Successfully implementing an AI solution into a SaaS offering is based on three data science building blocks common to all businesses -- a well-defined business problem, documented customer insights and technical alignment. From our partnering with many clients, we have learned to identify gaps in a company’s plans for an AI product solution. We share these three truths to support all SaaS startups in establishing successful AI-driven businesses.
If companies are not sure about their product’s AI readiness after reading, we encourage CEOs, CTOs and founders to reach out to our team.
Why assessing AI readiness is worth the effort
Simplicity before sophistication:
Software companies start out with a sophisticated vision for how AI will power their product. Sometimes that influences product decisions that limit future opportunities. We have seen greater success when there is a flexible infrastructure that allows a business plan (and nascent product) to start simple and evolve organically.
Experience before insights:
There can be no data warehouse with rich data until there is a consistent customer base to generate that data. When we help clients map out their data journey along with their customer journey, it is only based on a handful of willing users. We pinpoint potential opportunities for insights -- custom SaaS product ideas to combine with data science. Then, we can begin to know what the data pipeline needs to look like and start to enrich the data asset by connecting to the right data sources.
Alignment before implementation:
While clients depend on our data science expertise to accomplish a smooth implementation, we are only part of the solution. We have seen the most success when we combine our skills with the domain knowledge from within the business. True technical alignment of product and data and technical infrastructure drives a smooth implementation.
#1 Flexible infrastructure leads to sophisticated AI
When we start engaging with any client, whether they are fully-funded or just getting started in their first round, we evaluate the business problem and how well it has been defined.
Why is it so critical for a startup to know what customer problem they solve? The data structure needed to capture customer insights starts with a good understanding of the customer problem and how the product solves that problem. We encourage clients to pay for only what they need at that stage.
Small businesses can develop their product with flexibility to scale for the AI services to be integrated within the product.
Pace your growth with parallel paths. With limited resources, it’s hard to incorporate the necessary science while maintaining rapid growth. Solidifying your business foundation is more important in the long run, then forcing an AI model to emerge before its time. Reviewing business models and refining market opportunities is a worthwhile step before investing in a data science SaaS strategy.
Besides, there needn't be a rush. No matter what stage a startup is in, there will be a time and place for artificial intelligence and machine learning. In fact, one study by Andreesen Horowitz recently found companies are typically taking more than one week but less than six months to produce a model. With the right strategy and resources, this will not be a problem.
Start with microservices. When we meet with clients, despite having their product at the MVP stage, we ensure there is an infrastructure that considers future growth. Accuracy comes from feeding quality data into the AI product solution. It can be a waste of time for any startup to get to year two or three and want to take it to the next level with AI, only to find they have to start over and lose that acquired learning. Building a product around fixed AI tools and off-the-shelf models limits how the product can learn from the customer. We recommend introducing custom microservices to best handle unknown future needs.
Business infrastructure needed before building a flexible AI solution
When and where in the product can we generate new data elements?
Is my business model accounting for the value of certain data elements?
What motivates the customer to come back to the product each time?
How can we supplement the data model until we have enough customers?
We know business leaders want to be confident the product will handle future growth. We hear from clients it helps to have that outside perspective to verify everything is sound or to assist in creating a checklist to ensure a solid plan.
#2 Measurable customer insights lead to monetizable data
Clients come to us looking for that unique combination of product and customer data that sets them apart. They know future business growth and investor confidence depends on finding that data-specific MOAT that ensures a long-term competitive advantage. Since developing a data MOAT takes time and our clients are normally looking for more immediate gains, we recommend focusing on finding the most valuable customer data first.
While we of course advocate for an agile approach to development, we also encourage clients to have a vision for what customer experience they are building. That experience should include understanding the customer profile by knowing their motivations and behaviors. We see many early software companies believe they know their customer, but they have skipped an important first step, quantifying the value of the data output to the customer and regularly measuring their satisfaction with the end-to-end experience.
Know how the customer really uses the product. When a startup is still defining the business model, it also is inevitably still trying to figure out who is the right target market. Most software companies will have a user persona developed to build the product. By studying the customer touchpoints, we can find the most common patterns and learn new information about customer behaviors.
Know which data to capture. The second challenge in collecting customer insights for the data strategy is identifying when and how to get the data. When there is not a clear understanding of the customer journey, it is difficult to build out data capture mechanisms. For example, when a product is offering to recommend decision support, it has to have the beginning and end steps of the decision linked to behaviors from the customer.
Get creative in overcoming low data volume. But the most critical showstopper to building an AI solution for early stage companies, is a lack of data. Companies with a small customer base cannot amass enough volume of data to feed a data model. It’s ok to start small though. When data volume is low, we design a process putting the "human in the loop" like small data tools, to be more efficient, that also does not handicap AI development. This way, the product can mature to later support large volumes of data.
Customer insights needed to build a data-rich AI solution:
What data elements does the customer onboarding experience need to capture?
How will the different types of users interact with my product?
How will the customer feel when they cannot get to the data they want?
Incorporating data science and AI-powered products into your plans depends heavily on how the customer engages. With the right data science strategy, the more a SaaS business knows about its user needs, the more clarity it has for where the product should go. Having a product roadmap aligned to the customer journey also enables your product development process to accumulate key data elements necessary for a rich data strategy.
#3 Technical alignment leads to a smooth AI implementation
Algorithms and statistics require a certain amount of data and domain expertise to develop. We have helped clients evaluate their team’s abilities in designing and developing with data science in mind. Through our experience, implementation for a custom AI model is most successful when we have the right mix of business and technical knowledge.
Outsourcing data science doesn't hurt a product, technical debt will. Business founders should first look to streamline acquiring data without also acquiring loads of throw away work. Startups with a strong business plan and a good starting set of data can still need a technical gap analysis before a smooth implementation is possible.
Transfer domain knowledge organically. Most founders bring domain knowledge as part of their startup strategy. Their knowledge is solid about the industry and which customer problems to solve. Focusing on first creating a data strategy that builds upon the business plan and the customer insights will make it easier to educate all partners and vendors. Companies with an immature product can focus on sharing domain knowledge through product demos and customer interviews. They can build persona profiles and terminology glossaries so new resources can ramp up quickly. Through organically transferring this knowledge, they are also doing the hard work of documenting the technical history.
Keep data science in the product loop. As the product flow evolves and scales, early collaboration with data science experts can also be a way to smooth operations as the business grows. When data science teams understand how to link the AI solution to the product, the business can avoid amassing unnecessary technical debt and have a clear understanding of how to integrate the AI solution.
Technical expertise needed to develop an AI implementation plan:
Do we have all the right tools and data science expertise?
How should we mine data, through a software house or internally?
Where and how will the data be stored?
Can we handle rewriting the data integrations in 18 months?
Ensure AI solution readiness for your startup
When founders and investors dream about the possibilities of AI amplifying their product value, they need to first make sure they aren’t missing the clarity, quality and expertise that comes from having a data science strategy aligned with the business plan and customer experience goals. With help from a dedicated team partnering alongside in-house domain knowledge, you can design a data science strategy unique to your SaaS business.
Discuss your needs with our team. >> Schedule a call