User Review( votes)
Artificial intelligence (AI) has big promise to solve problems in almost every industry. AI-supported, AI-fueled, AI-based technologies are now present and capable of automating tasks in retail businesses and wealth management, to name a couple. These automations reduce error, manage increasingly vast datasets, and free up humans to do intelligent, strategic tasks. At the enterprise level, AI-architecture is transforming capacity and steadily shaping the way businesses of the future operate.
Connecting to Core Systems of Commerce Businesses
Operationalizing machine learning or AI at scale is a key priority for the world of retail and commerce. Enterprise tech stacks leverage AI and predictions for high-frequency, ambiguous situations. Active learning and continuous improvement of AI are embedded in business applications and workflows. Making use of these requires contextual stitching of signals to create a single unified view of the truth, which empowers teams to make contextual decisions in the present. While the technological frameworks have existed for the better part of a decade, most businesses have been unable to overcome the barrier of applying technology in real world contexts, or at scale.
Most merchants haven’t figured out how to use the tooling, and even companies at the enterprise level may lack the expertise to do so. The reality is that retail and commerce business leaders recognize these tools exist, and have value, but are blocked from realizing that value because of inflexible systems or production workflows.
Commerce will not slow, and no retailer in the world can halt operations to either add in AI/ML or to use them better. So the key struggle becomes, how to learn from machine learning that is running in production?
The potential of said systems lends urgency to the question. For example: data organized by machine learning and AI systems may give retailers the ability to predict whether someone will or won’t buy a particular product. Retailers can send an offer that will increase the likelihood of a purchase. The challenge is that conditions change, meaning the model requires continuous adaptation and updating to solve new problems.
Hypersonix is one company that has built a backbone, with multiple AI intelligence algorithms, through a combination of micro-services powered by a foundational enterprise AI layer. Historically, enterprises have struggled with value extraction from data because their data is organized in silos and hard to access and analyze. It is incredibly hard for enterprises to strategize how to get the right product to the right customer at the right price through the right channel.
According to researcher Mehran Rowshan, decisions to manufacture, distribute, price, promote, assortments to carry and offerst to generate are carried out in information black holes often powered only by gut instinct, static rules or, in the best of circumstances, through historical analysis. Rowshan continues, “the inability to combine historical analysis and future demand predictions to make the best decision in the present often is the cause of lost opportunities and unmitigated business risks.”