How To Nail Your GenAI Product Data Strategy

Enterprises are investing heavily in new applications that leverage generative AI to boost productivity and improve decision making, and many startups hope to capitalize on the opportunity with innovative new products. But how do you build and deploy a successful AI-powered product?

First, don’t build a technology in search of a solution; build a technology that addresses a real-world business need. That means looking at what businesses are really trying to achieve, whether that’s improving customer support, growing sales, increasing supply chain efficiency or something else entirely.

Given that data is central to generative AI, you also need to have the right data strategy for your AI-powered product. This is important for you as an application builder, because it determines your costs and the speed at which you can innovate, and for your enterprise customers, for whom data governance and security are paramount. Nailing this data strategy will be the difference between success and failure.

Use Data To Drive Insight and Actions

In virtually any line of business (LOB), the overall goal is to start with data, derive an insight from that data and then take an action based on that insight. The power of AI is that it can greatly simplify and shorten that process from data to action, in part through the power of large language models (LLMs).

This approach of going from data to insight to action is a good rubric to keep in mind when conceiving your application or tool. It can apply to products built for engineers, such as tools that help developers write code or manage infrastructure.

It also applies to the business users in marketing, product management, finance or other LOBs who will use your product. Most businesses have a ton of information about their customer interactions and spending habits, and they want to know who to target with which specific offers to increase sales. Instead of waiting for an engineer to build a SQL query, a marketer can interact with an AI-powered application in natural language to gain insights and recommended actions. Those actions create more data, which helps the system learn and improve, creating a positive loop.

Solving a real business need is one piece of the puzzle, but an application still needs to be differentiated to succeed, especially when your competitors building AI applications all have access to the same models and tools. This is where a strong data management strategy becomes critical, because enterprises care deeply about the security of their data.

Make Governance and Security Your Foundation

Concerns about governance and security are top of mind for enterprises, and they want to protect their sensitive data from inappropriate access. This is especially relevant when running AI applications against enterprise data.

One way to achieve this is by not requiring customers to move their data at all. Most Software as a Service (SaaS) apps require companies to upload their data to a third party, which means the SaaS provider is responsible not just for managing the application code, but also for properly securing the customer data the apps use. They ingest the customers’ data within their own app platform.

You can give potential customers far more confidence by bringing your application to their data where it already lives, instead of needing them to upload it to your platform. Achieve this by deploying your application code within the perimeter of a customer’s own cloud platform, or by connecting your application directly to the customer’s data platform. In each case, the customers’ data stays where it is, allowing them to decide what permissions they want to grant and to monitor how their data is used.

Enterprises also need to know that their data won’t be used to train models in a way that benefits competitors. Be very explicit about your data policies, including guarantees about how a customer’s data will and will not be used.

Provide Flexible, Usage-Based Pricing

Most enterprises are experimenting quickly with a variety of AI tools and don’t want to be tied to an annual contract or even a monthly SaaS license. For many AI-powered applications, usage-based pricing is highly attractive because it allows customers to pay only for the value they receive.

To determine the right pricing model, think about what your application provides — what’s the prime unit of value? If it’s a product for data transformation, you might charge based on the number of records transformed. Eventually, we may see enterprise AI products that charge based on the number of questions end users ask.

To further encourage adoption, provide a way for the customer to try the product at no charge before committing to purchase. Ultimately, you want to make the experience as friction-free and risk-free as possible.

Build Trust Through Transparency

End users need to trust your product to use it, so avoid the classic “black box” problem of AI by being transparent about how it arrives at answers and recommendations. For example, if you offer a system that helps sales reps decide which specific offers to make to a customer, to build trust, show the sources of data used alongside the recommendations.

You might even display the level of confidence in a recommendation, so that end users have as much information as possible to make their decision and aren’t being asked to blindly trust an algorithm that they don’t understand.

Optimize Your Development and Operating Costs

Most startups have limited funding, and there are ways to manage the costs of generative AI while still building a powerful product. For example, there are many different LLMs to choose from, and your selection will depend on what capabilities you need in terms of model size, model type and performance. You want the models that provide the functionality you need at the least cost.

Fine-tuning a model is technically challenging and requires specialized expertise, which means hiring expensive engineering talent in a competitive hiring market. Retrieval augmented generation (RAG) can be a more cost-effective approach for model training and provides sufficiently accurate results for many applications.

It takes a lot of experimentation to arrive at these decisions, so engineers need a development environment that allows them to test different models and training patterns in a way that’s cost-efficient and doesn’t lock them into an approach that turns out to be incorrect. Generative AI projects are susceptible to technical debt, but these debts can be managed if engineers are thoughtful and deliberate about the development process.

Conclusion

As enterprises and startups ride the wave of generative AI, they must anchor their strategies in solving genuine business needs. The differentiator isn’t solely the technology but also the data strategy that underpins your AI application. Bringing your AI application to the data — emphasizing security, governance and operational transparency — is not just good practice; it’s a competitive advantage.