Yesterday, I wrote about the last mile of AI and building sticky business applications through predictive technology. My final post in this series explores the tremendous overhype we’re seeing around AI, especially as larger players like Salesforce enter the game, and touches on when we should expect mass adoption.
Vendors in our space often over-promise and under-deliver, resulting in many churn cases, which, in turn, hurts the reputation of the predictive category overall. At first, this was just a problem with the startups in our space, but now we’re seeing it from the big companies as well. That’s even more dangerous, as they have bigger voice boxes and reach. It makes sense that the incumbents want to sprinkle AI-powered features into their existing products in order to quickly impact thousands of their customers. But with predictive, trust is paramount.
Historically, in the enterprise, the market has been accustomed to overhyped products that don’t ship for years from their initial marketing debuts. However, in this space, I’d argue that overhyping is the last thing you should do. You need to build trust and success first. You need to under-promise and over-deliver.
Can the Giants Really Go Deep on AI?
The key is to hyper focus on one end-to-end use case and go deep to start, do that well with a few customers, learn, repeat with more, and keep going. You can’t just usher out an AI solution to many business customers at once, although that temptation is there for a bigger company. Why only release something to 5% of your base when you can generate way more revenue if it’s rolled out to everyone? This forces a big company to build a more simplified, “checkbox” predictive solution for the sake of scale, but that won’t work for mid-market and enterprise companies, which need many more controls to address complex, but common, scenarios like multiple markets and objective targets.
Such a simplified approach caters better to smaller customers that desire turnkey products, but unlike non-predictive enterprise solutions, predictive solutions face a big problem with smaller companies — a data limiting challenge. You need a lot of data for AI, and most small businesses don’t have enough transactions in their databases to machine learn patterns from (I also would contend that most small companies shouldn’t be focusing on optimizing their sales and marketing functions anyway, but rather on building a product and a team).
So, inherently, AI is biased towards mid-market / enterprise accounts, but their demands are so particular that they need a deeper solution that’s harder to productize for thousands. Figuring out how to build such a scalable product is much better done within a startup vs. in a big company, given the incredible focus and patience that’s needed.
AI really does work for many applications, but more vendors need to get good at solving the last mile — the 80% that depends less on AI and more on building the vehicle that runs with AI. This is where emerging companies like Infer have an advantage. We have the patience, focus, and depth to solve these last mile problems end-to-end — and to do it in a manner that’s open to every platform — not just closed off to one company’s ecosystem. This matters (especially with respect to the sales and marketing space, in which almost every company runs a fragmented stack with many vendors).
It’s also much easier to solve these end-to-end problems without the legacy issues of an industry giant. At Infer, we started out with AI from the very beginning (AI-First), not AI-Later like most of these bigger companies. Many of them will encounter challenges when it comes to processing data in a way that’s amenable for modeling, monitoring, etc. We’re already seeing these large vendors having to forge big cloud partnerships to rehaul their backends in order to address their scaling issues. I actually think some of the marketing automation companies still won’t be able to improve their scale, given how dependent they are on legacy backend design that wasn’t meant to handle expensive data mining workloads.
Many of these companies will also need to curtail security requirements stemming from the days of moving companies over to the cloud. Some of their legacy security provisions may prevent them from even looking at or analyzing a customer’s data (which is obviously important for modeling).
When you solve one problem really well, the predictive piece almost disappears to the end user (like with our three applications). That’s the litmus test of a good AI-powered business application. But, that’s not what we’re seeing from the big companies and most startups. It’s quite the opposite — in fact, we’re seeing more over generalization.
They’re making machine learning feel like AWS infrastructure. Just build a model in their cloud and connect it somehow to your business database like CRM. After five years of experience in this game, I’ll bet our bank that approach won’t result in sticky adoption. Machine learning is not like AWS, which you can just spin up and magically connect to some system. “It’s not commoditizable like EC2” (Prof. Manning at Stanford). It’s much more nuanced and personalized based on each use case. And this approach doesn’t address the last mile problems which are harder and typically more expensive than the modeling part!
From AI Hype to Mass Adoption
There aren’t yet thousands of companies running their growth with AI. It will take time, just like it took Eloqua and Marketo time to build up the marketing automation category. We’re grateful that the bigger companies like Microsoft, Oracle, Salesforce, Adobe, IBM and SAP are helping market this industry better than we could ever do.
I strongly believe every company will be using predictive to drive growth within the next 10 years. It just doesn’t make sense not to, when we can get a company up and running in a week, show them the ROI value via simulations, and only then ask them to pay for it. Additionally, there are a variety of lightweight ways to leverage predictive for growth (such as powering key forecasting metrics and dashboards) that don’t require process changes if you’re in the middle of org changes or data migrations.
In an AI-First world, every business must ask the question: What if our competitor is using predictive and achieving 3x better conversion rates as a result? The solution is simple — adopt AI as well and prop up the arms race.
I encourage all emerging AI companies to remain heads down and focus on customer success and last mile product problems. Go deep, iterate with a few companies and grow the base wisely. Under-promise and over-deliver. Let the bigger companies pay for your marketing with their big voice boxes which they’re really flexing now.
Doing so, you’ll like succeed beyond measure — and who knows, we may even replace the incumbents in the process.