For decades, mergers and acquisitions (M&A) have been seen as a strategic tool for expanding market share, increasing asset size, and optimizing cash flow. These deals typically revolve around familiar questions: what is the target company's market share, tangible assets, EBITDA, and synergy potential?.
However, with the advent of the AI era, the value structure of businesses is changing. Technology is no longer a supporting component, but has become a core competency that determines long-term competitive advantage. Therefore, the mindset regarding M&A must also shift.
M&A in the AI era is not just about buying businesses to scale up. It's a strategy of buying to restructure, buying to integrate data, buying to AI operations, and building a new operating system for the business.
1. Traditional M&A: Market Share, Assets, and Cash Flow
In the traditional model, the motivation for M&A typically focuses on three main factors:
Market share – Acquiring competitors to expand market share or eliminate competition.
Tangible assets – Factories, real estate, brands, distribution systems.
Cash flow – The company has stable EBITDA and generates consistent profits.
Business valuation is primarily based on:
- EBITDA multiple
- Discounted Cash Flow (DCF)
- Net worth
The goal is to optimize capital structure and improve financial performance after the merger. This approach remains valuable. However, in a rapidly changing environment with big data and 24/7 markets, tangible factors are no longer sufficient to create a sustainable competitive advantage.
2. M&A in the AI Era: Data, Infrastructure, and Automation
As AI becomes central to operations, the focus of M&A shifts to less visible assets with higher long-term value:
Data AssetsIt's not just about customer data, but about the ability to collect, standardize, and utilize data in real time.
Technology infrastructureAPI system, cloud platform, computing power, data architecture, and AI integration capabilities.
Automation CapabilityThe process can be standardized and integrated into an AI Operating System.
In this context, a traditional business may be valued higher if:
- It has a clean and exploitable data structure.
- It has a clear process and is easy to AI.
- There is a sufficient technological foundation for integration.
The value no longer lies solely in current profits, but in the potential for transformation after AI integration.
3. Businesses can no longer be valued solely by EBITDA.
A business with modest EBITDA but high-quality data, standardized processes, and AI integration capabilities may have the potential to significantly increase profit margins after restructuring.
In the age of AI:
- Profit margins can be improved through automation.
- Personnel costs can be reduced thanks to AI agents.
- Operational risks can be controlled using the AI Risk Engine.
- Decision-making speed is significantly shortened.
These factors change how investors perceive the value of a business.
A company with a robust AI Operating System can achieve a higher valuation due to:
- Scalability
- Growth rate
- Low level of individual dependence
- Real-time risk control capabilities
This is why M&A in the AI era cannot rely solely on financial reports.
4. AI Operating System: A New Valuation-Boosting Factor
The AI Operating System is not a single piece of software.
It is an integrated system comprising:
- AI Financial Engine
- AI Risk & Compliance
- Multi-Agent System
- Data Infrastructure
After an M&A, instead of maintaining the old structure, the business can be restructured by:
- Data normalization
- Process automation
- Integrating AI for pricing and monitoring.
- Develop a unified operating system.
Value is not added simply through cost reduction, but through upgrading the operational structure.
In the long term, businesses without an AI Operating System will face limitations in terms of growth rate and risk control. Conversely, AI-powered businesses can scale without a corresponding increase in operating costs.
5. Buy to Transform: The "Buy & AI Transform" Mindset“
One of the prominent strategies of M&A in the AI era is:
Acquire traditional businesses and AI-ize their operations.
Instead of building from scratch, this model helps:
- Shorten time to market entry.
- Leverage existing teams and systems.
- Reduce testing risks.
- Accelerate the AI integration process.
The real value lies not in the acquisition itself, but in the subsequent transformation phase. The target business becomes the platform for deploying the AI Operating System.
6. HVA and its pioneering strategy in M&A linked to AI.
In this context, HVA positions itself not only as a financial investor, but also as a pioneer in AI-integrated M&A models in Vietnam.
HVA's strategy doesn't stop at acquiring shares.
The goal is:
- Integrating AI Financial Engine into operations
- Data and process standardization
- Building a Multi-Agent System
- Restructuring the governance model
Through this approach, each transaction not only increases assets but also enhances the core capabilities of the ecosystem. HVA pursues a long-term mindset: M&A is not just about scaling up, but about building a smart operational foundation for the next decade.
Conclude
The AI era is changing the value structure of businesses. M&A is no longer simply a game of market share and tangible assets.
In the new environment, data, technology infrastructure, and automation capabilities have become central elements. Businesses are valued not only on EBITDA, but also on their ability to integrate and operate AI operating systems.
M&A in the AI era is a strategy for restructuring core competencies, and pioneering organizations in the "Buy & AI Transform" model will be the ones reshaping the game.
