# Sensors Data Successfully Hosts Sensors AI Salon in Hong Kong, Exploring AI Growth Team Practices with Industry Decision-Makers

- Link: https://www.thailand-business-news.com/pr-news/sensors-data-successfully-hosts-sensors-ai-salon-in-hong-kong-exploring-ai-growth-team-practices-with-industry-decision-makers
- Published: 2026-09-08T14:25:00+07:00
- Author: PR Newswire

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.

Sensors Data in Hong Kong

**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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