Productizing AI: Why It's Not Just About Tech And Processes
Productizing AI: Why It's Not Just About Tech And Processes
Achieving Success Through Data, Deployment, And Diversity
On June 25, Srujana Kaddevarmuth (Senior AI CoE Leader) joined me on “What’s the BUZZ?” and shared how you can scale your AI product across the enterprise. At a time when Generative AI moves from pilot projects to production deployments, these insights are especially important. Productizing your AI applications is a critical step on the path to a sustainable operating model. This includes data democratization, building models in ways that are flexible and adaptable, and ensuring diversity of experience, gender, and ethnic background on your AI product team. But where should you start? Here is what we’ve talked about…
Harnessing the Power of Data for AI Products
In today’s data-driven world, organizations are increasingly focusing on data democratization and AI productization. An enormous volume of data presents both challenges and opportunities. Data democratization ensures that data is accessible to all levels of an organization, fostering a culture where data-driven decision-making is the norm.
AI productization, on the other hand, involves transforming insights from exploratory data analysis into scalable models that can power products. This process is not just about building algorithms but deploying them efficiently in production environments. The primary benefits of applying a product mindset to AI development are: enhancing human capital efficiency, fostering innovation, and standardizing technology capabilities across the organization. This not only reduces attrition but also allows AI experts to focus on innovative projects, driving further advancements.
Standardizing AI capabilities ensures that these technologies can be deployed consistently across various geographies and channels, making them easier to manage and monitor. By embracing data democratization and AI productization, organizations can unlock significant value and drive business outcomes more effectively.
Addressing the Three Risks of AI Products
Deploying AI at scale comes with its own set of challenges and risks, such as a lack of resource investment, production system malfunctions, and legal implications due to biased data. To mitigate these risks, it is important to have model flexibility, computational efficiency, and robust model wrappers.
The earlier in the lifecycle you can identify risks, the easier it will be to mitigate them. Flexible models can adapt to real-life scenarios without major architectural redesigns, which is crucial given the rapid evolution of technology. Computational efficiency ensures that models run effectively in production, balancing accuracy with functional usage and runtime.
» Model accuracy is important in the proof of concept stage. But in the production stage, it's equally important to focus on the functional usage, computational efficiency, and runtime. «
— Srujana Kaddevarmuth
Model wrappers play a vital role in maintaining the quality of AI deployments. They connect data feeds to the models, ensuring a constant flow of fresh data for continuous learning. This approach helps detect deviations early and prevents project failures. By focusing on these principles, organizations can deploy AI responsibly, ensuring that their models remain relevant, efficient, and unbiased.
Building Inclusive AI Products Starts With Building Inclusive Teams
Productizing AI goes beyond the technical and process aspects of building AI; it extends to diversity as well. With AI algorithms influencing all aspects of our lives, it’s crucial to have diverse perspectives in the development process. AI leaders need to address the gender gap, along with ethnic and neurodiversity gaps, to create more inclusive AI products.
It fosters innovation by bringing varied viewpoints and experiences into the problem-solving process. Moreover, diverse teams are better equipped to identify and mitigate biases in AI algorithms, ensuring fairer and more equitable outcomes. But, increasing diversity does not just start once employees start at your company. In fact, it starts at a much earlier age. Corporate social responsibility programs and education at an early age need to provide opportunities for girls to view STEM as a viable career option.
Closely connected with this is also seeing women in leadership roles as role models. At work, this effort involves not just recruiting but also retaining and supporting diverse talent throughout their professional journeys.
Summary
Data democratization and AI productization are essential for leveraging the full potential of data within organizations. Responsible AI deployment ensures that these technologies are implemented efficiently and ethically, while diversity in tech enhances innovation and fairness. By focusing on these areas, businesses can harness the power of AI to drive meaningful outcomes and create a more inclusive future.
How can you unlock the full potential of AI in your business?
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