The Commoditization of AI: How SMEs and Large Companies Navigate the New Competitive Landscape

Artificial intelligence is no longer the privilege of Big Tech. Learn how the democratization of AI is revolutionizing the competitive landscape and what strategies companies of all sizes are adopting to stay competitive.

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The Great Leveling: When AI Becomes Accessible to All

The year 2025 marked a momentous turning point in the artificial intelligence market. As highlighted by industry analysts, while costs to customers are falling toward zero, the fundamental question emerges as to how companies can maintain their competitive value in a landscape where the most advanced technologies are rapidly becoming commodities.

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The commoditization of AI is no longer a future prediction, but a tangible reality that is transforming the rules of the game for companies of all sizes. The democratization of artificial intelligence enables small companies and startups to leverage sophisticated algorithms that were once accessible only to tech giants with enormous resources.

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AI's "Sputnik" Moment: The DeepSeek Case

The event that best symbolized this transformation was the launch of DeepSeek in January 2025. The Chinese startup demonstrated that cutting-edge AI models can be developed with only $5.6 million, a fraction of the $78-191 million needed for GPT-4 and Gemini Ultra.

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Marc Andreessen, one of Silicon Valley's most influential venture capitalists, called the launch of DeepSeek "one of the most amazing and impressive breakthroughs I've ever seen -- and as open source, a profound gift to the world."

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The Impact Of Commoditization On Companies Of Different Sizes.

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Large Companies: From Technological Differentiation to Strategic Value

Large corporations are facing a strategic revolution. As Databricks experts point out, "companies can realize huge efficiency gains by automating basic tasks and generating data intelligence on demand, but this is only the beginning."

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Microsoft, for example, reported that more than 85 percent of the Fortune 500 are using Microsoft AI solutions, with 66 percent of CEOs reporting measurable business benefits from generative AI initiatives. The company has developed innovative strategies such as:

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Copilot Business Transformation: Accenture used Copilot Studio to grow its Center of Excellence team, achieving significant annual savings and reducing IT demand for short-term applications by 30 percent
Seamless Integration: Transformation of existing processes rather than simple technology overlay

SME: The Opportunity for Democratization

For small and medium-sized enterprises, the commoditization of AI represents a historic opportunity. As one industry expert notes, "AI commoditization democratizes access to powerful AI capabilities, promoting competitive advantage and innovation across industries."

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Specific benefits for SMEs:

Reduced barriers to entry: Access to previously prohibitive technologies
Optimized operating costs: Automation of costly manual processes
Accelerated scalability: Ability to compete with larger players
Agile innovation: Rapid experimentation with new business models

However, as experts warn, "quality control, scalability, ethical considerations and market saturation pose significant challenges for companies adopting commoditized AI solutions."

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The Three Pillars of Competitive Advantage in the Post-Commoditization Era

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1. Strategic Problem Selection.

Organizations emerging in 2025 have recognized that sustainable AI advantage comes less from the technology itself and more from three interdependent factors, starting with strategic problem selection and framing.

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It is no longer about applying AI to obvious use cases, but about developing systematic approaches to identify high-leverage business problems where AI can unlock disproportionate value.

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Sector Case Study:

Manufacturing: Manufacturing companies can use data resources from digital manufacturing equipment to optimize the health of their machines
Financial Services: Construction of specialized models based on their deep experience in the field

2. Superiority of Proprietary Data

While the models themselves have become commoditized, proprietary data remains a powerful differentiator. As highlighted by data strategy experts, "as AI capabilities become increasingly commoditized, proprietary data emerges as the critical differentiator for sustainable competitive advantage."

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Strategies for Building a "Data Moat":

Systematic collection through strategic partnerships
Incentive mechanisms for users who provide valuable data
Deployment of physical sensors to capture unique real-world data
As experts point out, "The most effective data moats often accumulate through consistent and deliberate efforts over time."

3. Excellence in Integration

The most successful implementations incorporate AI capabilities seamlessly into existing workflows, creating intuitive experiences for employees and customers.

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This integration expertise-the ability to redesign processes around AI capabilities instead of simply layering technology on top of existing systems-has emerged as perhaps the most scarce and valuable skill in the current environment.

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How Companies Are Adapting Their Strategies.

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The Portfolio Approach: Large Companies

Effective AI strategies take a portfolio approach, where one part of the portfolio develops a strong "ground game" to achieve many small wins through a systematic approach.

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Components of the Portfolio Strategy:

Systematic Ground Game:
- Automation of routine tasks
- Incremental improvements in productivity (20-30%)
- Focus on measurable ROI
Transformative Big Moves:
- New business models
- Reinvention of core processes
- Applications that revolutionize industries

The Agile Approach: SMEs and Startups

Smaller companies are taking advantage of their natural agility to:

Rapid Experimentation: Testing new AI use cases with limited investment
Vertical Integration: Focus on specific market niches
Strategic Partnerships: Collaboration with AI vendors for access to advanced capabilities

As one industry expert notes, "companies that build domain-specific solutions or layer proprietary data on commoditized models will have the advantage."

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Sectors at the Front Line of Transformation

Healthcare: Pioneering AI Innovation

The healthcare sector is driving AI adoption, with a particular focus on workforce transformation, personalization, technology upgrades, and eliminating "process debt" from pre-AI processes.

Transformative Applications:

Assisted diagnosis systems based on multimodal AI
Revenue and operating volume optimization
Support for clinical staff shortages

Financial Services: Reinventing Fintech

There has been a resurgence in the fintech space with native AI companies focused on solving old problems with new platforms and business models.

Emerging Trends:

Automation of due diligence and compliance
Risk assessment systems based on proprietary data
Algorithmic trading platforms democratized

Manufacturing: The Age of the Digital Twin

By 2030, many companies will approach "data ubiquity," with data embedded in systems, processes, channels, interactions and decision points that drive automated actions.

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Challenges and Risks of Commoditization

Risks for Large Companies

Erosion of Technology Moats: As MIT experts warn, "once AI becomes pervasive, it no longer provides companies with an advantage over rivals."
Pressure on Margins: Need to reinvent value propositions
Integration Complexity: Companies face technical hurdles in integrating multimodal and multi-agent systems with existing IT infrastructure

Challenges for SMEs

Quality Control: Difficulty in ensuring high standards with commoditized solutions
Scalability: Managing growth while maintaining efficiency
Ethical Considerations: Navigating complex privacy and bias issues without dedicated resources

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The Crucial Role of Human-AI Collaboration.

Redefinition of Work Roles

Research shows that collaboration between humans and artificial intelligence could unlock up to $15.7 trillion in economic value by 2030, but this will depend on measuring the strengths and skills of both.

Evolution of Competencies:

Declining Skills: Routine information processing, basic analysis
Growing Skills: Creative problem solving, emotional intelligence
New Skills: AI agent orchestration, content curation, strategic thinking

Emerging Partnership Models

The research identifies three main types of daily interactions between workers and AI: machines as subordinates, machines as supervisors, and machines as teammates.

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In 2025, organizations will begin to leverage AI agents to transform entire job functions, such as talent acquisition, with proactive passive candidate sourcing capabilities and outreach automation.

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Implementation Strategies for Success

AI Maturity Framework

Although 92 percent of companies plan to increase AI investments in the next three years, only 1 percent of leaders call their companies "mature" in the deployment spectrum.

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Stages of Evolution:

Nascent (8%): Minimal AI Initiatives.
Emerging (39%): Pilot projects that show value
Development (31%): Changing specific workflows
Expansion (22%): Scale across departments.
Mature (1%): fundamentally integrated AI

Practical Recommendations

For Large Companies:

Develop balanced portfolio strategies
Investing massively in data superiority
Adopt modular approach to "avoid vendor lock-in and rapidly implement new AI advances without constantly reinventing tech stacks"

For SMEs:

Focus on "domain-specific applications" that leverage proprietary data
Agile experimentation with controlled budgets
Strategic partnerships for access to advanced capabilities

Governance and Risk Management

The Governance Imperative

In 2025, business leaders will no longer have the luxury of addressing AI governance inconsistently or in isolated areas of the business. A systematic and transparent approach is required.

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Essential Components:

AI governance committees with decision-making authority
Risk management frameworks aligned with standards such as NIST AI RMF
Continuous monitoring for bias, transparency, and compliance

Shadow AI: The Hidden Challenge

In enterprise environments, "employees are driving adoption from the bottom up, often without oversight," creating significant Shadow AI risks.

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Mitigation Strategies:

Proactive discovery of all AI tools in use
Granular policies based on data sensitivity
Implementation of "templates that can identify and classify information as employees share data"

Future Trends: Toward 2030

Multimodal AI Systems

The multimodal AI market exceeded USD 1.6 billion in 2024 and is estimated to grow at a CAGR of 32.7% from 2025 to 2034. Gartner predicts that only about 1 percent of companies were using the technology in 2023, but the figure is expected to jump to 40 percent by 2027.

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Edge AI and Distributed Processing

As AI applications become business-critical, the limitations of the traditional cloud-based approach are pushing companies toward Edge AI to reduce latency, improve data privacy, and increase operational efficiency.

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The Age of Autonomous Agents

Google predicts that AI agents, multimodal AI, and enterprise search will dominate in 2025, with a focus on "agent governance" to support "diverse agents going everywhere and working across all these different systems."

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Conclusions: Navigating the Post-Commoditization Future.

The commoditization of AI does not represent the end of innovation, but rather the beginning of a new era where value shifts from technology to organizational capabilities. As the research points out, "the era of AI experimentation is behind us. We have entered the era of AI operationalization, where lasting advantage comes from organizational capabilities built around the technology."

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The companies that will prosper will be those that:

They build sustainable data moats
They excel in AI-human integration
They maintain agility in adopting new technologies
They develop robust but flexible governance

As the MIT researchers conclude, "companies must cultivate creativity, determination and passion. These are the same pillars of innovation that have always distinguished great companies; AI doesn't change any of that."

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FAQ: AI Commoditization and Corporate Strategies

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Q1: What exactly does "commoditization of AI" mean?

A: Commoditization of AI refers to the process by which AI technologies that were once unique and high-margin become indistinguishable from other products in the market, leading to increased competition and lower prices. As highlighted by industry analysts, this process is accelerated by the decline of AI token costs toward zero and the democratization of access to sophisticated capabilities.

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Q2: How can an SME compete with large tech companies in the era of commoditized AI?

A: SMEs have several advantages in the era of commoditized AI:

Agility: Ability to experiment and pivot quickly
Vertical focus: Specialization in specific market niches
Reduced costs: Access to "sophisticated algorithms that were once accessible only to tech giants"
Strategic Partnerships: Collaboration with AI vendors for advanced capabilities

Q3: What are the main risks of AI commoditization for companies?

A: The main risks include:

For large companies: Erosion of existing technology advantages, pressure on margins, complexity of integration
For SMEs: Challenges of "quality control, scalability, ethical considerations and market saturation"
For all: Shadow AI risks, regulatory compliance, external vendor dependence

Q4: How long does it take to implement an effective AI strategy?

A: Research shows that more than two-thirds of leaders launched their first generative AI use cases more than a year ago, but only 1 percent consider themselves "mature" in implementation. A typical roadmap includes:

0-6 months: foundation and quick wins
6-18 months: Scaling and advanced integration
18+ months: Complete business transformation

Q5: What skills do employees need to develop in the era of commoditized AI?

A: Key competencies include "creativity in problem solving and innovation, emotional intelligence and interpersonal skills, and the ability to quickly acquire new skills or adapt to changing circumstances." In addition, they become crucial:

Prompt engineering and curation of AI content
Orchestration of digital agents
Strategic thinking and business acumen

Q6: How can companies build a sustainable "data moat"?

A: Experts recommend a systematic approach that includes, "deliberate collection through strategic partnerships, incentive mechanisms for users who provide valuable data, and deployment of physical sensors to capture unique real-world data." It is critical to remember that the most effective data moats are built over time through consistent efforts.

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Q7: Which sectors are benefiting most from AI commoditization?

A: Leading sectors include healthcare, technology, media and telecommunications, advanced industries, and agriculture. Healthcare is leading the way with focus on workforce transformation and personalization, while financial services is seeing a resurgence in fintech with native AI solutions.

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Q8: How to manage the risks of "Shadow AI" in the enterprise?

A: Effective management requires: "proactive discovery of all AI tools in use, granular policies based on data sensitivity and roles, continuous monitoring with risk classification." It is essential to move from "block and wait" strategies to proactive governance approaches.

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Q9: What is the typical ROI of investments in AI?

A: Currently, only 19% of C-level executives report revenue increases greater than 5%, with 39% seeing moderate increases of 1-5%. However, 87% of executives expect revenue growth from generative AI within the next three years, suggesting that full value will be realized in the medium to long term.

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Q10: How to choose between proprietary and open source AI solutions?

A: The choice depends on several factors:

Open Source: Greater flexibility, reduced costs, transparency, but requires in-house technical expertise
Proprietary: Dedicated support, easier integration, but higher costs and possible vendor lock-in
Experts recommend a "modular approach to avoid vendor lock-in and rapidly implement new AI advances"

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