For over two decades the IT market has operated according to a relatively simple model.
A company has a problem. It buys a system. It implements the system. It trains employees. It learns to use the system. And then it tries to achieve the intended business goal.
This is what implementations looked like:
- CRM,
- ERP,
- warehouse systems,
- e-commerce platforms,
- marketing tools,
- project management systems.
For years nobody was surprised. After all, software was a tool meant to help achieve a specific result.
But today a question arises that a few years ago would have sounded absurd: Will companies still buy software?
Or will they start buying ready-made competencies delivered by artificial intelligence?
This is no longer a futuristic vision. The first signs of this change are already visible.
How has the IT market worked so far?
For decades we bought tools.
An example?
A company needed better sales management. - It bought a CRM.
It needed reporting. - It bought a BI system.
It needed marketing automation. - It bought another tool.
The problem was that buying a system never automatically meant achieving the goal.
Buying a CRM did not guarantee increased sales.
Buying an ERP did not guarantee better management.
Buying a marketing platform did not guarantee more customers.
A company bought the ability to perform the work. Not the effect itself.
And that is precisely what may change.
Software-as-a-Service - the model that dominated the world
First came SaaS (Software-as-a-Service).
Instead of buying a license and installing a system on their own servers, companies began using subscription services.
Thousands of platforms emerged:
- CRMs,
- HR systems,
- helpdesks,
- e-commerce platforms,
- marketing tools.
The model proved a huge success.
But it had one drawback - it still required operation. A person did the work. The system was only a tool.
What does AI change?
For the first time in history a tool begins to perform part of the work autonomously. This is a fundamental difference.
Example.
Classic CRM:
- stores customer data,
- allows adding notes,
- generates reports.
AI agent:
- analyzes customers,
- detects churn risk,
- proposes actions,
- prepares messages,
- triggers workflows.
And in the future it may do even more.
And that is why people increasingly talk about a new model.
Outcome-as-a-Service
This concept will appear more and more often.
Outcome means result.
We no longer buy a tool. We buy the effect.
Example.
Today a company buys a marketing automation platform.
Tomorrow it may buy: "acquisition of 100 qualified leads per month".
Without caring about the technology working in the background.
For the client the result matters. Not the tool.
This is a very similar change to the one we observed with the cloud.
Few people today think about which server an application runs on. What matters is the availability of the service.
The same could happen with AI.
Agent-as-a-Service
An even more interesting direction is Agent-as-a-Service.
That is the possibility of "renting" competencies.
Not a person.
Not a system.
AI agents.
Imagine a company that needs sales support.
Instead of hiring an additional person or buying another tool, it uses an agent responsible for:
- lead analysis,
- preparing offers,
- qualifying customers,
- reporting.
The company does not buy functions. It buys competence.
This is a very big change in the way we think about technology.
Glossary of terms
SaaS (Software-as-a-Service)
A model of delivering software as a subscription service.
Outcome-as-a-Service
A model in which the client pays for a business outcome, not for the tool itself.
Agent-as-a-Service
A service that provides specialized AI agents performing specific tasks.
AI Agent
A system capable of analyzing information, taking actions and achieving specific goals.
AI Orchestration
Coordination of many agents, processes and systems to accomplish a business task.
What might a company look like in a few years?
Imagine a medium-sized enterprise.
Today it has:
- CRM,
- ERP,
- helpdesk,
- accounting system,
- marketing platform,
- reporting system.
Employees log into many applications every day. They switch between screens. They export data. They create reports. They move information.
In a few years another model is possible - the employee communicates the goal.
Layer of AI agents:
- collects data,
- analyzes the situation,
- takes actions,
- reports results.
Systems still exist, but they cease to be the center of work - they become data sources.
Does this mean the end of traditional SaaS?
No. At least not in the next few years.
Business systems will still be needed.
ERP will not disappear.
CRM will not disappear.
E-commerce platforms will not disappear.
However, the way they are used will change.
Increasingly the user will not work directly in the system. They will collaborate with a layer of intelligence that will perform most operations.
Where can companies make mistakes?
The biggest mistake is believing that AI will solve everything by itself.
It doesn't work that way.
An agent without data is useless.
An agent without integration is limited.
An agent without oversight can make wrong decisions.
Therefore the future is not about replacing all systems with AI. It's about intelligently connecting them.
Red flags in implementations
🚩 A company implements AI without organized data.
🚩 The agent does not have access to real processes.
🚩 Validation and human supervision are missing.
🚩 AI operates next to the organization instead of within its processes.
🚩 Full autonomy is expected from day one.
Such projects very often end in disappointment.
How to prepare for this change?
You don't need to build an army of agents right away. It's worth starting from the fundamentals.
1. Organize your data
Data will be the currency of the new AI economy.
2. Build an API-first architecture
Integrations will become even more important than they are today.
3. Document processes
An agent cannot take over a process that nobody understands.
4. Think about outcomes
Don't ask:
"Which tool should we buy?"
Ask:
"What outcome do we want to achieve?"
Real-life example
Imagine a customer service department.
Classic approach:
- ticketing system,
- manual analysis of requests,
- manual assignment of cases,
- reporting.
Agent approach:
- the agent classifies requests,
- determines priority,
- assigns to the appropriate department,
- prepares the response,
- monitors deadlines,
- reports results.
The employee still exists. But their role changes from operator to process supervisor.
The most important conclusion
The future of the IT market may not be about buying more systems. It may be about buying the ability to achieve specific results.
Just as companies stopped buying their own servers and started using the cloud.
They may stop buying more tools and start using competencies delivered by AI agents.
This does not mean the end of software. It means a change in its role.
Software will increasingly become invisible. The visible thing will remain the result.
And after all, business has always been about the result.
