The BSPK CEO argues that the "customer of one" is now an operating model, and that the brands that win will be the ones that reorganize around it.
San Francisco, CA - October 08, 2026 - BSPK, the AI customer intelligence platform that turns retail data into revenue for consumer brands, today announced that founder and CEO Zornitza Stefanova has published a new Forbes Technology Council article, AI Moves Customer Intelligence From The Back Office To The Boardroom.
The article makes a direct case. AI now lets a brand understand each customer individually and know who is ready to buy right now, across millions of store and website visitors. Stefanova argues that the tools alone won't deliver that value. The companies that capture it treat customer intelligence as a mission for the whole organization, led from the top, instead of a project owned by one analytics team.
What does it mean to move customer intelligence into the boardroom?
For decades, the visible work at consumer brands was brand building, assortment and flagship windows. Understanding individual customers happened out of sight, in CRM and marketing teams that built segments from their own slice of data. E-commerce saw one version of the customer, the store saw another and customer service saw a third.
Stefanova uses propensity scoring to show how that has changed. Propensity scoring estimates which customers are most likely to buy and when. It used to reach the business as a campaign list or a quarterly report. With AI, a store associate, a service agent, a merchant or the CEO can ask a plain-language question and see who is ready to buy today. That is the practical promise of modern clienteling: frontline teams walk into every conversation knowing who they are serving and why the moment matters.
Once a whole company thinks about customers as individuals, its investment choices change too. Staffing, the number of sales advisors, store count and the role of each channel all become open questions.
"The customer of one has moved from aspiration to an operating model," Stefanova writes.
Why do data silos hold back AI in retail?
A reliable read on a customer's intent draws on e-commerce, point-of-sale, CRM and the human knowledge held by store teams. In most brands, each of those sources belongs to a different department. E-commerce owns web data, retail operations owns POS, marketing owns the retail CRM and associates know who came in, what they tried on and what they said.
No single team can assemble that picture alone. When leadership commits to unifying product, customer and sales knowledge, every interaction an associate records feeds the models in return, and the intelligence compounds over time.
What results are brands seeing from AI customer intelligence?
The article cites McKinsey research finding that companies setting bold, AI-backed growth targets can capture nearly three times more value than those with conventional targets. The same research finds that every $1 spent on an AI initiative needs about $3 more for change management, and that high performers are three times as likely to have redesigned their workflows around AI.
Stefanova adds results from her own work with brands. She has seen propensity-driven engagement produce conversion rates up to eight times higher than traditional upsell and cross-sell approaches in some cases. She is careful to say not every brand should expect the same lift. The bigger gain comes from directing attention to the customers most likely to engage.
"Every brand I work with already has the data to know which customers are ready to buy," said Stefanova. "What's missing is a company-wide decision to act on it together. That decision belongs to the CEO, not only the CRM team."
What should CEOs and leadership teams do next?
The article closes with four steps for executives:
Lead AI as a transformation of the customer experience at every touchpoint, with executive ownership and a change-management budget alongside the technology.
Start with human questions. What value, convenience and incentive does the customer get from engaging? Design the process before choosing any software.
Connect your data and give shared ownership to the teams that hold each source.
Bring machine learning into conversion models, train them on real-time associate and client activity, and measure success at the level of each customer relationship.
The same groundwork matters as agentic commerce grows and AI agents start researching and buying on behalf of shoppers. Brands with connected first-party data and strong direct relationships will be the ones those agents can recognize and recommend.
Frequently asked questions
What is AI customer intelligence? AI customer intelligence uses machine learning to combine e-commerce, POS, CRM and in-store data into one view of each customer. It shows teams who is most likely to buy, when and through which channel.
What is propensity scoring in retail? Propensity scoring estimates how likely each customer is to purchase in a given window. AI makes those scores available in real time to associates and executives, not only analysts.
How does clienteling connect to AI customer intelligence? Clienteling is the practice of building one-to-one relationships between store associates and customers. AI tells associates which clients to contact and why, and each interaction they log improves the model. Clienteling 101 covers the basics.
Who should own customer intelligence inside a brand? Stefanova argues it should have executive sponsorship, with shared ownership across e-commerce, retail operations, marketing and store teams.
About BSPK
BSPK is the AI customer intelligence platform that turns first-party retail data into revenue for consumer brands. It connects POS, e-commerce and CRM data with the client interactions store associates capture every day, so brands can treat every customer as an individual at scale. Teams use BSPK for 360-degree client profiles, smart client lists, task-driven follow-ups and messaging across SMS, WhatsApp, WeChat, email and more. Learn more at bspk.com.
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