Guide: How To Implement AI In Your Business?

“AI entered our workflow at BlazeKin because I was looking to eliminate bottlenecks that ate into productivity, and allow our team to focus on the creative, high-impact work they’re passionate about. The approach was simple: find processes that slowed us down but had repeatable steps—things like data analytics, client follow-ups, and campaign reporting. AI handled these smoothly, freeing up our people to engage in more creative problem-solving, and we quickly saw improved performance. For us, the initial payoff was time and focus; the real value has been in building team agility and morale.

For companies considering AI, I’d say start with areas where the impact is immediate, and work outward from there. The goal isn’t to overhaul your whole operation; it’s to add efficiency where it’s needed most. Keeping the data clean and accessible is foundational; I knew from experience that AI’s accuracy relies heavily on reliable data streams. And in terms of team readiness, a little goes a long way—basic training and setting up a support system was key to getting everyone on board.

The results have been both practical and strategic. AI has streamlined our daily operations, and the team has embraced the freedom it gives to innovate without the distraction of manual tasks.”

“At Anamap we’ve used AI for two main purposes. One is to add value to the product we’ve created, and the second is to act as “the keeper of all knowledge” at the company. 

In the first use case our product ingests data about how our customers have instrumented their analytics tracking so that anyone within their organization can ask for details about what data their website or app is collecting and use that information to inform their analysis. We decided to use AI for this because it allows users to communicate their analytics needs in the language of intent. User interfaces have a limited amount of room and are often simplified to the best of a company’s ability, but they are not intuitive to every user because not every human thinks the same way. 

An AI language interface is better able to understand what a user is trying to do despite using a wide variety of words and phrases. This flexibility has enabled users to find information in our product in a way that reduces the friction that they may experience when having to navigate the graphical user interface.

The second use case was connecting all of our internal documentation from standard operating procedures to our customer relationship management documents. This AI agent largely acts as the knowledge keeper for our business. It’s essentially like having one person in your organization that remembers all the details of your past work who you can send an instant message to and get a useful reply. 

The value of not having to dig through old files to find the obscure information we’re looking for is invaluable. Beyond that, sometimes what was written down doesn’t perfectly match what you are searching for. The intent-based search of AI is tremendously helpful here. A simple example of this is a situation where I remembered taking a note in a document that I thought was about “sales data”. The note actually referenced “new customer revenue” which is essentially the same information but called something different. 

If I searched for “sales data” using a traditional search algorithm it would not turn up the document I’m looking for. An AI agent that had read the document would understand that “new customer revenue” is likely a form of “sales data” and would surface that information immediately.

Many companies are afraid of trying to implement AI, but it’s much more straightforward than many would assume. For the most part, the best way to feed AI documents for tokenizing (the process of making it more “searchable” for the AI) is taking whatever information you have and converting it to a simple text document. 

For databases or JSON or other storage forms of data this can be achieved by using plugins that will help or simply finding a way of describing the data in plain text. If you had a customer object like { name: “John Doe”, age: 25, country: “United States” } you could simply represent that as a text string like “There is a customer named John Doe who is 25 years old and is located in the country of United States”. It’s not perfect English (because it is missing a “the”) but the AI will still be able to utilize it effectively without any issues. 

In summary, figuring out how to make your input data more digestible to AIs is the first step, and it’s not as complicated as many companies imagine. Once you have that step done many AI platforms allow you to simply upload your document as part of the file search UI, and then you can give the AI a system prompt similar to “Only answer questions about X, Y, and Z and only rely on data within the attached files”. After that you’ll have a working AI agent that understands the information you’ve provided, and you only need to do fine-tuning.”

“We’re constantly researching trends in business, marketing, and technology. These last few years AI technology has developed increasingly more sophisticated features to add value to most businesses. We decided to implement AI into our products and workflow to help us attain a competitive advantage by allowing us to work smarter and faster.

We went back to our business plan and started examine each area of our business that can be improved with AI. Likewise, we examined each of our products and services carefully and analyzed each individual task to determine if that task is best completed by a human or AI.

Furthermore, we examined the benefits of outsourcing our tasks to AI, and compared them to the potential risks of doing so to determine if the benefits outweigh the risks. Completing a risk-benefit analysis before implementing any new change (such as AI) is helpful for any organization to ensure success.

For example, any task that is somewhat simple, or repetitive, such as building online listings, or sending out automated requests for reviews, referrals or surveys, these have been outsourced to AI and automation tools. 

We’ve also implemented AI in more complex tasks that can be completed digitally. For example, we’ve implemented AI in our website’s web chat to answer questions and book appointments. Our partner’s CRM also offers AI technology to analyze phone calls and provide feedback for staff coaching opportunities.

The main benefit to implementing AI is that delegating tasks to AI saves significant amounts of time, allowing us to focus our time on more productive activities. The biggest risks to implementing AI that we’ve observed is making sure that the quality and accuracy of work are not impacting our clients.

For any company considering implementing AI, it’s helpful to conduct a risk-benefit analysis to determine if the benefits outweigh the risks. Part of this analysis includes examining your business plan and model to identify areas of the business that can be improved with AI or automation.

One of the most important aspects of implementing any new change (such as AI) is to track performance results to evaluate the success of the implementation. For example, we track metrics to determine how our new AI technology is helping us and our clients grow our business.”