• AI usage is becoming mainstream, and the business challenge is moving from “whether to adopt” to “how to manage AI effectively.” 

• The next competitive advantage will come from a practical AI Operating Model, not from simply deploying more AI tools. 

• AI Agents will reshape business operations, workflows, and management models. 

• Governance should not only reduce risk; it should also help companies improve AI ROI. 

• Hong Kong businesses need to build people, process, data, and governance capabilities together to realize long-term AI value. 

For Hong Kong businesses, AI adoption is no longer the main question. As AI becomes part of everyday knowledge work, the real challenge is how to manage it securely, consistently, and measurably. Although recent Microsoft and Hong Kong research focuses on education, it reflects a broader market signal: AI usage is becoming mainstream. Business leaders now need to move beyond individual experimentation and build an AI Operating Model that connects use cases, data, governance, security, skills, and measurable business outcomes. 

Microsoft’s latest research shows that 92% of students and education leaders, and 88% of educators, have already used AI. At the same time, 58% of education leaders say their institutions are already implementing or scaling AI. 

Hong Kong research also found that more than 90% of teachers and students have used AI tools. Although these studies come from the education sector, the message for business decision-makers goes beyond schools and classrooms. They reflect a broader trend: AI is gradually becoming an everyday tool for knowledge workers, much like email, Microsoft Teams, or cloud services, and is being embedded into daily workflows. 

What businesses should focus on is no longer whether employees will use AI, but whether the organization has the capability to manage how AI is used, how data risks are controlled, how workflows change, and how business outcomes are measured. 

Over the past two years, the main AI questions for many businesses were usually: 

• Is AI mature enough? 

• Is it worth the investment? 

• Will employees be willing to use it? 

Today, these questions are becoming less important. 

More employees are already using AI proactively to handle tasks such as: 

• Drafting documents 

• Summarizing meetings 

• Analyzing data 

• Creating presentations 

• Searching and organizing knowledge 

The new challenge businesses now face is this: 

How can businesses prevent AI usage from becoming fragmented? When different departments select their own tools, create their own workflows, and develop their own usage habits, organizations can easily face scattered knowledge, inconsistent security standards, duplicated investment, and outcomes that are difficult to measure. 

Business Impact 

The future gap between companies may not come from who owns the most advanced AI, but from who can place AI into the right workflows, roles, governance structures, and measurement systems more effectively. 

Our view is that Hong Kong businesses do not need to wait until every technology is fully mature before planning ahead. The earlier they establish clear usage principles, data foundations, and measurement methods, the easier it becomes to avoid fragmented pilots, duplicated investments, and solutions that are difficult to scale later. 

Simply put, what is an AI Operating Model? 

An AI Operating Model is the operating mechanism an organization uses to manage AI usage, risks, governance, skills, and business outcomes. As AI enters daily operations at scale, leadership teams need to answer more than “which tool should we use?” They need to clarify who owns AI, how results are measured, which workflows are suitable for AI, which decisions still require human judgment, and how risks are controlled. 

Management Consideration 

AI should not be managed by IT alone. Successful organizations usually establish cross-functional collaboration, bringing together IT, Security, HR, Operations, and business leaders to define AI priorities, use cases, risk boundaries, and success metrics. 

This is also what many businesses overlook when adopting Copilot or Azure AI: tools can be deployed quickly, but the operating model needs to be led by management and designed together with business processes.

Sources: Microsoft 365 powered by Work IQ: Built to Support How You Work 

Microsoft’s recent AI developments point to an important trend: 

What are AI Agents? 

AI Agents are AI work units that can understand goals, perform multi-step tasks, and interact with systems. Unlike traditional chatbots, an AI Agent does not only answer questions. It can help gather information, execute tasks, follow up on processes, and even trigger workflows. 

This means that businesses will no longer manage only employees and systems. They will also need to manage an increasing number of AI Agents participating in daily work. For leadership teams, this creates new questions: Which tasks can be delegated to agents? Which processes still require human review? How should agent permissions, accountability, and risks be defined? 

Governance Is No Longer Just About Compliance 

In the past, when businesses discussed AI governance, they usually thought first about: 

• Regulations 

• Compliance 

• Information security 

These are, of course, important. 

However, the value of governance goes far beyond risk control. A good governance framework helps employees understand which data can be used, which processes are suitable for AI assistance, and which outputs require human review. This improves user confidence, allows best practices to be replicated across teams, and makes ROI easier to measure. 

We recommend that businesses view governance as an accelerator, not a restriction. When employees clearly understand what data can be used, which workflows AI can support, and where human oversight is required, organizations can move faster in deploying high-value AI use cases safely and consistently. 

Instead of continuing to ask which model is the best, leadership teams should focus on the following questions. 

1. Do we know how our employees are using AI?

Without visibility, there is no effective management. 

2. Is our data ready?

If business knowledge is scattered across: 

• Email 

• SharePoint 

• File Server 

• Personal files 

AI will struggle to deliver real value. 

3. Have weestablished an AI Usage Policy? 

Employees need to know: 

• What information can be entered into AI 

• What information must not be entered 

• How to validate AI-generated content 

4. Do we have anAI Skills Development plan? 

Microsoft’s research highlights that the need for continuous training and support is increasing. AI capability will gradually become a core skill for delivering work effectively. 

5. How do we measure AI outcomes?

Businesses should focus on: 

• Productivity improvement 

• Time savings 

• Process efficiency 

• Customer experience improvement 

Instead of simply counting the number of prompts used.

From a market perspective, Hong Kong businesses are moving from AI experimentation toward scaled adoption. From 2024 to 2025, many organizations were still exploring Copilot, generative AI, or automation scenarios. In 2026, the management conversation is shifting toward a more practical question: how can AI be connected to daily operations, departmental goals, and measurable business outcomes? 

1. FromCopilot to Agents 

Businesses are starting to move from personal assistant use cases toward workflow automation. For example, sales teams can use Copilot to organize meeting notes and generate follow-ups; finance teams can use AI to classify invoices and prepare reports; IT teams can use AI to support service desk triage and knowledge base search. 

2. FromAdoption to Value Realisation 

Success is no longer measured by how many licenses are purchased. The real question is whether AI can shorten processing time, reduce repetitive work, improve customer response speed, or support faster internal decision-making. 

3. FromTool Management to Workforce Management 

In the future, businesses will not only manage employees. They will also manage an increasing number of AI Agents participating in daily work. This means organizations need to rethink role design, permissions, process oversight, and accountability for outcomes. 

SUPERHUB Recommendation 

Businesses should establish a four-layer AI Operating Model: 

People 

• Skills development 

• Change management 

Process 

• Workflow optimization 

• Automation strategy 

Governance 

• Security 

• Risk management 

• Responsible AI 

Technology 

• Copilot 

• Azure AI 

• AI Agents 

Only when these four areas develop together can businesses build a sustainable competitive advantage. 

If your organization is preparing to move from AI pilots to scaled adoption, the first step may not be to introduce more tools. It is to assess whether your current data, processes, governance, and employee skills are ready. SUPERHUB can support businesses with AI readiness assessment, Copilot adoption roadmap, and Azure AI implementation framework, helping AI evolve from a personal productivity tool into a managed, measurable, and scalable business capability.

1. Why do businesses need an AI Operating Model before adopting AI?

An AI Operating Model defines ownership, priority use cases, data rules, risk controls, and success metrics. It helps businesses move beyond scattered pilots and scale AI into daily operations. 

2. How can AI governance reduce risk while improving ROI?

AI governance gives employees clear rules on data use, human review, and safe AI practices. This reduces risk while making successful use cases easier to repeat and measure. 

3. How should Hong Kong businesses start Copilot adoption?

Start with high-value, low-risk use cases such as meeting summaries, document drafting, knowledge search, and customer follow-up. Pair adoption with policies, training, and outcome tracking. 

4. What areas does anAI readiness assessment cover? 

It reviews data readiness, permissions, security, workflows, employee skills, governance policies, and practical AI use cases to identify where AI can create value safely. 

5. How should businesses measure the real impact of AI adoption?

Measure outcomes such as time saved, process efficiency, reduced repetitive work, faster response times, lower error rates, and improved customer experience.

Microsoft’s latest research reflects an important reality: AI is no longer a future trend. It is already part of the present. 

Therefore, the next question for businesses should no longer be: 

“Should we use AI?” 

Instead, they should ask: 

“How are we going to manage AI?” 

The most successful businesses in the future may not be the earliest to deploy AI, but those that build an AI Operating Model earlier than others. 

In the AI era, true competitive advantage does not come from the tool itself. It comes from whether a business can systematically embed AI into its organization, workflows, and decision-making to continuously create measurable business value. 

For business leaders, the most practical starting point is to identify three to five high-value, low-risk business scenarios, supported by clear data governance and usage policies. This allows AI to move from experimentation into a real operating capability. 

Source: Microsoft’s New AI in Education Report highlights widespread adoption and increasing demand for support – Source

Application and Development of Artificial Intelligence in Primary and Secondary Education | 團結香港基金 OUR HONG KONG FOUNDATION