Building AI-Powered Products Without ML Teams

Recent industry reports indicate that companies successfully integrating AI are significantly outpacing their competitors in both growth and efficiency, making AI a key differentiator in 2024. However, despite the excitement, many businesses are still struggling to translate the AI hype into meaningful product improvements. Having worked in the tech industry for over a decade, I’ve witnessed how we can sometimes become overly optimistic about new breakthroughs. Looking to the future, nothing feels impossible, especially when we’re surrounded by stories of innovative ideas transforming industries and creating billion-dollar businesses.

In hindsight, it’s evident that history tends to repeat itself. From the Dot-com Boom to Social Media, the Mobile and Smartphone Revolution, Blockchain, Crypto, NFTs, the Metaverse, and now AI, each cycle has fueled product development, venture capital investment, and customer excitement. However, much of the buzz around these trends has often been more about marketing hype than actual business value. While technologies like blockchain and NFTs created excitement, AI has already proven to be a transformative force across industries.

Having been involved in over 50 software product builds, I’ve seen these cycles both distract and propel product builders. The difference lies in how leaders approach new technologies. Some get sidetracked by the buzz, while others harness it to improve customer experiences and create lasting value.

This article will help you cut through the noise, leverage AI to improve your product, and ultimately build a more successful business without needing a specialized machine learning team.

First things first

AI isn’t just another hype cycle; it is the result of decades of technological evolution, from early expert systems to today’s machine learning. Its adoption can either be a lasting advantage or a costly distraction, depending on how it’s used. While OpenAI’s launch of ChatGPT in 2022 brought generative AI into the mainstream, AI has been transforming industries like healthcare, automotive, and finance for over a decade. 

It’s important to distinguish between AI-native and AI-powered companies, as these terms are often confused but mean different things.

AI-native companies are built entirely around AI, with AI as the core of their business model and operations. For example, companies like OpenAI or DeepMind rely on AI as the primary product. On the other hand, AI-powered companies—like Netflix or Spotify—use AI to enhance certain aspects of their business, such as personalization or recommendations. Still, AI isn’t the foundation of their entire business model. While many companies start as AI-powered, they can evolve into AI-native organizations over time as AI becomes central to their operations and differentiation.

This article is for companies looking to become AI-powered. I’ll highlight pre-built solutions available on the market that allow businesses to transform their products into AI-powered solutions with relatively low investment. There’s no need to build custom models from scratch or have a team of experienced machine learning engineers. Instead, using the tools and processes I’ll share, your existing engineering team can fine-tune pre-built models to meet your specific needs.

First-principles thinking for integrating AI into your product 

When integrating AI into your product, taking a first-principles approach is essential: Why do you need AI? What are the most valuable parts of your product? How are these areas functioning now? What can AI do to make them better?

1. Start with a clear business objective

AI should solve real problems. Don’t start with the technology; start by identifying key challenges where AI can make a meaningful impact. Your business objective should be apparent from the outset, as it will serve as the benchmark for measuring AI’s success.

2. Map out specific use cases and AI integrations into your product

Focus on areas where AI can deliver measurable value rather than just being a fancy add-on. The most popular use cases include personalization (like recommendation engines for e-commerce platforms), automation (such as chatbots for customer service and workflow automation in SaaS tools), and operational enhancements like predictive analytics in project management software, fraud detection in fintech apps, and user behavior analysis in social media platforms.

3. Pick the right AI platform for your use case

Choosing the right AI platform is crucial. Depending on your needs, you may prefer specialized AI platforms (like Clarifai for image recognition) or general platforms (like OpenAI, Hugging Face, or Google Cloud AI) that support a wide range of use cases. Hugging Face, in particular, offers a large collection of pre-trained models—especially for natural language processing (NLP)—that can be easily fine-tuned and adapted to fit your specific needs. Additionally, consider your infrastructure: open-source platforms like Hugging Face allow for customization, while managed services offer convenience, although potentially at a higher long-term cost.

4. Evaluate the data you have access to

AI thrives on high-quality, structured data. Before integrating AI, assess the quality and quantity of your data. If there are gaps, create a strategy for collecting more data through user interactions, third-party sources, or internal processes. However, recent advancements like transfer learning (where a model trained on one task is adapted for a similar task) and few-shot learning (where the model learns to perform tasks with only a few examples) allow many AI models to perform well even with smaller or less-structured data sets.

5. Ensure AI can scale with your product

Select AI platforms that can scale as your product and user base grow. If you’re using pre-built models from platforms like OpenAI, AWS SageMaker, Hugging Face, or Google Cloud AI, much of the scaling infrastructure is managed for you. However, it’s still crucial to implement monitoring tools and maintain data pipelines to ensure optimal performance. Tools like AWS CloudWatch or Datadog can monitor performance and model drift, while Airflow or dbt help manage data flows. Even with pre-built models, some retraining and adaptation may be required as your data evolves. Investing in these practices early can provide greater control and scalability as your product grows.

Main AI platforms with models to integrate into your product

How it works in practice

At OAK’S LAB, our process for integrating AI into products follows these steps:

1. Defining clear use cases and requirements

We collaborate with clients to identify high-impact AI use cases that align with measurable business outcomes. This process begins by asking key questions such as:

• What business challenge are we trying to solve with AI?
• How will AI improve efficiency or reduce costs in this scenario?
• What measurable outcome will define success for this use case?

2. Data collection and preparation

We help clients set up scalable, compliant data pipelines to feed pre-built models. Data is cleaned, structured, and prepared to match the format expected by the pre-built models. Security measures, including PII removal, are implemented to maintain compliance with regulations like GDPR, CCPA, and HIPAA.

3. Model selection and fine-tuning

Using pre-built models from platforms like Hugging Face or OpenAI, we fine-tune these models to meet specific use cases. This involves adjusting model parameters and using techniques like prompt engineering to ensure the model behaves as required and achieves optimal performance.

4. Model integration and deployment preparation

We ensure models are integrated smoothly into our client’s infrastructure—whether cloud-based, on-prem, or hybrid—while prioritizing security, data privacy, and compliance. This includes using APIs and microservices. We’ve found LangChain to be a great tool to integrate the models into our applications and optimize the workflows, alternatively tools like Vercel and LlamaIndex are quite useful as well. We also implement real-time feedback loops and error monitoring to refine model outputs and address performance issues, ensuring the AI adapts effectively to real-world data, user interactions, and our human feedback layer.

5. Testing and QA

We conduct comprehensive testing, including performance and integration tests, to be sure that the models function effectively within the product. Real-time monitoring during slow rollouts allows us to detect and resolve any issues quickly.

6. Deployment to production

Using deployment strategies like blue-green (where two identical environments are used, one for testing new changes while the other serves users) or canary rollouts (where new updates are gradually rolled out to a small subset of users before full deployment), we minimize risks when pushing pre-built models into production. We track KPIs to evaluate success and make adjustments as necessary.

7. Post-deployment monitoring and maintenance

We continuously monitor model performance and data accuracy to ensure that the models remain effective over time. When the same model plays multiple roles, we monitor its performance in each task to ensure it meets the desired outcomes. The MLOps practices allow us to make ongoing updates and improvements, ensuring models adapt to changing data or requirements.

5 tips for integrating an AI solution into your product

Tip 01. Use pre-trained models and open-source solutions

Leverage pre-trained models like GPT-4 or utilize transfer learning to reduce development time and costs. Open-source libraries (such as those from Hugging Face) can be tailored to your specific needs, offering flexibility and scalability without the need to develop models from scratch.

Tip 02. Iterative development with rapid prototyping and feedback loops

Treat AI integration as an ongoing experiment. Ensure your product development timeline, tech team budget, and development process allow for flexibility, with an iterative approach and rapid prototyping to test, fail fast, and optimize the pre-built models for your specific use cases. Use A/B testing (comparing two variations to see which performs better) or multi-armed bandit strategies (where multiple variations are tested simultaneously, and the system dynamically shifts more traffic to the better-performing options) to compare different pre-built models or configurations and identify the most effective approach.

Tip 03. Financial sustainability and cost-effective scaling

Control costs by measuring ROI and optimizing compute resources when using pre-built models. Use tools like AWS Cost Explorer or Google Cloud Cost Management to monitor expenses in real-time. Additionally, third-party calculators can help estimate costs based on average input and output, which can be particularly useful for understanding costs per action. You can test usage patterns to determine average consumption, allowing you to create cost-per-unit estimates for specific actions. This can be updated post-deployment to compare estimated vs. actual costs. Consider cost-saving options like spot instances when scaling model usage. Understanding your cost structure and continuously refining it ensures these expenses are reflected in your revenue model, helping you run a financially sustainable business.

Tip 04. Upskill your team in AI literacy and prompt engineering

You don’t need an experienced ML team to develop custom models. Instead, focus on upskilling your existing team in prompt engineering and AI literacy. While the effectiveness of an AI model depends heavily on the quality of the prompts it receives, other factors like data quality and model fine-tuning also play a role. Investing in your team’s ability to create and refine prompts will still lead to significantly better outcomes.

Tip 05. Leverage multiple models to improve outcomes

There isn’t one model that rules them all. Consider using a combination of models to enhance performance and accuracy for more complex use cases. Here are three practical approaches:

• Multi-Model Ensemble: Combine multiple models that specialize in different tasks. For example, one model might extract data, another summarizes it, and a third evaluates the summary. This method allows each model to focus on its strengths, leading to more comprehensive and accurate results.

• Multi-Agent Systems: When different models need to collaborate, consider using multi-agent systems. These AI agents (models) communicate and exchange feedback, working toward a shared objective. This coordination improves the system's overall performance by leveraging each model’s insights.

• Multi-Model Feedback: Implement feedback loops where one model critiques the output of another. This process enables models to learn from each other, refining predictions over time and enhancing accuracy through continuous iteration.

Tip 06. Integrate human feedback for continuous model improvement

Incorporating human feedback through human-in-the-loop (HITL) processes can significantly enhance model performance. The system learns from mistakes and improves over time by having users or experts review and correct model outputs. You can also leverage active learning, where ambiguous outputs are flagged for human review, or integrate user feedback directly into the system to continuously optimize based on real-world interactions.

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Feel free to reach out if you’re exploring integrating AI into your product or building an AI-powered product from scratch. I’d be happy to help you on your journey to build a product with successful business outcomes.