Category: Future of Work

Updated on May 13, 2024

Getting Started with AI

By Yolanda Lau

With all the hype surrounding artificial intelligence (AI), it can be intimidating to figure out how to get started. 

What is AI? What’s the big deal with ChatGPT? What does GPT stand for? How should I use AI? Which AI tools should I use? Should I pay for AI? 

These are questions that come up in every workshop, training session, and conversation I have with folks just getting started in AI.

Let me help you answer these questions and more. 

Brief history of AI

Artificial intelligence is a field that has been around since the 1950s. It generally refers to the concept of computer systems that are capable of human reasoning, but even computer scientists don’t all agree on the definition of AI. It encompasses many different tools and technologies including predictive AI, generative AI, machine learning (ML), deep learning (DL), natural language processing (NLP), natural language understanding (NLU), sentiment analysis, computer vision, large language models (LLMs), robotics, neural networks, and more. 

You’ve been using products and services that use AI — and you’ve been using them for decades. All of the recommendation engines — Amazon, Netflix, Spotify — use ML for predictive AI. Alexa, Siri, and Google Assistant all use AI technologies NLP and NLU to understand speech. Even Gmail, Google Docs, or Grammarly helping you complete a sentence is AI. 

Generative AI

Earlier types of AI were predictive, meaning they  analyzed existing data to make predictions about future outcomes.

Generative AI creates new content based on patterns learned from training data. When ChatGPT was released in November 2022, it was the first publicly available generative AI for conversational use. This was groundbreaking because it made AI accessible to the masses in an interactive, conversational format. It showcased the ability of AI to generate human-like text on demand, opening up a world of possibilities for how AI can assist with content creation, problem-solving, analysis and more.

But generative AI is only as good as the data it’s trained on. So if it’s been trained on biased data, you’ll get biased output. And these AI systems are hungry for data (which is why they have free versions to get you to give it data), so it’s best to assume that anything you type into it could be used as training data and could one day become public. 

What does GPT stand for?

In ChatGPT, GPT stands for “Generative Pre-trained Transformer.” ChatGPT tells me, “This refers to a class of language models developed by OpenAI that are trained to generate human-like text based on input prompts. They use transformer architecture and are pre-trained on large datasets to understand and generate text.”

Ironically, GPT also stands for “General Purpose Technology.” GPTs are versatile technologies with broad applicability across various sectors that leading to significant productivity enhancements and transformative societal changes. GPTs have historically shaped economies and societies, driving progress and innovation.

Examples include electricity, which revolutionized manufacturing and communication; the steam engine, enabling mechanized production and transportation during the Industrial Revolution; computers, which have changed how we process data; and the internet, connecting people globally and facilitating information exchange. We can be pretty certain that AI is also a GPT that will revolutionize industries through automation and intelligent systems. 

Frameworks for thinking about AI

If you’re just getting started with AI, these frameworks may help: 

  • Helpful intern: AI can take on tedious research and drafting tasks, freeing up your time for higher-level work. But like an intern, it needs context and lots of guidance and oversight. And just like you wouldn’t fire an intern if it can’t do things perfectly right away, you’ll have to be patient with AI and train it to understand what you need. 
  • Thesaurus for whole thoughts: AI can suggest alternative phrasings to refine your writing and convey your points more effectively. This is how I personally use AI most frequently. 
  • Personalized search engine: AI can quickly find relevant information and insights tailored to your specific queries, without you having to sift through pages and pages of search results. .  
  • Editor: AI can proofread your work, offer editorial suggestions, and help ensure consistency of voice and style. I have more tips on using AI for rewriting here. 
  • Teacher/coach: AI can explain complex topics, provide practice problems, and offer feedback to accelerate learning. I love using AI to help my children get additional practice problems and to help me learn something I’m confused about. “Can you help me understand what this block of code is doing?” is something I ask ChatGPT regularly. 
  • Idea Generation / Brainstorming Buddy: AI can help generate novel ideas, explore “what if” scenarios, and inspire creative thinking. I also love it for brainstorming brand names or company names.

Underscoring all of these frameworks is that AI today is a co-intelligence. It’s best used to augment or enhance your existing capabilities. 

Because it was trained on human data, it responds the way humans do. Be polite and it behaves better than you berate it. But like humans, it is fallible, prone to making up stuff (“hallucinations” in AI-speak), unpredictable, and full of all of our human biases. And yet, as of May 2024, it is not human (one day we’ll get to artificial general intelligence but that’s a topic for another day). 

I like to think of it this way —  every single one of us now has immediate 24/7 access to personal assistants. 

Those who don’t know how to use AI will be replaced by those who embrace it. 

AI Tools

If you’re just getting started, you’re likely wondering which tools to start using. The complicated answer is to use them all. These tools are improving rapidly and new tools are being released that its impossible to find an up to date guide for which tools are best at that moment. 

The best way to discern what’s best for your use case at that moment is to try it out for yourself. Generative AI use is still so new that if you dive in to how it can solve your specific problem, you could very well become the expert on using generative AI for that use case. 

That said, these are the basic tools I’d start with for generating text, images, songs, presentation decks, text-to-speech, and even text-to-video. 

Text Generation (LLMs):

Each different large language model has its pros and cons. I don’t want to go deep into them but I’ll briefly describe each of the main LLMs. 

  • OpenAI’s ChatGPT: tends to be a bit too flowery and robotic but seems to be the best with code.
  • Anthropic’s Claude: has the best privacy policies and can be HIPAA compliant.
  • Meta’s Llama: as of May 1, 2024, this open source LLM seems to be better than free ChatGPT and it’s incorporated into every Meta product including Facebook and Instagram. Now that OpenAI is releasing ChatGPT 4o to all free users, ChatGPT will once again be the best free LLM.
  • Perplexity: cites its sources and functions mostly as a better search engine (all the other LLMs will make up sources).
  • Inflection’s Pi: this one is the most conversational and is built to be supportive and empathetic.
  • Microsoft Copilot: built in to every Windows machine and is built on ChatGPT
  • Google Gemini: has a more natural, conversational tone and is linked up to most Google services. 

For your specific use cases, you’ll want to try several of the models to figure out which one works best for you. Personally, I use a mix of all of these — yes, all of them — and others.   

Image Generation:

Today’s image generation tools use what we call diffusion techniques and are improving rapidly. But they often still generate extra body parts (especially limbs and fingers) and weird text that is almost, but not quite, correct. It’s always best to carefully review a generated image before using it. 

  • Adobe Firefly: currently free (as of May 2024) and designed for commercial use as it’s been trained mostly on Adobe-owned images. 
  • Canva: incorporates several different AI image tools in its Magic Studio. 
  • Ideogram: 100 free images every day. 
  • Midjourney: must use Discord to create images. Not sure what Discord is? Move on to another tool. 
  • Stable Diffusion: released in August 2022 before ChatGPT came on the scene. 
  • DALL-E: included in the paid version of ChatGPT. 

Generative AI for Songs:

Creating your own songs in a specific style on a unique topic is genuinely delightful. Reggae, hard rock, jazz — it can do it all. 

  • Suno: more melodic and better lyrics. I have a friend who now prefers suno music to human-created music. 
  • Udio: more customizable and better with chord progressions. 

Presentation decks:

If you’re like me and have given lots of presentations, you’ve always dreamed of having your PowerPoint presentations made for you. Canva has certainly made it easier to make gorgeous presentation slides, but you still have to make them. The next generation of tools takes it to the next level:

  • Beautiful.ai: easy-to-use presentation software with smart templates for quick, professional designs. As of May 2024, no free tier. 
  • Gamma: instantly personalize decks and webpages with AI, ideal for GTM (go-to-market) teams.
  • Tome: converts text into visually appealing slides, supports 100+ languages, geared towards sales and marketing professionals. 
  • SlidesAI: quickly generates professional slides from any text, integrates with Google Slides.

Text-to-speech:

  • ElevenLabs: text to speech in any voice, language, and style. 
  • Resemble: clone your own voice.

Text-to-video:

  • Canva: turn photos or videos into talking heads for any project. 
  • Synthesia: no free tier but they do have a free demo. 

Photorealistic tools like OpenAI’s Sora and Irreverent Labs aren’t publicly available, yet.

Again, these lists are by no means comprehensive. They are a starting point. There are also lots of niche products that might work very well for specific uses. These tools are changing every day and once you gain some confidence you’ll want to continue exploring and experimenting with all the latest technologies and advancements. What doesn’t work in one AI tool today may work very well in it next month. There is no handbook. 

I’ve been giving talks on the future of work since 2020, experimented with generative AI since Stable Diffusion and ChatGPT came on to the scene in 2022, and I’ve taken graduate-level coursework in artificial intelligence, neural networks, natural language processing, and generative AI. But even I don’t call myself an expert because the technologies and tools keep changing. Experiment and iterate and become your own expert.

Almost every single AI tool has a paid version and a free version. As I mentioned above, the free versions exist so these companies can get more data — from you! The free versions are generally good enough, but the paid versions are significantly better. If you can’t afford it, the free versions will be just fine. But if you can, I’d pay up for paid versions of the top 3 LLMs of today — ChatGPT, Gemini, and Claude. 

Tips for using AI

Finally, I’ll end with a few quick tips for using AI. 

  • Iterate; If an Al tool’s output isn’t perfect, work with it with different inputs or settings. Have a conversation to help it understand what you need. 
  • Verify: Always double check information generated by AI. Triple check it if it’s important. Trust, but verify, as my favorite Russian proverb warns.
  • Share wisely: Be mindful about privacy settings and the information you share with AI tools. I personally don’t share anything with AI that I wouldn’t be comfortable becoming public. 
  • Experiment: Try out lots of different AI tools to find the ones that best fit your needs. Just because AI can’t do it today doesn’t mean it won’t be able to do it next week. 
  • Human-in-the-loop: Use AI tools as an assistant, not a replacement for human interaction and thought. 

If you’re interested in going deeper, I share more tips on prompt engineering here. 

Start exploring AI 

Diving into AI opens up a world of possibilities. This powerful tool is accessible to everyone. Use it as your own personal assistant for anything you currently find tedious or that usually requires another human being — practice for an interview, create calendar event import files, and much more. Stay curious and adaptable as AI continues to evolve. The future belongs to those who can effectively work with AI.

So dive in and start exploring! 

Yolanda Lau is an experienced entrepreneurship consultant, advisor, and Forbes Contributor. She is also an educator, speaker, writer, and non-profit fundraiser.

Since 2010, she has been focused on preparing knowledge workers, educators, and students for the future of work.

Learn more about Yolanda here.

Yolanda is also a Founding Board Member of the Hawai’i Center for AI (HCAI), a non-profit organization. HCAI envisions a future in which all of Hawaiʻi’s residents have access to AI technology that effectively and safely serves their individual and collective well-being. Hawai’i Center for AI promotes the beneficial use of AI to empower individuals, communities, and industries throughout Hawai’i. We are committed to understanding the ways AI will help grow the state’s economy, help our institutions evolve, and transform our society. Through collaboration, education, and service, we drive research, innovation, and community partnerships to build a sustainable, prosperous, and policy-driven future for Hawai’i.

Updated on May 8, 2024

The Enduring Relevance of Coding Skills

By Yolanda Lau

In the age of AI, many say that coding is a dying art. But I think it’s more essential than ever.

Ever since ChatGPT was launched in November 2022, it has upended how we work, think, and even play. Generative artificial intelligence (AI), based on large language models (LLMs), have made huge improvements since then. Multi-modal AI — which can process info from multiple modalities such as images, video, text, and audio — makes some time consuming and tedious tasks seem effortless. 

With generative AI, anyone can complete computer programming coding projects. I’ve had many non-coder friends tell me that AI has helped them build their first website, app, or other coding project. For that reason, people keep asking if learning to code has become obsolete. 

I believe learning to code remains highly relevant.

Here are 13 reasons why:

1. Fundamental understanding

Learning to code is like getting a backstage pass to technology. When you understand code, you get to see how everything works behind the scenes. Coding helps people understand the fundamental concepts and principles behind how software and technology work. This understanding allows people to use AI in more powerful ways and helps to interpret AI-generated results. 

2. Problem-solving skills

Coding teaches students how to break down complex problems into smaller, manageable parts and develop step-by-step solutions. These problem-solving skills help people solve problems of all types. Want to solve climate change, the problem-solving skills learned with coding will help you ideate more effective solutions. These skills are valuable in many areas of life and work, regardless of whether the actual coding is done by AI or not.

3. Logical thinking

Programming necessitates a structured approach, where every line of code builds upon the previous one, akin to constructing a building’s foundation before adding floors. This process fosters logical thinking, where individuals learn to anticipate potential outcomes, identify patterns, and discern cause-and-effect relationships. Such skills extend beyond coding, enabling individuals to dissect complex real-world problems and devise systematic solutions. Whether troubleshooting a malfunctioning device or strategizing in business, the ability to think logically is indispensable.

4. Learning to experiment

Learning to code encourages students to embrace experimentation and take risks. Through coding, students can quickly prototype and test their ideas, learning from their failures and iterating on their designs. This process of experimentation fosters creativity and innovation, enabling students to develop unique solutions that push the boundaries of what’s known and possible.

5. Collaboration with AI

As AI becomes more prevalent in programming and software development, people who know how to code can better collaborate with and guide AI systems.  Programmers will need to guide AI by providing context, defining requirements, and validating its generated code (debugging). As a result, educators will need to emphasize problem decomposition, testing, and debugging – skills that are important in collaborating with AI for coding. 

AI is great for data analysis and repetitive tasks, but it lacks human intuition and understanding. Conversely, programmers bring these strengths to the table, ensuring the relevance, accuracy, and ethical considerations of AI-driven solutions. When we work together with AI, we maximize the potential of both and create more robust and nuanced outcomes.  However, students need to be taught to be skeptical of AI-generated results and take ownership of verifying and validating them. If students become over reliant on AI, they’ll short-circuit the important learning process.

AI-generated code can provide a starting point, but it often needs human intervention for refinement. Programmers can evaluate the code, identify areas for improvement, fix errors, and tailor solutions to specific needs. This critical evaluation is essential, especially as technology and user requirements evolve.

6. Computational thinking

Moreover, learning to code develops computational thinking, a fundamental set of skills and thought processes essential for solving complex problems across various disciplines. Computational thinking encompasses problem-solving skills, logical thinking, and other key components such as decomposition, pattern recognition, abstraction, and evaluation. By learning to code, students develop the ability to break down complex problems into smaller, manageable parts (decomposition), identify patterns and similarities within and across problems (pattern recognition), focus on the essential features of a problem while ignoring irrelevant details (abstraction), and assess the effectiveness and efficiency of their solutions (evaluation). 

These skills are invaluable in navigating an increasingly complex and technology-driven world, even as AI advances and automates certain aspects of programming.

7. Foundation for systems thinking

Learning to code also provides a strong foundation for systems thinking, which involves understanding how different components of a system interact and influence each other. Coding exercises, such as debugging and optimizing algorithms, help students anticipate unintended consequences and develop strategies for building more resilient and adaptable systems. By breaking down complex problems into manageable components, identifying patterns and relationships, and understanding the interconnectedness of different elements within a system, students develop a systems thinking mindset that is transferable across many domains, from software development to business management, public policy, and beyond.

8. Emerging technologies

Coding skills are not only relevant to AI but also to other emerging technologies such as AR, (augmented reality), VR (virtual reality), and MR (mixed reality). As these spatial computing technologies continue to advance and gain popularity, there will be an increasing demand for programmers and developers who can create immersive and interactive experiences. Students with coding skills will be well-positioned to contribute to the development of AR, VR, and MR applications, which have the potential to revolutionize various industries, including education, entertainment, healthcare, and more.

9. Data science and analysis

Coding skills are essential for data science and analysis, which play a critical role in today’s data-driven world. Every two days, we create as much data as was created since the dawn of humanity through 2003. That statistic alone should tell you that data science and data literacy are crucial skills. As organizations collect and process vast amounts of data, there is a growing need for professionals who can write code to clean, analyze, and derive insights from complex datasets. Those with coding skills can leverage powerful libraries and frameworks to manipulate and visualize data, build predictive models, and support data-driven decision-making. These skills are invaluable across all industries and functions, including business, retail, healthcare, entertainment, finance, and scientific research.

10. Responsible AI development

As AI-generated code becomes more advanced and widely used, it is crucial for people to understand the code they are working with to prevent unintended consequences. AI systems can sometimes “hallucinate,” generating code that seems plausible but may contain errors, vulnerabilities, or even malicious elements. Without a solid understanding of coding principles and best practices, people may inadvertently release software that is prone to hacks or behaves in unexpected ways. By learning to code, students can develop the skills necessary to critically evaluate AI-generated code, identify potential issues, and ensure the development of safe, secure, and ethical software.

11. Critical thinking skills

Learning to code teaches critical thinking in a different way than writing does. While both coding and writing require logical thinking and problem-solving skills, coding demands a more systematic, step-by-step approach to breaking down complex problems into smaller, manageable parts. Coding also requires students to anticipate and handle potential errors or edge cases, fostering a more rigorous and detail-oriented form of critical thinking. Moreover, coding encourages students to think algorithmically and develop efficient, optimized solutions to problems. These unique critical thinking skills are invaluable not only in programming but also in a wide range of fields and everyday life situations.

12. Tackling complex global challenges 

As we’ve established, learning to code equips students with computational thinking and systems thinking skills. These mindsets and skills are necessary to tackle the world’s most pressing problems. Trying to solve climate change? Tackling the impending water insecurity? Solving the problem of responsible AI implementation? All of the world’s most complex challenges require individuals who can break down problems, identify patterns, and develop innovative solutions. Coding provides a foundation for understanding and leveraging technology to address these challenges, enabling students to become active contributors to a better future.

13. Career opportunities

Even with AI improving rapidly, demand for programmers and software developers is still high. Having coding skills opens up a wide range of career opportunities and allows students to be active participants in shaping the future of technology.

While AI can automate certain aspects of programming and coding, it does not eliminate the need to learn these skills. Coding provides a foundation for understanding technology, develops valuable problem-solving and logical thinking skills, enables collaboration with AI, and opens up career opportunities. 

When the printing press was invented, scribes were not rendered obsolete but adapted to new roles as typographers and printers. The invention of the camera did not eliminate the need for artists but rather opened up new artistic possibilities and genres, such as photography and film. The introduction of the typewriter did not replace the need for writers but instead changed the way they worked and made the writing process more efficient. 

The advent of calculators and computers did not eliminate the need for mathematicians but rather allowed them to tackle more complex problems and develop new mathematical theories. When spreadsheets became commonplace, people theorized that accountants would become irrelevant but accountants simply shifted their work to other areas. The development of computer-aided design (CAD) software did not replace the need for architects and engineers but rather enhanced their ability to design and model complex structures. 

When AI started being used in radiology, people theorized that radiologists would no longer be needed — but instead of becoming obsolete, there is now a shortage of radiologists. In all of these cases, technologies changed how humans worked but did not eliminate the need for those functions. 

Similarly, rather than replacing the need for human coders, AI is likely to change the nature of programming work, requiring programmers to have coding skills and the ability to work effectively with AI tools.​​​​​​​​​​​​​​​​

Preparing for the future

In a world where AI can write essays, create art, and even compose music, it’s fair to wonder if learning to code is still worth it. But as I’ve discussed above, learning to code is absolutely still a worthwhile endeavor. Lest you think I’m not practicing what I preach, I’m actively learning Python for technical AI coding projects. 

As educators, we should integrate coding into our curriculum to prepare students for the future — less for the sake of coding and more for the mindset and frameworks that learning to code develops. This includes emphasizing problem decomposition, testing, debugging, and using AI as a copilot. In many ways, how AI is changing how we teach coding is the same as how AI is changing how we teach anything. We must move from teaching basic skills (in this case, syntax) to higher-order thinking. 

Students, embrace the challenge of learning to code, learn to be comfortable being uncomfortable, and don’t be afraid to make mistakes. 

And to anyone reading this, recognize the importance of coding skills in today’s digital world and take steps to learn — and you’ll develop new ways of thinking that will help you become better problem solvers. 

Together, we can foster a generation of creative, critical thinkers who are equipped to navigate and shape the future.​​ 

Yolanda Lau is an experienced entrepreneurship consultant, advisor, and Forbes Contributor. She is also an educator, speaker, writer, and non-profit fundraiser.

Since 2010, she has been focused on preparing knowledge workers, educators, and students for the future of work.

Learn more about Yolanda here.

Yolanda is also a Founding Board Member of the Hawai’i Center for AI (HCAI), a non-profit organization. HCAI envisions a future in which all of Hawaiʻi’s residents have access to AI technology that effectively and safely serves their individual and collective well-being. Hawai’i Center for AI promotes the beneficial use of AI to empower individuals, communities, and industries throughout Hawai’i. We are committed to understanding the ways AI will help grow the state’s economy, help our institutions evolve, and transform our society. Through collaboration, education, and service, we drive research, innovation, and community partnerships to build a sustainable, prosperous, and policy-driven future for Hawai’i.

Published on May 8, 2024

Why Students Still Need to Learn to Write in the Age of AI

By Yolanda Lau

In an era where machines can write your emails and papers for you, why should students still learn to write?

Yes, generative AI has gotten pretty good at natural language generation. In fact, studies have shown that people tend to prefer AI-generated writing!  Personally, I find off-the-shelf AI-generated writing to be too flowery and kind of stale. But in many cases – especially for lower-skilled writers – AI  pumps out pretty decent written content. 

But I firmly believe that students still need to learn and develop writing skills. 

Here are 9 reasons why:

1. Clear thinking 

Clear writing is a reflection of clear thinking. When someone isn’t able to express their thoughts clearly, I question the depth and clarity of their understanding. The process of writing helps students organize thoughts, identify gaps in their understanding, and think more critically about complex ideas. By learning to write clearly, students learn to think logically, express their ideas coherently, and make well-reasoned arguments. This skill is invaluable in everyday life (not just in school). Thinking clearly and making informed decisions are important in everything we do..

2. Critical thinking and problem-solving skills

Writing is a powerful way to develop critical thinking and problem-solving skills. When writing, we have to analyze information, evaluate arguments, and make informed judgments. This process helps develop the ability to think critically about complex issues, consider multiple perspectives, and construct logical, well-reasoned arguments. In addition, writing often requires students to break down complex problems into manageable pieces, devise creative solutions, and communicate their ideas effectively. These skills are essential skills for tackling complex problems in a world of accelerated change.

3. Creativity and originality

From the earliest days of humanity, we’ve been creative. Humans have been creating songs, stories, dance, art, and more since the time of cave-dwelling. Creativity and storytelling are core to who we are as human beings. 

Human writers bring unique perspectives, experiences, and creativity to their writing, producing original content that goes beyond what AI can currently generate based off of patterns and prediction. I think of the richness of the human experience and what will be lost if humans forget how to use their own words and ideas to express themselves. While I believe the future is a world where humans are augmented by AI, it’s imperative we don’t fully outsource writing and other art forms.

4. Effective communication

Writing is a fundamental form of communication that enables everyone to express their ideas, thoughts, and feelings in a clear and compelling manner. Whether crafting an essay, composing an email, or creating a report, writing helps students develop the ability to convey complex information in a way that is easily understood by others. This skill is essential in personal and professional contexts, where the ability to communicate effectively can be the difference between success and failure. Yes, AI-generated writing can help with this. But students still need to learn what makes communication effective. And the best way to learn that is to do it themselves. 

5. Personal and professional growth

Writing is a skill that requires continuous practice and refinement. Engaging in the writing process helps students develop their voice, style, and confidence as writers. Finding your voice can play a big role in personal and professional growth.

6. Human connection

Writing is often a deeply personal and emotional act. Human-written content can forge connections, evoke empathy, and inspire others in ways that AI-generated text may struggle to replicate. Writing is powerful. Again, while I believe AI-enhanced humans are the future, we cannot fully outsource writing and other art forms that allow us to connect with each other. 

7. Developing empathy and understanding

Writing encourages students to explore and understand diverse perspectives. This teaches empathy and open-mindedness. Fiction has been shown to improve empathy and to increase helpful behaviors. By engaging in writing, students become better people and citizens. They develop a deeper appreciation for the complexity of human experiences and learn to value diverse voices.

8. Inspiring innovation

The awe-inspiring, futuristic worlds imagined by science fiction writers have frequently sparked the curiosity and ambition of scientists, leading to the development of groundbreaking technologies. Star Trek and the Jetsons have inspired the creation of cell phones, laptops, and home cleaning robots. I’ve always loved historical fiction and science fiction. But even I remember being an undergrad at MIT and being astonished by how deeply passionate my classmates were about science fiction. Writing is so powerful that it can literally lead to the future – by inspiring and driving innovation across various fields.

9. Collaboration with AI 

Finally, I’ll address the elephant in the room. Yes, the future is a world where writers use AI to craft better writing. Paradoxically, that makes it more important to be a better writer. Confused?

As AI writing tools become more common, students who have strong writing skills can better collaborate with and guide these tools. As AI thought leader Ethan Mollick wrote in Co-intelligence, writers are often the best at working with AI to create writing. They can provide the necessary context, specify requirements, and edit AI-generated content to ensure it meets their intended purpose and audience. 

Because generative AI currently creates writing by guessing what the next probable word is, it often creates generic, inaccurate, one-dimensional text. Writers who can describe the effects they want the words to create are able to use AI to create more powerful prose. With their editing skills, good writers are able to guide the AI to improve their writing. And those who are familiar with a variety of different tones and styles can use that knowledge to prompt the AI more effectively.

In short, good writers are better at reviewing, editing, and adapting AI-generated text to fit specific needs or preferences.

Writing in the Age of AI

While AI can assist with writing tasks, it does not eliminate the need for students to learn and develop their writing skills. The process of writing fosters critical thinking, creativity, effective communication, personal growth, empathy, and the ability to inspire innovation. 

Rather than replacing human writers, AI writing tools are changing the nature of writing work, requiring students to have both strong writing skills and the ability to collaborate with AI.

As is true of many aspects of life, process is equally important as outcome. In teaching writing, process is more important than outcome.

Yolanda Lau is an experienced entrepreneurship consultant, advisor, and Forbes Contributor. She is also an educator, speaker, writer, and non-profit fundraiser.

Since 2010, she has been focused on preparing knowledge workers, educators, and students for the future of work.

Learn more about Yolanda here.

Yolanda is also a Founding Board Member of the Hawai’i Center for AI (HCAI), a non-profit organization. HCAI envisions a future in which all of Hawaiʻi’s residents have access to AI technology that effectively and safely serves their individual and collective well-being. Hawai’i Center for AI promotes the beneficial use of AI to empower individuals, communities, and industries throughout Hawai’i. We are committed to understanding the ways AI will help grow the state’s economy, help our institutions evolve, and transform our society. Through collaboration, education, and service, we drive research, innovation, and community partnerships to build a sustainable, prosperous, and policy-driven future for Hawai’i.

Updated on May 12, 2024

Learn AI Large Language Model Terms with ChatGPT

By Yolanda Lau

Last month, I read Nichole Sterling’s LinkedIn post where Kavita Tipnis-Rasal prompted ChatGPT to “Explain the top 10 terms in LLMs to a non technical audience with funny examples” and laughed out loud. The explanations created by AI about AI were great for a non-technical audience.

So, I fed the same prompt into ChatGPT. I ran it three times. Then, I curated, edited, and combined it into this list of terms used in Large Language Models (LLMs). I hope you enjoy it as much as I enjoyed what Tipnis-Rasal shared via Sterling.

Large Language Models (LLMs): Imagine you have a super-smart friend who knows everything about a wide range of topics, from ancient history to modern technology. Now, imagine if you could shrink your friend down and put them inside your computer. That’s essentially what a Large Language Model (LLM) is—a super-smart program trained on massive amounts of text data to understand and generate human-like language.

Natural Language Processing (NLP): Imagine you have a magical translator that can instantly convert your dog’s barks into human language, allowing you to understand exactly what they’re saying. NLP is like that magical translator, but for computers—it helps them understand, interpret, and generate human language, enabling tasks like translation, sentiment analysis, and chatbots to communicate with us in a way that feels natural.

Tokenization: Imagine you’re making a sandwich, but instead of cutting it with a knife, you break it into smaller, manageable pieces with your hands. Tokenization does something similar with text, breaking it down into smaller units, like words or even smaller parts.

Embedding: This is like putting different ingredients into your sandwich to give it flavor. Embedding takes words or phrases and converts them into numerical representations, which helps the model understand their meaning and context.

Attention Mechanism: Picture a teacher in a classroom paying extra attention to some students while explaining a lesson. Similarly, an attention mechanism in LLMs helps them focus on different parts of the text, giving more weight to important words or phrases.

Fine-tuning: Imagine you’ve mastered the art of making a grilled cheese sandwich, but now you want to tweak it a bit by adding different cheeses or toppings. Fine-tuning in LLMs is like adjusting the model’s parameters or training it on specific data to improve its performance for a particular task, like translation or summarization.

Transformer Architecture: Think of a group of robots working together to assemble a giant puzzle. In LLMs, the transformer architecture organizes layers of neural networks to efficiently process and understand large amounts of text data.

Beam Search: Imagine you’re exploring a maze with multiple paths, trying to find the quickest way out. Beam search in LLMs is like looking ahead and considering several possible sequences of words to generate the most coherent and accurate output.

Loss Function: This is like a scoreboard that tells you how well you’re doing in a game. In LLMs, the loss function measures the difference between the predicted output and the actual output, helping the model learn and improve over time.

Gradient Descent: Picture a hiker trying to find the quickest route down a mountain by following the steepest slope. Gradient descent in LLMs is an optimization algorithm that adjusts the model’s parameters in the direction that reduces the loss, helping it converge towards better performance.

Overfitting: Imagine a tailor making a suit that fits one person perfectly but doesn’t look good on anyone else. In LLMs, overfitting occurs when the model learns to perform well on the training data but struggles to generalize to new, unseen data.

Bias-Variance Tradeoff: Think of Goldilocks trying different bowls of porridge—not too hot, not too cold, but just right. In LLMs, the bias-variance tradeoff involves finding the right balance between flexibility (variance) and simplicity (bias) to build a model that generalizes well to new data without overfitting.

Fine-tuning: Suppose you’ve mastered the art of baking cookies, but now you want to experiment with new flavors like bacon or wasabi. Fine-tuning in LLMs is like tweaking a pre-trained model to specialize in specific tasks or domains, much like adding unique ingredients to a familiar recipe.

Pre-training: Imagine you’re a student preparing for a big exam by studying a wide range of topics beforehand. Pre-training in LLMs involves training the model on vast amounts of text data, teaching it general language understanding skills before fine-tuning it for specific tasks like translation or summarization.

Attention Mechanism: Think of attention as a spotlight on a stage, highlighting different actors as they perform. Similarly, an attention mechanism in LLMs directs the model’s focus to relevant parts of the input text, helping it understand context and relationships between words more effectively.

Generative Models: Picture a magician pulling rabbits out of a hat, except instead of rabbits, they’re generating realistic-looking images or text. Generative models in LLMs create new content based on patterns learned from the training data, allowing them to generate coherent paragraphs, poems, or even stories.

Perplexity: Imagine trying to decipher a secret code written in a language you don’t understand. Perplexity in LLMs measures how well the model predicts the next word in a sequence, with lower perplexity indicating better performance, much like cracking a code with fewer guesses.

Backpropagation: Picture a team of detectives trying to solve a crime by retracing their steps and identifying clues along the way. Backpropagation in LLMs is an algorithm that calculates how much each parameter in the model contributed to its error, allowing it to adjust and improve its predictions over time, similar to detectives refining their investigation based on new evidence.

Long Short-Term Memory (LSTM): Imagine you have a forgetful friend who struggles to remember things from the past. Now, picture giving them a magical notebook that helps them selectively remember important information and forget irrelevant details. LSTMs are like that magical notebook for language models, allowing them to maintain long-term context and selectively retain or forget information, making it easier to understand and generate coherent text.

Neural Network: Picture a bustling city with interconnected roads and highways. Now, imagine each intersection is a neuron, and the roads are the connections between them. A neural network in LLMs is like this city, with layers of interconnected nodes working together to process and analyze text data, much like how traffic flows through the city to reach its destination. The more complex the network (or city), the more sophisticated the language understanding and generation capabilities of the model.

I hope these playful examples help demystify the technical jargon surrounding Large Language Models!

Yolanda Lau is an experienced entrepreneurship consultant, advisor, and Forbes Contributor. She is also an educator, speaker, writer, and non-profit fundraiser.

Since 2010, she has been focused on preparing knowledge workers, educators, and students for the future of work.

Learn more about Yolanda here.

Yolanda is also a Founding Board Member of the Hawai’i Center for AI (HCAI), a non-profit organization. HCAI envisions a future in which all of Hawaiʻi’s residents have access to AI technology that effectively and safely serves their individual and collective well-being. Hawai’i Center for AI promotes the beneficial use of AI to empower individuals, communities, and industries throughout Hawai’i. We are committed to understanding the ways AI will help grow the state’s economy, help our institutions evolve, and transform our society. Through collaboration, education, and service, we drive research, innovation, and community partnerships to build a sustainable, prosperous, and policy-driven future for Hawai’i.

Updated on April 1, 2024

From Paradise to Progress: AI’s Potential for Hawaii

By Yolanda Lau

Growing up in Honolulu, I’ve always been in awe of our islands’ history and its potential. Hawai‘i is my home, my inspiration, and the place that taught me the importance of community, resilience, and embracing change. It’s with this spirit that I’m thrilled to announce my role as a Founding Board Member for the newly established Hawai‘i Center for AI (HCAI).

Throughout my career, I’ve seen the transformative power of technology. As an entrepreneur and advisor, I truly believe that Artificial Intelligence (AI) has the potential to be a game-changer for our island community.

AI: A Double-Edged Sword?

AI is an exponential technology that has the potential to transform every industry, sector, and job function. On an individual level, it will likely change how we live, work, learn, and play.

According to a recent report by ResumeBuilder, 37% of companies using AI say they replaced workers with the technology in 2023 — and 44% report there will be additional layoffs resulting from AI efficiency. While AI could lead to increased inequality, it also has tremendous potential for good. Recent studies suggest AI could potentially benefit lower-skilled workers more than higher-skilled ones, potentially reversing the trend of increasing inequality. Moreover, AI could make elite expertise more accessible and increase the value of middle-skill workers. Experts think that workers in 80% of occupations will save time with AI — as much as 20% of their time.

This is especially relevant for Hawai‘i, where our dependence on tourism has left our economy vulnerable. With daily arrivals to our islands continuing a downward trend in 2024, we must diversify our economy. And with most of our food arriving as imports, our islands are susceptible to supply chain disruptions. Having recently visited West Maui, I am reminded of how climate change and our colonial history have put us at risk. But I see AI as a powerful tool to help diversify and strengthen our economy.

Fortunately, there are several organizations working hard to diversify Hawaii‘s economy. Accelerators like Elemental Excelerator, Mana Up, Blue Startups — providing mentorship and funding to local entrepreneurs — and other organizations (like Purple Mai‘a) are fostering innovation and creating new business opportunities across various sectors. HCAI joins these efforts by focusing on the transformative power of AI.

Leveling the Playing Field: AI for Everyone

The beauty of AI is it creates a level playing field. Powerful AI tools like OpenAI’s ChatGPT, Anthropic’s Claude, Microsoft Copilot, and Google’s Gemini are now available to everyone. Whether you’re a CEO of a big corporation or a small business owner, we all have access to the same powerful AI tools. Stop and think about that for a minute. You can access the same cutting edge technology that is available to the world’s billionaires. This means AI has the potential to be a great equalizer, providing everyone with the tools they need to succeed, regardless of background or resources.

But access is just one piece of the puzzle. We need to use AI thoughtfully to truly benefit our communities. Generative AI can streamline tedious tasks, freeing us to focus on creative, meaningful work. Predictive AI can empower businesses to optimize operations and reduce waste. These are just a few examples of how AI can be harnessed for good.

Building a Thriving, Sustainable Hawai‘i

Some worry AI will take jobs away. But I believe AI, if leveraged responsibly, will actually create new opportunities for Hawai‘i’s workers. By automating repetitive tasks, AI frees us up to focus on more meaningful, uniquely human work. Plus, organizations are looking for people with AI skills. By embracing AI, we can position our workforce for the jobs of the future.

Working with HCAI Co-Founder and Board President Nam Vu and Founding Director Peter Dresslar, I’m excited to shape HCAI’s mission, vision, and programs. Our goal is to promote the beneficial use of AI to empower our people, communities and industries. Through education, research and innovation, we’re working to make sure our islands benefit from AI in a way that aligns with our values and culture.

Imagine:

  • AI helping local farmers increase yields and get more fresh produce to our markets and tables, reducing our reliance on imported foods.
  • Our hotels leveraging AI for sustainable resource management.
  • Small businesses leveraging AI to streamline operations and compete effectively.
  • More of our keiki getting AI literacy education to prepare them for the careers of tomorrow.

This is just the start — there’s so much more we can do with AI to lift up our islands’ economy and our people. This is the future we’re working towards at HCAI, and I’m thrilled to be a part of it.

Empowering Our Community With Responsible AI

Like any new technology, AI requires thoughtful implementation. With the right approach, AI can help build a more sustainable, resilient and prosperous Hawai‘i. One that honors our past while positioning us for the future.

That’s why HCAI is committed to ensuring AI is used for good — for Responsible AI. This means AI that:

  • Empowers individuals: Every local business owner should have access to AI to automate tasks, freeing them to focus on what truly matters.
  • Strengthens our economy: AI can help us diversify our economy, revolutionize our agricultural industry, increase local food production, and reduce waste. It can also help our sustainable tourism sector personalize experiences and minimize environmental impact.
  • Protects our environment: AI can be used to monitor environmental conditions, predict and mitigate natural disasters, and optimize energy usage.
  • Fosters cultural preservation: AI can be used along with traditional knowledge to document, analyze, and share Native Hawaiian language, traditions, historical artifacts, and mo‘olelo (stories) passed down through generations.
  • Bridges the digital divide: HCAI offers resources and training to ensure everyone in Hawai‘i has the skills and knowledge to benefit from AI.

Join Us in Building a Brighter Future

We are a unique community, deeply connected to our land and each other. This makes us perfectly positioned to be global leaders in Responsible AI. We can create a model for the world, one that fosters innovation, equity, and a deep respect for our cultures.

I hope you’ll join me in supporting our important work. HCAI isn’t just for techies — it’s for everyone who wants to see Hawai‘i thrive. Whether you’re a business owner, an educator, a farmer, a student, or simply a concerned citizen curious about AI, there’s a place for you at our Hawai‘i Center for AI. Get involved, ask questions, and explore volunteer opportunities to ensure AI serves our island community.

Together, let’s build a Hawai‘i where AI uplifts and empowers us all, honoring our past while positioning us for a brighter future.

Yolanda Lau is an experienced entrepreneurship consultant, advisor, and Forbes Contributor. She is also an educator, speaker, writer, and non-profit fundraiser.

Since 2010, she has been focused on preparing knowledge workers, educators, and students for the future of work.

Learn more about Yolanda here.

Yolanda is also a Founding Board Member of the Hawai’i Center for AI (HCAI), a non-profit organization. HCAI envisions a future in which all of Hawaiʻi’s residents have access to AI technology that effectively and safely serves their individual and collective well-being. Hawai’i Center for AI promotes the beneficial use of AI to empower individuals, communities, and industries throughout Hawai’i. HCAI is committed to understanding the ways AI will help grow the state’s economy, help our institutions evolve, and transform our society. Through collaboration, education, and service, HCAI drives research, innovation, and community partnerships to build a sustainable, prosperous, and policy-driven future for Hawai’i.

Updated on March 20, 2024

Transforming Tomorrow: Harnessing the Future of Work

By Yolanda Lau

The future of work unfolds with boundless potential and transformative opportunities awaiting. As we stand on the cusp of exponential change, a new era emerges in which the skills imperative for success diverge from those of the 20th century. The rise of automation, artificial intelligence, and the gig economy is changing how we work. To thrive, companies and individuals must be proactive in preparing themselves for the future of work.

Embracing Automation

The rise of automation is one of the most significant changes in the future of work. Automation encompasses various technological advancements aimed at reducing or replacing human labor with machinery or software — and is expected to reshape almost every industry. Many low-skilled jobs, such as assembly line work or data entry, will likely be automated, leading to a transformation in job roles and skill requirements. Automation isn’t about replacing humans entirely — instead, it’s about augmenting human capabilities and efficiency. Companies must invest in training and development programs to ensure their employees have the skills required to adapt to this evolving landscape. While automation may displace some jobs, it will also create new opportunities for those who can design, build, and manage automated systems.

Integrating Artificial Intelligence

In tandem with automation, artificial intelligence (AI) is revolutionizing the future of work. AI technologies — including machine learning, natural language processing, computer vision, and robotics — are increasingly integrated into various aspects of business operations. While many fear that AI will lead to widespread job loss, the reality is more nuanced. AI has the potential to enhance productivity, streamline processes, and unlock new possibilities. However, individuals who resist or fail to adapt to AI may find themselves at a disadvantage in the job market. Companies must invest in AI education and training to empower their workforce to leverage AI technologies effectively. Every senior executive should be thinking, “How can my team use AI to augment themselves?” By embracing and integrating AI, companies can gain a competitive edge and drive innovation in the evolving landscape of work.

Navigating the Gig Economy

Simultaneously, the gig economy continues to grow, redefining traditional notions of employment. More people are taking side gigs, to hedge against potential layoffs and to sharpen and learn new skills. And more individuals are gravitating towards freelance or contract engagements, thanks to the rise of online platforms facilitating flexible work arrangements. To adapt, companies must embrace the liquid workforce — and learn to cultivate and work with a virtual talent bench engaged in project-based work.

Shifting towards Project-Based Work

The future of work will also see a shift towards project-based work. This trend is driven by the need for agility — and for organizations to constantly be responsive to changing market conditions. Project-based work allows companies to quickly assemble a team of experts with the necessary skills to complete a specific project, rather than maintaining a large permanent workforce. Companies must invest in project management and collaboration tools — and create a documentation-first culture — to ensure that their employees and contract workers can work effectively in project-based teams.

Cultivating Skills and Adaptability

To prepare for the future of work, companies must be proactive in developing their employees’ skills and abilities. This can be achieved through a variety of methods, such as on-the-job training, online courses, and formal education programs. Companies must also invest in leadership and management development programs to ensure their employees have the leadership skills required to succeed in a rapidly changing workplace.

Individual Agency in Career Development

Individuals must take ownership of their career development and professional growth — honing their skills through lifelong learning and side gigs. By actively cultivating adaptability and resilience, individuals can position themselves as indispensable assets in the dynamic landscape of the future workplace. Individuals must also be proactive in building their personal brand and cultivating professional networks. Today, everyone is a brand — and individuals must curate their online presence and narrative to authentically reflect their values, expertise, and aspirations. Doing this stratgeically can help find uncover new opportunities. When combined with a strong network of weak ties, individuals can leverage diverse connections to achieve their career goals.

The Imperative of Work-Life Fit

The future of work is also likely to see a greater emphasis on work-life balance, or as I prefer to call it, work-life fit. We’re already seeing this with some countries exploring a four-day work week and others have made it illegal for companies to contact employees outside of the workday. Executives, policymakers, and workers are realizing that work-life fit is essential for both individual well-being and organizational success. Companies must adopt flexible work arrangements, offering employees the ability to work from home or on flexible schedules. They must also invest in creating a workplace where mindfulness, compassion, and grace are commonplace.

Take Action to Transform Your Future

The future of work is here — and it’s teeming with promise and transformation. The time for action is now. Whether you’re a company leader or an individual contributor, the future of work awaits. Embrace change, augment yourself, invest in growth, and seize every opportunity that comes your way. Together, we can shape a future where innovation thrives, and success knows no bounds. The journey starts today — let’s make it count.

Interested in the Future of Work? Join the Work of the Future #FutureOfWork Facebook Group.

Yolanda Lau is an experienced entrepreneurship consultant, advisor, and Forbes Contributor. She is also an educator, speaker, writer, and non-profit fundraiser.

Since 2010, she has been focused on preparing knowledge workers, educators, and students for the future of work.

Learn more about Yolanda here.

Updated on February 24, 2024

Tips For Scaling Customer Success

By Yolanda Lau

If you’re building a startup, you’re probably wondering how to start and scale customer success (CS). While the first step is to hire the right first customer success lead, the next step is scaling customer success to increase the impact and turn CS into a profit center that increases revenue. Here are my top tips to help you scale customer success at your startup. Hopefully, your first CS hire has already created a culture of operational excellence and started accelerating your one-to-many strategy, as most of my tips for scaling customer success rely heavily on customer success ops.

Meet customers where they are.

Scaling customer success means you’ll need to make it more effective and efficient. At Liquid, we’ve found that it’s imperative to meet customers where they are and where they prefer to engage. While some customers enjoy talking to a customer success manager, others prefer to get help on their own online. Some users prefer emailing, while others like chat. Some prefer personalized meetings, while others find more value in attending group office hours or webinars. Offering one-to-many experiences increases CS productivity — but more importantly, offering a multitude of options allows your customers to get help in ways that they perceive to be most valuable and efficient. Add new initiatives prudently; do so only when you have confidence that it fills a need. In addition, you should be analyzing the metrics of each new initiative to assess effectiveness. Experiment and iterate.

Build a knowledge base.

At Liquid, we recently released a customer-facing knowledge base (KB) based on customer requests for a dedicated help center to help themselves and to direct their additional users to train themselves up. Since its launch, KB usage has grown rapidly while also decreasing the volume of communications from customers. To be clear, our KB doesn’t prevent users from contacting humans — it simply helps users help themselves before asking for help.

When building out your KB, start with the most complex features and sticking points — get feedback from CSMs and support staff about what needs to be covered. As new support requests come in, support staff should create new KB articles. Similarly, as new features get released, work with your product team to add new KB articles at each release. Always include images and videos to be inclusive of different learning preferences. Don’t wait to put out your KB; in my experience, it’s better to put out a 60% completed solution and iterate and add to it.

Lastly, look at KB metrics to gather additional insight. For example, what are people clicking on? This might give you insight into problem areas. Who is looking? A customer with lots of KB views might need extra attention; a customer with a sudden drop in views might be at risk of churning out. What are people searching for? Repeat searches might mean your product team needs to resolve some underlying issues.

Create (and automate) repeatable systems and processes.

Scaling customer success also means dealing with an increasing number of customers. To manage volume, you need repeatable systems and processes. Operationalizing processes by creating playbooks and other documentation helps your team provide consistent service quickly and efficiently. When done properly, this also allows you to provide pooled CS where customers are not assigned a single CSM but instead get help from whoever is available. Start working on this early and iterate often.

As you work on this, also segment your customers and determine how your approach will differ for each segment. For some companies, it may make sense to segment by account value but for others, segmenting by behavior may be more suitable. Growth potential should also be considered in segmentation, along with other factors specific to your industry, company and product.

Another way to improve the efficiency of your CS department is to take your repeatable processes and systems one step further and automate where possible. Be strategic in your use of automation. At Liquid, we use Zapier to automate a few customer success emails and have a few other automations to provide more value to our customers at scale.

Separate customer support and customer success.

While customer success is meant to be proactive, customer support or customer service is reactive by nature. When the same team members manage both support/service and success, the most urgent requests (typically service requests) get worked on first. Unfortunately, this means the proactive work — of managing customer health and actively reaching out to customers who may be at risk of churning — sometimes falls lower down the list. In addition, the skills needed for customer service are different from those for customer success. From my experience, companies achieve better results when separating the reactive customer service team from the proactive customer success team early on.

Know when to grow your customer success team.

Dave Blake, CEO of Client Success, has some great tips on when to add additional staff to your customer success team. Specifically, he recommends looking at three factors:

• Annual Contract Value (ACV) Target Per CSM: Each CSM should be handling about $2 million in ACV.

• Product Complexity: The more complex your product, the fewer accounts each CSM can handle.

• Volume Of Customers Per CSM: Each CSM can generally only create meaningful relationships with about 50 customers (sometimes a bit more if automation is used).

Assess these factors against your own product to determine when it’s time to grow your customer success team. I’ve found that about 30 customers is the sweet spot — with automation required to manage more than that.

Scaling customer success will allow you to deliver more value to your customers, keep them happy and ultimately get them to grow their business with you. Whether you start with operationalizing processes, adding automations, building out a knowledge base, or separating customer service from customer success, be sure to meet customers where they are. Deliver more value in their preferred channels and your customers will eagerly turn into advocates, referring new customers.

This article was originally published in Forbes.

Yolanda Lau is an experienced entrepreneurship consultant, advisor, and Forbes Contributor. She is also an educator, speaker, writer, and non-profit fundraiser.

Since 2010, she has been focused on preparing knowledge workers, educators, and students for the future of work.

Learn more about Yolanda here.

Updated on February 24, 2024

Let’s Go Liquid: One Million Liquid Businesses

www.goliquid.io

By Yolanda Lau

Today, we launched a new website for Liquid, the operating system for agile businesses. I’m so excited to share what we’ve been working on, and so proud of how far we’ve come from when we first started our consulting firm together many years ago.

With that in mind, I wanted to share some thoughts on our vision for Liquid and the future of work, how we got here, and why I believe we’re building something important.

Shifting from role-based work to project-based work

For years, I had seen my friends — fellow MIT alums — leaving their careers behind because they couldn’t find a way to make work fit into their lives. Whether it was making more time for their children or parents (or not wanting to be tied to a job and an office), highly educated women were leaving the workforce in droves. I had long found project-based work to be a means of finding work-life fit and I wanted to build a way to make it easier for anyone to do project-based work. And as a long-time consultant, I had seen for myself how tapping into on-demand talent enable businesses to scale quickly and efficiently. I realized project-based work was a win-win for all parties involved.

Project-based work and the future of work

I’ve been a big proponent of project-based work as a critical component of the future of work. Project-based work allows small business owners, entrepreneurs, creators, and enterprises to grow and scale their businesses while also expanding opportunities to bring in diverse talent. Working with on-demand talent is a good business decision for agile businesses. On the talent side, project-based work is a means to find work-life fit, particularly for lifelong learners eager to strengthen and develop their capabilities.

Starting FlexTeam

It was with that goal in mind that I co-founded FlexTeam — we wanted to build a world where previously sidelined professionals could engage in challenging, meaningful work when it fit into their lives. In addition, we envisioned a world where anyone could build an agile business, bringing on just-in-time talent to complete only the specific work that needed to be done.

Building FlexTeam’s platform

As we built FlexTeam, we initially used half a dozen different platforms to get signatures, manage contracts, agree to scopes of work, make payments, etc. We realized a tech platform was needed to manage this. Bringing on a CTO, we built an operating system for our modern consulting firm. Our proprietary platform allowed us to vet, onboard, manage, and pay FlexTeam’s consultants, while also enabling our clients to approve scopes of work, communicate with our team, and make payments.

Going Liquid

Liquid @ Techstars LA

FlexTeam grew to over 700+ independent contractors working on strategy projects with hundreds of clients, from SMBs to Fortune 500 companies. But we realized that to accelerate our vision for the future — to help more businesses Go Liquid — we needed to reduce the friction for every business to work with on-demand talent.

We saw several main trends: workforce was becoming more flexible and global and agile companies succeed by tapping into the best non full-time workers. But working with on-demand talent can be really difficult because it’s not a regular recurring expense like payroll — and there are additional compliance issues and challenges with controlling costs / work.

So we created and spun out Liquid — joined Techstars LA, and raised venture capital. Today, the Liquid platform streamlines the way a business’ finance, operations and talent management teams work with its vendor and supplier networks in the U.S. and abroad. We simplify contracting and global payments while ensuring financial controls and compliance.

The Future of Work

It’s widely accepted that COVID-19 has accelerated the shift to the future of work. In 2020 alone, wages and workforce participation of independent workers rose by 33%. In 2021, we’ve seen the “ Great Resignation” / “ Big Quit” — which has led to further growth in the percentage of independent workers — and businesses becoming more comfortable with remote work, asynchronous work, hybrid work, and project-based work.

While others talk about the future of work being remote, I believe it’s much bigger than that. The future of work is about everyone and every business working together in a way that works for all parties involved. It’s a distributed hybrid world where some folks will remain employees, but more and more people will become independent contractors — some working through agencies, and others working directly with companies (with some growing to build their own agency and hiring their own subcontractors). The companies that thrive in the future of work are those that Go Liquid, embracing on-demand talent and virtual talent benches.

Liquid and the Future of Work

At Liquid, we are building the operating system for agile businesses to enable everyone and every business to succeed in the future of work — project-centric contracting, work orders, purchase orders, and payments for agile businesses and their global vendor networks. In addition, our platform allows businesses to quickly understand and control their variable non-employee costs. We want to help every company Go Liquid, and we want to make it easier for people to Go Liquid.

Every day, I wake up excited to build Liquid because I know we are changing how work gets done; we are building the future of work. I love our hectic startup life (for example, I previously led Marketing for Liquid on an interim basis) but I primarily spend my days leading Customer Experience and Customer Success at Liquid. With a consulting background and experience as a trusted advisor, I’m thrilled to be building this critical (and growing) department. I love partnering with startup founders, finance leaders, HR leaders, COOs, Chiefs of Staff, and other operations leaders to help them scale their businesses while saving them (and their teams) time and money. It’s been a joy to connect with our customers and ensure they are getting exactly what they need from our platform. I’ve seen our customers take their businesses from idea to Series A and from seed to Series B — and I love knowing that our platform has supported their rapid agile growth.

Today, hundreds of businesses spanning marketing agencies, startups, production companies, social enterprises, and SMBs are using our platform. These businesses use Liquid to contract, onboard, manage, and pay their on-demand talent and vendors in the U.S. and in 175+ countries across the globe. Thousands more are #GoingLiquid and engaging the liquid workforce in ever growing numbers. In just the last year, Liquid has securely processed millions of dollars in global payments. Our customers love our platform, rave about our helpful customer support team, and rely on Liquid on a daily basis. Liquid is becoming the operating system for agile businesses.

And we’re just getting started.

If you’re as excited as I am about what we’re building and our vision for the future, read more from our CEO. Then, visit our new website (www.goliquid.io) to learn more about Going Liquid or subscribe to our newsletter to follow our journey.

Are you ready to Go Liquid?

Interested in the Future of Work? Join the Work of the Future #FutureOfWork Facebook Group.

Yolanda Lau is an experienced entrepreneurship consultant, advisor, and Forbes Contributor. She is also an educator, speaker, writer, and non-profit fundraiser.

Since 2010, she has been focused on preparing knowledge workers, educators, and students for the future of work.

Learn more about Yolanda here.

FlexTeam  is  a mission-based micro-consulting firm, co-founded by Yolanda Lau in 2015, that matches talented mid-career women with meaningful, challenging, temporally flexible, remote project-based work opportunities. FlexTeam’s clients are businesses of all sizes across all industries and sectors. FlexTeam’s most requested projects are competitor / market research, financial models, and investor decks. FlexTeam is also the team behind Liquid.

Updated on February 24, 2024

Time For HR To Manage The On-Demand Workforce

By Yolanda Lau

In many companies, freelancers are often the “hidden” workforce. The human resources (HR) team is not always involved in the hiring and management of freelancers. But as organizations shift to being project-based versus role-based, HR must take the lead in managing the on-demand workforce. We must rethink the role of HR in enabling the future of work.

I find there are five key areas that HR leaders should first focus on as they take the lead on managing the on-demand workforce.

Establish Hiring Practices

The HR team has the best expertise for hiring and engaging freelancers. Your recruiting resources are experts at sourcing and assessing talent — managers should be leveraging these recruiters to find the best freelance talent.

And just as your company has defined practices for hiring employees, hiring practices also must be defined for engaging freelancers and independent contractors. It’s just as critical when working with freelancers.

Set the minimum standards for what needs to be completed before a freelancer can be engaged for the first time. For example, specify that at least two interviewers should speak to the freelancer to assess qualifications and fit. Always require background and reference checks. Decide whether a non-disclosure agreement (NDA) is mandatory or define the conditions that trigger this requirement. Establish benchmarks for hourly rate ranges for the types of freelancers your company will most frequently engage.

Provide Internal Training

The HR department should also provide training for managers on best practices for working with freelancers and independent contractors. This training is important for boosting project success rates and ensuring that managers understand compliance requirements.

When working with freelancers and independent contractors, managers need to be aware of local, state, and federal regulations regarding engaging freelancers. For example, the IRS considers the behavioral control, financial control, and relationship of the parties when evaluating worker classification. Understanding the regulations is critical to ensure workers are not misclassified as freelancers instead of employees, which exposes your company to legal penalties and liabilities.

Standardize The Onboarding Process

It’s a best practice for companies to have standardized onboarding processes for their employees. The HR team should establish a standard onboarding process for freelancers and independent contractors. This process will be distinct from the employee onboarding process and reflect the different needs and requirements related to freelancers.

For example, rather than collecting a W-2 during onboarding, your company will need to collect a W-9 from U.S. freelancers or a W-8 from foreign freelancers. Your onboarding process should include all the essential documents for a freelance engagement, like an executed contract, NDA, etc. Onboarding should also include the critical “welcome” elements, such as information on any tools that the freelancer will need to access.

Standardizing the freelancer onboarding process ensures that all the essential documents are completed for every engagement and saves your company significant time.

Set Performance Evaluation Guidelines

Establishing performance evaluation processes and standards is a key element of any HR team’s responsibilities. Performance evaluations with on-demand workers are both similar and different from evaluating employees.

Performance assessments should be done on a project-by-project basis and should include a recommendation as to whether the worker should be considered for future projects. In addition to the project evaluations, the skill sets of each worker should also be tracked. Insights into skill sets help hiring managers source the best fit for upcoming projects from already vetted on-demand talent.

Determine Supporting Tools And Systems

Many companies manage their freelance engagements through a hodgepodge of tools. For example, some companies manage freelancers entirely manually, tracking information in various spreadsheets. Manual management of freelancers can become time-consuming and is prone to errors. And with an assortment of tools, it’s hard to have complete visibility of all freelancer and independent contractor activity and expenses.

The human resources team should take the lead on defining the central tool or system for the company, making it easy for finance, legal, HR, and line managers to collaborate and have complete visibility of on-demand workforce engagements. Freelance and vendor management systems, like the solutions my company offers, provide the integrated capabilities critical for on-demand workforce management.

With a central tool or system, you can more readily develop an external talent bench to support your company. An on-demand talent bench can help your company be much more agile as well as digitally ready.

Leading The Development Of The On-Demand Workforce

HR should be leading the sourcing and development of all talent, not just internal talent. HR leaders have the expertise and the right skills and resources on their teams to develop an on-demand workforce successfully. A blended talent strategy will supplement and complement internal teams. It’s time for HR to lead the transition to the future of work.

This article was originally published in Forbes.

Interested in the Future of Work? Join the Work of the Future #FutureOfWork Facebook Group.

Yolanda Lau is an experienced entrepreneurship consultant, advisor, and Forbes Contributor. She is also an educator, speaker, writer, and non-profit fundraiser.

Since 2010, she has been focused on preparing knowledge workers, educators, and students for the future of work.

Learn more about Yolanda here.

FlexTeam  is  a mission-based micro-consulting firm, co-founded by Yolanda Lau in 2015, that matches talented mid-career women with meaningful, challenging, temporally flexible, remote project-based work opportunities. FlexTeam’s clients are businesses of all sizes across all industries and sectors. FlexTeam’s most requested projects are competitor / market research, financial models, and investor decks. FlexTeam is also the team behind Liquid.

Updated on February 24, 2024

Embrace Lifelong Learning To Thrive In The Future Of Work

By Yolanda Lau

In the future of work, the critical skills for success are increasingly soft skills like emotional intelligence, adaptability, and resilience. Success in the future of work requires becoming a lifelong learner. The world is changing faster than ever, and only through lifelong learning will we have the capability to adapt along with it.

Here are 11 strategies to develop a habit of lifelong learning.

1. Ask why.

Think back to when you were a child. Chances are you drove your parents a little crazy with all of your questions. Be like a child and always ask why — question everything. But be open to changing your mind. Seek out counter opinions and acknowledge alternative viewpoints — and you’ll learn more from these perspectives and push yourself further.

2. Learn to love challenges.

Challenges stimulate learning and bring a sense of fulfillment. I love challenges and welcome struggles and obstacles as the things most worth doing are often hard. Without challenges, we stagnate. While challenges and bumps in the road can be uncomfortable, these opportunities are the ones from which you will learn the most. Get ready to take risks by setting stretch goals for yourself. A willingness to take risks doesn’t mean you need to take on every challenge — it’s about taking measured risks that push you beyond your current limits.

3. Embrace failure.

Failing is something you do because you’re pushing yourself to do more. Every failure is an opportunity to learn. Don’t be ashamed or afraid of failure. You likely aren’t challenging yourself if you haven’t failed some of the time. I’ve found that failure has frequently been my best teacher, and my successes are a result of growth and learning from past failures and mistakes. Embrace failure and mistakes as opportunities to integrate valuable feedback and information. After all, as Albert Einstein once said, “Failure is success in progress.”

4. Practice mindfulness.

I’ve found that mindfulness is an essential soft skill to learn as it amplifies your other soft skills. Mindfulness can boost your mental agility, self-awareness, and resilience. Plus, taking a mindful brain break can boost your productivity and effectiveness while increasing the “divergent thinking” that results in new ideas. A plethora of apps and programs, such as Headspace, Yoga Ed., and Calm can help you practice mindfulness and build this essential lifelong learning skill.

5. School is only the beginning.

School should function to build a foundation for lifelong learning. Lifelong learners realize that learning doesn’t stop when school ends. Never stop seeking opportunities to learn, prioritizing both surface learning and deep learning. While surface learning is quick and easy, deep learning takes more effort. Both are valuable.

Coursera, Udemy, and EdX are great for consuming content. Cohort-based courses like Maven, On Deck, and Ascend take learning to the next level by bringing together groups of learners.

6. Be open to feedback.

Be proactive about asking for feedback. Surround yourself with mentors, personal advisors, and coaches and be willing to ask for help. I’ve found that having a community and network of peers and advisors has been essential in not only solving day-to-day problems or identifying new opportunities, but also in fueling my personal development. Frequent feedback has helped me to continually grow personally and professionally.

7. Become a polymath.

In the past, it paid to be a specialist — to accumulate as much knowledge as possible in only one area. But in the future of work, polymaths and expert generalists have the advantage. Developing deep knowledge in multiple areas, ideally with cross-disciplinary awareness, makes it easier to uncover unexpected connections and convergences. In a world where data is everywhere, pattern recognition and intuitive thinking have become more important than ever. Being an expert generalist or polymath requires continuous education — lifelong learning.

8. Teaching brings mastery.

In my experience, teaching brings mastery. I’ve been a teacher or teaching assistant for everything from entrepreneurship to ESL, citizenship to physics, biology to coding, sustainability to app building — each teaching opportunity was a window to deepen my understanding. Answering questions on the fly is the quickest way to test your knowledge and learn what you don’t know. Having spent my career advising entrepreneurs and small business owners, I’ve constantly been learning as I teach. I love it when I get to tell a founder, “I don’t know,” as it gives me something to learn.

9. Stay curious.

Talk to strangers. Be present in conversations and look for things that stimulate your curiosity. Pull on those threads and be open to learning from strangers. Follow your curiosity, and you never know where it will lead you. Sign up for that yoga teacher certification course, take that cooking class, try out a new sport, go for that art class. You need to keep that sense of wonder you had as a child to spark inquiry and continual exploration. This curiosity and openness will fuel your lifelong learning.

10. Prioritize process over goals.

Life is not about completing a series of goals. Most of us have had long and winding career paths, which didn’t necessarily make sense at the moment. When you prioritize the process of learning over the goals of completing the class or diploma, you’ll open yourself to new opportunities. Changing your mindset gives you the flexibility to follow your curiosity and may lead to opportunities you would never have otherwise thought of.

11. Give yourself the gift of grace.

But give yourself the gift of grace. It’s okay if you don’t know something. Embrace this as a challenge and as an opportunity to learn something new. It’s also okay to sprint and rest. In fact, giving yourself breaks is the best way to recharge and nurture curiosity. Breaks give your brain space to integrate your learning, developing connections between seemingly unrelated areas.

Become A Lifelong Learner

Lifelong learning isn’t just about preparing for the future of work. Lifelong learning also brings joy and a deep sense of empowerment and fulfillment — making life more meaningful. Grow and succeed professionally and personally by embracing lifelong learning.

This article was originally published in Forbes.

Interested in the Future of Work? Join the Work of the Future #FutureOfWork Facebook Group.

Yolanda Lau is an experienced entrepreneurship consultant, advisor, and Forbes Contributor. She is also an educator, speaker, writer, and non-profit fundraiser.

Since 2010, she has been focused on preparing knowledge workers, educators, and students for the future of work.

Learn more about Yolanda here.