You can integrate generative AI in two ways: by taking an existing AI model or by building your own one. Creating your own AI model from scratch is time-consuming, costly, and not always necessary. We suggest using an existing gen AI model and adjusting it to fit your business needs. This way, you’ll not only be able to accelerate time to market, but also save resources.
Further on, we will discuss how exactly you can implement a generative AI project. But let’s start with why you shouldn’t put the gen AI implementation on the back burner.
Generative AI is a “when, not if” question
Today, an increasing number of business owners recognize the potential value of generative AI, namely its ability to improve the competitiveness of any organization using it. Following the New Deloitte Survey, 79% of respondents believe that gen AI is capable of transforming their organizations in less than three years.
The world is more than interested in generative AI. But when it comes to adopting it, businesses don’t seem to rush. In the end, gen AI is a relatively new technology, so it seems more rational to wait until it becomes more polished and only then implement it.
However, we wouldn’t recommend following this approach. From our perspective, generative AI will be adopted by most companies, from big corporations to SMBs, in the near future. So if you want to stay ahead of your competition, the sooner you start with gen AI adoption, the better.
Be the first or be the best, which one matters most?
Remember Apple’s “There’s an App for That" campaign back in 2009? Well, it appears that soon we’ll be saying the same for generative AI.
When the App Store launched, many businesses saw this as an opportunity and rushed to develop their own apps to attract the first users. Now, with generative AI taking the spotlight, the market is experiencing a similar trend.
But just like the first mobile apps two decades ago, the first generative AI models aren’t perfect. And they shouldn’t be. By implementing generative AI now, you will be able to get a competitive advantage and stand out among others. Don’t worry about being the best for now. You can always improve your solution after the initial launch.
To implement gen AI, we recommend the “taking” approach. Following this approach, you can achieve a faster time to market with relatively low project development costs.
Some of the leading companies use this approach to develop new value propositions on top of their existing products.
For example, Duolingo, a leading language learning app, integrated GPT-4 to build two AI-powered features within their new subscription tier Duolingo Max ‒ Roleplay and Explain My Answer.
Roleplay is an AI conversation partner where learners can practice real-life communication skills by talking to various characters. The AI behind this feature ensures that responses are personalized to the learner’s input, creating a dynamic and engaging learning experience. Explain My Answer provides rules when a user makes mistakes. For example, a learner can enter the chat after answering incorrectly and ask Duo to offer more details on the response.
Luis von Ahn, the CEO of Duolingo, stated that integrating gen AI technology led to a 6.6 million increase in paid subscriptions, resulting in $157.8 million in revenue.
Here is how to get started with implementing generative AI.
Choose a suitable gen AI model
There are two types of generative AI models that you can use to integrate AI ‒ open-source and commercial LLMs available via APIs. What’s the difference?
The first difference between these two types of models is pricing. Off-the-shelf services use different pricing models. For example, some gen AI tools, like Google’s Vertex AI, charge based on characters for input and output text, starting from $0.0005 per 1,000 characters. Others, like OpenAI’s GPT models, use token-based billing, with prices ranging from $0.001 to $0.03 per 1,000 tokens. Different providers have varying definitions of characters and tokens, so review their documentation to find the best fit for your needs. For visual content creation, services like DALL·E 3 charge per image, ranging from $0.04 to $0.12 depending on size and quality.
Open-source AI models, on the other hand, are free to use but integrating them into software entails several costs, including hardware expenses (powerful GPUs or CPUs can be expensive), cloud computing fees, electricity, and maintenance costs for running and upkeep of hardware, data storage charges, and more.
When it comes to flexibility and customization, open-source tools are more adaptable but demand specialized skills, potentially resulting in higher staffing expenses to accommodate the expertise required for customization and development.
Integrating commercial models into existing business ecosystems, including cloud services, enterprise software, and data analytics tools is easier compared to integrating open-source AI tools because the latter often lack standardized integration protocols.
Finally, open-source models might take longer to deploy due to customization needs and more challenging integration with business systems. On the flip side, proprietary models are easier to implement, so the deployment is going to be quicker.
For a better understanding, here is a table comparing these models in brief:
To help you better navigate the gen AI models available, here’s a list featuring both free and paid LLMs.
Where is your competitive advantage?
You might be asking: how can I gain a competitive advantage if we all use the same generative AI model? The answer is a strong data foundation. Without relevant data, you won’t be able to go beyond basic answers.
To successfully implement gen AI, you have to ensure it can access your data across multiple clouds, data storage systems, and vendors. Establishing a robust data governance framework to maintain data quality, security, and accessibility is essential.
Fine-tuning the model to your own data
To feed relevant data to your AI model, you have to connect it to your internal systems, such as CRM or PIM, and even third-party services (if required). To efficiently integrate such systems, you need a loosely coupled technology architecture. When all your services are interconnected and have minimal dependencies between them, you can swap out or upgrade AI models without disrupting other parts of the system.
To improve the quality of your solution, you need to run your data through the model and test the results. With each training iteration, you will improve the AI model’s accuracy. Like a child at school, a gen AI model learns by repetition and exercises.
Build a pilot project
In our opinion, it’s not the best idea to dive straight into building a complex gen AI-powered solution. It’s better to start with a small-scale pilot project. It will help you quickly enter the market and see if your AI efforts are paying off. Evaluate KPIs and make sure an implemented solution is capable of achieving your business goals set at the beginning of the project. Further, you can iteratively adapt and train AI to improve its performance for specific tasks.
But things don’t end after the pilot project is deployed. As the gen AI model will keep receiving new data from users, the dataset will eventually start to differ from the one your model was trained on, meaning that the implemented gen AI model will start to decay. To make sure the model continues to perform at its best, you have to incorporate a monitoring system that supports the iteration and improvement of your gen AI model. The goal of such systems is to alert your development team to system-related issues, leaving them enough time to take action before a problem occurs.
As a result, your generative AI model will continue to evolve, while the development team will be able to keep their fingers on the pulse, ready to step in if needed. As a software development company, we work with clients looking to integrate generative AI. Reach out to us if you’re looking to build an AI-powered application.
