Here at Oakland, we’ve been talking about our Intelligent Agents for quite some time. Now, it seems everyone from Gartner to Microsoft is with us, except we have a new name: agentic AI. And it’s Gartner’s top tech trend for 2025.
But what is agentic AI? In its simplest form, the term describes semi-autonomous machine “agents” that can go beyond the chatbot and perform enterprise-related tasks without human guidance.
These AI agents can set goals, plan, and adapt their actions based on continuous feedback from their environment. This level of autonomy allows agentic AI to take on various roles, from managing routine processes to addressing complex collaborative projects across different platforms and systems.
Whilst there is still a lot of AI hype, there is also the realisation that AI technology will impact every business 74% of CEOs surveyed by Gartner in 2024 believe this is the case, and agentic AI has incredible potential. Wherever you are on your enterprise AI journey, Oakland is a data consultancy ready to help. Book a free AI workshop or speak to one of our experts.
What Sets Agentic AI Apart?
Agentic AI stands out because it can perform semi-autonomous actions within a business environment. Unlike traditional AI tools that primarily function as enhanced chatbots, agentic AI brings a higher level of cognitive engagement, reasoning, planning, and executing tasks that typically require human intervention. The technology is built on large language models (LLMs), which are trained to process and understand vast amounts of text, enabling them to undertake complex business functions that go beyond mere data entry or routine customer interactions.
Process Logging
This is a hugely important point – every step taken by our agentic AI is meticulously logged. Actions, decision points, and outcomes are all recorded in structured logs. These logs are invaluable for human review and provide a foundation for ongoing improvements and learning within the system through careful analysis and supervised learning.
Natural Language Processing and Task Parsing
Our agentic AI systems use state-of-the-art language models to break down instructions into actionable tasks. This process transforms complex natural language into structured task blueprints, meticulously crafted through advanced prompt engineering and template matching.
Task Planning and Sequencing
Agentic AI excels in organising tasks efficiently. It plans and sequences tasks into logical, branching pathways using well-defined action schemas and goal-oriented frameworks. These pathways consider all necessary steps while accounting for dependencies and constraints, ensuring a coherent and strategic approach to task completion.
Agentic AI vs. Generative AI
The distinction between agentic AI and generative AI lies in their capabilities and intended applications. Generative AI, a hot topic in recent tech discussions, primarily focuses on creating content – text, images, or code – from existing data. It excels in tasks such as drafting emails, generating reports, or producing artistic content, based on patterns it has learned from large datasets.
In contrast, agentic AI takes this a step further by generating content, making decisions, and taking actions based on its understanding. While generative AI can suggest actions, agentic AI can actually execute these actions within certain parameters, thereby acting as a semi-autonomous agent within an enterprise. This ability to not only suggest but also enact makes agentic AI a powerful tool for businesses looking to automate more complex and decision-driven tasks.
The Evolution of Business Automation
For decades, businesses have aimed to offload as many tasks as possible to machines – from calculations done by software like Excel to customer service interactions handled by chatbots. However, these tools have always been limited to specific, predefined tasks. Agentic AI changes the game by applying its reasoning capabilities to a wider array of activities, thereby enhancing productivity and allowing human employees to focus on more critical, less mundane aspects of their work.
Integrating Agentic AI into Modern Workflows
The integration of agentic AI into business workflows is transformative, enabling not just automation but smart automation. By understanding and acting upon natural language, these AI agents can manage and sequence tasks effectively, leveraging existing tools within the enterprise to enhance workflow. This capability allows businesses to augment their workforce’s productivity without necessarily expanding it, a critical consideration in today’s economic climate where efficiency and cost reduction are paramount.
The Practical Applications of Agentic AI
One of the most compelling uses of agentic AI discussed involves its deployment within large IT sales organisations. AI agents interpret and organise sales data, translating messy, inconsistently recorded entries into structured, actionable information. This capability is crucial for companies that must accurately understand their sales patterns and inventory needs, helping drive informed business decisions and strategic planning.
Oakland Agentic AI Case Studies
With the AI hype, you could be forgiven for thinking you might get left behind. For many businesses, if you’re in an industry which isn’t yet being reinvented by AI don’t worry you can afford to go at a more steady pace.
So where do we expect businesses will get the biggest bang for their AI buck? Gartner research shows that an average enterprise expects 47% of AI benefits from employee productivity, 33% from process improvement, and 20% from business model innovation.
Oakland is seeing the biggest benefits in smoothing out clunky, routine business processes to allow employees to concentrate on more interesting and complex problems. From our recent research, knowledge management is a key area in which Agentic AI technology can really start to prove business value (link to our report)
In a utility-sector example, frontline operatives must triage a high volume of alerts from their infrastructure network. Assessing the urgency of these alerts involves analysing complex, often unstructured data such as environmental factors, recent events, and historical infrastructure issues. Agentic AI steps in to streamline this process. By processing all inputs and applying reasoning capabilities, the AI can prioritise alerts and suggest actions, effectively replicating the decision-making process of human operatives. This saves time and ensures that critical issues are addressed promptly, enhancing operational efficiency and reducing risks.
Outside of knowledge management, we’ve found that large sales organisations often face challenges in managing extensive and fragmented product catalogues. With thousands of products listed, duplication and inconsistency in naming conventions, often due to decentralised data entry, can lead to significant inefficiencies. For example, the same product might be entered multiple times with vague or inconsistent descriptions, making it difficult to classify and report accurately. This lack of a unified taxonomy can create obstacles when aligning products with new organisational frameworks designed to improve financial reporting or operational insights.
Enter Agentic AI… Using Large Language Models (LLMs), businesses can process and understand product descriptions, cross-reference them with external data, and align them to standardised names and categories. These AI tools can manage vast datasets, accurately mapping products to a consistent taxonomy, even when descriptions are incomplete or unclear. Additionally, they can automate the classification process while incorporating exception-handling workflows for items requiring manual review. This approach enhances operational efficiency and provides more accurate and actionable data, enabling organisations to make informed decisions and deliver improved financial and operational insights.
These are just a few examples of the types of projects Oakland is currently working on, but there are hundreds of others.
Further Benefits of Implementing Agentic AI
Implementing agentic AI in business operations streamlines processes, significantly reducing the need for manual intervention and accelerating task completion. This technology enhances efficiency and boosts accuracy and consistency, minimising human errors and ensuring that tasks are executed reliably.
With its scalable nature, agentic AI can manage expanding workloads without corresponding increases in human resources. Furthermore, it liberates human employees to concentrate on strategic and creative tasks, thereby adding substantial value beyond routine activities.
Here are the benefits of incorporating agentic AI:
Enterprise-wide System Integration
Agentic AI streamlines integration by interacting with existing information systems much like a human user but without requiring bespoke API development. These agents can navigate multiple interfaces, seamlessly transfer data between systems, and ensure consistency across your technology stack. For example, an agent could efficiently synchronise customer data across your CRM, billing system, and support platform, managing format conversions and validation checks with ease.
Autonomous Operation and Self-correction
Agentic AI operates with minimal human oversight, thanks to its advanced error detection and resolution capabilities. These systems autonomously adjust their strategies when encountering obstacles, reducing dependency on human intervention. For example, a document processing agent might automatically detect and correct formatting inconsistencies and log its actions for future review, ensuring accuracy and compliance.
Boosted Team Productivity and Strategic Focus
Multi-agent systems can take over routine operations and complex analytical tasks that traditionally consume significant human resources. This allows teams to focus on strategic initiatives and creative problem-solving. A research agent, for instance, could continuously analyse market trends, compile data, and generate insights, freeing analysts to focus on developing detailed strategic recommendations.
Dynamic Real-time Optimisation
AI agents excel at improving operational efficiency through real-time monitoring and adaptive decision-making. They process streaming data, identify emerging patterns, and implement timely improvements, which is invaluable for complex, multi-step processes. For example, a manufacturing agent might adjust machine parameters and coordinate maintenance schedules to optimise production metrics and improve output quality.
Scalable Learning and Knowledge Management
A standout feature of agentic AI is its ability to learn from diverse interactions and apply this knowledge across different scenarios. This ability enhances decision-making and problem-solving across the organisation. For instance, a customer service agent could identify effective resolution strategies from thousands of interactions, share these insights with the support team, and refine its approach based on feedback and emerging trends.
These benefits underscore how agentic AI can transform various aspects of an organisation, driving efficiency, adaptability, and strategic depth across operations. Companies can unlock substantial potential by adopting agentic AI, positioning themselves for success in an increasingly automated and data-driven business landscape.
The Challenges and Considerations
Despite its benefits, deploying agentic AI comes with challenges, particularly concerning governance. As these AI systems can perform actions semi-autonomously, businesses must implement robust governance frameworks to ensure that AI actions align with company policies and ethical standards. This includes setting up processes to review and approve AI-generated decisions and actions, ensuring they are appropriate before being enacted.
The Future of Agentic AI
As we look towards 2025 and beyond, the landscape of agentic AI is promising but requires careful navigation. These AI agents could take on increasingly complex tasks within the business sector, potentially leading to a new era of productivity and innovation. However, this will also require businesses to stay vigilant about ethical and practical implications of semi-autonomous systems.
Agentic AI represents a significant leap forward in business technology. By shifting the cognitive load from humans to machines, businesses can streamline operations, enhance employee productivity, and focus on innovation and strategic growth. As we advance, it will be crucial for companies to balance the capabilities of agentic AI with thoughtful governance and a clear understanding of its role within the broader business ecosystem. This balancing act will determine how profoundly agentic AI can transform business operations in the coming years.
Strategic Implementation of Agentic AI
To successfully implement agentic AI, organisations should identify areas where agentic AI can make the most significant impact, focusing on processes that benefit from automation and decision-making capabilities. Partnering with AI specialists who can provide insights and support throughout the implementation process is crucial. Regularly reviewing the performance of AI systems and making adjustments as necessary to optimise their functionality is essential for maintaining efficacy.
Read our guide below for further insight into crafting a data strategy that integrates with AI.
Agentic AI is reshaping how businesses operate, offering powerful tools for automation, decision-making, and process optimisation. By understanding and integrating agentic AI, organisations can achieve greater efficiency, improve accuracy, and better allocate their human resources towards growth and innovation.
Looking to enhance your organisation’s data capabilities? Discover how a data consultancy like Oakland can assist you. Dive into our AI services or visit our main knowledge management guide for deeper insights into our support offerings.
