Article
    by Nortal

    Beyond copilots: The rise of AI agents and enterprise AI

    Autonomous and integrated AI is more than a collection of copilots – it’s a strategic necessity.

    Service

    Data and AI

    Copilots won’t transform your business. Autonomous AI agents will. 

    The AI conversation has been dominated by personal assistants and copilots - tools that help individuals work faster. But the real value of AI isn’t in helping a few employees write quicker emails or summarize documents. The next frontier is far more transformative: autonomous AI agents and Enterprise AI. 

    These aren’t just tools. They are strategic assets - AI systems that learn, act, and make decisions across your organization without constant human oversight. While copilots assist, AI agents can operate, optimize, and orchestrate entire business functions. 

    The companies already building these capabilities aren’t just getting more efficient - they're redefining how they compete, serve customers, and create new value. 

    In this article, we’ll explore: 

    • What autonomous AI agents and Enterprise AI really are
    • Why copilots are no longer enough
    • The levels of automation and how to evolve toward autonomy
    • Real-world use cases showing how leaders in manufacturing, public services, and supply chains are using AI agents today 

    The conversation around artificial intelligence (AI) often centers on personal tools like copilots and other AI assistants that can help with specific tasks at different levels of the organization. However, AI's true potential and value extend far beyond these tools. The two concepts that will transform how businesses operate and compete - and that every decision-maker should watch - are autonomous AI agents and Enterprise AI. 

    What are AI agents and Enterprise AI, and why should you care?

    AI agents refer to AI systems designed to act independently, make decisions, and perform tasks without constant human intervention. They can learn from data, adapt to new information, and execute complex processes, making them invaluable in dynamic environments. 

    Enterprise AI, conversely, encompasses a broader integration of AI technologies across an organization's entire ecosystem. This includes holistic data management and analytics in functions and processes from finance to procurement and from customer service to supply chain optimization. Enterprise AI aims to enhance decision-making, streamline operations, and drive innovation by leveraging AI at every business level. It is about creating a cohesive strategy that aligns with the company's goals and processes. 

    The need for autonomous agents and Enterprise AI arises from the limitations of the known personal AI applications like copilots. While copilots can assist with specific tasks, they lack the autonomy and scalability to address modern enterprises' complex challenges. Businesses today need AI solutions that can operate independently, integrate seamlessly with existing systems and business processes, and provide actionable insights that drive growth and efficiency. Essentially, they transform AI from a helpful assistant into a strategic partner that propels the business forward.

    The stepwise evolution towards autonomous AI agents

    While the first wave of generative AI brought us copilots and other personal AI assistants, it didn’t yet revolutionize our lives as advertised—or feared, depending on the viewpoint. Some even labelled the copilots as Clippy 2.0, referring to the notorious Microsoft Office assistant from the late 90s. While copilots have advantages over Clippy, assistants for humans can only increase productivity to a point. 

    The transformation from AI assistants to autonomous agents will revolutionize entire processes and how we do business. However, transitioning from manual human-led processes to full automation involves going through different levels, ranging from simple task automation to fully autonomous decision-making systems. The levels of automation can be categorized as follows: 

    Manual Systems:

    human intervention for all tasks and decision-making

    Assisted Automation:

    AI systems provide recommendations and insights, humans make decisions

    Partial Automation:

    human oversight for complex decisions, but AI systems perform tasks autonomously

    Conditional Automation:

    AI systems handle most tasks, but human intervention needed in limited conditions or scenarios

    High Automation:

    minimal human intervention, AI systems perform most tasks autonomously

    Full Automation:

    AI systems are independent, make decisions and execute without human involvement

    When climbing higher up on the automation level, data and AI solutions built for analytical and user-interface-focused needs will not suffice. New API-driven solutions are needed to drive and automate complex business processes without human intervention.  

    By understanding and leveraging these different levels of automation, organizations can gradually transition from manual systems to fully autonomous agents, unlocking new opportunities for efficiency and innovation. Despite the breathtaking speed of advancements and changes in the AI landscape, the shift to fully autonomous systems making independent decisions will not happen overnight. From our perspective, human oversight will still be needed in the short term.

    Capturing the value of Enterprise AI

    While some of Enterprise AI's value lies in increasing operational efficiency and unlocking the full potential of a company's data assets and processes, it ultimately enables organizations to discover new value streams, rethink their core processes, and innovate new businesses. 

    To successfully implement Enterprise AI, an organization needs a solid data and AI strategy backed up by a scalable enterprise architecture. The strategy defines a clear vision and key performance indicators (KPI). It directs the supporting governance structure and builds a robust data platform that includes secure data storage, efficient processing capabilities, and scalable cloud solutions. Seamless integration with existing operational and IT systems and processes requires interfaces and middleware that connect various data sources and applications within your organization. Our next article will discuss building data governance models and platforms in more detail. 

    Once prerequisites are met, adopting Enterprise AI can be transformative. It enhances decision-making by leveraging vast data for actionable insights, leading to better strategies and a competitive edge. It boosts operational efficiency by automating tasks, reducing costs, and freeing up human resources for strategic initiatives. Enterprise AI can help personalize customer experiences in real time, enhancing satisfaction and loyalty. Predictive analytics enables anticipating trends, behaviors, and risks, allowing businesses to mitigate challenges and seize opportunities.

    AI agents in action: examples from our customer verticals

    Business leaders may still be skeptical about integrating AI into the highest levels of strategic and tactical work. This might be due to security or privacy concerns, a lack of understanding of using and incorporating the tools into the core processes, or just mere fear of being “replaced by machines.” Luckily, we at Nortal have had the privilege to work with some of the most forward-thinking organizations in Northern Europe to envision the potential of AI agents and Enterprise AI. Here are some examples of use-case scenarios from our focus verticals.

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    Manufacturing: Real-time energy optimization 

     

    AI-powered solutions can continuously monitor and adjust energy usage in real-time, splitting tasks between AI and human agents. They go beyond traditional energy management systems by learning from historical patterns, forecasting demand, and automating energy-intensive tasks during off-peak hours. 

     

    Public Sector: Automating citizen services

     

    In public services like building permit applications, AI agents can interpret free-text requests, auto-fill regulatory forms, and generate draft decisions using RAG-based models. Human staff can review and approve these drafts, while the system learns and improves over time. 

     

     

    IT/OT Integration: Smarter factory setup

     

    AI agents can accelerate factory setup by automating tasks like asset hierarchy creation, device integration, and anomaly detection. They scan networks, update management systems, and tag IoT data for business intelligence systems in real time. 

    Procurement: Autonomous machine customers

     

    Enterprise AI agents can predict price movements in raw material markets, determine optimal buying times, negotiate deals, and automate purchases. They reduce cost volatility and mitigate supply chain risks while freeing human staff for higher-value tasks.

    From tactical tools to strategic transformation

    In all the cases described here, scalable Enterprise AI and autonomous AI agents don’t just boost productivity—they fundamentally reshape how organizations innovate, adapt, and grow. They empower businesses to respond to change faster, tap into new value chains, and reimagine what is possible. 

    The era of AI copilots is already behind us. The leaders of tomorrow are investing today in autonomous agents and enterprise-wide AI strategies. 

    If you're ready to move beyond task-based tools and start building AI that drives real business impact, let’s talk. 

    Contact us to explore how AI agents and Enterprise AI can help your organization unlock new opportunities, enhance performance, and build a scalable future. 

    Whether you're just getting started or scaling up your existing initiatives, our team is here to help you take the next step - with clarity, speed, and confidence.

    Get in touch

    Nortal is a strategic innovation and technology company with an unparalleled track-record of delivering successful transformation projects over 25 years.