Why your enterprise automation strategy needs more than just LLMs

Are large language models (LLMs) the silver bullet for your enterprise automation strategy? It’s a compelling idea. The promise of using a single, powerful model to process any document and streamline any workflow is tempting for any technology leader.

 

However, this simplistic view overlooks a crucial reality: successful, scalable enterprise automation requires a more nuanced approach. Relying solely on general-purpose LLMs for business-critical workflows is not just insufficient, it’s a recipe for failure.

 

Organisations are quickly learning that feeding documents into a generic LLM often leads to inconsistent results, hallucinations and a lack of the precision demanded by enterprise-grade operations. A recent survey commissioned by ABBYY found that while 98 per cent of business leaders are using generative AI, they have incurred significant hurdles. Nearly a third found training gen AI models harder than expected, and over a quarter struggled with integration into existing business processes.

 

The core of the issue is that mission-critical workflows, from customer onboarding and claims processing to supply chain logistics, demand more than what a standalone LLM can offer. They require accuracy, context and reliability. This is where a hybrid approach, combining the strengths of purpose-built document AI with the contextual understanding of LLMs, becomes not just beneficial but essential.

 

The LLM challenge: when general purpose isn’t good enough

 

The excitement surrounding LLMs is understandable. They have transformed how we interact with unstructured data. Yet organisations are discovering that these models, when used in isolation for document processing, have significant limitations.

 

For example, LLMs can struggle with complex document layouts, diverse formats or missing data fields. This leads to inconsistent and unreliable data extraction, which is unacceptable for processes where precision is paramount, such as in finance or healthcare.

 

Also, because LLMs are designed to generate plausible text, this can sometimes lead to them inventing facts or figures when they don’t know the answer. In a business context, where decisions are based on data, these hallucinations introduce significant risk and can lead to costly errors. In fact, our research found that 20 per cent of businesses had problems with hallucinations when implementing gen AI.

 

Ultimately, general-purpose LLMs lack the built-in validation, security and governance features that are standard in enterprise-ready solutions. Protecting sensitive data and ensuring compliance with regulations such as GDPR or HIPAA requires more robust controls than a generic model can provide.

 

Using LLMs alone for document processing may seem innovative, but it often leads to fragile, opaque, and ungovernable workflows. Real business value emerges only when generative AI is anchored by structured, reliable document AI.

 

The power of a hybrid approach: document AI and LLMs

 

The most effective path forward lies in a hybrid model that combines specialised document AI with the broad capabilities of LLMs. Document AI solutions are purpose-built to read, understand and extract data from any type of document with high accuracy. They are trained on vast datasets of business documents, enabling them to handle complex layouts and variations with precision.

 

When you integrate this specialized capability with an LLM, you create a powerful synergy:

• Document AI handles the heavy lifting of accurately ingesting, classifying and extracting structured data from unstructured documents
• LLMs then use this clean, reliable data to provide deeper contextual understanding, perform complex reasoning and even generate content

This hybrid approach to gen AI doesn’t just extract information; it turns it into actionable intelligence. Document AI ensures accuracy and structure at the point of capture, while LLMs enrich that data with contextual understanding and reasoning. In an insurance claims process, for instance, document AI precisely extracts details from accident reports and invoices, and the LLM can potentially interpret patterns to flag potential fraud or generate concise claim summaries for adjusters. Together, they deliver reliable automation with human-level insight.

 

Driving real business value across industries

 

The impact of combining intelligent document processing (IDP) with process intelligence is already transforming complex, business-critical use cases across various sectors:

 

Banking and financial services

 

In know your customer (KYC) and customer onboarding processes, IDP accelerates document verification and data extraction, while process intelligence provides visibility into the end-to-end workflow. This helps banks reduce onboarding times, ensure compliance and improve the customer experience.

 

Insurance

 

For claims processing, this hybrid approach can automate the collection and validation of various documents, from repair estimates to medical reports. This reduces manual handling, minimises claims leakage and allows employees to focus on providing better customer service rather than chasing paperwork.

 

Logistics and transportation

 

Customs clearance is a document-intensive process where errors lead to costly delays. Document AI can automatically extract and validate data from invoices, bills of lading and customs declarations, regardless of language or format, ensuring accuracy and compliance with ever-changing regulations.

 

Healthcare

 

Managing prior authorisations involves a high volume of complex medical documents. Automating data capture and integrating it with electronic health records (EHR) systems streamlines the entire workflow, enabling providers to get faster decisions and improve patient care.

 

Human resources

 

During employee onboarding, IDP can automate the capture and verification of data from critical documents, ensuring compliance and reducing manual errors. Process intelligence then offers a holistic view of the new hire experience, helping HR teams scale effectively.

 

Five steps to a smarter automation strategy

 

For CIOs and technology leaders, the path to successful enterprise automation is not about chasing the latest trend, but about building a solid foundation.

 

Understand your processes first: Don’t automate a broken process. Use process intelligence tools to gain deep, data-driven insights into how your workflows operate. Identify bottlenecks and inefficiencies to determine where automation will deliver the most significant impact.

 

Prioritise data quality above all: The success of any AI initiative hinges on the quality of the data that fuels it. Invest in tools and processes that ensure your data is accurate, accessible and AI-ready before it ever reaches an LLM.

 

Adopt a hybrid AI model: Recognise that no single AI tool is a panacea. Build a flexible AI architecture that combines the strengths of purpose-built solutions such as document AI with the capabilities of general-purpose models such as LLMs. This allows you to use the right tool for the right task.

 

Focus on business outcomes, not just technology: Tie every AI investment to a clear business objective and ROI model. Whether it’s improving customer satisfaction, reducing operational costs or ensuring compliance, your AI strategy must be aligned with measurable business value.

 

Establish robust governance: As you adopt AI, implement clear policies for data security, privacy and ethical use. This is especially critical as “shadow AI” – where employees use unapproved tools – becomes more prevalent. Strategic, top-down adoption with risk management as a priority is essential for unlocking corporate benefits securely.

 

The future of enterprise automation will be undeniably intelligent, but also practical. It requires moving beyond the hype of standalone LLMs and embracing a more strategic, hybrid approach. By grounding your AI strategy in high-quality data and a deep understanding of your business processes, you can build an automation framework that is not only efficient and scalable but also truly transformative.

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For more information, download the Process Automation Handbook: Navigate the Agentic Hype