The strategy many software companies are missing
A startup cannot leap from a first-generation product prototype to a perfect AI product solution. At least, not without creating new challenges and causing strain on the business.
This may feel like tough love, but we feel it’s our responsibility to be honest with our clients. We believe early software companies should not attempt building out complex and advanced AI without first ensuring they have the right strategy and right resources in place.
This is not a position we have come to overnight. After a decade of supporting startups in their hiring and managing of distributed development teams, it became clear sourcing and managing high quality data science, AI talents, was a challenge. Many organizations missed delivery dates and lost knowledge while trying to get internal teams and external partners to work together.
Sourcing and managing high quality data science, AI talent, was a challenge.
Insights from working with SaaS startups
Startup founders want to build a product with AI providing value and accuracy without fully understanding how to develop a data science strategy. Many early-stage software companies come to our team because incorporating artificial intelligence into the initial product release is not producing the desired results. We have seen teams frustrated with the outside tools they have chosen. We have also met with CEOs who did not know how to best build their product with AI from the start.
There is a common conflict among early-stage SaaS companies — patience versus speed. We are all faced with this challenge at some point in our lives. When we have a great idea, and the means to make it happen, we rush forth with passion and determination. It’s such a core part of human behavior that many cultures have coined phrases transcending generations — “putting the cart before the horse” or in Chinese culture we say, 本末倒置, which translates as “Take the branch before the root.” Simply put, in startups, the AI solution is the branch, and the software product is the root.
Very early-stage SaaS companies are not yet mature enough to take advantage of AI. It can be hard to hear for many CEOs, but the painful truth is they are normally missing a fundamental building block.
Different AI solutions used in SaaS products
Natural Language Prediction
Image recognition
Workflow Automation
Speech-to-text or Voice translation
Recommendation Algorithms
Machine Learning and predictive modeling
These small company leaders are full of the vision for their product, and they are aware of the value and power that comes with integrating AI into their solution. Some clients are looking to provide a stronger recommendation engine, while others want to generate real-time data elements through natural language processing. However they have designed the product in their mind, it has a fully automated model with 100% accuracy.
Unfortunately, this cannot be their reality, just yet.
Why a data science strategy avoids AI development pitfalls
Assessing the maturity of your company’s data science strategy is a must. Startups have to slow down before they can speed up. No, it’s not what a startup founder wants to hear. But it has to be said.
We recommend every client first design a data science strategy and then build a plan for how to get there. Skipping the fundamentals can cost a business in several ways:
Time lost: if the team has to rework AI/ML models, or worse, rework the product’s database or tech stack, velocity slows down.
Wasted investment: additional spending to update early work, and might mean explaining to investors why the ROI promised has not yet been realized.
Missed data opportunities: not having the right elements in place to capture data early, which can help with early learnings.
Why? Because in such early stages of their business, a startup SaaS solution has gaps in the customer data or the business strategy. There are several pitfalls to avoid, money that could be wasted or time better spent. For example, a company might decide renting software that auto-generates a model looks like the easy way to go for their first attempt at a machine learning algorithm. Then, it might take only a few months of growth to realize they will need a complete overhaul that eats into their investment funds.
Many times Gravitate has uncovered gaps in how the business model accounts for the scalable and trustworthy aspect of the data and how the AI solution will generate value. Starting to build AI into a product without an agile data science and AI approach could lead to problems and prove to be harder to scale with growth. We highly recommend business leaders use these 3 building blocks to know how their data will flow through their product and determine what kind of near-term value to measure before building out AI solutions.
What is a sustainable data science strategy
While we would love to design a custom algorithm for every client, we usually start first by evaluating their core business principles — customer, product, strategy, team and data. Then, we discuss how to design an AI solution for the product by building on top of a solid foundation. We can’t stress this enough. The solid foundation needed to generate a rich data strategy combines quality data with a clear product vision and customer understanding.
“A successful AI process is going to be tightly integrated with the product and the customer journey.” ~Qiuyan Xu, Founder, Gravitate AI
So what is included in this strategy? An intelligent product is the first part, and intelligent business operations come second. Most mature businesses operating an AI-driven product have built an AI strategy specific to their product, and then evolved by applying AI to business intelligence needs. Once both sides of the strategy are complete, you have a self-sustaining model of data in and data out.
A data science strategy includes:
AI product solution - defined, plans, and how to scale with customer trust
AI business intelligence processes - plans for sustaining the data value and consistently creating new insights
How to take an agile approach when creating an AI product solution
By creating a balanced strategy for developing an AI product solution, startup leaders are ensuring they can identify their needs for data structure, product integration, and implementation.
Data structure - Raw materials like data sources, schemas and relevant tables in databases or spark metastores and how to organize them
AI solution - what you make with the raw materials and solve the business question and provide customer value. Made up of a mix of algorithms/Statistics and models.
Implementation - how to quickly connect into the SaaS solution, and plan for longer term evolution while growing with their product
In our introductory meetings with clients, we find even businesses with a clear customer problem are lacking in other areas of team and data maturity. Why is this significant?
Mapping out the steps for developing a solution and coordinating a product implementation ensures we can successfully apply AI to the software. Ultimately, we focus on helping CEOs with their primary responsibilities of proving current product value and planning ahead to iteratively create future business growth. Working with each client, we demonstrate the connection between data science, the kinds of AI needed, and where it fits into the business.
Our data scientists work side by side with clients to develop an AI gap analysis that uncovers where the focus areas are within the current business model and product plans. In taking the time to review the current state, we are able to create recommendations for potential quick fixes, as well as guide the team in finding ways to gain efficiencies down the road.
Starting an AI Readiness Assessment
It’s never too late to get on the right track when it comes to creating a successful AI product solution.
After working with many clients to refine their strategies and improve sometimes fragile data structures, we’ve developed a series of assessments to verify a startup can successfully deliver an AI product solution.
If your teams need guidance with technical implementations or side-by-side teamwork to develop the data structure, Gravitate can design a team to suit your needs.
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