Businesses across the globe are acknowledging the necessity of utilizing artificial intelligence in becoming a data-driven organization, improving processes, better understanding customer needs, and driving innovation with data. The global AI market value is projected to grow from $47.7 billion in 2021 to $360 billion in 2028 at a CAGR of 33.6%. Despite a promising future, most companies are struggling to scale their AI initiatives. Large volumes of data, governance issues, finding and prioritizing a business use case, etc., are common barriers to AI adoption. To combat the above challenges, the field of MLOps has emerged.

The growing trend to incorporate artificial intelligence (AI) into various products across a wide range of industries has brought the convergence of AI and product development into sharp focus. Today's market environment is diverse and rapidly changing. Users demand more from businesses, and they are taking advantage of user data to gain insights, solve complicated business challenges, and deliver solutions in previously unimaginable ways. Businesses of all sizes are dabbling in AI and machine learning in order to provide more value to their users and delight their customers. And for this, an AI expert is required.

Get a free trial today and find answers on the fly, or master something new and useful. Product Managers are responsible for the successful development, testing, release, and adoption of a product, and for leading the team that implements those milestones. Product managers for AI must satisfy these same responsibilities, tuned for the AI lifecycle. If you're an AI product manager (or about to become one), that's what you're signing up for. In this article, we turn our attention to the process itself: how do you bring a product to market? The first step in building an AI solution is identifying the problem you want to solve, which includes defining the metrics that will demonstrate whether you've succeeded. It sounds simplistic to state that AI product managers should develop and ship products that improve metrics the business cares about. Though these concepts may be simple to understand, they aren't as easy in practice. It's often difficult for businesses without a mature data or machine learning practice to define and agree on metrics. Politics, personalities, and the tradeoff between short-term and long-term outcomes can all contribute to a lack of alignment.