Artificial Intelligence and Machine Learning have been buzzwords in the tech ecosystem for years, yet we’ve only scratched the surface of innovation that is possible. A simplified definition of Artificial Intelligence (AI) is the umbrella of machines performing “smart” tasks that humans would normally carry out. Machine Learning (ML) is a subset of that umbrella where machines access behavioral data and learn to reflect human actions, reasoning, and decisions.
As early-stage technology investors, we’ve been interested in AI for some time and have made early investments in AI-focused companies like Applitools, Text IQ, and Invisible AI. Over time, we’ve seen the sector grow and transform and we’re excited about the next wave of trends in AI.
Top trends we’re seeing in AI/ML in 2020 include:
- AI Governance is becoming a high priority as adoption increases.
- Key learnings say that AI/ML capabilities need to be embedded at the core of company systems, not just as an added product feature.
- Tech buyers are choosing verticalized AI solutions specific to their use case over generalist technologies.
AI Governance is becoming a high priority as adoption increases.
Increasingly, businesses are relying on AI/ML technology to remain competitive. As AI applications increase, businesses need to monitor their AI/ML algorithms to protect against “negative” outcomes which can include biased results and misinterpreted recommendations. Left unaddressed, AI Governance issues can result in a range of problems for a business from loss of revenue to regulatory and reputational risks. Most organizations are aware of the need for AI Governance, but to date, many responses have been reactive rather than proactive. We are seeing more organizations attempting to mitigate risks by setting up policies proactively to monitor AI/ML model operations and implement a use case-based strategy to govern data inputs and knowledge outputs.
AI/ML capabilities need to be embedded at the core, not just as an added product feature.
Gone are the days of implementing AI/ML features into existing legacy systems as an added benefit to the product suite. Buyers today are looking for the AI/ML technology to be embedded at the core of the internal systems. Many AI/ML solutions being marketed today are just features built on top of an existing software solution, which is why there are so many products required within individual business units. According to a survey of technology executives conducted by Forbes, 65% report that they are not yet seeing value from the AI investments they have made in recent years with 40% of organizations who are making ‘significant investments’ in AI not reporting business gains from the investments. Although tedious and time-consuming to develop, user-friendly core AI/ML products are primed to win in an oversaturated market of “feature” technology.
Tech buyers are choosing verticalized AI solutions specific to their use case over generalist technologies.
The AI/ML ecosystem has an underlying problem: there are many companies, but there are only a few big ones. According to a CB Insights report, more than 95% of AI/ML exits have been small M&As or acqui-hires with very few unicorn IPOs in the sector. Why? Most companies have been horizontally focused, meaning the product suite has a generic use case across many different verticals. Buyers are now looking for AI/ML solutions that are designed specifically to solve for their industry and company needs. AI/ML companies with vertical solutions are able to gather industry-specific data which is much more valuable to the buyer than unrelated cross-industry data. Finding a few targeted verticals and capturing majority market share will likely be the key to success for AI/ML companies scaling toward billion-dollar valuations.
We’re looking forward to following the shifts and advancements in AI/ML as we continue to invest in the space and will update our thoughts periodically.
Fundraising for your AI startup? Check out our post on How to Get Funding for Your AI Business.