This article is cross-posted and co-authored with OneWay Ventures
Generative AI has been present in consumer and B2B products for several years now. ChatGPT is taking it to the next level, but that doesn’t mean it’s the right fit for every product use case.
Here’s the reality - ChatGPT alone isn’t going to work for B2B startups building generative AI into their solutions. Why? It’s not a sustainable business model to use a foundational dataset trained on public consumer data.
Startup founders and their tech leaders are facing a classic business decision of build versus buy, but in this case - the “buy” is more of a “borrow”. With any build vs buy assessment comes an analysis of internal assets and strengths compared to what the cost/benefit would be for speed to market. We understand ChatGPT gets it done faster, but there are a few things to consider before you build your entire business offering around one trend.
Considerations for Product teams developing generative AI product solutions
Many companies are going all in on the borrowing approach and using a publicly-trained model for their B2B customer experiences. Here are a few strategic questions for product teams to discuss before incorporating ChatGPT into the product roadmap.
What will make your product unique if you’re using the same dataset everyone else is?
How will you control your AI data strategy when your product and customer experience depend on a training model you can’t control?
How will you manage your brand reputation and customer experience when you can’t calculate confidence levels on the results?
To dig into the viability of using ChatGPT in business solutions, we’ve polled a few experts who’ve been working with successful generative AI products built from proprietary data assets. And for those who like real-world examples, we’ve also rounded up two success stories for putting generative AI into B2B products.
Generative AI product development: 3 experts share what works and what to watch for
Davit Buniatyan, CEO of Activeloop, Eugene Malobrodsky, Partner at One Way Ventures, and Qiuyan Xu, PhD, Managing Director of Gravitate AI share their thoughts about the overnight popularity of ChatGPT. As it turns out, while these business leaders have varying experiences in industries and product development portfolios, they share similar views on how businesses can best develop generative AI product solutions.
Q: What are startups doing with ChatGPT or Google BARD that are NOT working, or you believe are not going to work?
Eugene: The current deployment of ChatGPT or Google BARD is great for answering simple questions or helping you write an email by just describing your thoughts. There are two major issues, though, that companies need to look out for. First, how do we understand that the answer we got is True? Dealing with Fake News in AI is an even larger problem than what we are dealing with today. The second problem looks inside corporations, to answer questions based on data on the intranet. Companies need to train the data set on the internal info, but not take data outside of their infrastructure or pay the cost of computing to do this.
Some start-ups are trying to add these technologies to their product without understanding the benefits to their customer or the cost of adding these features. Companies may do this just to increase their valuations because it is the new hot thing. The key is to identify a problem you are trying to solve for your customers and understand if adding this functionality really solves the problem for them.
Qiuyan: I can see how a startup leader might ask themselves “Why build custom machine learning models when there is already an incredible infrastructure that has a ready API to experiment with now?” Even though we are all about quick iteration and providing value fast, we have our own battle scars from jumping into a “quick solution” only to find out later that resources were wasted because of unclear requirements, which lead to half-baked designs. Many startups have innovation teams facing a fluid environment, where the requirements for a solution can change quickly. Asking “does this really solve the problem” should be the starting point. ChatGPT provides all of us with more temptation of AI instant gratification, which is great, but it could be more fruitful if we can savor it AFTER actually planning the big picture for a solution. Even just a little iterative planning and requirements gathering can avoid overhauling the technical infrastructure, code base, or database a few months down the road.
Potential pitfalls for startups building without an AI product blueprint
Missed opportunities to start collecting strategic data assets.
Missed opportunities to customize algorithms and datasets to deliver a differentiated B2B product.
Building without proper infrastructure, thereby incurring massive technical debt and other unnecessary costs.
>> Curious what an AI product blue print looks like?
Q: How can startups develop competitive, sustainable generative AI products?
Davit: If a startup company is going to develop a Data Moat - not just in the UX or training layer, it’s going to need to double down on its infrastructure. Just like a page from an economics textbook - as the barriers to entry into the generative AI product market drop, it all comes down to unit economics, and who can squeeze the most out of the data they have, as cost-efficiently as possible. We recently had an event with a speaker from Meta AI who shared that pre-training a foundational model requires wasting $30M just in computing costs on experiments before starting meaningful training.
We discovered that companies can unlock value fast by setting up a Deep Lake or a data lake optimized specifically for deep learning, which is what drove us to offer that to so many companies. Creating more robust and custom MLOps (or LLMOps) capabilities to give the generative AI the right context makes it more manageable, and definitely less costly.
Qiuyan: We ask our clients how they view their strategic data assets, which can be part of their Data Moat. Putting company data in the public domain is not going to develop into a differentiator. And neither is bringing that public data into a customer experience without any additional guardrails or proprietary datasets. Adding on algorithms and infrastructure is a recipe for success. We’ve seen it work really well when both technical and business sides come together to outline their best opportunities to differentiate with the unique data they have or that they can gather through their customer experience.
Key takeaway: Build AI products that are sustainable both in their unique differentiators and in how the business delivers the customer experience.
For more insights, you can explore other Gravitate AI blog posts. To learn more about our AI product strategy and product development services, including how we support companies in creating conversational AI products, visit our AI Product Blueprint Studio page.