Implementing and integrating Artificial Intelligence (AI) functionality into your product or service is no longer a luxury; it’s a necessity for companies that want to stay competitive in today’s market. In particular, Generative AI holds immense potential to help companies gain new, valuable insights from their existing data, regardless of its location.
While this new technology and its capabilities may seem daunting at first, implementing it effectively doesn’t have to be difficult. So how do you get started, and ensure that your Generative AI project will be a success? Here are five points you should consider when determining how to best proceed with Generative A tool selection and implementation.
1. Pick the right single use case to get started
Just like any IT project, establish a clear objective of what you hope to achieve with Generative AI. Be specific about your goals, and what the desired impact is. Examples include:
– Adding a Generative AI search box on your website to make it easier for your users to find relevant answers about your offerings.
– Enhancing your own product with built-in Generative AI capabilities to improve your user experience and product usability.
– Providing fast data-backed answers to customer or employee support questions, like an enterprise co-pilot or virtual assistant that boosts employee productivity.
– Extracting insights from various data sources, formats, and languages
Generative AI offers a wide variety of potential use cases. Select the one that aligns with your strategic objectives, and can quickly provide tangible benefits to your users, staff, and customers.
2. Testing is a must, but start with non-critical data
It’s easy to get carried away with the promise of Generative AI. Once you get started, you’ll quickly want to point it at nearly every data source possible. To ensure a smooth start, pick a use case that doesn’t require sensitive data to prove value. Although it’s easy to ensure private data remains private, avoiding starting with private data will make your evaluation go more smoothly, as you can avoid involving legal or compliance teams that would otherwise need to get involved.
Examples include historical customer inquiries, non-sensitive sales data, publicly available content from a website or documentation pages, or even public data from your competitors.
3. Spend your time wisely
Generative AI previously required you to select and integrate a number of disparate tools to process data, store it in a vector DB, and then you needed to develop a search UI and ranking engine. This meant that even experimenting with Generative AI in your environment required significant development and integration effort before you could see what Generative AI could do. Don’t believe the YouTubers claiming you can build meaningful AI tools by piecing things together. It will end in disappointment.
Instead, pick something to test that offers end-to-end functionality, including key features you’ll eventually require—things like data governance, multimodal (SaaS, hybrid, on-prem) deployment options, and SOC2 and ISO compliance certifications to ensure the highest of security standards are met.
Personalization is also important. You are unlikely to need advanced AI model personalization during your testing but as your Generative AI journey continues, the true power can be unlocked as you tune and evolve your AI model with your company’s domain knowledge and AI model training.
4. Show results quickly
A no-code tool and UI gives you the ability to rapidly implement and test, getting to results quickly. Pick the best use case, upload some sample data, wait a few minutes for data processing to complete, and run some sample searches. You’re now ready to demonstrate the power of Generative AI to your business leaders. They can even test it themselves.
Once you wow your internal teams, use CLI, API, and website widget capabilities to integrate Generative AI into any of your products, websites, or customer-facing tools that your business provides.
5. Expect growth and scaling
The right question: “Where can’t I use Generative AI in my business?” Understanding your possible search and data volume, data security, hosting, and processing requirements will help you make the best design, deployment and hosting decisions. The good news is that you don’t need to have every answer when you start, but keeping them in mind will help ensure you iterate your implementation in a common-sense way that will continue to scale.
Additionally, keeping your options open for future expansion through a UI, CLI, and API access to data-backed Generative AI answers, and automated data ingestion, updating, processing, and indexing keeps answers relevant and timely as your company’s knowledge grows.
We hope you’ve found this information useful and would love to hear about your experience (s) of testing Generative AI in your business.
And if you would like to gives us a try, sign up to Nuclia now!
