A customer inquiry arrives. Before someone can respond, information from e-mails, CRM and project documents must be collected. Such transitions cost time and make processes error-prone. AI agents, such as Microsoft Copilot Agents, in the company can provide support here: They process information, take into account the respective business context and carry out work steps within defined boundaries.
Their usefulness depends on the specific problem they solve. Seven use cases show where it is worth taking a closer look.
What are AI agents in the company?
AI agents are AI-supported software applications that can evaluate information for a defined task, derive next steps and use approved tools. Depending on the implementation, they answer questions, prepare decisions or initiate actions in connected systems.
For example, an agent can classify a request, search for suitable information and create a task in the CRM. To do this, he needs a clear order, suitable data and limited access rights. Not every process needs an agent: classic automation is often sufficient for fixed, clearly describable processes.
Seven concrete use cases for AI agents
1. Prepare
customer inquiries for service When a service request is made, information about the affected product, the previous progress or the urgency is often missing. An agent can structure the request, prepare queries and refer to relevant knowledge articles.
This gives service employees a better starting point for processing. Complex cases are handed over with a summary. Goodwill decisions or binding commitments remain tied to defined approvals.
2. Classify
sales inquiries A completed contact form says little about whether an inquiry fits your own offer. An agent can structure information about the company, needs and desired time frame and compare it with defined criteria.
This results in a proposal for responsibility and specific questions for the initial consultation. The evaluation should remain comprehensible and explicitly name missing information.
3. Prepare
customer appointments in a targeted
manner Before a customer meeting, relevant information is often distributed via CRM, call notes and open service cases. An agent can use it to create a briefing: What has been agreed? What questions are open? What projects are underway?
This allows the account team to concentrate more on the conversation. Assumptions about additional demand must be clearly separated from documented customer information.
4. Merge
project status and open tasks
In project-based companies, the status report is often created by manual enquiries. An agent can merge available task statuses, logs and project information and point out missing feedback.
The project manager reviews the design and assesses risks. Such an agent creates an overview, but only provides reliable information if the underlying systems are maintained.
5. Make
internal knowledge findable Which proposal template is up-to-date? How does a project handover work? A knowledge agent can compile answers from approved documents and display the sources used. This is especially helpful for new hires and cross-departmental tasks.
The prerequisites are up-to-date content, clear responsibilities and tested authorizations. Contradictory documents remain a quality problem even with AI.
6. Process
HR inquiries in a structured
way Recurring questions about internal guidelines or onboarding can be answered via an agent. Further concerns can be handed over to the HR team in a structured manner.
It is also conceivable to request missing information for an application and prepare a process to be approved. Personal information may only be processed in the intended authorization context.
7. Explain key figures and prepare
analyses If sales or project margins deviate from the plan, a time-consuming data search often begins. A data agent can support the query of approved key figures and compile anomalies for further analysis.
The prerequisite is a common definition of the key figures. A detected deviation does not explain a cause. Technical examination remains necessary before measures are taken from it.
What role do Microsoft technologies play?
Microsoft Copilot Studio offers an environment for creating and managing agents and workflows. Data sources and actions can be connected via suitable interfaces. The possibilities for your use case depend on the systems used, available connectors and configuration, among other things.
In a Microsoft landscape, for example, Dynamics 365 can be relevant for customer processes, SharePoint for documents, and Microsoft Fabric for processed analysis data. The decisive factor is what information the respective agent actually needs and what actions it is allowed to perform. Existing Microsoft licenses alone do not answer these questions.
Introducing AI Agents: From Prototype to Productive Process
Start with a clearly defined process. Determine what result is expected, who will review it, and how you will recognize improvement. Suitable metrics include processing time, rework rate, or completeness of a handover.
A prototype first demonstrates the basic feasibility. A tested use case must then also be able to deal with incomplete data, special cases and different authorizations.
For productive operation, monitoring, error handling, responsibilities and a regulated handling of changes are added. Also evaluate the ongoing license, usage and support costs.
How you can
get started with INKUBIT
In the INKUBIT workshop “AI Agent in a Day” you can select a concrete use case and build and test a first agent with Microsoft Copilot Studio. The result forms a starting point for further development and testing up to possible go-live.
Do you want to use AI agents in your company? Discuss with INKUBIT which process is suitable for an initial test and what data and integrations are needed for it.
Frequently asked questions about AI agents in the company
Which tasks are suitable for AI agents?
Clearly defined tasks are suitable, in which information from different sources is processed and next steps are prepared. Examples are service requests, appointment briefings or the search for internal knowledge.
How much does it cost to implement an AI agent?
The costs depend on data connection, process scope, usage, licenses and operational requirements. A simple prototype has a different effort than a productive solution with several systems and regulated releases.
Do AI agents work completely autonomously?
The scope of action is determined for the respective process. An agent can create proposals or execute approved actions. Companies should provide appropriate checks and approvals for high-impact decisions.
