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.
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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 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 labeled 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
Manual Systems:
human intervention for all tasks and decision-makingAssisted 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 building 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.
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Next level AI 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.
In the manufacturing industry, optimizing energy efficiency is typically crucial for cost savings, enhanced productivity, and sustainability. Integrating seamlessly with existing manufacturing processes, AI-powered solutions can continuously monitor and adjust energy usage in real-time. By splitting tasks between AI and human agents, each can focus on their strengths, reducing the need for human oversight as the system’s efficiency and reliability improve. AI agents go beyond traditional energy management systems, offering real-time adaptability and proactive insights, learning from historical patterns and operational data. Agents can forecast energy demand, suggest optimal scheduling for selling energy to the grid, and automate the energy-intensive tasks to be carried out during off-peak hours.
In many public sector online services, AI can significantly enhance processes where citizens now manually fill out forms that are then handled by contact center employees by hand. For instance, when applying for a building permit, citizens could describe their needs in free text, and AI will generate the necessary forms for review and acceptance. An AI agent can process the application using a RAG-based (Retrieval-Augmented Generation) solution that leverages regulations, internal guidelines, and historical data to create preliminary decision documents. The contact center employee could then approve, modify, or, if needed, transfer the application for further human processing. The AI model learns from approvals and rejections data, improving its quality and application acceptance rate over time.
It is complex and costly to build new factories or production lines, and the Information Technology/Operational Technology (IT/OT) solutions for them. AI agents can cut expenses and speed up the process by automating tasks like building asset hierarchies, integrating devices, and detecting anomalies and security vulnerabilities. They can scan networks for new devices, update asset management systems, and correctly tag Internet of Things (IoT) device data, providing real-time insights to business and analytics systems.
Procuring raw materials from fluctuating stock exchanges for manufacturing and other industries can be significantly optimized using Enterprise AI and adopting the “Machine Customers” concept, emerging on, e.g., Gartner's hype cycles. AI agents can analyze data to predict price movements and determine the best times to buy, thus minimizing costs and avoiding supply chain issues. They can autonomously negotiate and close the best deals while monitoring masses of data about changing inventory levels and predictions. Enhanced decision-making capabilities lead to better procurement strategies, diminishing risks of supply chain disruptions, and a competitive edge. By automating routine purchasing tasks, AI also improves efficiency, freeing human resources for more motivating and strategic tasks.
From tactical use of AI to strategic transformation
In all the cases described here, scalable enterprise AI and AI agents can boost innovation and agility. They offer opportunities for organizations to adapt to market changes quickly, explore new businesses and value chains, and drive strategic growth. Such opportunities should not be overshadowed by the louder hype surrounding personal copilots; instead, they should be explored side-by-side.
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