Introduction
AI is no longer an experimental project owned by the IT department. According to the IMF, AI could affect roughly 40% of jobs worldwide and lift global GDP by more than $7 trillion. McKinsey argues that AI + SaaS will be the biggest shift in software since SaaS itself. IDC estimates that global AI capital expenditure will rise from about $235 billion today to more than $630 billion by 2028.
This article looks at how AI is reshaping management strategy, business models, organizational structure, and platform business.
1. How AI Changes Management Strategy
Data-Driven Decision-Making
AI analyzes large volumes of data to predict market trends and customer behavior, helping companies define strategic goals. Decisions that once relied mainly on intuition are becoming data-driven, improving executive judgment.
Efficiency and Automation
AI automates repetitive work and increases the efficiency of human resources. Meta, Alphabet, Amazon, and Microsoft invested a combined $190 billion in capital expenditure over the 12 months through June, up sharply from $110 billion over the same period in 2020.
AI as a Strategic Collaborator
AI is becoming more than a tool. It is increasingly used as a strategic collaborator that provides data for validating strategic hypotheses and assessing whether strategies are working. Harvard Business School emphasizes that clear problem definition, strong data quality, and organizational readiness are central to building AI-driven business strategy.
2. AI + SaaS: A New Business Model
McKinsey argues that AI + SaaS is fundamentally changing the old SaaS paradigm. It combines AI's intelligence and automation potential with the scalability and accessibility of cloud software.
How Business Models Are Evolving
- Usage-based pricing: moving from fixed subscriptions to pricing based on AI consumption
- Outcome-based pricing: charging based on the results AI actually delivers
- Agentic SaaS: AI agents autonomously completing work on behalf of users
The best business model has not been settled yet. The key is not simply adding AI features to an existing product, but redesigning the business model itself around AI.
3. Platform Business and Digital Transformation
Platform Team Transformation Strategy
Forrester argues that successful IT platform transformation requires platform leaders to go beyond technical specialization and develop business judgment. The mindset has to shift from technology management to business outcomes, supported by a product-centered IT approach.
Five Elements of a Successful Platform Business
- Network effects: value increases as more users join
- Two-sided market management: balancing both suppliers and consumers
- API economy: open infrastructure that lets external developers and partners connect easily
- Data utilization: AI-driven matching, recommendation, and forecasting
- Ecosystem building: a service ecosystem centered around the core platform
4. The Agentic Organization: A New Operating Model
McKinsey's concept of the "agentic organization" argues that AI is creating the biggest organizational paradigm shift since the industrial and digital revolutions. Humans and AI agents become integrated into a model that raises speed, intelligence, and adaptability to previously impossible levels.
Core Changes
- Employees shift from task executors to managers of AI agent teams
- Departmental boundaries blur as agent-based workflows become central
- Decision speed accelerates by multiples or even orders of magnitude
- The role of middle management shifts from "collecting reports" to "orchestrating agents"
5. ESG Management and AI
AI is becoming a core engine of sustainability and ESG management. It can outperform humans in environmental data analysis, carbon optimization, and ethical supply-chain oversight.
At the same time, AI itself creates environmental costs: massive power consumption in data centers, the impact of AI chip manufacturing, and the energy required for training. The real challenge is to use AI while also managing the environmental cost of AI.
6. Agentic Commerce: The Future of Shopping
The era of agentic commerce, where AI agents shop on behalf of users, is arriving quickly. Visa has reported a 4,700% surge in AI-driven traffic to US retail sites and introduced an AI commerce framework called the Trust Agent Protocol.
Implications for e-commerce operators:
- Provide structured product data in formats AI agents can read
- Prepare for agentic commerce protocols such as Google UCP and OpenAI ACP
- Move beyond classic SEO into a new field of "agent optimization"
7. The Economic Impact of AI
Goldman Sachs projects that annual AI-related capital expenditure could exceed $1 trillion over the next decade. The Brookings Institution argues that AI could accelerate economic growth across industries including electricity, finance, healthcare, and information technology.
For the Korean economy, the Bank of Korea sees AI as a key force for offsetting labor shortages in an aging society and improving productivity. Deloitte highlights cost-optimization strategy as critical in AI economics, and Samsung SDS argues that AI cost management will become a core enterprise capability.
Closing
AI is changing business in ways that go far beyond technology. It is fundamentally altering corporate culture and organizational design. The challenge of this era is not simply "adopting AI" but redesigning the business around AI.
Companies need to move quickly on strategic AI investment while also accounting for organizational change, workforce management, and ethical risk. The gap between companies with a clear AI strategy and those without one will keep widening.