How AI Workflows & LLMs Can Automate Business Processes

How to use AI to automate tasks and business processes

Whether through accessible no-code platforms, comprehensive enterprise solutions, or custom AI applications, choosing how to apply AI for your use case and business context is probably the most important step to using AI effectively to automate tasks and processes.

No-code AI workflow builder for OCR, IDP, and LLM orchestration

One of the most accessible ways to integrate AI into business processes is through no-code AI workflow builders. These tools are designed for tasks like invoice processing and contract reviews that require orchestrating OCR, intelligent document processing (IDP), and large language models. 

No-code solutions for building AI workflows empower firms to automate transforming unstructured data into usable formats without requiring extensive technical expertise. The power of these tools is their ability to extract and transform key information from complex documents such as invoices, contracts, and forms, making the right data available for integration into business systems and workflows.

A prime example is V7 Go, an end-to-end solution for document processing and data extraction. Applying advanced OCR capabilities and IDP, it converts both complex documents as well as scanned images into editable and searchable digital text, significantly reducing manual data entry time and error rates. 

Importantly, V7 Go incorporates LLM orchestration to enhance document comprehension and contextual understanding. This allows businesses to deploy AI models that can perform tasks such as summarizing long reports, extracting insights, and even automating customer support responses. Its no-code interface ensures that users can design and implement workflows quickly, making it an ideal choice for companies looking to boost productivity and accuracy without significant investment in technical resources.

Enterprise AI for process automation

For larger organizations, enterprise AI solutions offer robust frameworks for process automation, designed to handle complex and large-scale operations. Companies like IBM and AWS provide comprehensive platforms that integrate AI with existing enterprise systems. These are, however, much more complex to implement as they typically rely on multiple modular solutions.

IBM's AI solutions, for instance, incorporate machine learning, AI-driven analytics, and natural language processing to automate tasks across IT operations, customer service workflows, and supply chain management. These enterprise tools are engineered to scale with business needs and provide flexibility to customize AI models to specific enterprise requirements. This process of course requires significant technical and business resources.

These traditional frameworks are particularly beneficial for organizations with the infrastructure to support advanced AI deployments as part of enterprise process automation efforts.

Building custom AI solutions based on model training or fine-tuning

For firms that require tailored AI applications, building custom AI solutions through model training or fine-tuning offers unparalleled flexibility and precision. As custom implies, this approach involves technical knowledge and hands-on configuration work to prepare data and train and deploy the customized model. 

Building a custom AI solution starts with data preparation, which involves gathering and organizing data that accurately represents the use case domain so that AI models are trained on high-quality inputs. Annotation tools like V7 provide key infrastructure for effective data labeling and annotation to streamline the process of working with extensive datasets.

Once prepared data is in hand, the next step is model training. This typically involves developing/refining machine learning algorithms to perform specific tasks. Fine-tuning pre-trained models is a great way to expedite this process.  Adapting existing algorithms to new applications requires less data which saves time, cost, and effort.

Building custom AI solutions, while a non-trivial project, has the benefit of addressing unique business scenarios and opportunities – and can achieve automation that aligns most closely with strategic goals.

Best practices for implementing AI automation

Successful AI implementation for intelligent workflow automation does not happen in a vacuum; taking into consideration the bigger picture of business needs and requirements will have a major impact on implementation outcomes.

A successful AI workflow must balance the power of automation with practical considerations including governance, data security, and scale.

The good news is that there is already a lot of great advice available, and it’s just waiting to be used to help put your implementation of AI on the right track.

Understand the problem and break it into steps

Before introducing AI into a workflow, thoroughly understand the problem you aim to solve. Complex workflows can often be broken into smaller, more manageable components. Each step should align with what AI can solve effectively given current capabilities. For instance:

• Identify where AI adds value. Does your workflow involve unstructured data (e.g., scanning invoices or extracting insights from customer reviews)? AI excels at structuring this data.

• Divide tasks by best-fit technology solution. Take a financial reporting process as an example. AI could extract figures from raw documents, but final calculations, such as tax adjustments or projections, should use specialized Python libraries like NumPy or pandas.

By isolating tasks like these, workflows won’t over-rely on AI in areas where alternative tools are more accurate or cost-effective.

Select the right model for each task

Choosing the right AI model should not feel like a guessing game. Selecting the right-fit model for a particular project minimizes waste and drives reliable results. Balancing accuracy, performance, and resource costs is possible when selecting AI tools. Consider:

• Optimize for cost vs. performance. If your use case doesn’t demand high complexity, GPT-4o mini could reduce token costs while delivering adequate results. However, GPT-4o might be necessary for deeper or contextual understanding. For example:

  • A chatbot for basic FAQs could suffice with a simpler model.

  • Researching nuanced legal cases might demand the higher sophistication of GPT-4o.

• Apply multimodal models where needed. Does your workflow require text, image, or voice data processing? Multimodal models like GPT-4V (vision enabled) could be valuable, but only if features will be used in full.

Combine AI and traditional coding when possible

AI automation is not necessarily about replacing existing tools—it should complement them. Consider hybrid workflows where both AI and traditional automation approaches are combined for maximum efficiency. It's a mistake to force AI into every step of a process; focus on where it can save time or add insight where traditional methods fall short.

For example:

• Financial workflows: AI can extract or analyze text from annual reports, while numerical calculations (e.g., interest rates or portfolio metrics) are better handled by proven Python-based libraries.

• Data pipelines: AI can parse and clean raw data (e.g., social media sentiment analysis), but the integration and storage could rely on Python scripts or SQL-based databases for infrastructure.

Ensure data quality and relevance (or not)

When developing a new AI model,  optimal performance requires high-quality input data. Models rely on the clarity, consistency, and relevance of data fed into them. In fact, when developing custom AI models, data labeling is a critical part of the process.

That said, when applying existing AI tools and out-of-the-box solutions, advanced AI workflow automation platforms are now able to do the data heavy-lifting for you without additional training. They can work with low-quality source data – think low-light snaps of crumpled receipts – and still extract everything you need.

In all cases,

• Use tools that can integrate information from multiple platforms, such as shared databases or systems of record.

• Look for models pre-trained on domain-specific data. A healthcare chatbot, for instance, will perform better if trained using medical conversations rather than generic public datasets.

Optimize AI prompts to reduce costs and improve accuracy

The majority of AI workflows can and should be optimized with the right prompting techniques. It's about understanding your model, refining your approach, and iterating prompts accordingly. By paying attention to prompts, you can dramatically improve your AI's performance. While AI model quality matters, in reality, a good model combined with a very good prompt can surpass the results of a very good model with a bad prompt.

Prompt engineering is a strategic approach to communication with AI with the goal of crafting the right type of request to elicit the best responses from AI systems. For instance,  a well-designed prompt can guide an AI system in a more targeted direction, generating more accurate and useful results.

Prompt engineering is as much a science as an art. Poorly designed prompts can lead to inaccurate results, wasted compute resources, and higher costs.

A few prompting tips:

• Be specific. Detailed context in prompts guides AI towards targeted outputs. For example, instead of asking, “Extract key points from this report,” specify: “List the three most important financial metrics from this annual report.”

• Consider token efficiency. Long prompts can inflate token usage. Trim prompts for concise formatting and just enough detail.

• Create templates. If workflows involve repetitive tasks, consider standardizing prompt templates to ensure consistency while scaling.

Start small, then scale

Ambition often outweighs feasibility in AI implementations. Begin with small projects to get started automating with AI workflows. This approach helps refine processes, address challenges, and demonstrate value before full-scale deployment. For example, a corporate insurance firm might start by automating parts of the claims process to gauge effectiveness before extending AI solutions across all operations. Gradual scaling helps establish AI automation that is both manageable and sustainable.

• Test on specific use cases. For example, a law firm integrating AI for document review could start with non-critical operational documents before tackling client-related contracts. AI can address some parts of due diligence with basic data extraction and automation.

• Define performance metrics like processing time reduction or accuracy improvements. Use these metrics to validate value before scaling to larger projects.

A phased approach reduces implementation risk while delivering incremental value.

Embed governance and compliance from the start

Implementing AI automation involves navigating governance and compliance requirements. Establish clear policies and frameworks to ensure that AI applications follow industry standards and regulations, including data protection, privacy, and accountability considerations. Integrating compliance into AI workflow design phase can avoid future high-cost setbacks.

• Document processes and outcomes to ensure transparency and accountability for AI-driven decisions.

• Be vigilant with sensitive data. For instance, when handling medical or financial documentation, anonymize data and comply with standards like GDPR or HIPAA.

• Assign oversight roles. Define roles and responsibilities to ensure effective oversight and compliance and corrective action when violations/deviations are found.

Monitor and iterate

AI workflows aren’t static—they evolve as needs shift and models improve. Continuous improvement is part of maintaining the health and effectiveness of AI workflows. Regularly evaluate automation performance and make adjustments when needed. Collect feedback from users and stakeholders to identify changes to underlying needs and business processes.

Maintain AI workflow performance by:

• Scheduling periodic evaluations of AI outcomes compared to original benchmarks. This can uncover declining performance or new opportunities for optimization.

• Learn from user feedback. Stakeholders that interact with outputs from the workflow are your frontline for feedback to ensure ongoing alignment with business goals.

• Update models and systems. Because AI tools rapidly advance, reviewing system upgrades and new versions of AI models is a must.

Can businesses effectively integrate AI to automate workflows?

The short – and sweet – answer is: Yes.  

But...

With fast-moving advancements in AI technology, it requires being open to innovation. Effectively integrating AI to automate workflows takes a firm-wide appetite for experimenting with new tools and willingness to let go of old ways of working.  Successful implementation of AI automation is not just a technical challenge; it requires organizational and cultural readiness for change.

That said, getting started, even with a single workflow, can be the spark that ignites a firestorm of innovation. With the right AI solution, you can effortlessly integrate AI into workflows and empower teams to experiment with automating time-consuming tasks like document processing and data analysis – freeing up valuable time for more strategic work. 

Try V7 Go. It’s an intuitive, no-code platform for AI workflow automation. No AI expertise required. Just drag and drop your data, select the desired outcome, and let AI do the rest.