Digitalization
Information becomes available digitally.
Why companies still leave potential on the table despite digitalization
Most companies have digitalized a great deal in recent years. ERP and CRM are running, documents are available digitally, teams work together through shared tools. And still there are numerous manual steps in between.
Employees look up information, transfer data between systems, chase approvals or prepare decisions. Each individual activity looks small. Taken together, they cost a considerable part of the working day.
This is what typically happens
- Data and information are looked up or transferred by hand
- Emails are read and forwarded
- 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.
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.
Digitalization makes sure data is available digitally. Classic automation handles fixed workflows according to clear rules. AI automation goes beyond that. It grasps connections, responds to the context at hand and supports decisions instead of just working through a fixed scheme.
Not every activity is necessarily fully automated. By integrating AI, routines are taken off people's hands and employees are supported in the decisions that really count.
Automation
Fixed rules take on individual work steps.
AI automation
AI understands information, supports decisions and takes 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
- follows if-then logic
- needs structured data
- works through fixed workflows
AI automation
- understands the context
- processes unstructured information
- responds flexibly to new situations
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 process orchestration
On the third level, 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 interlock into larger workflows – the basis for automating a company end to end.
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
Typical areas of use
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
Typical areas of use
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
Typical areas of use
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.
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.
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
A company in which digital coworkers are a firm part of value creation and people can concentrate on strategic decisions.
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.
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?
Trust needs more than promises: it grows out of transparency and the ability to step in at any time.
Why companies implement AI automation with agenic
Successful AI automation is rarely the result of a single technology. It grows out of 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. Where a task calls for a different solution, we go beyond it. At the center is always the process you want to improve. The technology follows from that.
AI projects from practice
A selection of AI applications that are in productive use today and take on typical tasks in very different areas.
Assessing tenders
From extensive documents in SharePoint, a consistent profile takes shape, complete with references to the original sources – for a fast initial assessment, without switching to a separate tool.
Evaluating documents
An AI summarizes long documents, extracts the relevant information and matches it against defined criteria.
Sorting mailboxes
Incoming emails are categorized automatically, assigned to the right places and given follow-up actions.
Creating profiles
Employee profiles are created automatically, with personal data left out and the AI improving only the wording.
Making knowledge accessible
An assistant based on Copilot Studio guides employees through content and applications on the intranet.
Setting up projects
New projects are created automatically and the matching structures are generated in the connected systems.
Is your company ready for AI automation?
How quickly AI automation takes effect in your company depends above all on four things.
Processes
Are your workflows documented and the 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 named?
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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