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4 steps to cook up a solid generative AI data strategy

By

Jay Shah

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Generative artificial intelligence (GenAI) makes lofty promises to transform organizations and their teams through natural language outputs. You’re promised faster, smarter, and more efficient tech but never told how to create an effective strategy to maximize AI’s functionality. The key to generative AI success is a strategy that aligns your data with your business priorities.

While generative AI’s capabilities are revolutionary, real transformation occurs when data, IT, and business strategies are working together. Without a data-centered business strategy, even the most advanced AI models can fail to deliver real value to your bottom line. You’ll end up with a bunch of underperforming tools in your toolkit.

So, how do you ensure that your business is making the most of generative AI?

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The critical role of your generative AI data strategy

Generative AI isn’t magic. We’re taking a very practical approach to generative AI at Elastic. We know its technical capabilities can produce hours of work in seconds, but for real results, generative AI needs a solid foundation. Like with any work, effectiveness and productivity only happen with defined goals and the right information.

We can think of generative AI as a great meal. To deliver top nutrition and flavor, it needs great ingredients. In the case of implementing generative AI in an organization, data is the ingredients, and data strategy is the recipe. Without either, generative AI cannot deliver real business value. 

The secret sauce? A quality generative AI data strategy — for which you need high-quality, transparent, and governable data fueling your large language models ( LLMs). No amount of strategy can compensate for poor data. Good data, however, is defined by how relevant it is and how easily you can access and actionalize it. 

So, how do you build a solid generative AI data strategy?

Step 1: Craft a generative AI data strategy to align with business goals

Implementing generative AI starts with a clear vision: What is it intended to achieve? What specific business problems should it solve? Where does the scope start and end? Setting a strategic "generative AI north star" ensures an end user experience that prioritizes the right things, not just the shiny new things. Defining your goals is the first step to crafting your generative AI data strategy.

Every stakeholder group in your organization will have different goals and definitions of success. These goals crystallize when you evaluate your requirements, determine success criteria, and identify which use cases will move the needle. Is the goal to reduce customer churn? Automate repetitive tasks? Enhance sales forecasting? Once business leaders and IT align on these goals, you can begin to design a data architecture that supports your business. 

Ultimately, consider this: A meal tastes only as good as its ingredients. The same is true with generative AI — it is only as good as the data it relies on. Investing in the quality rather than the quantity of data is the crux of a successful data strategy. To define what good data is for your team, reverse engineer the results you want. Think of the goals you set and ask what data you need to achieve them.

In the kitchen, you’ll need the right tools to bring your dinner vision to life. The same is true for implementing generative AI: Enacting your data strategy requires a unified data model, clear data governance and access policies, scalable infrastructure, and a suite of monitoring tools.

Establishing clear objectives for generative AI initiatives

Rule of thumb: When working on generative AI apps, stay SMART. Set specific, measurable, achievable, relevant, and time-bound goals to ensure that your generative AI initiative remains focused on delivering value. For example:

  • Increase customer service resolution speed by 30% within six months using a generative AI chatbot.

  • Reduce internal document search time by 40% through conversational search capabilities.

  • Improve sales forecast accuracy by 15% in Q4 using AI-enhanced analytics.

These goals guide implementation and create a framework for measuring ROI that considers your overall data strategy. At every step in your process, don’t forget to ask: What data do I need to augment my LLM with to achieve these goals? Is it quality data?

Step 2: Evaluate the right tools and technologies for your business

Once you’ve defined your goals, it’s easier to pick the right tools. When evaluating tools, consider:

  • Scalability: Can the solution grow with your business needs?

  • Integration: Does it connect seamlessly with existing AI systems?

  • Security and compliance: Does it meet your organization’s standards?

  • Flexibility: Can it quickly adapt to new data sources, LLMs, and emerging capabilities like model context protocol (MCP)? 

  • Cost: Is the cost structure suitable for your organization as you add more use cases?

  • Deployment time: How quickly do you need to show this delivers business value? 

  • Relevancy and accuracy: How important is it that answers are unique to your organization?

Some challenges can be solved with off-the-shelf solutions, while others may demand customized approaches that fit the unique context of your business. Either way, the right solution should enable you to operationalize generative AI without completely replacing your current tech stack.

There are many things to look out for. When the technology is treated as an add-on, organizations are at risk of creating the dreaded swivel-chair scenario, forcing users to constantly switch from one type of AI tool to another. You also don’t want to create tool sprawl, which will become unmanageable over time. Instead, the key to sustainable success is to embed generative AI directly into existing systems and workflows.

Think of generative AI as an enhancement layer that makes your current tools smarter, faster, and more user-centric. For example, embedding generative AI into a CRM can automate account status summaries and suggest next-best actions for sellers, while integrating it into employee knowledge bases can simplify internal information retrieval for your entire workforce.

Once initial integrations are in place, establish a feedback loop to refine and expand its use. Start with a minimum viable product (MVP), gather insights from real users, and iterate quickly. This not only builds momentum but ensures the AI solution evolves in direct response to real business needs. And at every step, your “AI North Star” is tracing your path forward.

Step 3: Bridge the gap between tech and business outcomes

The role of IT is undergoing a major transformation. No longer relegated to backend support, IT leaders are now at the helm of business innovation. Today, generative AI has turned IT into a strategic function.

IT leaders are uniquely positioned to bridge the gap between technology and business outcomes. In today’s data-driven organizations, IT leaders don’t just manage infrastructure — they’re shaping your data process. Ensuring relevant data starts with them. By collaborating closely with business teams, IT can turn enterprise data into actionable insights while also building and customizing the tools that empower teams to work smarter. Whether it’s developing self-service generative AI tools, enabling secure access to trusted data, or ensuring systems are scalable and compliant, IT is now a catalyst for transformation across the business.

And it’s important to note that implementing AI requires more than technology. It requires a shift in mindset, culture, and workflows. Employees need to feel empowered, not replaced. Leaders must prioritize transparency, training, and inclusion. Consider providing hands-on training and AI bootcamps to upskill employees. Recognize and reward AI-led innovations, and encourage dialogue about the ethics and impact of AI.

Step 4: Measure your generative AI app’s business value

AI is an investment, so ensuring that it solves real business problems is key to justifying the cost and effort. Deploying AI alone is not enough. It must be integrated seamlessly into workflows to deliver measurable outcomes. This starts by implementing a feedback loop and continuous monitoring, and clearly defining success KPIs. 

Building effective AI solutions demands thoughtful, responsible planning from the start, including ethical considerations, responsible data usage, rigorous testing, and evaluation at every stage. Revisit the goals you set when you kicked off this initiative and ask:

  • Does it solve a priority business problem?

  • Can it be measured effectively?

  • Is it scalable and sustainable?

  • Is it secure and compliant?

By embedding these principles into your AI strategy from the strategy phase, you can ensure that your solutions are trustworthy and aligned with core business values.

The real business impact of generative AI

Chatbots, assistants, agents, summarization, and conversational search are just some of the potentially high-impact use cases of generative AI. When implemented strategically, generative AI can have tangible outcomes from time savings to productivity boosts, new revenue opportunities, and even culture shifts. 

For example, take our own ElasticGPT, our internal generative AI assistant. We started with a problem we wanted to solve — empowering employees to find the information they need — and established our main KPIs and knew how we would measure them. In the first phase after launching, employees were using the generative AI experience to find relevant information. 

Within 90 days of launching, 10K queries had been successfully answered and developers saw a 99% satisfaction rate. We were able to prove its business impact, which encouraged teams across the organization — including legal, marketing, and product — to use ElasticGPT to improve their efficiency.

Learn more about our generative AI success in the ElasticGPT case study. 

Explore additional GenAI and data resources

The release and timing of any features or functionality described in this post remain at Elastic's sole discretion. Any features or functionality not currently available may not be delivered on time or at all.

In this blog post, we may have used or referred to third party generative AI tools, which are owned and operated by their respective owners. Elastic does not have any control over the third party tools and we have no responsibility or liability for their content, operation or use, nor for any loss or damage that may arise from your use of such tools. Please exercise caution when using AI tools with personal, sensitive or confidential information. Any data you submit may be used for AI training or other purposes. There is no guarantee that information you provide will be kept secure or confidential. You should familiarize yourself with the privacy practices and terms of use of any generative AI tools prior to use. 

Elastic, Elasticsearch, and associated marks are trademarks, logos or registered trademarks of Elasticsearch N.V. in the United States and other countries. All other company and product names are trademarks, logos or registered trademarks of their respective owners.

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