AI for businesses – benefits, use cases and implementation
AI is being used more and more in businesses, but identifying its potential and putting it into practice still raises questions. Where should you start, which tasks can AI automate, and how do you turn an experiment into lasting business value?
AI can help, for example, process orders, find information in a company’s own documents and automate repetitive tasks. The best results come when the solution is designed around a real need and connected to the company’s existing systems.
In this guide, we go through the key principles, use cases and benefits of AI. You’ll also get practical guidance on choosing the right use case, running a pilot and rolling out the solution.
How to get started
- Book an AI Sandbox demo and see in practice how AI solutions work and what opportunities they offer your business.
- Map out your opportunities in an AI Sprint and choose your first use case with the help of our experts. Fixed price €1,190 + VAT.
- How to get started
- What is AI and how does it work?
- Key types of AI in business
- AI adoption in businesses is growing
- Where is business use of AI heading?
- What are the benefits of AI for a business?
- AI for businesses: use cases across industries
- How do you identify the right AI use case for your business?
- Levels of AI adoption in a company
- From AI pilot to production
- Practical examples of AI solutions
- AI Sandbox makes the possibilities visible
- AI risks, security and responsibilities
- AI Sprint helps you find your first AI project
- Want to find the right AI solution for your business?
What is AI and how does it work?
Artificial intelligence refers to systems that perform tasks requiring, for example, reasoning, pattern recognition or language understanding. AI recognizes patterns in large amounts of data, analyzes data, processes natural language and produces output such as text, forecasts or recommendations.
An AI system receives data as input, such as text, images, audio or measurement data. The model processes the information based on its training and the material available to it, and produces an answer, classification, forecast or other result.
In traditional software, behavior is based on precisely defined rules. AI is particularly well suited to tasks where the information varies and not every situation can be described with unambiguous rules. For example, orders arriving by email may contain the same required information in very different formats.
In business use, AI is often combined with the software’s other logic, data sources and validations. This allows the information it produces to be used in a controlled way as part of business processes.
Key types of AI in business
In machine learning, a system learns patterns from data without every rule having to be programmed separately. It is used, for example, in demand forecasting, anomaly detection, quality control and predictive maintenance.
Generative AI (GenAI) produces new content such as text, images, audio and code. Businesses use it, for example, in content creation, software development, document processing and customer service.
Advances in generative AI have made it much easier to process free-form text, documents and speech. This opens up opportunities to automate tasks where interpreting information has previously required a lot of manual work.
Large language models understand and generate natural language. They can be used, for example, to summarize reports, classify messages, extract information from documents or handle questions in a conversational format.
A language model connected to a company’s own data and systems can also support more complex tasks. Its performance depends heavily on the information, instructions and tools available to it. We cover this in more detail in our article on context engineering.
RAG (Retrieval-Augmented Generation) connects a language model to a company’s own data sources. When a user asks a question, the system retrieves relevant information, for example from documents or web pages, and uses it to build the answer.
The answer can include source references that let the user check the original information. RAG is well suited, for example, to technical customer service, internal information retrieval and making use of large document collections. Read more about RAG AI chatbots for businesses.
An AI agent performs tasks according to defined goals, permissions and procedures. It uses AI models and, where needed, other software or tools to complete the task.
For example, an agent built for order processing reads emails, identifies the order details and prepares them for transfer to the ERP system. The software’s business rules and user approval ensure that the data is handled correctly.
When an agent is connected to a company’s processes, it can process information and trigger actions in different systems. This kind of system-integrated implementation is also called functional AI.
AI adoption in businesses is growing
The use of AI in businesses is growing rapidly. According to Statistics Finland, 38 percent of Finnish companies used AI technologies in spring 2025, an increase of 14 percentage points in one year. AI use was especially common in the information and communication sector and in companies with at least 100 employees.
Finland is also one of the leading countries in Europe in AI adoption. According to Eurostat, 37.8 percent of Finnish enterprises used AI in 2025. Among EU countries, only Denmark ranked ahead of Finland.
However, companies are adopting AI at different paces. In some organizations, employees use individual AI tools, while in others AI is already part of the company’s services, systems and daily processes.
Especially in SMEs, identifying suitable use cases, a lack of expertise and limited resources can slow down progress.
Where is business use of AI heading?
According to a Gartner forecast published in August 2025, 40 percent of enterprise applications will feature task-specific AI agents by the end of 2026, up from less than 5 percent in 2025.
Agents are increasingly built into off-the-shelf software, and companies are also building them into their own processes. At the same time, using agents increases the need to define permissions, validations and responsibilities.
In June 2025, Gartner predicted that over 40 percent of agentic AI projects will be canceled by the end of 2027. The main reasons are escalating costs, unclear business value and inadequate risk controls.
The forecast is a reminder that a technically working solution alone doesn’t guarantee success. At the start of a project, you need to define what problem is being solved, what benefits are being sought and how the results will be measured.
The EU AI Act sets risk-based requirements for the development and use of AI. The obligations apply in phases, and what they include depends on factors such as the AI system’s intended purpose and risk category.
Companies must take into account requirements related to, for example, AI literacy, transparency and high-risk systems where they apply to their own operations.
What are the benefits of AI for a business?
The biggest benefits of AI often come from tasks that involve processing large amounts of information, repeating the same steps or searching for answers across multiple sources. When such tasks are automated, staff have more time for expert work and customer service.
| Use case | Example | Business benefit |
| Information retrieval | An AI chatbot answers based on the company’s own documents | Customers and employees find information faster |
| Order processing | AI reads email attachments and extracts order lines into the ERP system | Less manual data entry |
| Customer service | A chatbot answers common questions and routes difficult cases to staff | Faster service and more time for experts |
| Reporting | AI compiles summaries from data, meetings or system logs | A better basis for decision-making |
| Sales and marketing | AI helps identify buying signals and create content | More targeted communication and faster work |
| Software development | AI supports writing code, software testing and documentation | Faster development and better quality |
| Security | An AI agent monitors vulnerabilities and compiles the relevant changes | Experts get a situational overview faster |
| Production and maintenance | AI detects anomalies and predicts maintenance needs | Fewer outages and less unnecessary work |
The benefits of an implementation depend on the available data, current ways of working and how the solution is connected to the company’s processes. That’s why it pays to define goals and metrics before development begins.
AI for businesses: use cases across industries
In manufacturing, AI is used, for example, in production optimization, quality control, predictive maintenance and processing technical documents. AI can analyze data collected from equipment and systems and help experts quickly find the instructions they need.
Read more about the potential of AI in manufacturing.
In retail, AI supports demand forecasting, inventory management, product recommendations and customer service, for example. The most common customer questions can be handled automatically, allowing staff to focus on more demanding service situations.
In professional services organizations, AI speeds up information retrieval, document analysis, reporting and compiling information. For example, key information can be found in extensive contracts or guidelines without going through every document manually.
In education, AI supports preparing learning materials, information retrieval and personalizing learning. In the public sector, it is used, for example, to develop administrative tasks and information services. In these use cases, data protection, accessibility, transparency and clearly defined responsibilities are especially important.
In software development, AI helps with generating and analyzing code, software testing, documentation and building prototypes.
AI is also used to understand how legacy systems work and to plan their modernization. At the start of a project, it can speed up turning an idea into a testable prototype.
Read more about prototyping with AI.
How do you identify the right AI use case for your business?
Your first AI project should be chosen based on a real business need. A useful use case is often found in a process where the same work is done repeatedly or information moves slowly between systems and people.
You can map out potential use cases with questions like these:
- Which step creates the most manual work?
- Where is information scattered or hard to find?
- Where do experts spend their time on repetitive checking?
- Where do errors cause costs or delays?
- Where are decisions made based on incomplete information?
- Where do customers have to wait too long for an answer?
OpenAI breaks down the identification of AI use cases into three steps: identify opportunities suited to AI, teach the organization the basic AI use cases, and prioritize ideas based on impact and implementation effort. Common use cases highlighted by OpenAI include content creation, research, coding, data analysis, ideation and automation.
In practice, it’s best to choose a well-defined use case for your first pilot, one where the benefits can be seen quickly. It could be, for example, one of these:
- classifying requests for quotation
- reading orders from emails
- a search bot for internal guidelines
- a support tool for customer service
- automatic report summaries
- document pre-processing
- enriching product data
- support for software testing
Starting too broad can easily turn AI into a heavy project. A well-defined pilot helps test the benefits, risks, data and user experience before a wider implementation.
Levels of AI adoption in a company
Companies are at different stages in their use of AI. Adoption can be described in four levels that help you identify where you are now and what your next step should be.
Individual employees use tools such as ChatGPT for brainstorming, editing text or looking up information. There may not yet be shared goals, guidelines or metrics.
Next step: Define basic rules for using AI and identify a few tasks where its benefits are worth testing systematically.
AI tools are already used regularly, for example in content creation, analysis or software development. However, use is mainly based on individual tools.
Next step: Find out in which tasks connecting AI to your company’s own data or systems would bring more value.
AI works as part of the company’s workflows. It retrieves information, interprets documents, automates tasks and makes use of integrations between systems.
Next step: Measure the impact, improve reliability and ensure the security, quality control and maintenance of your solutions.
AI is part of the company’s products, services or core business processes. It can be used to develop new service models and build a competitive advantage.
Next step: Develop solutions with a long-term view and evaluate new opportunities in line with your business goals.
From AI pilot to production
A working AI demo shows that an idea is technically feasible. A solution meant for a company’s daily use, however, has to work with real data, connect to the necessary systems and also handle exceptions.
After the pilot, the solution can be taken into production in stages.
- Evaluate the pilot results
Find out whether the solution worked with real data and whether the intended benefits were achieved. Metrics can include, for example, time saved, fewer errors or faster processing times.
- Build the necessary integrations
The solution is connected to, for example, the enterprise resource planning (ERP) system, customer relationship management (CRM) or document management. At the same time, the data flows and the responsibilities of each system are defined.
- Build in validations and approval steps
AI-generated results are checked as required by the intended use. For example, order data is validated before it is saved, and human approval is included in important decisions where needed.
- Test and roll out
The solution’s functionality, security, performance and error handling are tested before a wider rollout. Users are trained in the new ways of working.
- Monitor and improve
After rollout, the quality of the results, costs and performance are monitored. As AI models, data sources or business needs change, the solution is developed further.
Taking a solution into production requires expertise in software development, integrations and AI technologies. At Hurja, these come together in the same development team.
As a Microsoft CSP partner, we can also build solutions in the Azure cloud and take care of the cloud services they require. This is especially useful when the solution is tied to the company’s Microsoft environment or its data management.
Explore our custom AI solutions and how we take them from idea to production.
Practical examples of AI solutions
AI use cases are easiest to understand through real implementations. Our case examples show how AI is combined with a company’s own data, software and processes.
Case: RAG-based AI chatbot for a website
AI chatbot improves the website service experience
Read more
Case: Odoo AI order processing agent
An AI agent that processes orders into Odoo ERP automatically
Read more
AI Sandbox makes the possibilities visible
For many companies, AI feels abstract even when there is plenty of interest. The benefits of AI are often appealing, but its practical possibilities are hard to grasp without concrete examples.
That’s why we use our AI Sandbox demo environment, where we present ready-made AI demos that solve real-world challenges. The demos show how AI:
- automates manual routines
- interprets documents using natural language
- analyzes and summarizes data
- makes recommendations at the right point in the user journey
- streamlines internal processes and decision-making
AI Sandbox is a quick and concrete way to see, through a range of demos, what can already be built with AI and what opportunities there might be in your own services. Once you see AI in practice, coming up with ideas becomes easier. You start to spot places in your own processes where a similar solution could save time, improve the customer experience or reduce errors.
AI risks, security and responsibilities
When adopting AI, you also need to consider questions related to data handling, reliability and responsibilities. In solutions that use a company’s own documents, customer data or business data, these issues are a key part of the design.
Before rollout, it’s important to define at least the following:
- what data the AI may process
- where the data is located
- who can use the solution
- how answers are checked
- what is logged
- who is responsible for errors
- how the solution is updated and tested
- when human approval is needed
At the same time, AI regulation is evolving quickly. The EU AI Act entered into force on August 1, 2024, and aims to support the responsible development and deployment of AI in the EU. Read more in our article The AI Act sets out the rules for the use of artificial intelligence.
Not all AI solutions fall into the heavily regulated high-risk categories, but every company should take the basics seriously. Controlled use of AI builds trust both within the organization and with customers. Our development work is also guided by our ethical principles of AI.
AI Sprint helps you find your first AI project
Once a company has identified opportunities to use AI, the next challenge is often choosing what to implement first. There may be several ideas at once, and their benefits or scope haven’t been assessed yet.
AI Sprint is our fixed-price service in which we map out your company’s AI opportunities and choose the first use case to implement together.
The sprint includes a preliminary survey, a workshop led by our experts and a written report that brings together the proposals, their assessment and the next steps.
- Where would AI bring the most value to our business specifically?
- Which use case should we pilot first?
- What data and systems does the solution need?
- What kind of implementation would be realistic?
- How will the benefits be measured?
- What would the first phase cost and how long would it take?
After the sprint, you’ll get a clear proposal for how to proceed and a preliminary estimate of the cost and schedule of the first implementation.
Want to find the right AI solution for your business?
Does your company have a process that would save time if automated, or data you’d like to make better use of? Your idea doesn’t need to be fully formed yet.
We help you assess the opportunities, choose the right approach and put the AI solution into practice. If the best starting point is a simpler automation or integration, we’ll tell you that too.
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Artificial intelligence (AI) refers to computer systems and algorithms that can perform tasks that normally require human intelligence. AI is not a single solution but a collection of different techniques and methods. It’s important for companies to be able to choose the right AI solution for different business needs.
In business use, AI is often based on machine learning, where a system learns to recognize patterns in data and improves its performance through experience. AI doesn’t operate independently without guidance. It uses the data, rules and goals it has been given to support decision-making and automate selected processes.
Functional AI refers to AI that doesn’t just analyze or produce content but also takes concrete actions according to predefined goals and rules. It combines decision-making, automation and system integrations into practical action.
For example, functional AI can interpret incoming information, choose a suitable course of action and trigger the next step without separate human instruction. In businesses, this often takes the form of AI agents that process orders, route workflows, update systems or support customer service as part of everyday processes. Its value comes from analysis and action happening seamlessly within the same solution.
Common use cases include customer service, information retrieval, order processing, reporting, supporting sales and marketing, and software development. In manufacturing, AI is also used in quality control, production optimization and predictive maintenance.
It’s best to start with a well-defined use case. First, identify a process with a lot of manual work, scattered information or slow decision-making. Then assess the data, security, users and implementation effort. A good first step is a small pilot whose impact can be measured.
Yes. Even in a small company, automating a single time-consuming task can deliver significant benefits. The right starting point depends on the company’s processes, the available data and the intended benefit. Lightweight, well-defined solutions can be developed without a large software project.
AI can speed up work, reduce manual routines, make information easier to find, support decision-making, reduce errors and improve the customer experience. The benefit always depends on the use case and how well the solution is integrated into the company’s processes.
AI risks include, for example, incorrect answers, inadequate security, use of the wrong data, lack of transparency and unclear responsibilities. Risks are reduced when the use case is well defined, data handling is carefully planned and a human remains responsible at critical points.
The success of an AI project depends heavily on the implementation partner’s expertise. Especially in solutions that are integrated into a company’s systems, you need knowledge of software development, security and integrations in addition to AI technologies.
When comparing partners, pay attention to the following:
Experience with real-world implementations
Can the partner show solutions that customers use in their daily work? Completed client projects help you assess their experience in real business environments.
Software development and integration expertise
Can the partner connect AI to your existing systems and also take care of user interfaces, business logic and the software’s other functionality?
Security and data management
How is your company’s data handled? How are access rights, logging, data retention and the necessary validations implemented?
Understanding of business needs
Does the design start from your company’s goals and real problems? A good partner can also explain why a particular approach fits the need.
The option to start small
Can the idea be tested before a larger investment? A limited assessment or pilot helps evaluate the benefits, costs and how well the collaboration works.
Continuous development and maintenance
Will the partner look after the solution after rollout as well? AI models, data sources and the company’s ways of working change, so the system must be able to evolve.
If your company needs help identifying opportunities or choosing a technical approach, take a look at our AI Sprint.
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