Intelligent Applications have been and continue to be a focus of our investing. These apps sit on top of the infrastructure a company chooses, the data they collect and curate, the machine learning they apply to the data and the continuous learning system they build. In this deep dive we talk about why intelligent applications are a central component to our investing themes and where we see the opportunities for company creation and building.

Intelligent apps are applications that use data and machine learning to create a continuous learning system that delivers rich, adaptive, and personalized experiences for users. These intelligent apps range from “net-new” apps like those powering autonomous vehicles and automated retail stores to existing apps that are enhanced with intelligence, such as lead scoring in a CRM app or content recommendations in a media app.

Intelligent apps will have a massive impact on the way we work, live, and play, and we have already been blown away by the potential in what we are seeing companies build today. Some of the most exciting intelligent apps we have seen do at least one of the following:

Enable completely new behaviors

Some of the most impressive demonstrations of machine learning are those that use AI to create new business processes and markets that completely change the way people do things. One high-profile example is Amazon Go stores using computer vision to completely change the supermarket or convenience store experience by removing the checkout process.

Another great example is Textio. Textio offers an AI-powered ‘augmented writing platform’ which draws on massive amounts of historical data to help companies write better job descriptions that will attract higher quality applicants. Both of these examples use AI to create new processes that result in better experiences and better outcomes for their users.

Drive 10x (or better) process improvements

AI automation and insights can also be used to optimize existing processes and workflows. Automation using AI is at the cornerstone of what every enterprise is going through in terms of digital transformation. For example, UiPath’s RPA platform allows companies to drastically reduce costs by automating a wide variety of software based tasks using UiPath’s “robots.” While the UiPath platform is early in its journey to becoming an intelligent app, it is already helping its customer drive 10x process improvements.

Suplari also uses AI to improve existing business processes, namely to analyze purchase behaviors to better understand how to drive cost reductions and manage supplier risks. While Suplari’s customers may have individual processes to reduce software costs through deduplication or to identify opportunities for savings in contract renewals, using AI to proactively identify the best opportunities allows their customers to realize large efficiencies in their procurement processes.

Integrate silos (data and workflows) and capture value

Another great opportunity for AI companies is to combine data and processes to allow companies to combine different parts of the value chain and capture more value. For example, Affirm uses machine learning to approve consumer loans and uses these loans to help ecommerce companies improve shopping cart conversion rates.

One of our portfolio companies, Amperity, literally combines different silos of customer data. Companies that have customer data stored in disparate systems and tools can’t easily leverage this data to get a full picture of their customer base. Intelligently stitching these silos together drives significant business results for Amperity’s clients who can now clearly see the stitched 360-view of their customers and use it to market and sell products in a more intelligent way.

Trends Converge

Now is an exciting time for investors and entrepreneurs to be focusing on intelligent applications because of the momentum and growth of several important technology trends:

• Massive computational power and low-cost storage are creating the infrastructure to train machine learning models
• More data is generated and stored than ever before in many different fields like healthcare, autonomous systems, and media
• Availability of good-enough capabilities at the edge to do a lot of the inferencing work at the edge as opposed to having to round-trip to the cloud
• Continued improvement and development in tools and frameworks make it easier for companies and developers to begin using machine learning
• New “user interfaces” using voice, vision, and touch are bridging the gap between the digital and physical world

As these trends make it easier for entrepreneurs to build intelligent applications, we have been developing our own frameworks to understand how all of these pieces fit together to create value for customers. Generally, we think about the intelligent application ecosystem in three main parts:

• The Data Platform Layer
• The Machine Learning Platform Layer
• The Intelligent Applications and “Finished Services” Layer

The Machine Learning Platform Layer

As an early believer in the potential of AI and machine learning, Madrona has made several investments in the machine learning platform layer, including companies like Turi, Lattice, and Algorithmia. This layer of the intelligent app stack is meant to make it easier for other developers and applications to make use of machine learning by providing the tools and automating tasks such as model training, model deployment, and model management.

The ML platform includes machine learning frameworks like TensorFlow and PyTorch, managed services and tools like Amazon Sagemaker and TVM, as well as “Model as a Service” providers in the form something like AWS Marketplace that can help developers and companies develop and deploy ML models in specific environments. While many of these tools have been developed by large companies or acquired by large companies, we believe there continues to be interesting opportunities at this layer because deploying and managing machine learning systems continues to be very difficult.

As an example, while the major cloud providers have made large investments in software and hardware to train ML models in the cloud, using those models for inference at the edge continues to be a difficult problem on resource-constrained devices. Xnor.ai is a portfolio company in this segment that uses software optimizations to improve the quality of machine learning predictions on edge devices that have limited power or bandwidth.

Overall, we believe that while frameworks and tools have been improving, advanced techniques like reinforcement learning still need frameworks and tools that are easier to use, and there are many interesting opportunities to continue improving the ML platforms that intelligent apps depend on.

The Data Platform Layer

A precursor to using AI effectively and building intelligent applications is having a “data” strategy. Having a unique data strategy that could be a combination of public data sets and proprietary data sets enables companies to provide unique and differentiated value. This is a necessary first step, before you can use the data to train models and build a continuous learning system that is a core part of building an intelligent application.

Within the Data Platform layer, we think of companies and products from portfolio companies, Datacoral and Snowflake, as well as those from Databricks and Amazon’s Redshift, which offer customers different ways to connect, transform, warehouse, and analyze data in order to be used in an ML platform. What we’ve seen at this layer of the stack is that getting data into the right place, in the right format, in order to be used for machine learning continues to be very difficult, and simplifying this process is extremely valuable to customers.

Additionally, access and ownership to data itself is a key part of the data platform layer. By this, we mean that companies need to be thoughtful about their data strategies in order to find ways to gain access to, generate, or combine different data sources in order to create unique data assets. As we are seeing frequently in the news these days, companies also need to be thoughtful about data privacy and making sure customers understand what data is being used, shared, and how.

The lines between the Data Platform Layer, the ML Platform Layer, and Intelligent Apps themselves can be quite blurry, especially as companies try to offer their customers a broader set of services or learn their way into new customer needs. However, we do see a distinction between companies that are focused on helping customers manage their data vs. helping customers manage their ML models.

 

Ultimately, we are looking for companies that can benefit from the virtuous data cycle – where more data creates better user experiences, leading to better user engagement, leading to more data, and ultimately better user experiences again.

Intelligent Applications and “Finished Services” Layer

Within the Intelligent Applications and Finished Services layer, there are several ways to segment the market. We like to think about verticals – applications that focus on a specific industry such as healthcare or insurance – and horizontals – cross-industry applications such as marketing automation or robotic process automation. One of the principles that we follow when looking for these types of opportunities is to find areas where data is becoming digitized and/or more data is being collected than ever before.

For example, one promising vertical for intelligent apps is healthcare. Technology and regulatory trends have driven the healthcare field to rapidly digitize many different types of records – from basic medical histories, to insurance claims, to x-rays, MRI scans, and ‘omics’ data (e.g., genomics, proteomics, biomics). This digitization of healthcare is creating new levels of visibility into patient and population health data, and ML will be a critical tool to help decision makers make sense of these new data sources.

Workforce productivity is another promising area for horizontal intelligent applications because more data is digitized than ever before in HR and employee engagement across industries. One example of a horizontal intelligent app is Madrona Venture Labs spinout company, UpLevel, which uses unstructured data from tools like Slack to help managers get better insights on how to best engage their teams and drive productivity.

In addition to vertical and horizontal apps for business users, we also include other types of “finished services” in this bucket. This can include services like Amazon Rekognition or Amazon Forecast, which help application developers add image and video analysis or time series forecasting models to other applications. In this case, the end customer for a product may not be a consumer, but the product is a “finished service” which can be plugged into a customer-facing application.

In each of these use cases, we are looking to find companies that deeply understand customer pain points and use machine learning as a tool to solve customer problems, rather than starting with a technology and searching for use cases.

Areas of Opportunity

We believe that every successful application built today will be an intelligent application, and that is why we think there is a huge amount of opportunity for entrepreneurs in this space. In particular, we would love to see more companies that are building at the nexus of multiple large markets, companies with unique data strategies, and companies with great ML teams (because AI continues to be very difficult). Four specific areas where we are excited to meet new companies are:

• AI for Healthcare – More healthcare data is digitized and stored than ever before, and this is creating massive opportunities to reduce costs while improving quality of care and operations. The intersection of the biological sciences with computer science is going to be a difficult area to break through, but the potential value created will be huge, and we are looking for entrepreneurs who are ready to take on these challenges.
• AI for Work – More and more, companies want to measure and become data-driven about productivity, hiring, and employee wellness. Traditionally, HR and workforce data has been incredibly hard to collect and analyze, but new applications like Slack and Workday are creating opportunities for startups like Polly and UpLevel to analyze workplace data to generate insights for employees and managers.
• Automation – Robotic Process Automation (RPA) vendors are one set of companies building early intelligent apps that can analyze a business process and improve productivity through automation, but they will not be the last. We think there will also be opportunities to build vertical “RPA-like” businesses in specific industries, automation of manual work that can be dangerous and expensive, and new types of autonomous systems like autonomous vehicles.
• “End-to-End AI” – Many companies have a section of their pitch explaining how valuable their data will be. We always encourage companies to think about the best use cases for their data, and, if it makes sense, execute on those use cases themselves. Some of our favorite examples in this category are companies like Climate Corp, which started with an ML system for predicting weather, found that they could use their predictions to sell weather insurance to farms, and eventually built an end-to-end farm management software system to capture more data and use it to write insurance policies.

Conclusion

During a recent CIO roundtable, we debated whether machine learning was an over-hyped or under-hyped technology trend. The answer in most people’s minds was both. There are incredibly high expectations for machine learning, and many of those expectations are not grounded in the reality of what ML can do today.

However, we believe that as we move forward, the ability to build new applications and continuously improve systems and processes using machine learning will be a core part of any app, and machine learning will be immensely impactful in every fabric of the society that we work and live in.

Current or previous Madrona Venture Group portfolio companies mentioned in this blog post: Algorithmia, Amperity, Datacoral, Lattice, Snowflake, Suplari, Turi, Xnor.ai, UIpath