Hi Ashwini,
When it comes to AI - I believe there are 3 types of customers:
Sceptical types - they have been bitten by past failures or failure stories (e.g. chatbot nightmares). It is hard to convince them that all AI is not the same.
Excited/Curious types - who believe this is brand new technology and want to know what it does, how its different etc.
I want magic types - those that believe in a Sci-Fi version of the world
Do you encounter the same? What’s your strategy for dealing with the different types :)?
Glad to have yo with us, super impressed by your podcast episode & moreover candid answers with FactorDaily.
My questions is around building the ML/AI product as a “system of intelligence” e.g. classification based ML engine with growing intelligence over an already data stack of “system of records” e.g. CRM data within the product roadmap & taking this into wider customer audience. I have 2 questions on early customers choice & pricing.
As any new ML/AI offering is looking to nail the business problem waiting to be baked by user feedback should we dog food with existing customers and give it enough gestation period to delivery outcomes ? Before we move to new customers. Is this how we shd plan the product rollout as initially biased outcomes is highly probable. So tolerance level of existing customers will be more compared to new ones. Happy to hear your thoughts.
How do we price an ML / AI offering as an add-ons. Take e.g. if our SaaS ACV is say $5K, should be price it as a premium add-ons initially & then move to a value-based pricing e.g. X no. of predicted conversions * unit cost. Any good playbooks on pricing such products will be super helpful
Hahaaa! That right there is your lead list generation strategy, no? It’s not been easy, we’ve taken time to figure out the high intent vs. low intent folks in this space. But it directly has a bearing on which of those categories they fit.
That said - You do have a 4th. The folks who are ready, see the need for change, and can be educated or already know how this works. There is a growing market of these folks. More and more people know exactly what to ask for, when and how. So much so that many of them are building their own teams to work with AI.
When one’s product/brand narrative is based on a tech(AI in your case), and given that tech changes at a fast pace and tends to get commoditised, how do you manage the risk of having to change the narrative every so often?
WRT 1. YES. If you don’t understand how your ML system works, its RoI, you can’t put it out there with new customers. In a way, this is like any big new product feature you launch. The downside, though, is that ML features can be disruptive (in a negative) if rolled out wrongly. It can create incorrect data, can bias your systems and really hurt your business. So test extensively with a smaller base before rolling it out. Unless, it’s simple automation of workflows.
Pricing entirely depends on value add. If your ML add-ons are providing incremental outcome of some kind, then customers will be willing to pay more. So test your hypothesis like you do with any other product feature. Talk to customers, build, iterate and evaluate value before pricing it. 2 things I’ve seen: customers pay for incremental growth in revenue or costs saved by AI.
Glad to connect. I know my marketing team is a happy customer of Recotap
I worry less about commoditization with AI and more about fighting against the status quo. AI is not getting commoditized anytime soon. There will always be verticals and horizontals that have a place and we’re barely getting started. I’d say as a market we’re about 4-5 years from seeing AI in the market on scale.
The status quo, however, is a big problem. Inertia is a big problem. That’s who we’re fighting
Another one on the quick team building. If we have to sensitise our engineers to learn & become well versed with all 3 - diagnosing the business problem, finding right AI/ML tools / algos to solve for them ( regressions, classification, anomaly etc. ) & then running fast iterative sprints. What kind of learning playbooks you will suggest.
Is there a resource for engineering managers like us to guide team to build ML sensitivity understanding within broader engineering team. Which can be used as a continuous learning framework, toolkit, courses & resources. For e.g. https://hackernoon.com/tagged/machine-learning.
Excited to hear your thoughts on skill building on ML/AI for teams.
Abhi - I can tell you that we have something coming … to address just this. In 9 months We’re building a platform focused resource center. But that’ll have to wait.
Meanwhile - Datadog, Datarobot - are all companies that have great reading materials for engineering managers and more. If it helps at all - I’d be happy to offer an hour or so of my team’s time to help you answer any questions you have - if you’re interested.
I think it will be absolutely great to steal 30 mins from your team / experts based on your convenience. I’ll ask Astha to share your details to get in touch with you. I really appreciate this help
Thank you so much for taking the time out to answer all the questions with such care and in such depth!
It’s been so great to hear about your journey and learn from your nuanced perspective on the AI space and the enigma it can be.
There is, undoubtedly, a ton of great startup and SaaS advice in there, but I esp. loved how you phrased this: “You can differentiate and build a massive organization that can fire up bellies full of ambition while being thoughtful, empathetic and caring. These are not mutually exclusive.” What a great, much-needed thought!
So glad we could get a chance to host you and get to learn from you. Thank you, again!
This is such a useful frame to separate hype from real potential for not just AI-related developments but any kind of technological advance (leap :)) we hear about. Definitely forces one to pay conscious attention.
And I’m saving this, such an important reminder for both founders and product people:
We can fool ourselves into believing something is true, if the flash quotient is high I’ve noticed this doesn’t last. Customers move onto the next thing and stop using this. We have actively advised many of our customers to not use some of our initial features, despite their being a very high demand for it in a particular period. We stopped offering those features and let our customers to go shop for them elsewhere. 3.5 years in, I can tell you it was one of the best decisions I’ve ever made. Every little thing that doesn’t move you forward, sets you backward in some way.
Thanks for sharing that in-depth reflection, Ashwini!
You’ve put it so well. How wrestling with the regular challenges of building for a new category gets considerably more complex when everyone in the market also has not-so-helpful, deep-rooted preconceptions of what is/isn’t possible.
Love how you’ve thought about this all along. This, especially:
I can tell you when we crossed our 3 year mark, for me it was all about helping the team undo, unlearn, rethink, remake and rebuild themselves not as people building bleeding edge tech but about helping them think of themselves as people who have a responsibility to help the world around them become AI-Natives. I can tell you this is one of the best things that’s happened to the org as a whole, maybe we should have done it sooner. But the responsibility that comes with enabling people with AI is a very real one and that can help counter the fear and the high threshold for change.
It’s not been easy, we’ve taken time to figure out the high intent vs. low intent folks in this space. But it directly has a bearing on which of those categories they fit.
