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How Enterprise AI Architecture Is Transforming Every Industry From Commerce To Wealth Management, And Beyond

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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.

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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. 

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. 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. 

Overcoming the huge diversity of formats and data structures, enterprise data silos, inconsistent decision processes and archaic, brittle enterprise systems, the team at Hypersonix has built a sophisticated, two-step process. 

CTO Kumar Srivastava explains it this way, “Our platform focuses on the entire world of commerce: everything from manufacturing to shipping to pricing to returns. It’s extremely broad. We address the question of how large enterprises connect thousands of different signals so the enterprise can make better decisions for what to build, how to ship it, how to price it, how to promote it when to take it out of circulation and more. What we fundamentally believe is every enterprise cares about getting the right products to the right customers at the right time through the right channel.”

 This requires restructuring all enterprise decisions to be fundamentally grounded in a demand forecast that predicts the demand for every product at every location at any given point in time. This is incredibly hard, as not only enterprises have massive product catalogs but also because each product in each location can have very different patterns, purchasing behaviors, and consumer preferences. To make things even more complex, product catalogs, consumer preferences, product purchasing behavior and local conditions are constantly, changing and in flux. 

Most enterprises are completely unable to keep up with such dynamic environments, often falling back to static rules or gut decisions. Hypersonix’s products and technology stack is designed to first bring together disparate data sets in a unified data model for commerce and then subsequently, enable highly contextual and customized algorithms to be executed at a very high frequency. 

The operations of enterprise companies aren’t the only area in which AI is supporting growth; the people themselves, and more specifically their money, are also getting a boost.

Private Banking for the People

As start-ups proliferate, and the number of high net-worth individuals grows, innovation is developing in wealth management and the private banking industry. More than 8% of American adults are millionaires, and this wealth is not segmented to the west. Asheesh Chanda spent over 15 years working in finance, but when he went to sign up for a personal banking account, found that even being a millionaire brought him up short. Chanda recognized there are many people like him, who have similar amounts of personal wealth, but no opportunity for quality banking products or advice. They want a private banking account, but are stuck with retail.  

Kristal is a platform that uses AI to democratize private banking. Chanda discovered that one important aspect of the private banking experience is personalization. He noticed that the existing model of private banking was very human-dependent, and thus had the inherent human weaknesses of bias and limited information.

Chanda describes the dilemma: “If you work with an advisor in a private bank, they will most likely refer to funds they are most knowledgeable about and familiar with. If you approach that individual for personal portfolio recommendations, you’ll get an opinion shaped by that person’s personal experience. We felt that this human bias can be solved by using AI. AI can take in large sets of data and identify the best choice among a large set of products.”

The innovators at Kristal felt they could take this further than data analysis and interpretation, and developed a system to provide a standardized advisory at scale. Chanda explains, “In a traditional model, your investing experience is limited to the advisor you’re attached to and their knowledge and understanding. With AI, you can pick the investment experience of a few people, merge it with AI, and deliver it to hundreds of thousands of investors. Personal advisory at scale with a very consistent quality. Different investors in different parts of the world experience the same quality without being limited by their personal experience.”

As the platform evolved, Kristal became about more than creating customized portfolios. The intelligent system can help people rebalance portfolios and provide recommendations at the click of a button. Today, Kristal uses algo to make stock and ETF recommendations; clients can save time on research and use the algo ranking instead. 

Especially after the crash of 2008, people are cautious about humans. The shift to algo was a reasonable one, but algo itself is also limited. For instance, the algo can tell us that markets crashed, but it can’t tell us why.

Kristal provides a hybrid model, leveraging the computing power of the algo complimented by the investing experience of humans. This is an optimum mix of an advising model, which is AI-enabled. Chanda sums it up this way: “In the next 5-10 years, it will be algos that will be trading in the market more than human traders themselves. Algos will take a more central role in making investment decisions on behalf of the clients. It’s natural that an advisory system also uses an algo. It will likely become a base expectation for algos to be part of an advisory capability. Just like a website and an app has become a given, we believe that in the private wealth management industry, algos will become part of any progressive advisory practice.”

Buying, selling, and trade are all three areas where AI is making systemic changes, for the good of humans.

Driving Digital Transformation Worldwide

In all sectors, AI is changing the way business gets done. Systems that can relieve manual processes, refine insights, and speed up progress are now in play. This unlocks endless possibilities, which may increasingly be accessible not just to tech geniuses or billionaires, but to regular people with a vision for positive impact.

At a high level, platforms like this are driving innovation faster, helping customers find new opportunities and accelerating digital transformation worldwide.