AI and Business Strategy: Will Automation Shrink Teams or Create New Competitive Advantages?

Key Facts

AI cuts costs but can shrink markets: Tools like OpenAI, Midjourney, and Stability AI reduce production costs while compressing existing value chains.
Higher productivity ≠ more jobs: If demand lags behind technology, companies produce the same output with fewer people.
When productivity creates new markets: In some cases, technological gains trigger entirely new industries and roles instead of contraction.
Core competencies in the age of AI: Competitive advantage shifts to problem definition, validation, architectural thinking, systems perspective, integration, and decision ownership.

In the previous article, we examined AI through the lens of John Maynard Keynes and the history of technological change. If we accept that automation does not eliminate work entirely but transforms its nature, a new business question emerges: What happens when technology evolves faster than new markets and roles can form?

For business leaders, this is not a theoretical but a structural issue.

When Productivity Rises but Demand Does Not

With artificial intelligence, the dynamic looks like this:

Tools are becoming more powerful.
Companies operate more efficiently.
But new areas and tasks do not grow at the same pace.

As a result, businesses are making the same product in the same market, but with fewer employees. Work volume decreases. Competition among specialists intensifies.

From a cost perspective, this may look like optimization. From a market perspective, it may signal contraction rather than expansion.

A Business Case Study in AI Disruption: The Stock Illustration Market

A clear example can be found in the stock illustration industry.

Until a certain point, the market thrived on freelance illustrators, stock image platforms, and licensed visual content. With the arrival of generative tools such as OpenAI (DALL-E), Midjourney, and Stability AI, the cost of producing acceptable-quality images dropped sharply.

Companies began generating visuals internally. As a result:

Demand for mass-market stock illustration declined
Income for many stock artists decreased
The broader visual content market survived
But the low-cost stock segment contracted significantly

The lesson for business is clear: AI reduces production costs, but it may also compress entire segments of value chains.

The Opposite Scenario: When Productivity Creates New Markets

However, cost reduction does not always lead to a decline in employment. There is also the opposite scenario, where increased productivity triggers a new wave of demand for specialists.

For example, the emergence of compilers and high-level programming languages made software development easier and more affordable. This opened the door to mass-market software, web applications, mobile development, and many other fields.

As a result, the number of jobs increased, and entirely new competencies emerged.

Core Competencies in the Age of AI

If AI changes the structure of work, what capabilities become strategically important for professionals and organizations?

Core competencies you should have in the age of AI

Precise problem definition

The first critical factor is the ability to clearly formulate the task. AI models perform poorly with vague requirements. When initial conditions are unclear, outputs may look convincing but fail in real-world application.

Therefore, the following abilities become a competitive advantage:

Decomposing complex problems
Defining constraints
Setting measurable quality criteria

It is no longer about writing code faster, but about understanding what code is really needed (and whether it is needed at all).

Verification and quality control

The second important skill is checking the result. Though generation has become fast, correctness still requires attention. The code may compile, the text may sound logical, the requirements may look structured, and yet contain contradictions or hidden risks. The speed of creation is increasing, but the responsibility for quality remains the same.

Organizations that fail to invest in validation processes may accumulate invisible technical and operational debt.

Architectural thinking

The importance of architectural thinking is becoming increasingly significant. It is now easy to obtain fragments of a solution, but the more difficult task is to integrate them into the existing system.

New fragments must align with current architecture and not duplicate the logic that has already been implemented. The cheaper the generation, the easier it is to produce large volumes of material without sufficient quality control.

Systems perspective and consequence analysis

AI increases the number of possible solutions. Hence, the role of a systemic view (understanding interrelationships, dependencies, and constraints) is growing. Speed of response becomes less important than the ability to evaluate consequences.

Integration as a structured process

For users, integration becomes a separate skill. This does not necessarily mean complex automation via API. It can be a well-thought-out workflow.

For example:

Analyzing requirements
Forming user stories
Refining acceptance criteria
Identifying risks
Preparing test cases

AI may assist at every stage, but the structure of the stages is defined by humans. Without structured integration, AI usage becomes fragmented experimentation rather than operational leverage.

Responsibility and decision ownership

Another aspect is responsibility. The model can offer a solution, but the legal, technical, and reputational consequences remain with the specialist.

Remember that the easier creation becomes, the higher the standard of oversight must rise. Fast output does not automatically equal business value.

Market Expansion or Market Overheating?

In an environment of cheap generation, two macro-scenarios are possible:

Expansion with new ideas and directions.
Overproduction with excessive solutions and declining differentiation.

A specialist who understands why a product exists and what problem it solves is in a more stable position. AI does not replace professionals. It raises the level of thinking required from them.

From Executor to Structural Shaper

Each of the skills described performs a specific function.

Clearly formulating the task reduces the risk of creating solutions that do not initially correspond to the real problem.
Checking the result helps to distinguish a convincingly formulated answer from a correct one.
Architectural thinking allows you to consider the impact of new solutions on the entire system, not just on a separate fragment of it.
A systematic perspective helps you see the interrelationships and consequences of changes.
Integration turns the use of AI into a structured process, rather than a set of disparate actions.
Responsibility keeps the specialist in the role of the one who makes the final decisions.

These skills do more than just increase personal effectiveness. They determine whether a specialist will remain simply a faster performer or become someone who changes the very structure of work. In the first case, AI reduces the number of participants in the process. In the second, it creates new roles and directions.

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