How to integrate AI into existing workflows

So rather than identifying a problem with an existing system or workflow, ripping the whole thing apart, and starting from scratch, actual AI automation is going to be a little more nuanced. The most effective way forwards is to integrate AI solutions into these existing workflows seamlessly.

Of course, this requires careful planning. You'll need a strong strategy, defining what needs AI integration, why AI is needed, and how the integration will take place. For instance, a law firm might find its document review processes inefficient and decide that AI can accelerate the process and improve accuracy. 

This is the what and the why, but where is the how? This law firm will probably find that some components of the existing workflow are just fine but that certain core aspects can be greatly improved through AI automation.

We'll look at this example — and another — in more detail later on.

For now, however, let's take a look at how you can achieve AI workflow integration within your own organisation.

Identifying the right processes to automate

The whole point of AI automation is that it brings you tangible benefits — things you can point to and say, "This is better than it was before." So instead of simply shoehorning AI solutions wherever you can, you need to find the right processes to automate, and target your AI workflow integration.

Work backwards from your results

To start with, identify what's not working for you. Perhaps it's taking too long to bring products to market, or these products are simply too expensive once they arrive there. Recognising a key operational failing gives you a strong indication of where AI solutions can be applied.

Seek out bottlenecks and chokepoints

Working backwards, you can identify what is causing the operational problem. For example, perhaps it is taking too long to prototype and test your products — smart automation can help with this.

Start with the most obvious candidates

Now, you can go into greater detail, finding specific operational tasks that are ideal for automation. This will help you implement AI without compromising on quality. In fact, by selecting repetitive processes that human teams struggle with, you should be able to actively improve quality.

Outline metrics for analysis

This is where you circle back to the beginning. You started by identifying the operational results that weren't working, so now you need to look for changes in these results. Make sure these AI automation benefits are measurable — cost savings or timeframe reductions, for example — so you can analyse the success of the integration.

Effective collaboration with AI specialists

To get the best out of AI implementation, it's best not to go it alone. Instead, work with an AI specialist — someone who can guide you through the process and make sure you remain on track for effective integration. Effective collaboration with specialists requires a few key steps.

Open communication channels

You'll need to remain in communication with your specialists, scheduling regular meetings and using communication and project management tools like Teams and Azure DevOps. With these channels in place, in-house teams will be able to work directly with specialists.

Ensure stakeholder engagement

All your key stakeholders will need to be on board with the appointment of the specialist. This includes leadership, who must understand the role of the specialist, and team members, who need to recognise the benefits the specialist will bring.

Outline project goals and technical specifications

You and your specialists need to be on the same page from the outset, moving towards the same goals and KPIs and working with the same technical specifications.

AI use cases in business

How does implementation actually work in practice? Take a look at a couple of AI use cases, and learn how they can be applied in business..

Example #1: Legal document reviews

Let's start with the potential problem — legal document reviews take too long, and efforts to accelerate this result in inaccuracies.

This is a clear candidate for implementing AI automation. Fine-tuned language models trained on labelled datasets drawn from existing legal documents can provide a significant advantage. Through training, these AI solutions essentially become specifically designed for the task at hand, delivering excellent results in areas such as classification.

AI automation is actually far more reliable for this sort of task than manual processes. Therefore, quality is not only not compromised — it's also enhanced.

Example #2: Data migration and import

Another time-consuming problem is data migration and import. Many platforms require data imports to populate the environment for their users - but this source data is invariably unstructured and complex to manipulate and structure.

AI solutions can simplify this process by training models on example datasets to understand the context of the data to be imported. The AI solution can then contextualise the content and translate the unstructured data to a structured format ready for direct import into a system or alternatively surface that data to an end user who can subsequently validate and make any final amends before completing the import to the platform's database.

Best practices

Adopting the right best practices can help you achieve seamless integration.

Implement change management

Integrating AI with your existing workflows means change, a lot of change. Investing in user training and maintaining consistent communication will help you manage that change effectively.

Prioritise data security

While the regulatory landscape around AI is constantly evolving, adhering to existing standards like GDPR will give you the foundation you need to remain compliant in the future. Adopting encryption standards like AES-256, or utilising techniques like differential privacy to support statistical insight without revealing individual details, will also help you prioritise security.

Utilise integration technology

AI implementation is about supporting systems rather than replacing them. Approaches like RESTful APIs, support effective AI workflow solution integration. Alternatively, offerings like Azure Cognitive Services provide ready-to-use APIs designed specifically for simple AI integration.

Pitfalls to avoid

The other side of best practice is the pitfall — the common mistakes you need to avoid.

Going too big early on

The low-hanging fruit should be your starting point for implementing AI automation. This means the obvious tasks and processes that can easily be automated. Too many businesses try to adopt AI solutions wholesale at the outset and get overwhelmed. Starting small and scaling up can avoid this issue.

Implementation without consideration

AI implementation needs to be problem/solution-oriented. You'll need to spend time considering what problems you are facing and identifying how AI can provide the solution. Without preliminary auditing and analysis, you won't know which areas of your business are best suited to automation.

Failing to track

The only way to know whether or not integration is successful is to measure your progress. Not only will this tell you when you've arrived at your goal, but it will also keep your teams motivated as you move forwards.

Meaningful steps towards AI-based efficiency

Successful AI automation integration isn't about overhauling your entire system. Rather, it's about making small but meaningful steps towards change. Identifying areas of operational friction and making small improvements are great ways to begin your journey.