Crafting Strategy: Infusing AI into Your Product’s Core

“What industry thought leadership reveals is that organizations are investing in tools to improve productivity,” shares EIG Founder & CEO, Jake Miller. “The beauty of Generative AI is that it takes data from multiple sources in an unstructured format and makes it easier to unlock value from that data.”

“The same is true for building Generative AI into products,” Jake continues. “You don’t have to spend hundreds of thousands of dollars building custom API’s. Gen AI makes it easier to accelerate your time to insights and value affordably and reliably at scale.” 

In the rapidly evolving landscape of technology, integrating Artificial Intelligence (AI) into product development is more than just a trend—it’s a strategic imperative. As product teams embark on this journey, crafting a clear vision and a robust strategy becomes paramount. This involves not only envisioning how AI can enhance your product but also conducting a thorough competitive and market analysis to understand the AI landscape and your position within it.

AI has come a long way from its early days of basic rule-based systems. Today, AI and Machine Learning (ML) are driving innovation across industries, from personalized recommendations on platforms like Spotify to advanced diagnostic tools in healthcare. The state of AI is characterized by significant advances in deep learning, the accessibility of pre-trained models, and the proliferation of cloud-based AI services. According to McKinsey’s recent survey, product and/or service development has the second most AI adoption with more than half of respondents planning to implement GenAI into product/service development over the next one to eight months.  Investment in AI is set to rise significantly due to generative AI advancements.

Deep learning, a subset of ML, has led to breakthroughs in image recognition, Natural Language Processing (NLP), and more. Models like GPT-4o and Claude have revolutionized how machines understand and generate human language. Additionally, platforms like TensorFlow Hub, PyTorch Hub, and Hugging Face provide access to powerful pre-trained models, reducing the barrier to entry for AI development. Cloud providers like AWS, Google Cloud, and Azure offer scalable AI services, allowing companies to leverage powerful computing resources without heavy upfront investments.

As someone who has been deeply involved in AI product development, what I’ve discovered is that building AI-native products requires a fundamentally different approach than traditional software. Designing becomes extremely challenging when simplifying, testing requires acknowledging unknown outcomes, and considering interactions demands the mindset of an architect/engineer along with the end user experience across multiple dimensions.

Our advice for getting started is to build a strong foundation around the basics: 

AI is not a magic wand. It’s crucial to begin by identifying a real internal or external customer need. The allure of AI can sometimes lead teams to develop solutions in search of problems. Instead, engage in rigorous market research, conduct customer interviews, and validate your assumptions through prototyping and testing. For example, Spotify’s recommendation engine wasn’t built in isolation; it was developed in response to the genuine need for personalized music discovery. In fact, their team put so much work into solving this problem that the company spun out their own research and development department where they share some of their learning. AI should be seen as a tool to solve specific problems, not just a buzzword to attract attention.

Before diving into model development, ensuring data readiness is paramount. This involves having access to data while also preparing and structuring it for effective use in AI models. The key aspects of data readiness include:

Data Collection: Identify all potential data sources and systematically collect data relevant to your problem. This might involve integrating with various systems, using APIs, or even manualmethods. For example, in healthcare, patient data may come from Electronic Health Records (EHR’s), wearable devices, and even patient self-reports.

Data Cleaning: Raw data is often messy, containing errors, inconsistencies, and missing values. Implementing processes to clean and preprocess this data with techniques like data normalization, outlier detection, and imputation of missing values is crucial. Clean data ensures that your AI models are trained on reliable and accurate information, leading to better performance.

Data Annotation: For supervised learning tasks, data annotation is critical. This involves labeling data with the correct outputs. In image recognition, for example, this means tagging images with the objects they contain. While this process can be time-consuming, it is essential for training accurate models. Tools like Labelbox or Amazon SageMaker Ground Truth can facilitate this process. And marketplaces like Amazon’s Mechanical Turk make it easier to source and to pay people to manually annotate your data.  

Data Augmentation: Sometimes, the amount of available data may be insufficient. Data augmentation techniques, such as generating synthetic data or applying transformations to existing data, can help expand your dataset. This is particularly useful in image and text processing, where variations of the same data can improve model robustness.

Data Governance: Establishing data governance practices ensures that data is managed and used responsibly. This includes setting up policies for data access, usage, and compliance with regulations like GDPR. Data governance helps maintain data integrity and protects against misuse, which is vital for maintaining user trust.

In our next article, we will share actionable strategies to evaluate Machine Learning vs. AI and how to discern the best path forward for your organization.