AI is making its impact across UK business. And the next wave of AI promises even more.

Instead of simply answering prompts, new ‘AI agent’ tools will be powerful and smart enough to act by themselves. For example, scheduling meetings, issuing invoices, and updating systems without continuous human oversight.

If implemented effectively and responsibly, they could feel like an extra pair of hands. A definite attraction for hard-pressed SMEs.

But implemented poorly, these new tools could quickly become extra bother – introducing errors, compliance risks, and operational headaches.

So, what do you need to know about AI agents? And how can they be introduced in a way that’s practical, safe, and commercially worthwhile?

What makes AI agents different from the AI tools you’re already using?

Many businesses are now familiar with generative AI tools. You may already use them to draft emails, produce reports or summarize documents. However, they rely on you to prompt them and decide what to do with their outputs.

AI agents represent the next stage. Instead of simply responding to instructions, they can complete tasks independently to achieve a defined objective.

For example, a standard AI tool might draft a customer response email. An AI agent could read incoming messages, identify the query, check internal records, send a reply, and update systems automatically.

By managing multi-step processes with minimal supervision, they help complete tasks that might otherwise require specialist knowledge. They also reduce administrative overhead and free staff for more valuable work. And, because they integrate with systems and operate continuously, agents can also handle routine tasks outside of normal working hours.

Crucially, they are not designed to operate without any human involvement. They still require supervision, particularly where decisions affect customers, finances, or regulatory compliance.

Early adoption

Adoption of agentic AI is still in its early stages. Only around 7% of UK businesses currently use agentic AI technology. However, projections suggest adoption could reach around 74% of organizations within the next two years.

For SMEs, that signals a growing need to understand the tools. Not necessarily to deploy them immediately, but to avoid being caught off guard when competitors begin integrating them.

The risks to understand before experimenting with AI agents

Although AI agents offer a lot of potential, handled badly, they can introduce new risks. And, because they can take direct action, mistakes can compound if controls aren’t in place.

Although many organizations are planning to deploy AI agents, only 21% currently have a mature framework for oversight. This creates the risk that businesses end up scaling automation faster than they can handle. The risks include:

• Loss of control over automated decisions - Agents given excessive autonomy can make errors affecting customers or finances. There have been cases of agents deleting large volumes of emails or making unauthorized financial decisions when safeguards were absent.

• Security and data protection challenges - Granting access to systems, accounts, and sensitive customer data can expose businesses to cyber and compliance risks. Systems can be vulnerable to “prompt injection” attacks, where malicious inputs manipulate tools into revealing information or performing unintended actions.

• Reduced visibility and accountability - If businesses rely on AI outputs without proper monitoring, it can become difficult to understand how decisions were made. This can create challenges when explaining outcomes to customers or regulators. There is also growing concern around “shadow AI agents,” where unapproved agents are deployed independently without organizational oversight.

AI agents aren’t inherently dangerous, but structured and supervised adoption is essential. If you’re still to take the plunge, experimenting with specific use cases and rolling out iteratively can be helpful.

How to start experimenting with AI agents safely

When getting started with AI agents, experimenting with specific use cases and rolling out iteratively can be helpful. There are a few simple things you can do to minimize risks and keep projects on track:

• Introduce agents into controlled, lower-risk workflows - A practical way to start is to begin with simpler tasks (e.g. scheduling, internal reporting, or summarizing data). These repetitive, rules-based tasks are measurable, low-stakes, and allow teams to see tangible benefits quickly.

• Expand through a focused pilot project - When you have the confidence to expand into more complex processes, starting with a single pilot project can identify limitations. At this stage, you can also refine workflows, and build understanding of how agents interact with existing systems.

• Keep humans in the loop and focus on measurable outcomes - At every stage, keeping humans in the loop is essential. This also helps establish where agents can work independently and where humans need to step in. Focusing on measurable productivity benefits like reduced admin, faster response times, and fewer errors is also important early on.

• Understand how data is stored and processed - Finally, it’s worth understanding where an agent stores and processes information. Regulation around AI is changing fast, so knowing where your data is being used and processed is important. If it’s not clear where your data is going, think twice about using the tool.

How standards can help

Deploying any AI tool is not without risk, especially when it’s operating autonomously. Standards can play an important role in proactively mitigating risk. Being able to demonstrate due diligence can also help build trust with customers, partners and regulators. Relevant standards include:

BS EN ISO/IEC 42001:2026 – Information technology. Artificial intelligence. Management system: Provides guidance for establishing, implementing, maintaining and continually improving an AI management system. The overarching purpose is to establish competence and confidence, and to maximize the benefits of AI.

BS EN ISO 9001 – Quality management systems: The most widely recognized quality management system in the world. It can be integrated and implemented alongside BS ISO/IEC 42001 to improve AI quality management.

BS EN ISO/IEC 23894 – Information technology. Artificial intelligence. Guidance on risk management: Provides strategic guidance on identifying and managing risks specific to AI systems. Important for responsibly handling AI risks throughout the lifecycle.

BS EN ISO/IEC 27001 – Information Security Management System: Sets out a framework to protect information assets through structured security controls. Important for safeguarding data that AI systems use and process.

BS ISO/IEC 42005:2025 – Information technology. Artificial intelligence (AI). AI system impact assessment: Designed as a companion standard to BS ISO/IEC 42001. It provides guidance for conducting AI system impact assessments that are well-documented and aligned with organizational AI governance.

AI agents aren’t a magic bullet, but if rolled out thoughtfully, they could become a powerful extension of your team. The key is to start small, maintain human oversight, and adopt clear governance and standards from the outset. Standards can help ensure that AI becomes an extra pair of hands, not extra bother.

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