AI agents: 3 tips to simplify your system landscape

Where you once might have needed dozens of Software-as-a-Service (SaaS) solutions, a single AI Agent can now independently perform countless processes and tasks. This reduces costs and complexity, and creates room for innovation. In this article, we explore how this works exactly and what the three key building blocks of a successful AI implementation are.

At first glance, it seems contradictory: you introduce an advanced AI solution, but at the same time, the complexity of your IT landscape decreases. To understand this, it is important to realise what an AI Agent actually does. An AI agent is not just a chatbot or virtual assistant, but an intelligent 'entity' capable of independently performing tasks and making decisions based on data and predefined objectives.

Previously, you had to implement a new SaaS tool for every functionality, process optimisation or automation. This meant constant new API connections, login procedures, licence costs and supporting systems. An AI Agent can centralise all these tasks. You no longer need to purchase a separate solution for every subprocess or specific application; one powerful AI system can fulfil multiple roles. This reduces the number of separate applications and systems, giving your IT landscape a more streamlined and clearer structure.

For an AI Agent to function properly, qualitative data is vital. If you do not have a reliable, consistent, and well-structured database, you risk errors or incorrect decisions. But there is more: in addition to structured data (such as customer data, product information or financial data), unstructured data (documents, emails, meeting minutes) is also of great value. It is precisely in these unstructured sources that the context needed to make decisions or take action often lies.

The database is therefore the 'brain' and knowledge base of your AI Agent. The more complete and qualitatively better this database is, the smarter and more accurate the output of your AI agent will be. Setting up and maintaining such a database is therefore an important part of your AI strategy. It requires data governance, security, privacy protection, and continuous optimisation.

The 3 most important ingredients for an AI Agent:

1. Ensure data quality: make sure data is clean, consistent, and up-to-date.
2. Data security: limit access to sensitive information and consider laws and regulations (such as the GDPR).
3. Data integration: integrate data sources so that your AI agent has access to all relevant information, without fragmented 'data silos'.

An AI agent is powerful, of course, but its value truly emerges when your employees (or even customers) can easily communicate with it. This is where the conversational interface comes in. Think of a chat window, a voice assistant, or another user-friendly frontend where you can give commands or ask questions. This interface makes interaction with the AI Agent easily accessible: an employee can, for example, ask a simple text prompt or spoken question to generate a report, plan a campaign, or analyse a customer query. The interface also provides feedback and suggests follow-up questions if necessary. This ensures a natural way for humans and machines to collaborate.

Why a conversational interface is important:

• Intuitive use: you don't need to be a programmer to give instructions.
• Higher adoption rate: employees embrace new technology faster if it is user-friendly.
• Real-time feedback: the AI can immediately indicate if it needs extra information and why.

The third and final pillar is, naturally, the AI Agent itself. This is the 'brain' that has been trained on large language models and is often equipped with specific functionalities to automate processes. Thanks to this understanding of language and context, the AI can answer questions, perform tasks, and even follow complex reasoning.

A prerequisite is that the AI Agent has API access to the data sources and other business systems required to perform actions. The agent then goes beyond advising: it actually takes action. Think of placing orders, sending emails, updating customer profiles, and much more.

Example applications:

• Automated customer support: the AI Agent handles simple customer queries or sends updates to the customer.
• Process automation: the AI agent can manage internal workflows, such as invoicing or HR processes.
• Reporting and analysis: with a single prompt, you can request real-time reports or data analyses, leading to faster and better decision-making.

Want to read more about AI Agents? Also read this article: AI Agents: what you need to know about them

With one AI Agent capable of performing various tasks, you need fewer separate SaaS solutions. This means lower licence and management costs and less integration headaches. Your IT environment becomes clearer, simplifying administration and maintenance. Because the AI agent takes over repetitive tasks, employees can focus on tasks that require human creativity and insight. This increases both efficiency and job satisfaction. The AI agent is, in fact, a digital colleague available day and night. By consolidating all data into one 'brain', the AI agent can make smarter and faster decisions. Employees are no longer dependent on countless separate data sources and tools, but can ask their questions directly via the conversational interface. This prevents silos and shortens lead times. AI technology continues to develop at a rapid pace. By investing now in the three pillars – a qualitative database, a user-friendly interface, and a powerful AI agent – you lay a solid foundation for future innovations. New AI models and functionalities can be integrated relatively easily into this existing structure.

5 steps to apply AI Agents

1. Define your AI strategy: identify the processes that can yield the most impact or efficiency gains when automated.
2. Get your data in order: ensure a reliable and secure data architecture. Invest in data quality and data integration.
3. Develop (or choose) an AI agent: select a technology partner or develop your own AI agent. Pay attention to scalability, adaptability, and privacy.
4. Create an optimal interface: focus on user-friendliness and adoption. A conversational interface must be accessible and intuitive.
5. Train, test, and continue to optimise: AI keeps learning and needs feedback. Establish a process for regular updates, monitoring, and improvements.

The idea that more AI technology would automatically lead to a more complicated business landscape is outdated. With the right approach, an AI agent can actually lead to fewer separate SaaS solutions, simpler processes, and a significant increase in productivity. The key lies in three elements: a qualitative (and well-integrated) database, a user-friendly conversational interface, and an intelligent AI agent that not only provides advice but also independently performs tasks.

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