Data
are looked up or transferred manually
Potential
Why companies still leave potential on the table despite digitalization
Most companies have digitalized heavily in recent years. ERP and CRM are running, documents are available digitally, teams collaborate through shared tools. And yet there are still countless manual steps in between. Each individual activity seems small. Taken together, however, they take up a considerable part of the working day.
Information
is received and passed on
Decisions
are prepared and wait for approval
Tasks
are coordinated across several places
Data is already plentiful in most companies. The real task is to connect information, decisions and workflows across the whole company. Only when that succeeds do digitalized processes become an intelligently automated organization.
Most companies have digitalized their processes. Few have automated them intelligently.
Definition
What does AI automation mean?
AI automation means having recurring tasks and entire process steps carried out with the help of artificial intelligence. It can start with individual activities and extend to end-to-end processes that connect several applications and business areas. The difference from classic digitalization is that information is not only available digitally, but is also understood and processed further. Integrating AI takes routines off people’s hands and supports employees in the decisions that really count.
Digitalization
Making information available digitally
Digitalization ensures that data and information are available digitally and can be used in further processes. It thus creates the basis for automated workflows.
Automation
Fixed rules for digital workflows
Classic automation takes over clearly defined work steps according to fixed rules. It is suited to recurring workflows in which decisions follow a set pattern.
AI automation
Taking context into account
AI automation processes information in its respective context and takes relationships into account. This allows it to support decisions and take on tasks depending on the situation.
Why now?
Powerful language models and a new generation of agent technologies are available today, many of them already built into applications such as Microsoft Dynamics 365 and Microsoft 365. Where the standard is not enough, we bring in custom technologies as well. What would have meant a development project of its own a few years ago can now be integrated into existing business processes step by step.
Why classic automation is no longer enough today
Classic automation still does excellent work – wherever rules are unambiguous, data is cleanly structured and workflows repeat reliably. In those places it remains the first choice.
Everyday work often looks different. A large part of processes sits in emails, documents, call notes and exceptions that cannot be pressed into a fixed scheme. This is where classic process automation reaches its limits. Artificial intelligence, by contrast, can read such content, classify it and respond to it depending on the situation.
Classic process automation
AI automation
The three levels of modern AI automation
Artificial intelligence in a company works on three levels. They build on each other and can be used individually or in combination. How many of them you need depends on the process – from supporting individual employees to orchestrating workflows company-wide.
AI assistants
AI assistants support daily work. They answer questions, create drafts, evaluate information and bring extensive knowledge to the point in a few sentences. The best-known example in the Microsoft world is Microsoft Copilot.
AI agents
AI agents go one step further. They take on clearly defined tasks within a business process, work independently and keep to the rules you set for them.
Intelligent processes
Several specialized agents, applications and systems mesh. Information flows between departments, tasks are handed on and the next steps are triggered automatically. Individual automations thus become larger workflows – the basis for automating a company end to end.
Areas of use
Where companies automate with AI today
AI automation becomes most tangible in the concrete business process. Today that shows most clearly in finance, in sales and in the supply chain.
AI automation in financial accounting
In the finance department, invoice checking, payment matching and preparing closes take up a lot of time. AI automation takes on most of the recurring steps and only puts before your employees what really needs a human decision.
- 01Invoice arrives by email
- 02AI reads the contents
- 03Matching against the ERP
- 04Proposed posting
- 05A person approves
AI automation in sales and service
In sales, speed often decides. When an inquiry comes in, the AI qualifies the contact, completes the CRM and prepares the next step. Your team steps in exactly when it gets personal.
- 01Lead comes in through the website
- 02AI qualifies
- 03The CRM is completed
- 04The appointment is prepared
- 05Sales takes over
AI automation in the supply chain
In the supply chain, every hour in which a problem is spotted early counts. If a delivery delay comes in, the AI analyzes the consequences, informs those affected and proposes alternatives before it turns into a larger bottleneck.
- 01Delivery delay detected
- 02AI analyzes the effects
- 03Stakeholders are informed
- 04Alternatives are proposed
- 05Measures are coordinated
These examples unfold their full effect as soon as the areas work together. Information from one process is directly available in the next and triggers the following steps there. How far that can be taken is shown in the next section.
Fully automating the company
From a single process to a fully automated company
With full automation, individually automated tasks are connected into end-to-end workflows. A customer order, for example, directly triggers the next steps in sales, service, supply chain and finance. The necessary information reaches the right places automatically, tasks are triggered and the areas involved continue working from the same data.
Employees define which rules apply, which decisions need approval and where they take over themselves. Recurring tasks and coordination between systems can be handled by AI agents and automated workflows.
The more of these workflows mesh, the more a company's processes connect across departments and systems. Step by step, individual automations become an agentic enterprise.
For workflows to work together across system boundaries, the applications involved have to be connected. For many common systems, interfaces and add-ons already exist as ready-made building blocks, so that data flows between ERP, document storage and specialist applications.
End-to-end automation makes companies faster and more scalable above all. When more orders, inquiries or transactions have to be processed, the manual coordination effort does not automatically rise to the same degree. Specialists gain time for the cases where experience, personal exchange or a deliberate decision is needed.
Agentic enterprise
From Copilot to the agentic enterprise
Where a company stands today varies widely. Some use a copilot for individual tasks, others already let complete process steps run independently. The following maturity model helps you place your own position and determine the next step.
- Level 1
AI delivers answers
Ask questions. Get information.
- Level 2
AI supports processes
Suggestions, analysis and recommendations.
- Level 3
AI takes on tasks
Recurring activities are automated.
- Level 4
Agents work together
Several specialized agents coordinate complex workflows.
- Level 5
Agentic enterprise
Digital workers are a fixed part of value creation.
You can start at any of these levels. A copilot in a single area can be the beginning just as much as an agent for a clearly defined process. Over time, further tasks and areas can be connected, until end-to-end, agent-supported workflows emerge.
The agentic enterprise describes this development at company level: people set goals and guardrails, while digital coworkers take on a growing share of operational workflows.
Framework & control
AI automation needs trust
As soon as AI takes on tasks independently, one question becomes more important than any feature. Can you trace and steer what is happening there? Productive AI automation therefore needs clear guardrails from the start.
Access
Which data may be used?
Governance
Which rules apply?
Traceability
Why was a decision made that way?
Control
Where does a person stay involved?
It comes from transparency and the ability to step in at any time.
Trust needs more than promises.
Why agenic
Why companies implement AI automation with agenic
Successful AI automation is rarely the result of a single technology. It comes from the interplay of business processes, data, enterprise applications and artificial intelligence. That is what agenic stands for.
Business applications: Deep experience in ERP, CRM and the processes behind them.
Microsoft AI: Copilot, agents and the AI technologies of the Microsoft ecosystem and beyond.
Software engineering: Custom AI solutions when the standard is not enough.
Connected operations: Connecting applications, data and processes intelligently.
Technology-open instead of platform thinking
Our focus is on the Microsoft ecosystem. But where a task calls for a different solution, we go beyond it. The focus is always on the process you want to improve. The technology follows from that.
Is your company ready for AI automation?
How quickly AI automation has an impact in your company depends mainly on four things.
- Processes: Are your workflows documented and responsibilities clear?
- Data: Is the necessary information available digitally, and is the quality right?
- Systems: Can your applications exchange data, or are there media breaks?
- People: Is there openness to AI, and have the first use cases been identified?
A short assessment often shows where your company stands today faster than any long analysis. In an initial conversation we classify your status and name the next sensible step.
FAQ
Frequently asked questions about AI automation
What is AI automation?
AI automation connects your data, applications and processes with artificial intelligence. Unlike pure digitalization, information is not only made available but also understood and processed further. Routine tasks can be taken off people's hands and decisions supported deliberately, without every step having to be triggered manually.
What sets AI automation apart from classic automation?
Classic automation follows fixed if-then rules and needs cleanly structured data. AI automation also understands unstructured content such as emails or documents and responds depending on the situation, without every individual case having to be programmed in advance. The two do not exclude each other. They often complement one another.
Which processes are particularly suited to AI automation?
Well suited are workflows that recur often, take up a lot of time and require manual steps today. Typical examples are invoice checking, lead qualification or supply chain monitoring. As a rule of thumb: the more information has to be brought together from different sources, the greater the benefit.
Do we need new software for it?
In most cases, no. AI automation builds on your existing systems, for example Microsoft Dynamics 365, Microsoft 365 or other enterprise applications. It complements your IT landscape instead of replacing it.
Do we need AI agents for AI automation?
Not necessarily. Many companies start with an AI assistant such as Microsoft Copilot and automate first tasks without agents of their own. AI agents come into play when a process is to run independently. What agents do exactly is explained on our AI agents page.
What is the difference between Microsoft Copilot and AI agents?
Microsoft Copilot is an assistant. It supports your employees as soon as they ask it something. An AI agent works independently on a defined task and does not have to be triggered again at every step. Put simply, the copilot helps with the work, while the agent does the work.
How do I start with AI automation?
The most sensible first step is taking stock. Which processes are documented, which data is available digitally, which systems are already connected? Our agent readiness check provides an initial assessment from which a suitable entry point can be derived.
What prerequisites should be in place?
Three things help most: documented processes, digitally available data and systems that can be connected. None of it has to be perfect to start. What matters is a clearly defined use case in which the benefit shows quickly.
Is AI automation possible in line with the GDPR?
Yes. AI automation can be implemented in a privacy-compliant way if it is clear from the start which data is used and where decisions stay with people. The Microsoft ecosystem provides governance and traceability features for this, which we set up together with you.
What does AI automation cost?
That depends on the scope, from individually automated tasks to cross-process solutions. Many AI features in Microsoft Dynamics 365 and Microsoft 365 are billed based on usage, in addition to your existing licenses. A manageable entry point that grows with the benefit usually makes the most sense.
What does it mean to fully automate a company?
Fully automating a company means automating recurring workflows and entire business processes as end to end as possible. Information is passed between applications and business areas, follow-up tasks are triggered automatically and AI agents take on defined tasks. Employees set goals, rules and approvals and take over for exceptions or decisions with greater consequences.
Does AI automation replace jobs?
The focus is on relieving routine. AI automation takes on recurring tasks, while employees set goals and guardrails and take over where experience, exchange or a deliberate decision is needed. Teams gain time for the work that really counts.
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Where can AI make a difference in your processes?
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