Every software company is building AI into their product. As we embark on the Rippit journey, something we spent a lot of time thinking about the last 2-3 years is what are the attributes of great products.
In the technology industry, there has been a lot of talk about Forward Deployed Engineers. These are people who work with the customers to customize the product to a customer's needs.
What they are primarily doing is building prompts for LLMs on behalf of customers.
This sounds like a great deal for the customer and it often leads to customers rewarding business to the company that offers more services and help.
Our pre-Rippit experiences said this is a death trap for the company and the customer within 24-36 months.
When a software company is small, early customers get the best employees as Forward Deployed Engineers. The software company is more desperate for early customers that they also don’t fully charge for the human-resources they provide. Both of these things have to change for any successful software company and as a result, the experience changes for early and future customers.
The average customer loses the best employees involved in their success. The average customer has to pay more for people-resources.
Because the company has committed to a strategy where humans fill in product gaps, the product gets harder and harder to use for customers.
What ends up happening is only the most important customers have good customer experiences.
I’ve lived this. I don’t think you can scale the Forward-Deployed Engineer model while delivering a high quality customer experience unless the customer is paying $500,000/Year +/- $250,000.
This is the honeymoon phase but it is very hard to hire enough high quality Forward-Deployed Engineers, so if the company becomes successful, they start to acquire more customers.
This is why I think Great AI Products have to be easy enough to use that a customer could learn it all by him or herself. It is the most important criteria when judging a Great AI Product.
That’s why we’ve committed to this strategy at Rippit - we’re not where we want to be but we are making progress on the way to perfection.
This also seems to be the overwhelming attribute of the best software products we use internally like Figma, ChatGPT, Claude, Ramp, Cursor, Snowflake, AWS, and more.
We felt even higher conviction when we evaluated the competition for alternatives for Voice-of-Customer software, Quality Assurance software, Experience Management software, or Conversation Intelligence Software - all of them require forward-deployed engineers and the signal was that they all had minimum price points of $25,000 to $50,000. That is way too much friction for many potential customers.
I’m not sure a single one lets you sign up for the product without talking to a human, which is often another signal for a complex product. The ones I researched were Qualtrics, Medallia, Enterpret, Chattermill, Unwrap, Loris.ai, Level.ai, Observe.ai, Cresta.ai, MaestroQA, Balto.ai, and UnitQ.
I think Great AI Products for businesses have to be so easy to use that you can sign up and learn it yourself within 10 minutes.
Not everyone in the industry agrees with me - in fact, I think most won’t.
Some will argue it’s different for products selling to B2B or to certain industries.
Some will argue that companies with Forward Deployed Engineers will use the learnings from customers to build an easier product - the reality is it’s very hard to keep the plane running just as fast while building an easier to use product and it’s a different product culture. It requires a founder to potentially slow growth down and rearchitect everything and only the exceptions have the courage to do that.
All will admit that deploying AI in all situations will get easier over time - similar to how making a website got easier from the 1990s to 2020.
There might be an exception but I’m not letting the exception be the rule.
Lastly, similar to how it’s harder to write less words then more words to get a point across - it’s harder to make easier-to-use software than harder-to-use software. I think people who argue for complexity are often scared to step up to the engineering challenge.
