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How to Effectively Implement AI in Your Business - In 6 Steps
6 months ago by Angelique Assaf

How to Effectively Implement AI in Your Business - In 6 Steps

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Artificial intelligence is taking over many industries. From virtual assistants to chatbots that are programmed to respond to a variety of customer questions, AI applications have become highly flexible and diverse. Driven by improvements in computing power, artificial neural networks, statistical algorithms, and data analysis, AI makes current technologies and processes much more efficient by utilizing all the data that large corporations collect.  And with the growth of AI leading to further innovation in the fields of natural language processing, machine learning, deep learning and computer vision, it has become easier to integrate AI algorithms into business operations.

Large corporations who have more resources have already started to invest in AI implementation, but there are still many SMEs and start-ups with more limited resources who have not. Because we believe that AI will continue to evolve and become even more crucial to the growth of all businesses, we have written a six-step guide which handily illustrates how to adopt AI into your business successfully.

1. Getting Acquainted with AI

Before starting to brainstorm ideas and discussing an AI strategy, the first thing you’ll want to do is familiarize yourself with precisely what AI is, its pros and cons, and what it can do for your business. To have an AI strategy approved and successfully implemented, company management may very well need to understand AI and its benefits. There are plenty of online resources that explain the basic concepts—here are some recommended by PC Mag:

·         Udacity's Intro to AI course and Artificial Intelligence Nanodegree Program

·         Stanford University's online lectures: Artificial Intelligence: Principles and Techniques

·         EdX's online AI course offered through Columbia University

·         Microsoft's open-source Cognitive Toolkit (previously known as CNTK) to help developers master deep-learning algorithms

·         Google's open-source (OS) TensorFlow software library for machine intelligence

2. Identify the Problem

Once everyone is familiar with AI, the next step is to begin brainstorming different ways AI can naturally fit in within your current business procedures, what issues it can solve, and where it can add value. In other words, how you can utilize AI to expand and enhance the capabilities of your existing offerings. 

At the brainstorming stage, it is crucial that you have a clear idea of what problem(s) you want to solve with AI. The more specific you are, the easier it will be to build a strategy around how AI can be the solution. For example, “needing to reduce the deficit created by the decrease in demand for a product” is more specific and clear-cut than “needing to decrease loss to profits.”

3. Feasibility and Value Study

The next stage is to review what potential value (business and financial) can be gained from each of the various AI applications you identified in the brainstorming stage. To understand the economic value of each solution, you will need the support and assistance of your company's top-level executives as they’ll have access to information that will help give a more accurate evaluation. Once you supplement your research with the data you receive from upper management, you’ll be able to identify which solutions you should prioritize.

Then, you’ll need to develop and test the prototypes of the AI software that you chose to implement. This test must be broad enough to estimate the actual cost, the timeline, and the optimal goal of the AI. The feasibility study is crucial, as the results will provide essential information that will shape the project’s next steps.

4. Data Preparation and Audit

It is important to note that before integrating machine learning (a key application of AI that allows systems to grow and improve without further programming) into your operations, you must clean your data. Cleaning your data allows you to prevent situations where poor input leads to flawed results and compromises the accuracy of your AI computations. To achieve this, we recommend you create a specialized team that will collect and combine all your data from various data silos and review them for inaccuracies and inconsistencies. Alternatively, ensure you source your data from a team of specialists—like Cedar Rose. We have been using artificial intelligence and machine learning processes for the last 3 or 4 years to process orders and allocate orders to researchers automatically, to clean, de-duplicate, structure, link and translate our data as well as to automatically determine the size, credit risk score and recommended credit limits for the companies whose data we hold.

Once you have clean data and a completed audit, the next step is to begin processing the results. This will involve sorting the results into categories, identifying vital features and scaling potential, dealing with missing information, and anything else that results in complete and precise data sets that are ready for analysis. As a final step, ensure that you compare this data to the one from the feasibility study to confirm the accuracy of your predictive models.

5. Incorporate Storage

Storage is a crucial requirement for the implementation of AI solutions. Improving your AI's learning ability is vital to getting the desired research results—and that requires having large quantities of data. That’s because the more data that the AI analyses, the more it learns, which allows it to produce more accurate models that better achieve your objectives. Therefore, fast and optimized storage that takes into consideration data consumption, workflow, and modelling are essential to your system running efficiently once it goes live.

6. Review and Integrate

Once your AI model is complete, we recommend that you teach your employees how to implement your new algorithm into their daily tasks. Use this training to ensure transparency regarding how the tech solves problems within a workflow. Achieving this transparency will be vital in making your employees feel comfortable with using AI, as well as convince them that AI is something that enhances their work and helps to boost company profitability, instead of threatening their positions within the company.

Once reassured, your employees will find that AI helps them to be more productive, makes their lives easier and their positions more stable; due to the enhanced success of your company!

  • AI
  • Artificial Intelligence
  • Business Automation
  • Data
  • Machine Learning