If you are not ready for fully automated AI as described above, there are plenty of augmented (human in the loop) use cases that will supercharge productivity, drive engaging customer experiences, and improve business efficiency.
This is the promise of agentic AI - a powerful technology designed to augment, not replace, human talent.
What is agentic AI?
Agentic AI refers to artificial intelligence (AI) that can complete tasks with little to no supervision. Often called ‘AI agents’ this intelligent software is often built on advanced models capable of understanding context, making decisions, and working autonomously. Designed with specific objectives in mind, these agents can adapt to their environment and improve their performance through feedback and interaction.
Beyond individual productivity tools, agentic AI introduces multi-agent systems. This is where groups of AI agents work together but each have specialised roles and tools tailored for specific tasks. These agents can interact with both human teams and other agents, collaborating, sharing insights, and delegating tasks to keep operations running smoothly and intelligently.
With this, organisations can look forward to a dynamic, collaborative workforce where digital agents handle routine tasks and teams can focus on innovation, leadership, and strategic growth.
Agentic AI in action
Imagine this: your business holds a wealth of knowledge, whether written down or in your employees’ heads. When a new hire joins, onboarding usually means endless reading and shadowing, with productivity taking weeks to kick in.
Now, picture an AI-powered onboarding agent that:
Delivers key information instantly
Answers questions in real-time
Accelerates onboarding for faster productivity
What are the benefits of agentic AI?
While agentic AI might seem like a buzzword initially, and maybe a world away from the AI plans you currently have for your business, it does bring a wealth of benefits. Organisations that start the journey now will experience ROI faster than those who wait.
Supercharged business productivity
AI agents handle repetitive tasks like data entry, reporting, and process monitoring - freeing your people to focus on creative problem-solving and decision-making.
According to Gartner®, “by 2028, at least 15% of day-to-day work decisions will be made autonomously through agentic AI, up from zero percent in 2024.”
Gartner, Top Strategic Technology Trends for 2025: Agentic AI, Tom Coshow, Arnold Gao, et al., 21 October 2024 GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved.
By deploying a multi-agent setup, tasks can be further broken down and distributed among specialised agents, improving throughput and consistency across departments.
You’ll outcompete businesses that don’t use AI agents
AI agents give you a serious edge with explainability, modularity, and fault tolerance built right in. Each agent has a clear role, so you can easily track how tasks are completed and refine processes with confidence. This level of transparency builds trust and keeps you ahead of the curve with technology you can trust.
Their modular design means you can update or replace agents without disrupting the entire system, keeping your business adaptable and future-proof. They’re also incredibly resilient. If one agent encounters an issue, others step in with more specialised skills to keep things running smoothly.
Add in the cost efficiency of using smaller, focused agent models instead of an expensive, one-size-fits-all approach, and you’ll be miles ahead of your competitors.
24/7 operations & cross-team collaboration
AI intelligent agents don’t need breaks. They can manage round-the-clock operations and collaborate seamlessly across teams.
Agentic AI examples
Augment teams with AI
A Business Development Agent identifies high value leads and transfers the prospect onto a human salesperson to close.
A HR Onboarding Agent assists new hires by coordinating introductions, sharing useful resources, answering questions, and prescribing mandatory training courses.
A Finance Agent detects invoice charge increases and notifies a Procurement Agent about potential cost savings.
This continuous collaboration reduces bottlenecks and ensures workflows stay efficient - even across global, distributed teams.
Data-driven insights in real-time
Agentic automation and AI can process large datasets in real time, delivering insights faster and more accurately than human teams working alone. Imagine an AI-powered market analysis agent identifying emerging trends and automatically notifying the sales strategy agent and the product innovation agent.
Scalable, flexible automation
As your business grows, so can your digital workforce – but without the linear costs of a traditional (fully) human workforce. Multi-agent systems can:
Expand with new agents for emerging tasks.
Reconfigure when business priorities shift.
Enhance with appropriate tools to solve unique industry challenges.
Workforce augmentation, not replacement
Consider the aggregate time savings across your teams if AI agents could handle or accelerate routine activities like customer service escalations, proposal writing, or pricing reviews.
By applying agentic AI to these regular activities, your teams can handle greater volumes of work or spend the time saved on higher-value activities like strategy, relationship-building, and smart decision-making. A critical advantage of agentic AI is that it enhances human capabilities, enabling workers to do more, without replacing them.
What are the challenges with agentic AI?
While the promise of agentic AI is transformative, businesses will need to navigate certain challenges and consider governance to ensure a successful outcome.
Shadow IT and governance gaps
Many organisations fail to recognise the risks of “shadow AI” - tools and systems implemented by teams without central oversight.
Risk: Without alignment, these systems may lead to inconsistent outcomes, security vulnerabilities, and privacy or compliance breaches.
Solution: Provide employees with access to a ‘known safe’ agentic AI solution with which they can collaborate and innovate. You could also establish a centralised AI governance team to oversee implementation, usage policies, and monitoring across departments.
Ethical and legal risks in multi-agent AI systems and decision making
While individual agents may behave ethically, their interactions in multi-agent systems and automated decision making can produce unintended or potentially harmful outcomes.
Risk: Collaborative AI systems might reinforce biases, make conflicting decisions, or unintentionally discriminate. These behaviours can lead to ethical dilemmas, regulatory violations, and reputational damage if decisions breach compliance laws or conflict with company values.
Solution: Implement explainable AI and test multi-agent interactions in simulated environments to identify and mitigate emergent risks before deployment. Collaborate with legal, compliance, and ethics teams to ensure AI behaviours align with the required standards, regularly auditing for biases and discrepancies.
Agent accountability and liability
Who is accountable when an AI agent makes a mistake? Many organisations do not address this upfront.
Risk: Lack of clarity leads to confusion during incidents and can harm customer trust.
Solution: Establish clear accountability policies, including escalation paths and human oversight checkpoints for critical decisions.
AI model drift and performance decay
Automated AI agents learn from data and environments that evolve over time, but businesses often assume solutions development is a one-time activity and overlook the need for regular updates and retraining.
Risk: Models may produce inaccurate results or make decisions based on outdated assumptions.
Solution: Schedule ongoing evaluations and retraining of AI agents to adapt to new trends, data, and regulatory changes.
Cultural resistance and misalignment
The shift to AI-driven processes can create friction if organisational culture and employee readiness are not addressed.
Risk: Employees may resist using AI, fear job loss, or fail to understand its potential benefits.
Solution: Build trust through transparent communication, emphasising how AI supports – and not replaces – the workforce, and involve employees in the AI design process.
Hidden costs of AI consumption, scalability, and sustainability
The financial and environmental costs of consuming and scaling agentic AI are often underestimated.
Risk: Costs can escalate due to retraining models, curating data, upgrading infrastructure, and providing ongoing technical support. Additionally, scaling AI systems to handle increased demand may require significant investment in compute resources, which can increase energy consumption and carbon footprints.
Solution: Leverage scalable AI solutions, such as modular multi-agent systems, to minimise disruptions and optimise resource use as your AI footprint expands, considering maintenance, retraining, monitoring, licensing, and usage fees to reduce costs and ensure a more sustainable use of AI.
Underestimating the human oversight workload
While AI agents reduce manual tasks, it often increases the need for skilled oversight, quality control, and decision-making support.
Risk: Teams may become overwhelmed by managing agents, undermining the efficiency gains AI is meant to deliver.
Solution: Clearly define human roles in AI oversight and invest in tools to monitor agent performance at scale.
How will agentic AI change the workplace?
Looking beyond the fact that agentic AI will transform processes and simply certain parts of our day-to-day working lives by handling tasks autonomously as discussed earlier – the human workforce plays a pivotal role in guiding, managing, and improving these digital agents. In the same way businesses once optimised manual workflows, the focus will shift to optimising agent collaboration and effectiveness.
Key human responsibilities in an AI-augmented workforce will include:
Agent management: Defining agent roles, assigning tasks, and overseeing performance.
Workflow optimisation: Continuously refining how agents collaborate to improve results.
Quality control: Monitoring the quality of agent outputs and providing feedback for improvement.
Decision logic tuning: Refinement of decision-making algorithms for accuracy and fairness.
Upskilling teams: Teaching employees how to train, manage, and collaborate with AI agents.
Humans won’t just work with agents—they’ll lead them.
This shift creates new opportunities for Prompt Engineers, AI Operations Managers, AI Quality Specialists, and Automation Strategists to ensure continuous AI platform optimisation and value.
Microsoft's role in advancing autonomous innovation
There is a plethora of platform options available for building your agentic AI workforce. But we recommend choosing one designed to integrate seamlessly with the core software systems in your business.
For example, if your organisation operates on Microsoft 365 or any other Microsoft technologies, the obvious choice would be to leverage Microsoft’s tried and tested enterprise-grade AI tooling, including:
Copilot: Embedded within Microsoft 365 applications like Teams, Word, Excel, and PowerPoint, Copilot assists users by automating tasks such as drafting documents, creating presentations, and summarising meetings.
Copilot Studio: A platform for building custom AI agents tailored to specific business needs, allowing users to create autonomous agents that handle routine tasks and integrate with existing systems.
AutoGen: A developer toolkit for creating intelligent, multi-agent systems capable of handling complex collaborative tasks using large language models.
Azure AI Foundry (formerly Azure AI Studio): A comprehensive platform for developing, customising, and managing AI applications and agents, including the Azure AI Foundry SDK and Portal for model discovery, evaluation, and deployment.
Azure AI Services: Pre-built APIs and services for tasks like speech recognition, face detection, and natural language processing, ideal for adding cognitive intelligence to AI agents.
Azure AI Agent Service (Preview): A new service designed for deploying and scaling enterprise-grade AI agents to automate business processes with a focus on security and scalability.
Microsoft AI tooling
How can agentic AI help you innovate?
Businesses are already using AI agents to innovate faster, streamline processes, and ultimately drive strategic growth. Let’s dive into a few real-world examples of Microsoft agentic AI technology being used to reshape these businesses for the better.
JetBlue: Personalised customer experience
JetBlue is taking customer service to new heights with AI. The airline uses AI intelligent agents to deliver personalised experiences, from booking to baggage claim. By leveraging tools like Microsoft Dynamics 365 and AI-powered chatbots, JetBlue provides customers with real-time flight updates, baggage tracking, and even personalised offers.
The result? Happier customers, smoother operations, and a brand that’s becoming known for its innovative use of technology.
Holland America Line: Digital concierge
Holland America Line used Microsoft Copilot Studio to build a digital concierge, “Anna,” to support customers with bookings and queries. This AI-driven agent handles thousands of interactions weekly, resolving issues and reducing call centre traffic. It uses intelligent features like multi-intent detection and custom entity extraction, enhancing both efficiency and customer satisfaction.
The result? Faster, more personalised service, and streamlined operations that improve the customer experience and business performance.
Orbital Witness: Legal tech automation
Orbital Witness leverages Microsoft Azure AI to transform the property search process. Their AI-powered platform uses machine learning to analyse legal documents, helping clients quickly identify potential risks in property transactions. By automating manual tasks, it saves time and reduces errors, empowering legal professionals to make faster, more informed decisions.
The result? Increased efficiency, reduced costs, and a smoother customer experience
Toyota: Enabling faster innovation
Toyota is using AI agents to unlock the collective wisdom of its engineers, enabling faster innovation. The AI helps with knowledge sharing, improving collaboration, and accelerating product development. By capturing insights from engineers’ work, AI agents reduce redundancy and enhance decision-making, allowing Toyota to innovate more efficiently.
The result? Quicker response times and better solutions for complex engineering challenges.
Pets at Home: Fraud detection AI agent
Pets at Home is leveraging Microsoft Copilot agents to automate processes and improve both the customer service and fraud detection. These automated AI agents streamline operations, allowing staff to focus on higher-value tasks while detecting fraudulent activities in real time.
The result? By deploying AI to tackle repetitive work and analyse patterns for potential fraud, the company enhances efficiency, security, and customer satisfaction.
The bottom line: Agentic AI unlocks new possibilities
Agentic AI isn’t just a tool for automation or worker productivity - it’s a business transformation enabler.
By combining intelligent automation, multi-agent collaboration, and human leadership, businesses can:
Unlock new levels of profitability and scale.
Make faster, data-driven decisions.
Empower their workforce with smarter, more effective tools.
The future of work isn’t humans vs. AI - it’s humans leading AI to build more effective, innovative, and resilient organisations.
The question isn’t whether your business should adopt agentic AI - it’s how quickly can you start harnessing its potential?
Ready to unlock the power of agentic AI? Book your readiness and opportunity audit today.
Speak with our expert team to explore how AI agents can transform your business. We’ll help you identify the right agent types for your use-cases, the infrastructure you’ll need, and how to unlock cost savings, efficiency gains, and groundbreaking innovation at scale with agentic AI.
