The Microsoft Ignite event in November 2025 sent a clear message: the future of work belongs to AI Agents. Almost every major product announcement – from Microsoft 365 to Dynamics to Azure – was centered on Agentic AI. New agent frameworks, advanced automation capabilities, self-learning systems, and domain-specific copilots for areas such as finance, security, development, sales, and operations. Many of these innovations are based on a crucial paradigm shift: AI is no longer merely reactive – it doesn’t just analyze, it acts. But what does that mean in concrete terms for businesses? What is the difference between AI Assistants like Microsoft Copilot and AI Agents? And how can businesses benefit from Agentic AI?
Why companies should rely on Agentic AI now and why this is not the same like AI Assistants
26 Apr 2026
AI Assistants boost productivity – but Agentic AI is transforming entire business processes. So, what are the differences, and how will Agentic AI be important to your business?
What’s the difference between an AI Assistant and an AI Agent?
The terms “AI Assistants” and “AI Agents” are often used interchangeably, but they are not the same. To understand the difference, it is important to first look at the underlying technology: Generative AI (GenAI).
GenAI forms the foundation for many modern AI applications. It enables systems to generate content such as text, code, or analyses. Both AI Assistants and AI Agents are based on this technology – but differ fundamentally in how they are used.
- AI Assistants: AI Assistants are applications based on generative AI. Tools such as Microsoft 365 Copilot, GitHub Copilot, or ChatGPT use generative AI to generate content, summarize documents, write text, analyze information, or suggest code based on prompts. However, they respond exclusively to user input and do not act independently. Their strength lies in supporting individual tasks – not in executing entire processes. The power to make decisions always remains with humans.
- AI Agents (Agentic AI): AI Agents go a decisive step further. They also use GenAI as a foundation, but expand upon it with capabilities for planning, decision-making, and execution. Instead of merely reacting to prompts, they act proactively and work independently toward a defined goal – all under human supervision and within set boundaries. To do this, they orchestrate multi-step processes, make decisions on their own, and access tools and systems such as CRM, calendars, or web services. AI Agents thus behave more like a digital employee, who completes tasks independently. One example is a sales agent that identifies leads, enriches data, and automatically generates personalized follow-ups. With these capabilities, AI Agents go far beyond traditional AI Assistants. The result is shorter decision-making cycles, higher productivity, and a clear competitive advantage.
A practical example from marketing
Let’s imagine the following task: “Plan and launch a campaign for a new product.”
A typical AI Assistant would: | Agentic AI, on the other hand, could: |
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It is therefore clear that AI Assistants and AI Agents are not the same thing, even though a recent IDC study suggests that many companies interpret the AI Assistants they are already using as Agentic AI: 47% of EMEA organizations state they are already deploying AI Agents at scale. However, what IDC considers true Agentic AI technology is still emerging and the technology underpinning AI Agents is still immature.1
IDC estimates, however, that the number of companies using Agentic AI will triple over the next two years. And that could pay off: In our last blog post, we wrote about why pioneering companies using AI generate four times more value than laggards – one of the reasons was the use of Agentic AI!
Examples of AI Agents with real business impact
Here are 5 potential business scenarios where Agentic AI can add value:
A Sales Agent can - Build pipelines
- Analyze CRM data to identify promising leads
- Enrich data with external company information
- Prioritize opportunities based on defined criteria
- Prepare personalized sales pitches
A Customer Service Agent can - Handle cases
- Ensure the accuracy of knowledge base data
- Interpret customer intent
A Marketing Agent can - Monitor campaign performance across channels
- Identify underperforming target groups or segments
- Adjust targeting parameters
- Create performance reports for stakeholders
A Finance Agent can - Consolidate data from various systems
- Identify and review inconsistencies
- Create monthly reports
- Proactively identify risks and anomalies
- Establish guidelines
- Review contract documents
- Assist in the search for suppliers
- A Supply Chain Orchestrator Agent can
- Evaluate current inventory, production capacity, and logistics
- Identify a potential shortage
- Simultaneously, it can communicate with a Supplier Negotiation Agent, which, within pre-defined authority and ethical constraints, automatically renegotiates contracts and secures raw materials from alternative suppliers
The ROI of Agentic AI
Agentic AI can therefore offer numerous benefits, because it doesn’t just assist with work – it executes work. Instead of producing outputs that humans must interpret and act on, agentic systems plan tasks, make decisions, and carry out multi‑step workflows across tools and systems – all with minimal human supervision. This is why organizations adopting Agentic AI increase their efficiency through automation, reduce workload on their staff, see faster returns, improve customer experience, have shorter decision-making cycles and higher productivity. According to Google Cloud, 88% of Agentic AI early adopters are seeing a positive ROI on GenAI.
How organizations can prepare for the agentic future
So, what steps can companies take to implement Agentic AI within their organization and reap the benefits?
- Identifying effective workflows: One of the key prerequisites for implementing Agentic AI is a clearly defined use case with measurable business value. Companies should not focus on individual tasks, but rather on end-to-end workflows that can be automated or optimized. Start by identifying a small number of workflows where increased autonomy can have the greatest impact in terms of improving efficiency, reducing costs or boosting revenue. You can then scale up on this basis.
- Ensuring high data availability and quality: Equally crucial are the availability and quality of the data, as agents make decisions and need access to up-to-date, consistent and relevant information to do so. In many organizations, however, data is still stored in silos or is inadequately structured. Only when data sources are integrated, processed and reliably accessible can agents realize their full potential.
- Establish agentic governance and guardrails: Agents require strict oversight. Organization must clearly define which decisions an agent is permitted to make and where human oversight is required. They must be able to monitor what their AI Agents are doing, why they are doing it, and what impact their actions have on the environment. They must gain real-time insights into the agents’ behavior, decision pathways and outcomes. Transparency, monitoring and compliance with regulatory requirements are crucial for building trust in the systems and minimizing risks.
- Train the workforce: Agentic AI is not purely an IT project. AI skills must be developed across the board and new roles defined in order to manage the use of AI Agents. Train the workforce and enhance their skills. This requires close collaboration between business departments, IT and data experts. New roles such as "Agent Orchestrators", who lead teams of AI Agents, train them and guide their focus, will be necessary.
- Providing technological architecture: Another crucial factor is technological architecture. Companies need an architecture that can do more than just GenAI. They need an environment that enables planning, decision-making logic, context storage and the orchestration of workflows. There are three architectural variants: the single-agent architecture, which is suitable for specific use cases of limited scope where a single AI Agent handles all perception, reasoning and actions for a specific workflow; the multi-agent architecture, in which several specialized agents coordinate to handle complex workflows; and the hybrid architecture, which combines autonomous agents for routine decisions with human-in-the-loop approval for high-risk actions.
What Konica Minolta Offers in the Field of Agentic AI
Konica Minolta is already taking concrete steps towards Agentic AI:
- The Agentic Document Extraction (ADE) solution can extract information from any document and convert it into structured, actionable data. Instead of training a single AI model for a specific document type, ADE uses multiple specialized AI Agents that work together to understand the structure and content of each document, regardless of whether it contains structured data, handwritten notes, images, or diagrams—even mixed document types can be processed. This independence from document type significantly reduces the need for retraining and enables companies to process a wide variety of different documents within a single solution. One potential application area is manufacturing environments where logs are created manually, and the information must subsequently be digitized and integrated into operating systems.
- In the procurement sector, Konica Minolta has developed a solution that identify potential savings in areas such as maverick buying (purchases made outside official procurement processes), supplier contracts, price fluctuations, inventory optimization, and more. Applying agentic principles, AI continuously analyses large volumes of transaction‑level data across invoices, purchase orders and ERP systems, autonomously detecting patterns, prioritizing findings and providing procurement teams with clear recommendations for action. This enables earlier intervention, more focused decision‑making and measurable business impact.
- Konica Minolta also ensures high data availability and quality. In its workshops, Konica Minolta works with its customers to analyze where the customer currently stands, which data sources are available, and what untapped data assets lie hidden within the organization. This dialogue provides clarity on which systems, departments and processes generate data – and where potential silos may arise. On this basis, Konica Minolta links all relevant data sources and brings them together in a central location. The company uses the Microsoft Fabric data platform as its technological foundation, which enables the integration of structured and unstructured data in real time. The result is a unified database that paves the way for Agentic AI.
Why businesses should act now
The transition to Agentic AI is no longer a vision of the future, but already a reality. Whilst AI Assistants have primarily improved individual productivity, AI Agents open up the possibility of rethinking and transforming entire business processes. Businesses that act early can automate complex workflows, boost their operational efficiency and significantly reduce manual tasks.
1 IDC eBook, AI in EMEA, 2025
