Four ways to integrate Artificial Intelligence into existing workflows

By Peter Philipp, General Manager – ANZ at Neo4j

With the power and sophistication of Artificial Intelligence (AI) tools growing rapidly, increasing numbers of organisations are looking for ways to integrate them into everyday workflows.

However, many are encountering significant challenges. From technical barriers to unstructured implementation strategies, organisations often struggle to scale AI initiatives. Indeed, industry research has found that only 26% have developed the necessary capabilities to move AI beyond proof-of-concepts stage.

Extracting maximum value from AI relies on taking advantage of its more advanced capabilities, with agentic, reasoning and domain-specific AI leading the way. Moving beyond the chat-style responses of generative AI, these systems are designed to think critically, make autonomous decisions, and unlock new levels of business innovation.

In this way, organisations will be much better placed to leverage AI not just as a tool, but as a driver of innovation. Four key ways in which the technology can be successfully integrated into workflows are:

1. Take a co-ordinated approach:

AI initiatives can often strike problems if clear goals have not been set from the beginning. Having a well-defined framework in place ensures that everyone is aligned on the purpose of the AI efforts, how the technology will be applied, and who is responsible for each phase.

It is essential to begin by defining clear objectives that outline the benefits that AI should deliver, whether that is optimising internal workflows, processing large data volumes, or enhancing products with new capabilities.

With these objectives in place, engaging all relevant stakeholders is then key. AI can impact various departments, from legal and compliance to IT and operations. Employing transparent governance ensures compliance with data regulations and ensures that everyone understands the project’s scope and benefits.

2. Always use high-quality data:

AI models, no matter how advanced, cannot deliver reliable results if trained on low-quality or inconsistent data. An important initial step is to perform a comprehensive data audit, cataloguing all potential data sources, both structured (like database entries) and unstructured (such as PDFs, emails, or images).

Identify any gaps, inconsistencies, or duplication, as the more complete and precise the data, the more effectively AI will perform. Equally important is ensuring compliance with data policies.

While establishing strong data practices may require an initial investment, they form an essential foundation that reduces errors and confusion as the system begins to generate real-world outputs.

3. Select the most appropriate AI model:

While Large Language Models (LLMs) can be powerful, they are often associated with high training costs, increased processing time, and require substantial computing resources. For many organisations, particularly those in specialised fields like manufacturing or pharmaceuticals, smaller domain-specific models (SLMs) can offer greater agility and effectiveness.

In many cases these models can outperform larger, general-purpose models and are typically more transparent, allowing for better understanding and oversight of how and why they generate certain results.

Also, building a model from scratch can be an overwhelming task, so it’s worth considering fine-tuning an existing framework. Many companies begin with a core model from a leading AI provider and then refine it using their own data.

This approach combines the latest AI advancements an organisation’s specialised knowledge, enabling faster deployment while still achieving targeted accuracy.

4. Integrate AI into existing workflows:

Rather than trying to overhaul the organisation’s entire IT infrastructure, a better approach is to gradually integrate AI components with existing systems and platforms.

For example, an option might be to introduce an AI-powered recommendation engine into a current e-commerce platform or to integrate an AI agent into a customer support system. These incremental changes allow employees to adapt to new workflows at a comfortable pace, reducing disruption and resistance.

Equally important is deploying flexible interfaces. For example, if graph database technology is being used, it might be prudent to implement a translator that converts user requests into a graph query and returns the results in understandable text.

It is also important to regularly monitor the entire IT infrastructure as this can identify potential bottlenecks and maintain efficiency, while knowledge graph structures provide a cohesive point of view of how various data sources and AI models interact.

Delivering true business value

The ongoing development of AI is leading to increasing numbers of organisations seeing the technology as strategic business investment. However successfully implementing it requires a thoughtful, structured approach to ensure scalability and effectiveness.

By understanding these four key strategies, organisations can build a solid foundation to fully harness the promise of the technology.