B2B SaaS + Generative AI: A strategic framework
Generative AI is the next big thing for your established B2B SaaS company and it’s going to be so easy. Slap some OpenAI API calls into your existing product and ship. Right?
Well, it’ll be a fun prototype.
However, successful production integration of GenAI is the culmination of systematic team training and the careful crafting of intelligent, context-aware interactions that enhance user experiences without compromising on quality or ethics. Moreover, ongoing maintenance to train and refine the GenAI models is critical to adapt to evolving data and user feedback.
Simply put, GenAI isn’t a plug-and-play solution; it’s a process, and it’s a commitment.
The framework
Because established B2B SaaS companies cannot implement and succeed with GenAI overnight, I am advising them to follow a framework that focuses on:
A slow buildup of a moat
Keeping risk in check
Proper education for internal and external stakeholders
Predicting and tracking regulation
The inherent complexity in balancing all of this appropriately
The idea is that you don’t want your company to move too quickly or too slowly. It should be innovating, but responsibly.
From this, 3 phases emerge:
Phase 1: The foundation
3-6 months
In the initial phase, I recommend investing in three distinct areas and manners:
Training your team on the basics: Your team will need time to learn the basics. And let’s face it — You do not want your team releasing high-impact, high-security use cases until they are truly ready.
Foundation models with public data and a human in the middle: Now is not the time to fine tune models based on massive amounts of your customers’ data. That will come. For now, simply integrate GenAI capabilities that are available outside of your product already, into your product. Also ensure the use cases you develop always keep a human in the loop. Think: Allowing a customer to summarize a single file interactively based on a public foundation model like GPT-4.
Community thought leadership: Make sure customers know what types of use cases your company will explore in the future, how to help get their data ready for that future, and what to generally expect once everybody arrives.
Phase 2: The leverage
6-12 months
In this second phase, invest in:
Training those interested in becoming experts: Once a significant portion of your team is proficient with GenAI, ensure that a subset of folks from here become experts. From advanced prompt engineering to fine tuning, and controlling for security concerns to understanding the regulatory environment, there is a lot to learn. To work towards a moat, you need these capabilities in-house.
Low-risk use cases that utilize fine-tuning, embeddings, and other advanced techniques based on individual customer data: Build upon your team’s “text book” and real world knowledge of the journey so far to begin delivering on use cases that are more proprietary to your business. Note, here, that it is still important to ensure the GenAI that your product offers is transparent and keeps a human in the loop. Think: An assistant that answers questions for an individual customer about that same customer’s data.
Advanced community thought leadership: Customers should understand that there is a shared responsibility model when it comes to the safe and responsible usage of AI. Ensure they know what you consider your responsibility to be vs. where they need to invest in their own training and policies.
Phase 3: The moat
As you enter the third phase, it’s all about leveraging your organization’s breadth of data along with the capabilities refined above to drive value that your competitors simply cannot touch.
Here, invest in:
Use cases that leverage advanced techniques based on data about your entire customer base: Think: Intelligent search within your product based on knowledge of the customer base’s domain.
Moderate-to-higher risk use cases: This may include cases where important decisions are being made, and there is the potential for GenAI to be relied on too heavily in making them. In my view, this is acceptable as long as implemented carefully, and as long as it’s clear to customers what their responsibilities are. Think: Features that allow customers to classify records they have in their database based on your company’s unique knowledge of the customer base’s domain and other similar data.
Community engagement: More than just ‘thought leadership’, true engagement with your community to ensure that as the GenAI capabilities in your product grow, your customers understand its limits and how to use it responsibly. You will iterate and refine your product based on this.
The journey
Your product wasn’t created overnight and neither will its GenAI capabilities - especially if you want them to become a moat. Embracing generative AI means embracing a phased, methodical journey. This will set both you and your customers up for long-term success.