The First 90 Days of AI Workflow Integration: What to Expect and How to Succeed

AI integration is all the rage across every industry, but this widespread increase has some severe downsides. Many people think of this as the next digital gold rush, so they are scrambling to get their share of the loot. But that is ruining the potential of Artificial Intelligence, and limiting its value. 

AI isn't some magic elixir you put into your workflow to instantly shoot your productivity into the stratosphere. It's a new tool, that requires ample learning time, so an AI integration needs to happen in small stages. In this article, we'll talk about what you should be doing to get the most out of AI integration in the first 90 days. 

Every business and company in every industry is lining up to integrate AI into their workflow to get on the bandwagon. But many have no idea why or how this technology could help them in their roles. This is the biggest reason behind failed AI integration projects. 

AI is a diverse tool, that has diverse usage, but when you think about including it in your workflows, you need to give it a very specific task that it can do flawlessly. Most businesses jump into the integration phase without determining the specific task. Shoehorning AI into everything isn't going to make your business better. 

That's why it's worthwhile to take some time to go over the details and consult some experts on possible areas of your organization's workflow to figure out where to integrate the AI. 

Gather Industry-specific Data 

Research is extremely important before you do anything with AI on a large scale. It's a big investment and has a lengthy acclimation phase, so the last thing you want is to have to roll back. 

The best way to find out what works and what does not is to follow the examples set by the industry giants of your specific niche, or your competitors. Watch their investment strategy, and their failure, that way you can avoid a lot of pitfalls. 

You might also want to subtly gather feedback from your consumer base to see how open they are to the use of AI. This step is crucial if you want to include it in your front-end products. 

Logistics Of It All 

Lastly, you'll want to reach out to a professional and consider the expenses, manpower, and other resources needed for your integration. AI isn't something you can just jump into with no infrastructure, and infrastructure can be more expensive than most people realize. That's why it's better to go over the numbers at the very start, so there are no surprises. 

At this point, you should also consider your industry standard for privacy, both your own and your client's. Getting this wrong can have serious implications later down the line.  

After you have the base idea in place, you need to start considering your workforce. Employees require varying levels of assistance in adopting any technology. In fact, one of the biggest problems for integration is often the employees. 

In most cases, employees often feel distressed by the thought that AI will soon replace them, and this fear keeps them from embracing the technology. You need to dispel their fear by laying out how none of the jobs will be lost, and how it's merely going to be used as a tool to boost efficiency.  

There are only three key points you need to pay attention to: 

1. Get at least some members of your management on board and have them champion AI.

2. Encourage a few field-level employees to champion the technology—this helps build confidence among their peers and fosters a more comfortable adoption of the software.

3. Brief your team about the upcoming changes. It's better to be upfront with your expectations and address their concerns regarding role changes or job displacements. You would rather dissuade all their doubts up front than have it fester and bring down the morale of an entire department.

After you're done with the research and have your team prepped for the upcoming system change, it is time to get to the technical aspect of it. At this stage, you can start building a solid foundation for your AI integration process and look for the necessary hardware and software resources. 

When selecting an AI solution, it's important to choose one that aligns with your specific business needs and build a strong foundation around it. Regardless of the industry, there are a few key factors to consider during setup.  

First, ensure the model you choose can handle a high volume of queries while maintaining a high level of accuracy. This is crucial for delivering consistent and reliable results. Additionally, the AI should be capable of recognizing when a query is too complex and transferring it to a human team member. This kind of smart escalation ensures that users receive the help they need without disruption, while also preserving a seamless and efficient workflow. 

Choose a solution that offers a high level of customization, as no two businesses are exactly alike. Customization allows you to fine-tune the AI to align with your existing workflows, systems, and operational structure. This flexibility is essential for ensuring the technology works seamlessly within your environment. 

Getting something too rigid and unchangeable will potentially have a negative impact on your overall efficiency. It's also best to subscribe to a solution provided by a leading LLM provider like Google, OpenAI, or Anthropic. 

When you have everything sorted out, and structures in place, do not implement AI on a large scale. Run some controlled tests, check how it affects the workflow, and how your employees adapt to it.  

You can slowly scale the implementation to one team or department at a time. This will give you a better understanding of your operational structure. Your employees will also get more time to warm up to the technology.  

Even with full acceptance, a new tech is still a new tech. Your employees will need time to adapt to any new tech. It'll certainly be a while before they can produce output far beyond their previous peak. So, be patient, and let things run their course. 

During this time, your primary goals should be to identify and resolve unexpected compatibility issues. You should also sort out people who seem too averse to the idea of using AI in their workflow. Have some meetings with them to clear things up or possibly reposition them for the greater good of your workflow. 

If your controlled tests seem to work well, then you can consider implementing the technology on a large scale. This phase does not bring any new challenges, and you'll mostly do the same thing in the testing phase just on a much broader scale.  

From here on out, you'll need to keep track of accuracy and integration improvement levels. Run some AI training programs routinely to get everyone used to the technology. You can take employee feedback to fine-tune your AI setup even further by making the user interface more accessible. 

Around this time, you can start to expect performance improvements. You should constantly check your performance matrices to find more ways to optimize. Look for new opportunities for AI implementation and possible future expansion plans.  

Hopefully, you now have a general understanding of how to implement artificial intelligence in a workflow. This template will work on pretty much any business model or organization.  

You can even use this same structure to implement AI into your personal workflow. To learn more about AI's capabilities for your organization, schedule a meeting with one of our AI experts today! 

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