What is a Copilot?

What is a Copilot?

A copilot is an AI-supported virtual assistant that is embedded directly in digital software applications, where it automates tasks or makes suggestions. While chatbots are typically intended for interaction with external users, a copilot works closely with internal specialist users within software applications and offers them context-related support. It understands voice input, analyses data, recognizes action contexts and helps people to work more efficiently. It accompanies them without taking over decisions completely.

Background and context

The idea of a digital copilot originally comes from the field of software development (e.g. GitHub Copilot) and was made possible by large language models such as GPT. Similar approaches are now being transferred to many other fields of application. The need for intelligent assistance is particularly high in data-intensive areas such as production planning, customer service and logistics.

The increasing complexity of systems, the need for intuitive operation and the desire for greater efficiency make the copilot approach attractive. Companies benefit from copilots because they not only increase processing speed, but also reduce the workload for employees. AI technologies, in particular speech processing and machine learning, enable a new quality of assistance: situational, dialog-based, adaptive.

INFORM has been developing software solutions for demanding operational processes for years. The first copilot functions are being used here. The aim is to make even complex applications easy to use for different user groups. And on our homepage a copilot answers all visitors' questions about INFORM without them having to click through the website themselves.

How does a copilot work?

A copilot is made up of several components:

• Language understanding: Using natural language processing (NLP), the copilot recognizes queries and interprets them in context.
• Generating suggestions: Based on large language models, the copilot can create suitable texts, answers or options for action.
• Data integration: Copilot accesses internal data sources (e.g. ERP, CRM) to integrate relevant information into its suggestions.
• Learning ability: Copilots improve over time through user feedback - for example through preferred formulations or recurring patterns.
• User interface: Copilots are usually seamlessly embedded in existing software environments (e.g. as a sidebar, chat module or action button).

These mechanisms enable the copilot to respond to individual work contexts and dynamically adapt its support.

Application examples

Copilots can be used in a wide variety of industries and processes. Below is an example from one of our customers of how a Copilot can offer companies concrete support:

Copilot in scheduling & logistics

In logistics processes - such as transportation scheduling - numerous similar requests and routine decisions are made every day. A practical example comes from vehicle logistics: an INFORM customer used a copilot to automatically pre-process around 100,000 transport requests per year (mostly by email). The system recognizes the relevant information in incoming e-mails (e.g. chassis numbers, delivery address, desired date), even if they are unstructured or incomplete. The copilot then generates a suitable response - stylistically flawless and including all the necessary details by automatically adding missing information from internal systems.

This automation has significantly reduced the workload of our customer's customer service team and response times have fallen considerably. In dispatching, this means that employees can concentrate on exceptions and problem cases, while the copilot takes over routine communication and standard decisions.

Further examples

Copilots help engineers in production, for example, to write, improve and test automation codes more quickly. Thanks to generative AI, simulation times can be significantly reduced and productivity can be increased throughout the entire industrial process. In the logistics sector, copilots act as digital assistants that support dispatchers in route planning. They analyze real-time data such as traffic conditions and delivery priorities to suggest optimal routes.

In finance, copilots support financial analysts in analyzing financial data by seamlessly integrating them into applications such as Excel and Outlook. For example, they can identify cash flow variances and create intelligent sales forecasts, making it easier to make informed decisions. These examples illustrate that copilots are more than just AI tools; they are specialized assistants that provide real-time contextual support, improving efficiency and decision making across various industries.

Benefits and challenges

Benefits

A copilot relieves specialist users of repetitive routine tasks, makes complex software applications more intuitive to use and supports faster decision-making. Particularly in data-intensive fields of work, it increases efficiency, reduces sources of error and improves the user experience through context-related suggestions.

Challenges

The quality of the recommendations depends largely on the availability and quality of the underlying data. Incomplete information or misinterpretations can lead to incorrect suggestions ("hallucinations"). In addition, data protection requirements, technical integration and acceptance within the workforce must be taken into account in order to successfully introduce the copilot.

FAQ - Frequently asked questions about copilots

What distinguishes a copilot from a chatbot?

A copilot works alongside internal employees, embedded in specialist software. It provides context-related suggestions, automates work steps and supports decision-making processes. A chatbot, on the other hand, conducts simple dialogs with external users - usually on websites - and is technically less deeply integrated.

What data does a co-pilot need?

A copilot can basically work with a general language model, but becomes much more helpful if it is enriched with company-specific data (e.g. process rules, product knowledge). This enables it to interpret terms correctly and provide suitable suggestions.

How do you get started with Copilot?

The best way is via a limited area of application, e.g. in planning or communication. A clearly defined goal, a good database and accompanying training are important. This creates realistic added value without being too demanding

Conclusion

Copilots offer a new form of intelligent support in everyday working life. They are not a substitute for specialist knowledge, but a tool for using it more efficiently. Companies that make targeted use of AI assistance benefit from better use of resources and faster processes - provided they integrate these systems responsibly and with a view to the specific benefits.