Beyond text, images, and code

By Henry Huang, Partner

Over the past 12 months, the growth and accessibility of foundational models (FMs) have created a whirlwind of conversations about new possibilities. From software and high-tech to the more traditional, businesses are (hopefully) asking: what can Generative AI do for me? 

Emerging venture-backed companies, like Jasper, have shown that along with curiosity is also a growing willingness to explore and experiment. In less than two years, more than 80,000 subscribers, many of whom are businesses, are using Jasper to create copy to meet the high-volume content demands of sales and marketing teams. There is more optimism today than ever that AI can create benefits for businesses that directly and meaningfully improve the bottom line. 

We’re still only at the beginning. Much of the recent focus of Generative AI apps for businesses is on producing increasingly better content, code, and data outputs, based on user prompts and descriptions. With the speed at which FMs are improving, it is easy to see that these types of business apps will continue to get better, require fewer humans in the loop, and be able to support more industries out of the box. 

However, the needs of businesses extend far beyond content creation and code development. There is an abundance of opportunity for Generative AI to support, if not entirely reinvent, the enterprise and business application stack. 

Consider the below response from ChatGPT, based on the prompt: “Aside from content creation, what are some other ways that Generative AI can improve a business’s bottom line?”

  • Automation: Generative AI systems can be used to automate certain tasks, freeing up time and resources for other activities.
  • Innovation: Generative AI can be used to generate new ideas or solutions that may not have been considered before, potentially leading to new products or services that can drive revenue.
  • Efficiency: Generative AI can be used to optimize processes and improve efficiency, resulting in cost savings that can contribute to the bottom line.
  • Personalization: Generative AI can be used to create personalized experiences for customers, which can lead to increased customer satisfaction and loyalty, and potentially increased sales.
  • Supply chain management: Generative AI can be used to optimize supply chain operations, including forecasting demand, optimizing inventory levels, and identifying potential bottlenecks.
  • Fraud detection: Generative AI can be used to identify patterns and anomalies that may indicate fraudulent activity, helping businesses to protect against financial losses.
  • Predictive maintenance: Generative AI can be used to predict when equipment is likely to fail, allowing businesses to schedule maintenance in advance and avoid costly downtime.
  • Financial analysis: Generative AI can be used to analyze financial data and identify trends or patterns that can inform business decision-making.
  • Product design: Generative AI can be used to generate new product designs or prototypes, potentially speeding up the product development process and enabling businesses to bring new products to market more quickly.
  • Improved decision-making: Generative AI can be used to analyze data and provide insights that can inform business decisions, helping businesses to make more informed, data-driven decisions.

Two observations: (1) The model's confidence in its own potential is remarkable! (2) When excluding content generation in the prompt, the model is clearly biased towards analytics-to-action/automation use cases.

Whether or not generative capabilities become features within existing platforms and apps, transform antiquated systems such as ERPs, or create new categories and business models altogether, the larger point is still valid. Within business apps, Generative AI opportunities are diverse and the surface area for potential innovation is massive.

Big ambitions, tons of hype, what can go wrong? There’s still so much to prove and too many considerations to touch on all of them, but here are a few thoughts for folks that are thinking about building business applications powered by Generative AI.

  • While the appetite for experimentation is growing, businesses will continue to hold a high bar for new technology investments, especially for emerging technologies in the current economic climate. The question that you should answer is not, “is it better than how businesses do things today?” Of course, it is. Instead, you should be asking, “how much better, is it obvious, and at what true cost to the customer?” 
  • Consider the audience and the task, not just the user. For example, is the audience internal employees or customers? Is the task collaborative and/or definitive? As my colleague, Jeremy, mentioned in “AI-assisted creativity,” generative models can be wrong and even sometimes respond with inappropriate, biased, or disturbing content. Keeping the audience and the task in mind will help inform your tolerance for errors, and balance between human-assisted and automated approaches.
  • There is limited defensibility in models. Given how nascent Generative AI is in the business context, a lot of early work and investment for new business app companies will be in model development. To support hyper-specific use cases, for example in the cases of verticalization, FMs will need to be seeded with new data, and new models to extend their capabilities will need to be built. However, most models built on widely available or obtainable data can be easily replicated and replaced. It’s important to not underinvest in defensible products upfront; for example, mechanisms for user or audience-led fine-tuning that generates proprietary data to further enhance models.
  • Businesses, especially those in highly-regulated industries, care about compliance. In the Generative AI field, there are still many open questions surrounding IP ownership, data privacy, security, and ethics. While independent and individual creators may not have to think too much about risks, businesses must. Generative AI business apps that take potential compliance risks into account early will have advantages, particularly in attracting certain types of customers.


At Madrona Venture Labs, we help entrepreneurs build their businesses from day one. We execute alongside founders, and we are currently busy experimenting with foundation models with the constant goal of finding unique, venture-scale businesses. 

We’re hosting Launchable: Foundation Models January 23-29th to bring together entrepreneurs, technologists, and leaders in Generative AI. Please apply today and get started building your Business App powered by Generative AI.

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