We hear about artificial intelligence almost every day. Chatbots, agents, automation and various other AI-related solutions are frequently mentioned. From the outside, it may seem that most companies are already working with highly advanced AI systems.
In reality, however, the development is much more gradual. AI adoption happens step by step, much like digitalization or the transition to cloud services in previous years. Most organizations are still at an early stage, and AI is often used only within individual departments or even at the level of individual employees.
International research, including studies by Boston Consulting Group, shows a similar pattern. While many companies today experiment with AI, only a relatively small share has already generated significant business value from it. In many cases, AI remains in isolated initiatives without strategic integration across the organization.
Based on my professional experience and observations in the market, I estimate that companies are currently at very different stages of AI maturity. To structure this landscape and make it easier to understand, the development of AI adoption can be divided into five maturity levels.
Level 1- Individual Productivity AI
In 2026, the majority of companies are still at this level.
At this stage, AI is primarily used as a personal productivity tool. It helps write texts, analyze information, generate ideas or produce code. However, it does not operate independently within business processes.
AI simply reacts to human prompts and does not initiate actions on its own. It is not integrated into company systems and does not make decisions on behalf of the organization.
This is the most common and easiest way to adopt AI, essentially representing productivity improvements at the individual level.
Approximately 70–85% of companies are currently at this stage.
Examples:
- Marketing: AI generates LinkedIn posts, advertising copy or product descriptions in multiple versions, which a human then edits and publishes.
- Legal: AI helps draft an initial contract structure or explain specific clauses that are later reviewed by a lawyer.
- Software development: AI generates code fragments, tests or documentation, which developers validate and integrate.
- Management: AI prepares meeting summaries or helps structure ideas for presentations.
At this level, AI essentially functions as “a smarter Google combined with a very fast intern.”
Level 2 – AI with Company Data Context (RAG)
At this stage, AI is connected to internal company data sources such as documents and databases. Instead of relying only on general internet knowledge, the AI can generate responses based on company-specific information. It becomes a knowledge assistant that helps employees navigate internal data.
However, AI still mainly provides recommendations or prepares information for human decision-making. Business processes themselves are not yet fully automated.
Even so, this level already delivers meaningful operational value.
Approximately 10–20% of companies are currently at this stage.
Examples:
- Internal knowledge assistant: An employee asks: “What is the company’s travel policy for the UK?” The AI finds the relevant document and responds with references to the exact policy section.
- Customer support: An operator asks AI about a specific product. AI searches company manuals and helps generate an accurate response.
- Contract analysis: AI checks whether a contract draft complies with the company’s standard legal conditions.
- Sales: AI analyzes CRM data and prepares a personalized proposal draft for a specific client.
At this stage, AI already creates structured operational value because it works with company-specific data rather than the entire internet.
Level 3 – Process AI (Agentic AI)
At this level, AI begins to participate in real operational workflows.
AI can analyze a situation, determine the next step and trigger actions within systems or tools. Typically, these actions occur under human supervision or with approval mechanisms.
AI becomes a digital colleague rather than just an information assistant.
This enables automation of repetitive and structured tasks. However, implementing AI at this level requires well-organized processes and reliable data.
Approximately 3–7% of companies currently operate at this level.
Examples:
- Task processing: AI receives a request → checks CRM → identifies the appropriate team → prepares a proposal → sends it for client approval.
- Finance: AI classifies incoming invoices → checks them against budget → prepares payment instructions → a human approves.
- IT support: AI analyzes an incident → checks the known issues database → executes a server restart script → informs the user.
- HR recruitment: AI analyzes CVs → compares them with job requirements → prepares a candidate shortlist → schedules interviews.
At this stage, AI truly becomes a digital coworker, not just an assistant.
Level 4 – Multi-Agent Systems
At this level, multiple specialized AI agents collaborate with each other.
Each agent has a specific role — for example analysis, planning, execution or quality control. These agents coordinate their actions and solve complex tasks that go beyond a single process.
The system begins to resemble a digital team with distributed responsibilities.
Humans define strategy and supervise outcomes, while day-to-day execution may be largely automated.
This level requires significant technological and organizational maturity.
Approximately 1–2% of companies are currently at this stage, and most implementations are still pilot projects or found in highly technology-driven organizations.
Examples:
- E-commerce: Customer behavior agent analyzes user activity on a website
Segmentation agent defines customer profiles
Offer generation agent creates personalized product recommendations
Pricing agent adjusts promotions or discounts
A communication agent sends personalized messages to the customer. All of these decisions happen within seconds without manual campaign planning. - Financial analytics automation: Data collection agent gathers data from ERP, CRM and accounting systems
Quality control agent detects inconsistencies or missing records
Analysis agent builds financial models and forecasts
Risk assessment agent identifies potential issues
Reporting agent generates executive-level summaries. In this scenario, reporting is no longer static, it becomes continuous financial situational awareness.
Level 5 – Artificial General Intelligence (AGI)
Artificial General Intelligence (AGI) represents a theoretical level of AI where systems could solve any intellectual task at a human level.
Such systems could transfer knowledge across domains, adapt to entirely new situations and improve themselves based on experience without needing retraining for each new task.
AGI could potentially formulate goals independently, develop strategies and make decisions in complex environments.
It is important to emphasize that AGI is not simply a more powerful chatbot or a collection of AI agents.
It represents a fundamentally different level of intelligence, the ability to understand, learn and reason in a general way.
In practical terms, one might imagine AGI acting as a digital chief strategy officer for a company, analyzing market trends, financial performance and team capacity, setting priorities, initiating new product development and adjusting strategy dynamically.
However, it is important to stress that this level does not currently exist in real enterprise environments. Discussions about AGI remain largely theoretical and belong more to future scenarios than to current business practice.
Where Should Companies Focus Today?
It is valuable to discuss how AI may evolve in the future. However, it is even more important for organizations to clearly understand where they stand today and what practical improvements can be implemented now.
Targeted application of AI solutions can help:
- increase operational efficiency
- reduce manual work
- strengthen competitiveness
The key question is not how advanced AI might become in the future, but how organizations start using it today.
At LTECH, we work with companies on system modernization, process automation and the implementation of AI solutions that deliver practical business value.
If you are considering how AI could be applied within your organization, feel free to reach out, I would be happy to share insights and ideas.