3 AI Strategies for Technology Leaders

As a technology team, you carry the responsibility for understanding Generative AI and leading its adoption in your organization.

While every team in the organization contributes to Generative AI's success, technology teams and their leaders set the tone by understanding AI tools, establishing processes and frameworks, and setting their organizations up for long-term success.

That great responsibility has the opportunity to bring incredible opportunities to create the foundation for how your company uses Gen AI. Its use cases are far-reaching, meaning your input could impact every employee and customer.

Ready to lead your organization to Gen AI success? Here are three leading strategies from our year in the AI trenches.

Set Up a Flexible (Rather than Traditional) Data-Cleaning Strategy

The foundation of successful AI implementation is strong data. Without accurate and relevant data, you won’t have trustworthy results. In fact, if you don’t keep data precise and updated, you might be pointing people in the wrong direction with your Gen AI results. Therefore, data cleaning plays a decisive role in improving data quality.

The sheer volume of data is overwhelming. Your organization curates more data every day than you'd ever be able to clean, so you'll never truly be caught up. There is always something more urgent or appealing to focus your attention on, and there's no perfect data-cleaning strategy.

Instead, the best strategy is to create a flexible data-cleaning process using these three steps:

1. Set up Gen AI tools to focus on narrow sets of content. For example, you could create a Gen AI tool that first narrows down the category of information before providing the specified result. This way, you can turn massive amounts of data into manageable sections.

2. Define a manual cleansing program, focusing on data that has regulatory, legal, or financial impacts on your organization. This program is for data that has to be completely accurate, that is crucial to be clean, and that has the most significant impact on your company.

3. Set best practices for creating, tagging, and cleaning data moving forward. Employees can use these policies and procedures to ensure that data stays Gen AI-ready. What needs to be done to your data for it to be clean and ready to input into a Gen AI system? Create a framework teams can follow, then expand your data cleaning strategy to less critical data. Make sure you are tagging content approved for generative AI with metadata and policies to expire or review when appropriate.

Plan for Scalability

Gen AI has incredible power and may feel like a magic box, but many tools and services have size limitations that organizations may reach quickly. After the excitement and buildup of launching a new Gen AI tool, nothing kills momentum and enthusiasm more quickly than hitting the limits of the service and having users wait hours to get results. As you implement new tools, knowing what processing capacity you’ll need to support the tool is challenging. Organizations often find themselves with a maxed-out Gen AI tool as they’re ready to introduce it companywide.

As you plan for release, prioritize scalability. Not everyone needs access to every AI tool at the same time. By setting guardrails for release, you can take a measured approach to scaling adoption strategically and responsibly. Understand the likelihood of usage and what peak usage could look like across your user base. But even with that research, be flexible: You don’t truly know what capacity you’ll need until it’s rolled out.

The key to scalability and matching capacity is to release the tool in waves. Phase releases of each tool and measure the capacity that each group uses. That information will provide a reasonably accurate picture of how much capacity the tool needs. As you expand the implementation to additional groups, you can scale accordingly and calculate the capacity you’ll need when the entire company uses the tool. For example, you may release a Gen AI tool that provides personalized customer recommendations first to a small group of customer service agents and track their usage, then expand to all employees working at one location before introducing the tool to the entire company.

In addition, it’s beneficial to consider how you’ll scale from the backend. Many services start with a trial or limited capacity. Aim to use technology for load balancing, throttling, and capacity management so you can make the rollout a good experience instead of pushing the tool — and your users — to the limit. It is also important to understand use cases and apply the correct models that are optimized for the task or have the right-sized context windows to complete the task.

Focus On Your Competitive Advantage, Not Being a Gen AI Company

Every company can benefit from Gen AI, but that doesn’t mean every company needs to be a Gen AI company.

Building custom tools is time-consuming and requires resources and expertise. While it may be tempting to invest in custom-built AI tools, the best option is almost always to integrate with an existing LLM. These AI giants have incredible resources and can quickly build and adjust their technology, providing you with updated AI tools without investing your resources in the development.

Your competitive advantage isn’t in building AI tools but applying them to your business. That means your organization’s time and resources are better spent figuring out how to use AI tools most innovatively and leveraging their power than creating them yourself.

There are LLMs and applications that use LLMs. You shouldn’t build your own LLM, and you may also not want to build your own applications on top of LLMs. If you can use the framework of one of the big software companies, whether Open AI, Google, Meta, or maybe others, they are likely to build the features you need. There will be a few big players, but there won’t be constant competition across a large swath of organizations.

But just because everyone has the same tools doesn’t mean they will use them the same way — that’s where your advantage comes in. Instead of pouring resources into developing AI (creating a sunk cost to build and maintain the tool), spend your time and resources creating innovative AI use cases and applications that help your company stand out. Take advantage of the AI experts and the products from external vendors.

Being a technology leader in the age of Gen AI is an incredible opportunity. By strategically shaping how your organization cleans and organizes its data and applies AI, you can set your company up for long-term success and leverage the power of AI to become an industry leader.