HONG KONG, Sept. 8, 2026 /PRNewswire/ — On September 4, 2026, Sensors Data successfully hosted the closed-door salon “Sensors AI Salon • Hong Kong” at the International Finance Centre (IFC), Central, Hong Kong. Under the theme “The Future of Enterprise Growth: Building Your AI Growth Team”, the salon brought together enterprise decision-makers and senior executives from aviation, banking & finance, hospitality, premium retail, property development, and utilities to explore practical paths of enterprise growth in the AI era through two keynote speeches and three panel discussions.
From Purchasing Growth Software to Hiring an AI Growth Team
Welf Sang, Founder & CEO of Sensors Data, delivered a keynote titled “From Data-Driven to AI Growth Team”, reviewing three stages of the company’s eleven-year journey: from its early focus on user behavior analytics and building a solid data foundation, to entering the marketing cloud space and helping enterprises turn insights into actions through the SDAF data closed loop (Sense–Decision–Act–Feedback), and to today’s full embrace of AI. He noted that Sensors Data has been developing the Hong Kong market for about two years.
Drawing on years of service practice, Sang shared his renewed thinking on data-driven growth: whether a data-driven approach can truly deliver growth depends first on the direction and quality of the business itself. Meanwhile, implementing data-driven strategies relies heavily on organizational capability, as talent who understand both business and data is scarce, costly, and hard to replicate. In his view, the new bottleneck lies in breaking the dependence on individuals and organizations.
Based on this, he offered three observations about the AGI era: AGI will arrive within the next three to five years; AGI will not eliminate enterprises but will reshape them; and general AGI is not the same as enterprise AGI — every enterprise needs to build its own “Mini-AGI”. He drew an analogy between enterprise growth and autonomous driving: growth likewise evolves from L2 (human-led, AI-assisted) to L3 (AI-led execution with human confirmation as the backstop) and then to L4 (AI operating autonomously once goals and boundaries are defined). Sensors Data’s three-year product strategy is precisely to build an L4-level AI Growth Team: from “delivering software” to “delivering workload” to “delivering results”.
“What companies have always wanted is not software, but growth.” Sang said the shift Sensors Data aims to drive in the collaboration model is from “purchasing a growth software suite” to “hiring an AI Growth Team”: a team composed not entirely of AI, but of human employees and AI employees working in concert — a “silicon-based plus carbon-based” combination.
He also introduced on site the Sensors AI 1.0 growth Agent platform that supports this vision: dozens of out-of-the-box AI growth workflows carry the complete growth process; three engines — the customer data engine (CDP), customer journey analytics (CJA), and customer journey optimization (CJO) — are packaged as Skills that Agents can invoke; and the Customer World Model enables Agents to genuinely understand an enterprise’s customers. In the three-tier product layout, on top of the Customer World Model and the growth Agent platform sits Sensors AGW (AI Growth Worker), an AI growth employee ready to be “hired”. Sang also shared Sensors Data’s own AI transformation practice: building dual world models internally so that the intelligence layer proactively drives customer operations and service closed loops — including the management AGW he uses every day, with which he can query any customer’s business status and health at any time within IM.
AI Employees, Living in Real Enterprise Workflows
You Chenjun, AGW Product Lead at Sensors Data, delivered a presentation and live demo titled “AGW: The Hirable AI Growth Worker”.
He pointed out that AI product forms have evolved rapidly, from Chatbot to Copilot to Agent. Yet when a general-purpose Agent is placed inside an enterprise as an “employee”, it falls short in three types of scenarios: business problems that require multi-person collaboration and have no standard answers; complex tasks whose execution paths keep changing; and process management that requires continuous tracking and accumulation. Real enterprise-grade work happens in IM communication, cross-system data retrieval, and person-to-person collaboration.
To address this, AGW is designed as a digital employee living in the enterprise IM: it proactively inspects data and raises alerts upon anomalies; it knows every member of the team and, when it cannot complete a step, proactively assigns the task to the right person and continues the process automatically once a reply is received; it retrieves and analyzes data across systems, connecting the enterprise’s internal systems one by one into callable capabilities; and more importantly, it consolidates the experience accumulated in each collaboration into Skills, upgrading individual experience into company standards to benefit the entire organization: “A personal assistant only makes the individual stronger; AGW makes the whole organization stronger.”
In the live demo, You Chenjun fully reproduced a growth analysis closed loop: AGA — the specialized version of AGW for data analysis — proactively alerted on an anomaly in first-charge conversion on an app release day, automatically aligned metric definitions, broke down the funnel, drilled down into user segments, and cross-validated multiple data sources to locate the root cause, provided role-specific remediation suggestions, and then continued to track the effectiveness of the fixes. Going one step further, the complete troubleshooting process was consolidated into a company-wide release-analysis Skill, which now runs automatically on every release. He stated that AGW’s design philosophy is “let everything happen naturally — do not reshape work, but follow it”: AI adapts to people, not people to AI.
Three Panel Discussions: Real Challenges from the Front Lines
Around the theme “Data Assetization and AI Transformation: New Growth Strategies for the Financial Services Industry within Compliance Boundaries”, three guests from banks and financial institutions discussed from the perspectives of IT, business, and marketing. The guests generally agreed that as AI projects move from pilot to scale, security and compliance are the first prerequisite; at the same time, they cautioned against “AI for AI’s sake”: first determine whether the problem genuinely exists and whether the process is sound, then decide whether to introduce AI. The key to implementation lies not only in technology but also in organizational coordination: involving business, IT, risk control, and frontline teams early, clarifying data ownership and definitions, and managing the integration between new-generation AI systems and existing core systems. On handling sensitive data, the guests shared practices such as data masking, using trusted models, and setting human red lines, and expressed the hope that AI could help bridge business breakpoints and shorten the approval chains of marketing campaigns.
The second panel focused on “The Next Era of Enterprise Growth: Operational Transformation and Omnichannel AI Execution”. Three guests from property development, premium retail, and hospitality discussed the common challenge of fragmented multi-channel data. Using coffee retail as an example, they noted that customers often experience products in-store first and convert through other channels later, so effectiveness cannot be judged by a single store visit; the same customer often appears under different identities across parking, consumption, accommodation, and membership systems, with inconsistent data definitions, making it difficult to form a complete customer journey and a unified view. The guests agreed that breaking down data silos and building a unified, trustworthy customer profile is the foundation of precise outreach and higher customer lifetime value; the key to marketing automation is delivering real value at the moment the customer needs it — push communications should stop proactively when information is overloaded or service issues remain unresolved. On implementation, they acknowledged that the bottleneck for most enterprises lies not in systems but in people: employees still tend to make decisions based on experience. One guest shared the practice of equipping the team with “data partners”, which notably improved efficiency after several months of operation. On AI’s role, the guests look forward to delegating repetitive work such as customer analysis and multilingual content generation to AI, transforming marketing professionals from executors into designers of customer experience, while strategic direction and key decisions remain in human hands.
The final panel, themed “The Next Era of Enterprise Growth: AI-Driven Operations and Customer Engagement”, featured two guests from aviation and utilities sharing AI practices in operations at scale. The utilities guest introduced how to build personalized customer journeys starting from the most mature data: from welcome communications for newly onboarded customers to e-payment guidance before bills are issued; and shared progress on building a unified customer profile (One Profile). The aviation guest used extreme weather as an example, noting that AI’s value at such moments is not to take over frontline service directly, but to help soothe traveler emotions and ensure their requests are heard in time, while focusing on root-cause analysis and solving problems at the process level, reserving frontline capacity for the most complex, high-value scenarios. Both guests agreed that AI should serve as the team’s customer strategy assistant, collaborating with humans in a human-in-the-loop manner: people are responsible for high-value judgments such as customer experience design, while AI handles repetitive tasks such as data retrieval and verification.
The salon concluded with a relaxed cocktail networking session. Guests engaged in in-depth exchanges on AI Growth Teams, digital employees, and industry implementation practices. Moving forward, Sensors Data will continue to bring the capability of “building an AI Growth Team” to more enterprises, helping more customers achieve the leap from data-driven to AI-driven growth.
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SOURCE Sensors Data
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