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AI is no longer a "Nice to Have" but a "Must Have"

Updated: Feb 10, 2019


Over the last 18 months, it would have been impossible to follow the technology ecosystem in Silicon Valley without hearing the deafening roar of all things Artificial Intelligence. Why is there such a frenzy around AI-based solutions?

It starts with market demand. AI-based enterprise software has moved from a “nice to have” to a “must have” for most large corporations.


This market demand will drive ever increasing revenue for the winners of the AI enterprise software race.

According to research by Statisitca, global revenues from AI for enterprise applications is projected to grow from $1.62B in 2018 to $31.2B in 2025 attaining a 52.59% CAGR in the forecast period.


The investment community has responded by putting billions into AI-driven startups. In 2017, over $15B USD was invested globally in AI startups.

This is an increase of 141% from 2016. In 2016 and 17 over 1,100 startups received an initial round of investment. (CB Insights – State of Artificial Intelligence Report 2018).


The result is AI for Everything! Well, at least machine learning for everything.

  • From Russia, there is DeepFish (using AI to help fishermen find a better place to fish);

  • From Sweden, there is Hoofstep (AI for the behavior of horses);

  • From the UK, there is IntelligentX (AI for making craft beer based on consumer feedback).

The graphic below from TopBots shows the current landscape for AI-based enterprise software


So what does this mean for founders and CEO’s of young technology companies?

Countless startups try to wrap themselves in AI / Machine learning clothing. It is rare to hear and enterprise software pitch that doesn’t include statements about the company’s machine learning (or deep learning) algorithm and how it will change everything.

The challenge is that just having AI is no longer a differentiator. Frank Chen, a partner at Andreessen Horowitz is more blunt. On his Machine Learnings site, he wrote:

I believe that [within two years] no investor will be funding startups calling themselves AI-powered startups (and no startup CEO will differentiate themselves as an AI-first company like Google) is because investors will assume the startup is using the best available AI techniques to solve the problem they are solving.

Not having state-of-the-art AI techniques powering their software would be like not having a relational database in their tech stack in 1980 or not having a rich Windows client in 1987 or not having a Web-based front end in 1995 or not being cloud native in 2004 or not having a mobile app in 2009. In other words, in a small handful of years, software without AI will be unthinkable.

So ambitious founders will need to invest some other way to differentiate themselves from the crowd — and investors will be looking for other ways to decide whether to fund a startup. And investors will stop looking for AI-powered startups in exactly the same way they don’t look for database-inside or cloud-native or mobile-first startups anymore. All those things are just assumed.

In this way,

AI is no different than any other foundational computer science technique that gets widely adopted. Every now and again, the industry invents a new set of “must adopt” computer science techniques that find their way into all important software we use.

As a founder or CEO, you need to have a coherent strategy for how AI will enable your solution.


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