enhances your understanding of AI and boosts confidence in its applications. By collaborating, you can explore various viewpoints on AI-related challenges and opportunities. Discussions with peers from different backgrounds help in addressing ethical considerations, improving AI algorithms, and shaping policies that govern AI use.

Engaging with a diverse community of AI professionals is crucial for fostering innovation and addressing ethical and societal challenges. By collaborating with data scientists, ethicists, and policymakers, you can gain a holistic understanding of AI's impact and ensure that solutions are equitable and sustainable. This multidisciplinary approach not only builds confidence but also promotes the responsible development and deployment of AI technologies.

To build confidence, it's crucial to engage with the public. Get involved in outreach programs, educational projects and community discussions. Use storytelling and real life instances to make AI more relatable and its advantages more concrete. Share stories of success where AI has made a positive impact in fields such as healthcare, education and environmental protection. Encourage hands on learning opportunities through workshops and interactive demonstrations so that people can witness AI in action. By actively participating in these dialogues and demystifying AI, you can bridge the gap between experts and the general public.

Use storytelling and real-world examples to make AI relatable. Sharing success stories where AI has positively impacted healthcare, education, or environmental conservation can make the benefits more tangible. Encourage hands-on experiences through workshops, interactive demos, or AI fairs where the public can see AI in action. By humanizing AI and illustrating its practical applications, you can transform abstract concepts into meaningful narratives, thereby enhancing public trust and enthusiasm for AI advancements.

Foster trust and confidence in AI! Focus on transparency: Explain how AI works and address potential biases openly. Prioritize human-centric design: Ensure AI complements human expertise, not replaces it. Deliver clear value: Showcase how AI solves real problems and improves outcomes. Communicate effectively: Clearly explain AI's capabilities and limitations to manage expectations. By prioritizing these principles, you can build trust and position AI as a powerful tool for positive change.

To build confidence in the AI industry, it's crucial to show measurable results and explain how the models work in detail. Make it clear to clients from the first conversation that 100% error-free automation is unrealistic. Break the process into smaller parts and choose the best AI for each task, as a fully end-to-end AI solution can be prohibitively expensive for most industries.