AI is hot, I mean really hot. VCs love it, pouring in over $1.5B in just the first half of this year. Consumer companies like Google and Facebook also love AI, with notable apps like Newsfeed, Messenger, Google Photos, Gmail and Search leveraging machine learning to improve their relevance. And it’s now spreading into the enterprise, with moves like Salesforce unveiling Einstein, Microsoft’s Cortana / Azure ML, Oracle with Intelligent App Cloud, SAP’s Application Intelligence, and Google with Tensorflow (and their TPUs).
As a founder of an emerging AI company in the enterprise space, I’ve been following these recent moves by the big titans closely because they put us (as well as many other ventures) in an interesting spot. How do we position ourselves and compete in this environment?
In this series, I’ll share some of my thoughts and experiences around the whole concept of AI-First, the “last mile” problems of AI that many companies ignore, the overhype issue that’s facing our industry today (especially as larger players enter the game), and my predictions for when we’ll reach mass AI adoption.
Let’s start with defining AI-First vs. AI-Later. A few years ago, I wrote about the key tenants of building Predictive-First applications, something that’s synonymous to the idea of AI-First, which Google is pushing.
A great example of Predictive-First is Pandora (disclosure: Infer customer). Pandora didn’t try to redo the music player UI — there were many services that did that, and arguably better. Instead, they focused on making their service intelligent, by providing relevant recommendations. No need to build or manage playlists. This key differentiation led to their rise in popularity, and that differentiation depended on data intelligence that started on day one. Predictive wasn’t sprinkled on later (that’s AI-Later, not AI-First, and there’s a big difference… keep reading).
If you are building an AI-First application, you need to follow the data — and you need a lot of data — so you would likely gravitate towards integrating with big platforms (as in big companies with customers) that have APIs to pull data from. For example, a system like CRM.
There’s so much valuable data in a CRM system, but five years ago, pretty much no one was applying machine learning to this data to improve sales. The data was, and still is for many companies, untapped. There’s got to be more to CRM than basic data entry and reporting, right? If we could apply machine learning, and if it worked, it could drive more revenue for companies. Who would say no to this?
So naturally, we (Infer) went after CRM (Salesforce, Dynamics, SAP C4C), along with the marketing automation platforms (Marketo, Eloqua, Pardot, HubSpot) and even custom sales and marketing databases (via REST APIs). We helped usher a new category around Predictive Sales and Marketing.
We can’t complain much — we’ve amassed the largest customer base in our space, and have published dozens of case studies showcasing customers achieving results like 9x improvements in conversion rates and 12x ROI via vastly better qualification and nurturing programs.
But it was hard to build our solutions, and remains hard to do so at scale. It’s not because the data science is hard (although that’s an area we take pride in going deep on), it’s the end-to-end product and packaging that’s really tough to get right. We call this the last mile problem, and I believe this is an issue for any AI product — whether in the enterprise or consumer space.
Now, with machine learning infrastructure in the open — with flowing (and free) documentation, how-to guides, online courses, open source libraries, cloud services, etc. — machine learning is being democratized.
Anyone can model data. Some do it better than others, especially those with more infrastructure (for deep learning and huge data sets) and a better understanding of the algorithms and the underlying data. You may occasionally get pretty close with off-the-shelf approaches, but it’s almost always better to optimize for a particular problem. By doing so, you’ll not only squeeze out better or slightly better performance, but the understanding you gain from going deep will help you generalize and handle new data inputs better — which is key for knowing how to explain, fix, tweak and train the model over time to maintain or improve performance.
But still, this isn’t the hardest part. This is the sexy, fun part (well, for the most part… the data cleaning and matching may or may not be depending on who you talk to :).
The hardest part is creating stickiness. In my next post in this series, I’ll talk about how to do that by solving AI’s last mile problem.