AIAgenticWorkflows:TransformingYourBusinessProcesses

AI agentic workflows go beyond basic automation by enabling systems to operate independently, make decisions, and adapt to changing conditions. Unlike rule-based automation, these workflows handle unexpected challenges and continuously improve through real-time learning. Businesses in the UK are already seeing benefits, including cost savings, faster processes, and increased productivity.

Key Takeaways:

• What They Are: Agentic AI workflows autonomously execute tasks, adapt to changes, and optimise over time.
• Why They Matter: UK companies report savings of over £29,000 annually and productivity boosts of 27%-133% with AI adoption.
• How They Work: These workflows break down complex tasks, use multiple specialised agents, and integrate with existing systems.
• Real Examples: Companies like Superdry, Virgin Money, and Bosch have used AI to cut costs, reduce errors, and improve efficiency.
• Legal Compliance: UK GDPR and the upcoming Data (Use and Access) Act 2025 require businesses to follow strict data protection rules.

Whether you're in retail, finance, healthcare, or manufacturing, AI workflows can streamline operations and improve decision-making. However, success depends on careful planning, employee training, and adherence to regulations. With the right approach, businesses can achieve a 250% ROI within the first year of implementation.

Key Parts of Agentic Workflows

How Agentic Workflows Operate

Agentic workflows are designed to constantly monitor their surroundings, make decisions based on real-time data, and seamlessly integrate AI components with human input. Using machine learning, these systems identify patterns within the data to guide decision-making. This combination of analysis and context gives the agents a deeper understanding of not just what’s happening, but why it’s important for a particular business goal.

At the heart of this process is the workflow orchestration system, which ensures smooth collaboration between AI components, automated processes, and human operators. It manages the flow of information across different agents and systems, maintaining consistency while enabling quick adjustments when needed.

Take ServiceNow AI Agents as an example. When they receive a customer support ticket, they compare the information with an internal knowledge base, review historical cases, and generate a concise summary for an IT specialist. Based on this, they recommend specific actions. The human specialist can then approve or tweak these recommendations, creating a collaborative decision-making process.

This ability to dynamically adapt makes agentic workflows highly effective for breaking down and managing complex tasks.

Breaking Down Complex Tasks

Agentic workflows excel at taking large, intricate goals and breaking them into smaller, more manageable subtasks. This happens dynamically, as agents constantly reassess priorities and adjust their strategies in response to new data or changing conditions.

For instance, in healthcare, a doctor submitting a prior authorisation request triggers a series of steps managed by different agents. One AI intake agent gathers patient information from medical records and insurance files. A validation agent then checks whether the request aligns with medical and insurance guidelines, recommending approval or flagging issues for further review. If approved, a communication agent updates the system and notifies the doctor and patient, initiating the next steps for treatment.

Another example is Claygent, which simplifies lead research by scanning web sources, enriching profiles, extracting relevant data, and generating tailored outreach strategies. It adjusts its approach based on the quality of the data it encounters.

Multiple Agents Working Together

The true strength of agentic workflows lies in their ability to deploy multiple specialised agents that work together to solve complex problems. Instead of relying on a single, general-purpose AI, these systems use a team of agents, each focused on a specific task.

Feature	Single Agent	Multi-Agent Collaborative
Expertise	Limited to one area	Specialised in various areas
Communication	Operates independently	Shares information seamlessly
Task Resolution	May struggle with complexity	Tackles challenges collaboratively
Efficiency	Slower for multifaceted tasks	Faster due to task division

This collaborative approach has proven effective across industries. For example, legal departments using multi-agent AI systems have cut contract review times by 60% while improving the accuracy of risk identification. These systems use document processing agents for intake, classification agents to categorise clauses, comparison agents to match against templates, risk assessment agents to evaluate liabilities, and summary agents to compile comprehensive reports.

In financial services, similar frameworks are used for investment document analysis. Different agents specialise in extracting numbers, processing text, spotting trends, and creating visual reports. The combined output significantly reduces the time human analysts would need to produce the same insights.

These multi-agent systems don’t just work internally; they connect with external tools through APIs, databases, and third-party applications. This allows them to access real-time data, execute actions in other systems, and stay synchronised across the business.

"Agentic AI platforms must be able to integrate with existing infrastructure to maximise their potential." – Lan Guan, Chief AI Officer at Accenture

For multi-agent systems to succeed, careful design is essential. The most effective setups focus on creating narrowly specialised agents, each with a clear role, rather than trying to develop all-purpose agents. Each agent is assigned specific inputs, outputs, responsibilities, and performance benchmarks, along with access to the tools it needs.

Insurance companies provide a great example of this principle in action through automated claims processing workflows. Document digitisation and OCR agents handle intake forms, data extraction agents gather claim details, policy verification agents confirm coverage, fraud detection agents flag suspicious activity, and adjudication agents calculate payments. Each agent focuses on its specific task, and together they create a streamlined system for managing even the most complex claims.

Real Uses for UK Businesses

Industry Examples

Businesses across the UK are adopting agent-driven workflows to enhance efficiency and productivity across various sectors.

Retail and Customer Service

Pets at Home is leveraging AI through Microsoft Copilot Studio to improve fraud detection and offer personalised pet care recommendations for its 8 million loyalty programme members and 10 million registered pets. In its veterinary practices, AI-powered transcription and automated scheduling are easing administrative workloads, allowing staff to focus on patient care.

Superdry has transformed its invoice processing with AI, increasing its touchless processing rate from 5% to 80%. This shift has reduced staffing costs, improved purchase order compliance, and provided better cash flow visibility. Similarly, Primark has adopted automated invoice processing with advanced matching capabilities, speeding up operations, reducing errors, and cutting costs.

Financial Services

Nationwide Building Society has integrated GPT-4 via Azure OpenAI Service to automate customer correspondence. This has reduced response times from 45 minutes to just 10–15 minutes, a 66% improvement. Routine back-office tasks are also automated, enabling staff to focus on more complex customer needs.

Virgin Money introduced "Redi", a virtual assistant developed with Dynamics 365 Customer Service and Microsoft Copilot Studio. Redi now handles 90% of customer inquiries, improving customer satisfaction and allowing contact centre agents to tackle more challenging issues.

Barclays has adopted AI for advanced data analysis and risk management. Real-time analytics identify and mitigate risks effectively, while automated fraud detection systems improve accuracy and reduce financial losses.

Manufacturing and Operations

Bosch has integrated AI into its production processes, optimising operations and supply chain management. Their AI systems manage production schedules, monitor inventory in real-time, and analyse product quality data to prevent defects, reducing waste and improving both efficiency and product quality.

Logitech has implemented AI-driven strategies, including automated data capture and intelligent workflow routing. These efforts have resulted in 83% straight-through processing, improved accuracy, better cash flow management, and enhanced operational control - all without increasing headcount.

Healthcare and Professional Services

Valorem Reply UK developed an AI-powered mobile app for a National Health Service organisation in Scotland. This app replaced traditional phone services with a digital platform, improving accessibility for patients while cutting operational costs.

"We're proud to have partnered with this national healthcare organisation to develop a cutting-edge mobile app that not only improves patient experience but also optimises operational efficiency." - Andy James, Partner, Valorem Reply UK

Amey, an engineering firm, uses mobile-friendly AI agents to provide frontline workers with instant access to safety information. These agents offer real-time troubleshooting and multilingual support, enhancing both safety and efficiency standards.

Insurance and Process Automation

Aon Ireland adopted FlowForma Process Automation to streamline workflows, automating tasks like HR onboarding, GDPR requests, insurer amendments, and contractor vetting.

"We can now build a fully functional flow in less than a day. FlowForma Process Automation offers a level of customisation that's been a game-changer, making our work - and the business's work - easier and more efficient." - Robbie Molloy, Aon's Product Manager

Early adopters of agent-driven AI in the UK have seen notable benefits. For instance, businesses that previously spent £9–£15 to manually process a single invoice can cut costs by nearly 70% with AI automation. Finance teams using AI have reported a 30% reduction in audit preparation time and a 40% improvement in reporting accuracy. However, achieving these benefits requires strict adherence to data protection and regulatory standards.

UK Legal Requirements

For UK businesses, implementing agent-driven workflows means navigating a complex legal landscape, primarily shaped by the UK General Data Protection Regulation (UK GDPR) and the Data Protection Act 2018, alongside industry-specific rules.

Data Protection Essentials

Under UK GDPR, personal data must be processed in a fair, lawful, and transparent manner. Companies are required to clearly define the purposes for which AI systems process data, ensuring these purposes are legitimate, necessary, and that the data remains accurate, secure, and up-to-date.

Risk Assessment and Impact Analysis

For high-risk AI applications, conducting Data Protection Impact Assessments (DPIAs) is mandatory. These assessments help identify potential risks to individual rights and freedoms and outline mitigation measures.

Automated Decision-Making Rules

When AI systems make decisions with legal or significant effects, GDPR requires transparency about the logic behind these processes. Organisations must also provide options for human intervention, explanation, and the ability to challenge decisions.

Vendor Agreements and Safeguards

Collaborating with AI vendors demands clear agreements on data processing roles and responsibilities. Organisations must ensure vendors comply with GDPR principles and include safeguards like data minimisation and retention policies in contracts.

Equality and Bias Prevention

The Equality Act 2010 requires businesses to ensure their AI systems do not discriminate based on characteristics like age, gender, race, or religion. Regular testing and monitoring are essential to detect and address any biases.

Adapting to New Regulations

The regulatory environment is evolving. The Data (Use and Access) Act, effective from 19 June 2025, will introduce new requirements, while the EU AI Act will impose stricter rules on high-risk AI systems, including those used for profiling and automated decisions. Non-compliance can lead to steep penalties, with GDPR fines reaching up to €10 million or 2% of annual revenue.

Steps for Compliance

To stay compliant, businesses should:

• Perform thorough due diligence before adopting AI systems.
• Clearly define data processing roles and responsibilities.
• Verify vendor transparency and adherence to GDPR principles.
• Design systems with data minimisation and fairness in mind.

Setting Up Agentic Workflows with Antler Digital

Antler Digital's Approach

Antler Digital partners with UK businesses to create bespoke agentic workflows tailored to their operational hurdles and growth ambitions. Their approach focuses on crafting solutions that integrate smoothly with existing processes while being scalable for future demands.

The process begins with a thorough workflow assessment to identify inefficiencies and opportunities for automation. Based on this, Antler Digital develops detailed implementation plans that deliver immediate efficiency improvements alongside long-term strategic advantages. For businesses with established systems, they specialise in phased integration, ensuring operations continue uninterrupted as AI-driven automation is introduced. The success of this transformation hinges on three key elements: technical implementation, change management, and staff training.

"The team at Antler Digital was able to take our complex ideas and turn them into a functional and user-friendly SaaS app. They brilliantly handle the frontend of our fintech both with design and development. We love working with them as an in-house team where they bring the expertise we needed." - Jeremy Taylor, CTO Wiserfunding

This testimonial reflects Antler Digital's ability to handle intricate business requirements while fostering long-term collaborations. Their work with Wiserfunding, including redesigning marketing platforms and creating bespoke risk management systems, highlights their commitment to delivering tailored, impactful solutions.

Their methodology is built on a cutting-edge technology stack that ensures solutions can adapt and scale as businesses grow.

Technology and Tools Used

Antler Digital employs a modern technology stack designed for scalable and efficient agentic workflows. Central to their approach is Ray, a distributed computing framework that allows AI systems to scale across multiple processors and machines. This is especially beneficial for managing complex multi-agent processes.

FastAPI serves as the backbone for building APIs, enabling seamless communication between system components. This Python-based framework integrates effortlessly with Ray Serve, ensuring machine learning models and AI agents work together efficiently. It also provides automatic documentation, simplifying ongoing maintenance.

For deployment and scaling, Antler Digital relies on Kubernetes containerisation, which dynamically adjusts computational resources to meet changing demands. This combination of tools forms a robust foundation that supports everything from simple task automation to intricate multi-agent systems. Their collaboration with DeZaan is a prime example, where a Jamstack architecture supported nearly two years of continuous platform enhancements.

Service Options for Workflow Automation

Antler Digital offers three tailored service models to meet diverse business needs and technical capabilities. Each model provides a different level of involvement and control, ensuring businesses can choose an approach that aligns with their goals and resources.

Service Model	Key Features	Best Suited For	Pricing Structure
Project-Based	Comprehensive planning, execution, and maintenance using modern technologies	Businesses with clearly defined automation goals and timelines	Custom quote based on project scope
In-House	Seamless integration with existing teams, offering full client control	Companies seeking to retain oversight while leveraging specialist skills	Custom quote based on team size/duration
Full-Service	End-to-end solutions, from initial scoping to ongoing maintenance	Organisations seeking complete workflow transformation without in-house tech teams	Custom quote covering full lifecycle

The Project-Based model is ideal for businesses with specific automation objectives that require a focused, finite solution. The In-House model suits companies looking to maintain control over their projects while benefiting from Antler Digital's expertise. For those seeking a fully managed solution, the Full-Service model offers comprehensive transformation, from planning to ongoing support.

"The SportsIcon platform is a testament to Antler's ability to build modern, scalable web applications. Their attention to detail and commitment to excellence are evident in every aspect of the platform, from its design to its functionality. I would highly recommend them to anyone looking to build a cutting-edge digital solution." - Alexi Yovanoff, COO & Co-Founder Sports Icon

This feedback highlights the quality and scalability of Antler Digital's solutions across all service models, ensuring businesses receive workflows that are both robust and adaptable to their evolving needs.

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Solving Problems and Following Best Practices

Data Protection and Legal Compliance

When it comes to AI workflows, ensuring adherence to data protection laws is non-negotiable. The UK's Data (Use and Access) Act 2025 (DUAA) has introduced key reforms to data protection requirements, directly impacting AI processes. For instance, a staggering 91% of UK business leaders admit that poor data quality hinders their operations and reduces the effectiveness of AI systems. The DUAA maintains the UK's EU data adequacy status while allowing for more adaptable automated decision-making frameworks.

A landmark case in March 2025, Dun & Bradstreet Austria (C-203/22), further clarified the responsibilities of data controllers. The European Court of Justice ruled that data controllers must provide individuals with clear, concise, and easily understandable explanations of the logic and principles behind automated processing - tailored specifically to each individual’s case.

An example of effective compliance can be seen in Nationwide Building Society, which uses GPT-4 through the Azure OpenAI Service to automate customer correspondence. This implementation slashed response times from 45 minutes to just 10–15 minutes - a 66% efficiency gain - while maintaining robust data security. To ensure compliance, businesses must adopt safeguards that align with the UK’s risk-based approach, requiring that data protection standards in partner countries are not “materially lower” than those in the UK.

Common Problems and How to Fix Them

Despite the increasing interest in agentic AI, its adoption remains low, with full deployment stuck at just 11%. Several hurdles stand in the way, including challenges with system integration, insufficient infrastructure, and resistance to change within organisations.

One major obstacle is the skills gap. A significant 78% of UK chief executives identify this as a challenge, with 68% specifically pointing to a lack of technology expertise. Adding to this, 85% of employees believe AI will affect their jobs within the next five years, which fuels cultural resistance. As Michael Green, UK and Ireland Managing Director at Databricks, puts it:

"AI tools are only as effective as the people trained to use them. Insufficient AI literacy remains a major barrier to successful deployment."

To counter this, organisations should prioritise transparency and education rather than imposing top-down mandates. For example, Amey, a UK engineering company, has empowered its workforce by deploying AI agents that provide instant access to safety information from 99% of their 118 million files stored on SharePoint.

Infrastructure readiness is another pressing issue. Gartner estimates that by 2028, 33% of enterprise apps will incorporate agentic AI, up from less than 1% in 2024. Tackling this requires businesses to improve their data maturity and integration capabilities. Starting with small pilot projects in high-impact areas can help organisations gain experience and confidence while minimising risks. Additionally, ensuring data quality from the start - using preprocessing tools to clean and standardise datasets - lays the groundwork for smoother implementation.

Addressing these challenges creates a foundation for a clear and actionable AI adoption strategy.

Steps for Successful Adoption

Given the complexities of legal compliance and system integration, a well-structured adoption plan is essential. Success with agentic workflows starts with defining clear objectives and identifying areas where AI can streamline operations or reduce errors.

The first step is assessment and planning. Organisations need to evaluate their current workflows, assess the quality of their data, and pinpoint integration needs. Selecting the right AI tools is equally important - factors such as cost, scalability, ease of use, and compatibility with existing systems should guide decision-making. Instead of rolling out AI across the entire organisation at once, many companies see better results by starting with small-scale pilot projects. These pilots allow teams to refine processes and build trust among stakeholders.

Employee training and ongoing support are also critical. Companies that invest in equipping their teams to work effectively alongside AI tools report an average of 20% savings in operational costs.

Finally, continuous monitoring and optimisation ensure long-term success. Metrics like accuracy, speed, and user satisfaction should be tracked regularly, with periodic audits to identify areas for improvement. As Michael Green emphasises:

"Without effective adoption across industries, the UK risks being a nation of AI ambition rather than AI execution."

The benefits of disciplined implementation are clear. A remarkable 91% of businesses using AI workflows report improved operational efficiency, and such organisations are 33% more likely to outperform their competitors. Moreover, companies that maintain systematic performance tracking and update their systems regularly scale operations at twice the speed of those relying on manual methods. This highlights the tangible rewards of a thoughtful and methodical approach to AI adoption.

Andrew Ng On AI Agentic Workflows And Their Potential For Driving AI Progress

Summary and Main Points

AI agentic workflows represent a major shift in how businesses across the UK can operate. These systems move beyond traditional reactive tools, functioning instead as proactive collaborators that reshape entire business processes. Research shows that organisations adopting these workflows can achieve a 66% increase in worker productivity, effectively compressing decades of efficiency gains into just one year. McKinsey reports that generative AI could add £2.1 trillion to £3.5 trillion in value globally, highlighting significant opportunities for UK businesses.

These advancements are already delivering measurable results. UK organisations have reported notable efficiency improvements, with frontline workers now able to access critical data in real time. On top of that, AI-driven workflows can improve task accuracy by over 41% when compared to traditional approaches.

However, achieving these benefits requires careful planning and execution. As noted earlier, success hinges on starting with high-impact use cases, ensuring excellent data quality, and adhering to UK regulations. Strategic planning is essential to unlock the full potential of these technologies.

Antler Digital exemplifies how AI integrations can transform business operations. Their tailored solutions span industries such as FinTech, SaaS, and environmental platforms. They offer services including project-based solutions, in-house team integration, and full-service technical management. With expertise in building scalable, modern applications, Antler Digital helps businesses enhance efficiency while navigating the complexities of AI implementation.

FAQs

What makes agentic AI workflows more adaptable and effective at decision-making compared to traditional automation?

Agentic AI workflows are remarkable for their ability to adjust on the fly and make smart choices. Unlike traditional automation, which sticks to rigid, pre-set rules, these systems can evaluate various factors, learn from past results, and tweak their actions in real time. This makes them especially capable of managing complex or unexpected situations.

While traditional automation is great for repetitive, straightforward tasks, it doesn’t evolve. Agentic AI, on the other hand, gets better over time by learning from fresh data and gaining new insights. For businesses looking to streamline operations and remain flexible in a fast-moving world, this approach offers a clear advantage.

What legal compliance requirements must UK businesses meet when adopting AI-driven workflows, and how can they ensure adherence?

Businesses in the UK integrating AI into their workflows need to navigate several legal requirements. These include adhering to transparency, non-discrimination, and data protection rules outlined in the UK GDPR. Additionally, they should keep an eye on future regulations, such as the proposed AI Act and the Artificial Intelligence (Regulation) Bill, which aim to introduce oversight bodies and promote responsible AI practices.

To stay compliant, organisations can take these steps:

• Address bias: Put measures in place to minimise bias and promote fairness in AI-driven decisions.
• Ensure transparency: Keep clear records explaining how AI systems function and make decisions.
• Enhance data security: Strengthen protocols to safeguard sensitive data.

Keeping up with regulatory changes and seeking advice from legal professionals can help businesses adapt to new standards while building trust and demonstrating accountability.

How can businesses effectively implement AI-driven workflows to enhance operations and ensure a seamless transition?

To make AI-driven workflows work well, businesses should start by closely examining their current processes. This helps pinpoint where automation can bring the most value and ensures the chosen AI tools match the organisation's specific needs and objectives.

A well-thought-out integration plan is key to making sure new systems blend seamlessly with the existing setup. Equally, providing training and support for staff is crucial. This not only helps employees get comfortable with the new workflows but also builds confidence in the technology. Keeping communication open between teams and tracking progress regularly can help smooth out any bumps during the transition.

Another critical step is to prioritise data security and stay compliant with regulations. Protecting sensitive information is non-negotiable for maintaining trust and operational stability. By following these practices, businesses can use AI to simplify processes and achieve meaningful growth.