Existing Customer Upgrade Path · Qlik Elite Channel Partner
Qlik AI Analytics: Start With What You Already Have
Qlik AI Analytics does not require you to rebuild your existing data warehouse,
applications, or governance framework. The QVDs, dashboards, data models,
and business logic accumulated over the years are already some of the most
valuable institutional knowledge your AI can use. At AIP International,
we help Hong Kong organizations assess these existing assets and define a
practical, executable path to AI-powered analytics.
AIP International Industrial Limited is a Qlik Elite Channel Partner,
providing licensing, solution design, implementation, deployment,
and support services in Hong Kong. We are an authorized partner,
not a subsidiary or branch office of Qlik.
IN ONE SENTENCE
Qlik AI Analytics means allowing AI to work with your data through the Qlik analytics layer, rather than allowing an AI model to query the data warehouse directly. Qlik acts as the governed layer that provides computational capabilities, metric definitions, access rules, and analytical context to AI.
For organizations already using Qlik, the barrier to AI adoption can be much lower than expected: existing QVDs, data models, application logic, and governance can often be reused. You do not necessarily need to build a new data platform just to introduce AI. This is one of the key points we use when helping enterprise IT teams evaluate their AI strategy.
Why Most AI Projects Struggle Is Not the Model
Qlik has cited BCG research from 2025 indicating that only around
5% of organizations are achieving AI value at scale. In our experience
working with organizations in Hong Kong, the obstacles are often found
behind the AI model itself. Three gaps appear repeatedly.
🧭
The Context Gap
A model may receive an isolated query result without understanding
what surrounds that number or which conditions have been excluded.
Without context, AI can produce a convincing explanation without
knowing what the business should investigate next.
💸
The Cost Gap
AI generates questions far more frequently than human users.
If every follow-up query goes back to the data warehouse,
consumption and infrastructure costs can increase rapidly.
This is one reason some AI pilots fail to progress beyond the
proof-of-concept stage.
🧠
The Reasoning Gap
Without consistent metric definitions, an AI system has no reliable
analytical baseline. If the same business question produces different
interpretations, users quickly lose confidence in the system.
Qlik AI Analytics: Four Existing Assets You Can Build On
If your organization has been using Qlik for several years,
you probably already have most of the assets shown on the left.
They are not technical debt to discard. They are the starting point
for your AI strategy.
From Existing Assets to AI Consumption · The Middle Layer Does Not Need to Be Rebuilt
WHAT YOU ALREADY HAVE
QVDs & Existing Data Models
Analytics Applications & Dashboards
Business Logic in Scripts
Access Control & Governance Rules
Intelligence Layer: Analytics Engine + Agent Orchestration
Reuse the same data models and metric definitions
to provide governed computation and analytical context
to AI applications.
No Need to Replace Your Data Warehouse or Models
WHO USES THESE CAPABILITIES
Analysts & Business Users
Built-in AI Agents
External AI Assistants via MCP
Automated Processes & APIs
The assets on the left represent both accumulated investment
and institutional knowledge. In many AI projects, the real work
is organizing this middle layer—not rebuilding everything from scratch.
QVDs and existing data models. These assets contain years of knowledge about your source systems: which sources are trusted, which fields require cleansing, and how relationships between datasets should be constructed. Rebuilding them is not simply a matter of engineering effort; it can also mean losing hard-earned business knowledge.
Business logic embedded in scripts. How do you define an active customer? How should returns be deducted? Should inter-region transfers count toward sales performance? These rules are often not documented anywhere else. They exist inside scripts and applications—and they are exactly the type of business context an AI model needs to understand.
Access control and governance. AI agents should operate within the same access boundaries as users. If access controls are already implemented at the data layer, much of this foundation can be reused. If permissions exist only at the dashboard level, the AI transition is a good opportunity to strengthen the governance model.
Open access through MCP. Qlik provides an MCP server that allows compatible AI assistants and agents to access governed analytical capabilities without necessarily creating another copy of the underlying data. For the latest product capabilities and official documentation, please refer to the official Qlik Agentic Analytics page .
Add an Intelligence Layer — Don’t Replace Your Existing Architecture
What IT teams usually resist is not new functionality,
but the prospect of rebuilding everything again.
The approach we recommend is the opposite:
keep your existing data warehouse, data models, and AI providers,
and add the intelligence layer on top.
No Need to Replace Your Data Warehouse
Your data can remain where it already lives.
Qlik provides the analytics and governed access layer.
Common cloud data platforms and lakehouse environments,
including AWS, Snowflake, and Databricks,
can be part of the architecture.
No Lock-in to a Single AI Provider
You can use built-in AI capabilities or connect
external AI assistants and agents.
When your preferred model provider changes,
the underlying analytics and governance layer
does not necessarily need to be rebuilt.
A Different Cost Structure for Analytics
Repeated analytical interactions can be handled
through the analytics layer instead of sending every
question back to the warehouse. For cloud platforms
where query volume directly affects cost,
this can become an important financial consideration.
Trusted Dashboards Come Before Trusted AI
If business users do not fully trust today’s numbers,
solve that problem first. AI does not automatically correct
inconsistent definitions. It can simply spread those
inconsistencies faster.
Based on Where You Are Today, Here’s Where to Start
Different starting points require different first steps.
This is the practical decision framework we use when assessing
Qlik environments in Hong Kong.
| Current Situation | Main Challenge | Recommended First Step |
|---|---|---|
| Still Using QlikView® | The latest AI capabilities are not centered on this product generation. | Assess application complexity and migration requirements. Migrate in phases rather than switching everything at once. |
| Qlik Sense® On-Premises | The availability of some AI capabilities may differ from the cloud environment. | Review compliance requirements and product capabilities, then determine whether a hybrid architecture or cloud transition is more appropriate. |
| Already Using Qlik Cloud Analytics® | Your subscription level and metric governance may not yet be fully aligned with your AI objectives. | Review which agent and AI capabilities are included in your subscription, then select one practical business scenario for a pilot. |
| Using Another BI Platform | Existing dashboard assets cannot always be transferred directly. | Run a parallel proof of concept using real business data and compare actual results—not demonstration environments. |
| Data Still Scattered Across Multiple Systems | There is no consistent data model or metric layer. | Start with data integration and metric definition. This foundation cannot be skipped if the goal is reliable AI. |
How We Deliver Qlik AI Analytics in Hong Kong
We do not recommend buying licenses first and deciding what to do later.
Our approach starts with your existing environment and business objectives.
Each of the four stages can be stopped or adjusted based on the results.
1
Existing Asset Assessment
Review your applications, QVDs, data models, business logic,
and access-control configuration. Identify what can be reused,
what needs to be cleaned up, and what should be retired.
The outcome is an actionable inventory—not just another strategy document.
2
Use Case Selection & Feasibility Assessment
Select a business problem with a clearly defined scope,
sufficient data, and measurable outcomes. If the environment
is not ready, we will tell you directly what needs to be
addressed before moving forward.
3
Pilot Implementation
Build the pilot using real business data rather than
demonstration datasets. The delivery includes model adjustments,
metric definition, access-control validation, and documentation
of actual AI agent behavior.
4
Rollout & Operational Support
Once the pilot is validated, we can plan broader deployment
and licensing. We provide ongoing operational support,
upgrades, and coordination with Qlik, with local support
in Hong Kong and communication in English, Traditional Chinese,
and Cantonese.
Frequently Asked Questions
No. The analytics and AI layer can be introduced on top
of your existing architecture. Your data can remain where
it currently resides, including common cloud data warehouses
and lakehouse platforms. In practice, the key work is usually
metric definition, data modeling, governance, and access control.
If your organization does not yet have consistent data sources,
however, data integration should be addressed first.
In many cases, yes—but they should normally be assessed and
cleaned up first. A common situation is that the same data
is loaded by several applications, metric definitions differ
slightly, or parts of the script have not been maintained
for years. The purpose of the assessment is to preserve
what works, consolidate duplication, and retain the business
knowledge already embedded in your Qlik environment.
Potentially, yes. Through Qlik’s MCP capabilities,
compatible external assistants and agents can access
governed analytical capabilities and data. Access and
governance remain controlled by the underlying platform.
This approach can also make it easier to change AI model
providers without rebuilding the entire analytics layer.
Actual availability depends on the Qlik version,
configuration, and subscription level.
Qlik analytics and AI capabilities are available across
different subscription structures. Pricing can depend on
factors such as users, capacity, and the capabilities
included in the selected subscription. Our approach is to
first understand your intended use case and then determine
which components are actually required, helping you avoid
paying for capabilities you do not need. Contact us for
current licensing and renewal information.
AIP International Industrial Limited is a Qlik Elite Channel Partner,
not a Qlik subsidiary or branch office. We support the Hong Kong
market with licensing, asset assessment, solution design,
implementation, migration, and local support. Qlik remains
responsible for the product roadmap, product functionality,
and its official support policies.
Third-party research figures referenced on this page are based on
publicly available Qlik materials citing BCG research published in 2025
and are provided for informational purposes only.
Qlik®, Qlik Sense®, Qlik Cloud Analytics®, Qlik Answers®, Qlik Predict®,
Qlik Automate®, Qlik NPrinting®, QlikView®, and Qlik Talend® are
trademarks or registered trademarks of QlikTech International AB.
Other product names mentioned are trademarks of their respective owners.
AIP International Industrial Limited is an authorized Qlik partner
and is not affiliated with or owned by Qlik.
Understand What You Already Have Before Deciding What to Buy
Tell us your current Qlik version, number of applications,
and the business problem you most want to solve.
We will provide an initial asset-assessment recommendation,
including areas where we recommend that you do not make changes yet.
Initial consultation is free.
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