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Reframing Generative-AI through the Lens of Three Business Classics

Generative AI, with its transformative capabilities, has sparked immense excitement and anticipation in the business world. However, to fully harness its potential and avoid the pitfalls that have plagued previous technological advancements, it’s worth taking a step back and reframing our thinking around this emerging technology.


Let’s revisit three seminal business classics – "The Innovator's Dilemma" by Clayton Christensen, "The Lean Startup" by Eric Ries, and "Zero to One" by Peter Thiel – to give us a fresh perspective on generative AI and its implications for businesses.


The Innovator's Dilemma, The Lean Startup and Zero to One on a Bookshelf

The Innovator's Dilemma and the Generative AI Gold Rush

Embracing Iteration and Customer Feedback

Focusing on Unique and Valuable Problems


 

The Innovator's Dilemma and the Generative AI Gold Rush


In his seminal work, The Innovator's Dilemma, Clayton Christensen argues that established companies often fail to innovate because they’re too focused on serving their existing customers and fail to see the potential of disruptive technologies.

This phenomenon, known as the "innovator's dilemma," can lead to complacency and massively missed opportunities.


We’re seeing a similar dynamic play out today in the market for generative AI. Established tech companies are pouring billions of dollars into developing and deploying generative AI platforms, but are largely ignoring the underlying tools and services that will bind these technologies together and ultimately make them useful.


The real value in generative AI lies not just in the platforms themselves, but in their ability to create new products and services.

The real value during the gold rush was not in the gold itself, but was the value embedded in the tools and services that enabled miners to extract it.


A gold rush shovel


And just like the gold rush – the real value in generative AI will be in the tools and services that enable businesses to use it to create new products and services.


90% of businesses believe that generative AI will have a positive impact on their industry.

For example, generative AI can be used to create new marketing content, customer service chatbots, and even new products. However, in order to do this, businesses need access to tools and services that can help them to train and deploy the generative AI models powering these products and services.


And this is where the opportunity lies for startups.


By developing tools and services that make it easy for businesses to use generative AI, startups can position themselves to capture a significant market share.

By developing these tools and services, startups can play a vital role in the development of the generative AI market. They can help businesses overcome the challenges of using generative AI and unlock its full potential.


Just as the companies that sold picks and shovels during the gold rush achieved greater long-term success and economic stability than the miners themselves, the companies that develop the tools and services that bind generative AI together will achieve greater success and stability than the companies that develop the technologies themselves.



Team of programmers working on a solution using generative AI


Embracing Iteration and Customer Feedback


Eric Ries, in "The Lean Startup," advocates for a "lean" approach to building successful startups. This approach involves testing ideas quickly and cheaply, and then iterating based on feedback from customers. By embracing this methodology, startups can avoid wasting time and money on products and services that are not viable.

The lean approach is particularly well-suited to businesses that are developing generative AI products and services. This is because generative AI is a rapidly evolving field, and it is difficult to predict what will be successful. By using the lean approach, businesses can quickly test ideas and get feedback from real customers, which can help avoid wasting time and money on non-viable products and services.


By testing ideas and getting feedback from customers early on, you can avoid wasting time and money on products and services that are not viable.

Here's a few examples of how we can use the lean approach to develop generative AI products and services:


AI-generated avatars

AI-generated avatars can be used in a variety of applications, such as customer service chatbots, marketing campaigns, and online training programmes. By implementing a lean approach, we can quickly test different types of avatars and get feedback from customers, which can help them to develop avatars that are both effective and appealing.


Language translation

Generative AI-powered language translation services can be used to translate text and speech in real time. By using the lean approach, we can quickly test different translation models and get feedback from customers, which - in turn - can help them to develop translation services that are both accurate and efficient.


Personalised video

Generative AI-powered personalised video services can be used to create unique videos for each customer. By using the lean approach, we can quickly test different types of personalised videos and get feedback from customers, which can help them to develop videos that are both engaging and effective.


The global generative AI market is expected to grow from $15.7 billion in 2023 to $108.7 billion by 2029, a CAGR of 30.5%.

Here are a few tips for using the lean approach to develop generative AI products and services:


Start with a problem

Don't start by developing a solution. Instead, start by identifying a problem that you want to solve. This will help you to focus your efforts and develop a product or service that is truly needed.


Build an MVP (a minimum viable product)

If you've built yourself a perfect product or service, you've launched it too late. Instead, get a minimum viable product (MVP) to market – one that has just enough features to solve the single problem you've identified. This will allow you to get feedback from customers quickly – and act on that feedback!


Get feedback from customers (quickly!)

Once you've built that MVP, get feedback from customers as soon as possible. This will help you to identify any problems with your product or service - then make improvements and redeploy the product. Quickly.


Iterate, iterate, iterate

Based on the feedback that you receive from customers, iterate on your product or service. This means making changes and improvements until you have a product or service that is both effective and appealing. And do it quickly. Again!



Woman working on a digital product using generative AI


Focusing on Unique and Valuable Problems


In "Zero to One," Peter Thiel emphasises the importance of finding a unique and valuable problem to solve when building a successful business. He argues that most startups fail because they try to compete in existing markets, rather than creating new ones.

In the generative AI landscape, the key to success lies in identifying unique and valuable problems that this technology can solve. This requires looking beyond the hype and excitement and focusing on the real needs of businesses and consumers. By addressing these unmet needs, startups can build businesses that have a transformative impact on the world.


Thiel's advice is particularly relevant to the generative AI market. The generative AI market is still in its early stages, and there is a lot of hype and excitement around the potential of these technologies. However, it is important to remember that generative AI is not a magic bullet. It is a tool that can be used to solve problems, but it is not a solution in itself.


The key to success in the generative AI market is to find a unique and valuable problem to solve. This means looking beyond the hype and excitement and focusing on the real needs of businesses and consumers.



Working on a digital product using generative-AI


Here are a few takeaways for businesses who are looking to grow their proposition around generative AI:


Think big

Don't be afraid to think big and aim to build a business that can have a transformative impact on the world.


Focus on solving a unique and valuable problem

Don't try to compete in existing markets. Instead, focus on finding a unique and valuable problem that generative AI can be used to solve.


Build a strong team

Surround yourself with a team of talented and passionate people who share your vision.


Be patient (but move quickly!)

Building a successful business takes time and effort. Don't expect to become an overnight success.


A Path to Success in the Generative AI Era


By combining the insights from "The Innovator's Dilemma," "The Lean Startup," and "Zero to One," we can develop a comprehensive framework for approaching generative AI and maximising its potential.

Firstly, businesses must avoid the trap of established markets and focus on developing the underlying tools and services that will enable the widespread adoption of generative AI. Secondly, startups should embrace the lean approach to quickly test their ideas, get feedback from customers, and iterate their products and services.


Finally, entrepreneurs should strive to identify unique and valuable problems that generative AI can solve, thereby creating businesses that have a meaningful impact on the world.


By following these principles, businesses can navigate the generative AI landscape successfully, drive innovation, and capture the immense opportunities that this transformative technology offers.

And lastly here's seven key areas Generative-AI is currently being used to improve -


  1. Designing new products and services Generative AI is being used to design new products and services that are tailored to the needs of individual customers, leading to the development of new products and services in a variety of industries, including retail, healthcare, and education.

  2. Improving customer service Generative AI is being used to create chatbots and other customer service tools that are more intelligent and responsive leading to improved customer service experiences and reduced costs for businesses.

  3. Automating tasks Generative AI is being used to automate a variety of tasks, such as data entry, scheduling, and report writing, leading to increased efficiency and productivity for businesses.

  4. Creating personalised content Generative AI is being used to create personalised content, such as marketing copy, social media posts, and product descriptions, leading to more cost-effective and targeted marketing campaigns and driving increased sales for businesses.

  5. Developing new AI models Generative AI is being used to develop new AI models that are more accurate and efficient, leading to advances in a variety of fields, such as computer vision, natural language processing, and machine learning.

  6. Improving cybersecurity Generative AI is being used to develop new cybersecurity tools and techniques that are more effective at detecting and preventing cyberattacks, leading to increased security for businesses and governments.

  7. Creating new forms of art and entertainment Generative AI is being used to create new art and entertainment, such as music, paintings, and video games, leading to new and innovative forms of entertainment for consumers.


 

A primer on Generative AI


Q: What is Generative AI, and why is it so exciting?

A: Generative AI is a type of artificial intelligence that can create new data or content from scratch. This is in contrast to traditional AI, which can only learn from and make predictions based on existing data. Generative AI has the potential to revolutionise many industries, from healthcare to entertainment.


Q: What are some of the potential benefits of generative AI for businesses?

A: Generative AI can help businesses save time and money by automating tasks, creating personalised content, and developing new products and services. It can also help businesses improve customer service, increase sales, and gain a competitive advantage.


Q: What are some of the ethical concerns surrounding generative AI?

A: There are a number of ethical concerns surrounding generative AI, including the potential for bias and discrimination in AI-generated content, the impact of generative AI on the job market, and the potential for AI-generated content to be used for malicious purposes.


Q: What are some of the challenges associated with developing and deploying generative AI models?

A: Developing and deploying generative AI models, or LLMs can be challenging due to the need for large datasets, specialised hardware, and expertise in machine learning.


Q: How can businesses evaluate and select generative AI solutions that are appropriate for their needs?

A: Businesses should consider a number of factors when evaluating and selecting generative AI solutions, including the type of data they have, the desired outcomes, and their budget.


Q: What is the current state of the generative AI market?

A: The generative AI market is still in its early stages, but it is growing rapidly. There are a number of major players in the market, including Google, Microsoft, and OpenAI.


Q: What is the potential impact of generative AI on society as a whole?

A: Generative AI has the potential to have a significant impact on society as a whole. It could lead to new forms of art and entertainment, new scientific discoveries, and new ways of solving problems. However, it is important to be aware of the potential risks and challenges associated with generative AI, and to develop policies and regulations to mitigate these risks.


Q: How can businesses prepare for and adopt generative AI in the future?

A: Businesses should start by educating themselves about generative AI and its potential benefits and risks. They should also begin experimenting with generative AI solutions to see how they can be used to improve their businesses.


Q: What are some examples of generative AI in use today?

A: Generative AI is being used in a variety of applications today, including:

  • Creating personalised marketing content

  • Creative digital human avatars that can interact in natural language

  • Generating synthetic data for training new machine learning models

  • Developing new drugs and therapies

  • Creating new forms of art and entertainment


Q: What is the future of generative AI?

A: The future of generative AI is bright. Generative AI has the potential to revolutionize many industries and to have a significant impact on society as a whole. As generative AI models become more sophisticated and accessible, we can expect to see even more innovative and groundbreaking applications of this technology in the years to come.

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