I’m excited to introduce a new series called “Applied AI Success Stories.” Over the past 20 years, we’ve invested in AI SaaS companies and predictive analytics, watching AI evolve from simple algorithms to machine learning and large language models (LLMs). With LLMs leading the charge, we believe we’re on the verge of a revolution that could transform nearly every job in the U.S. economy. The key to this transformation lies within the thousands of companies harnessing AI to reshape workflows and generate new value. This approach, known as Applied AI, rests on three critical pillars: data, algorithms, and workflows.
We believe so strongly in this approach that we built our fund and management company around it. A prime example is SaleSavant, a tool we've developed to identify investment prospects, talent for our portfolio companies, and their Ideal Customer Prospects (ICP). By gathering online data and using AI-driven matching algorithms, we uncover sales opportunities, target companies, and identify critical contacts—demonstrating the importance of applied AI to drive business growth.
We categorize the AI market into two parts: core technologies and applied AI. Core technologies, such as LLMs, are created by tech giants like OpenAI, Microsoft, and Google. Our focus, however, is on applied AI—companies that leverage these core technologies to address specific use cases. The success of applied AI depends on integrating proprietary data, effective algorithms, and redesigned workflows.
The Pillars of Applied AI: Data, Algorithms, and Workflows
Let’s break down these pillars, starting with data. To fully benefit from applied AI, a company needs access to proprietary data or data that are closely tied to its operations. This could include existing workflow data or external datasets, which open up new avenues for value creation. Organizations can incorporate additional data or build metadata models, allowing for smarter queries and previously untapped insights.
Next, the algorithms. These are the models that interpret data and drive workflows. How companies select, train, and optimize these algorithms plays a huge role in determining the impact of their AI solutions. There’s no one-size-fits-all approach—different AI models come with distinct benefits.
Lastly, workflows are often the most difficult aspect to change. Implementing AI often disrupts existing workflows, but adapting these processes is essential to unlocking the full potential of the data and the algorithms. When data, algorithms, and workflows come together, these three components create transformative value.
Upcoming Series: Real-World Applications of Applied AI Across Industries
This series will explore how AI is applied in industries, such as sales, customer support, finance, healthcare, and logistics. For instance, IntelePeer, one of our investments, uses OpenAI to automate call center tasks like scheduling and bill payments. Their technology can handle 75% of calls with minimal workflow changes.
We invite you to share your stories of applied AI, as we continue to highlight how companies are driving change through AI innovation. Stay tuned!
