It’s tempting to think AI adoption is limited by the technology itself. Headlines declaring the rise of robot doctors and approaching Singularity, contrasted with humorous memes of robots falling over, make us fear and sometimes doubt its capabilities. In practice, however, decades-old AI technology could unlock significant value, though many companies still have yet to adopt it. This is because the adoption of AI is determined by risk and trust. Thinking about AI adoption in this way enables us to more accurately anticipate opportunities for AI startups.
Gradual exposure to successful applications of AI in daily life or as part of trivial workflows builds trust. For example, machine learning algorithms encourage us to revisit abandoned, online shopping carts every day, so adopting AI-based software to make our jobs in enterprise sales and marketing seems natural. A nuclear power plant manager, however, has a wider mental gulf to bridge in imagining how the technology behind her Nest thermostat could safely automate dangerous maintenance procedures in her plant without extremely close supervision.
This curve predicts that the progression of AI will move from the consumer space to AI-enhanced applications, then applications and, finally, AI-enabled applications.
AI in the Consumer Space
We’ve experienced AI applications for over a decade in the consumer space, where the benefit from applying AI is high but the consequences of an incorrect prediction are low. Some well-known consumer AI applications include Google’s PageRank and suggested searches, Amazon’s product recommendations, and Netflix’s content recommendations. The right recommendation at the right time from such products leads to increased revenue for the company but the wrong recommendation does not cause more than some unintended humor for the consumer.
AI-enhanced workflow solutions
Similarly, in the enterprise space, AI was primarily and successfully applied in low-risk, high reward areas. Products were architected so that AI was applied as a layer on top of a workflow application that would function just fine without the AI. We call these applications “AI-enhanced.”
Constructor.io, a Zetta partner company, is an AI-enhanced company. Constructor applies machine learning to dynamically rank site search autocomplete suggestions and search results. The algorithm observes which search results visitors to the website are most likely to click on and ranks the search results accordingly. This dynamic ranking has increased conversions by 2-20% for Constructor’s e-commerce customers. If the machine learning layer fails completely, however, the website would still have a functioning search bar.
Other AI-enhanced workflow companies include InsideSales (AI-enhanced CRM), Lilt (AI-enhanced enterprise translation) and Teem (AI-enhanced office room management).
The AI ecosystem is in the middle of a shift in the risk curve to applications completely built around AI. We call this category “AI-centric applications.”
Zetta partner company Tractable is an example of an AI-centric application. Tractable uses deep learning-based computer vision to visually inspect damage to a car after a crash. Like a human inspector, the product estimates the cost of the damage and determines whether the damaged section should be repaired or replaced. The computer vision element is so central to this application that if the AI fails, the product would provide limited value to its customers. That said, no one is physically harmed if the product makes an incorrect assessment because a mechanic performing the repair can override the recommendation.
Other AI-Centric companies include x.ai (automated appointment scheduling over email), Falkonry (anticipating maintenance and repair of industrial equipment) and Focal Systems (retail inventory tracking and restocking).
We’re at the beginning of the next stage of the risk curve, seeing applications that are only possible because of recent advances in AI. We call these “AI-Enabled applications.”
Invenia is a company only possible because of AI. The company builds models that predict the demand and supply of electricity. They collected troves of proprietary data — on grid operations, energy usage, weather, etc. — and modeled the physical flows of power to build the best predictive models for energy usage. The company gets paid for its predictions because it improves the ability for the independent system operators (ISOs) of the electricity grid to avoid blackouts, on the one hand, or produce too much energy, on the other hand. Energy systems are so complex that machine learning is necessary to accurately model the system.
We have a general, moral imperative to maintain the quality of life for people all around the world. This is difficult to satisfy as populations increase and resources decrease. However, machine learning technologies are particularly good at solving complex optimization problems. There is a lot of risk in solving societal problems such as optimizing energy distribution, healthcare systems or food production with probabilistic methods. However, advances in machine learning technology make it possible – they just have to earn our trust.
This piece was co-authored by Ivy Nguyen, an associate at Zetta Venture Partners, and Ash Fontana, Managing Director at Zetta Venture Partners.