So, You're Thinking About Implementing AI...

Businesses everywhere are leveraging AI to answer questions and reveal information about their customers, operations, forecasting, R&D, and many other areas. The Hollywood approach portrays AI as almost sentient. The reality is less glitzy - but more powerful - and it happens within the framework of your current technology.

You don’t need to overhaul your business to start leveraging AI. By integrating on a smaller scale with ready-made solutions, you can still harness the benefits of AI and machine learning (ML), just like the big dogs. Widespread advancements in machine learning, computer vision, deep learning, and natural language processing (NLP) have made it simpler than ever before to layer an AI algorithm into your software or cloud platform. Applying these tools and working with AI experts, with an understanding of what your goals are, will make this process far more efficient, while not straining your budget.


So, you have a limited budget but would like to implement the use of AI. As a company, you’re wondering, “how can we even get started?” - much less compete with larger firms. The first step in navigating an AI implementation is to understand what you want to achieve and be prepared to want some things that may currently be beyond your grasp.

Practical AI business applications are all around. However, knowing your goals will give you faster “speed to value” and narrow your focus. The key to a successful application is that the implemented algorithms provide the answers to the simple questions and provide insight into the questions you never thought to ask. AI can be employed for just about everything, from collecting social data to drive customer engagement, to curating relationships with CRM, to optimizing logistics and efficiency in asset management.  

The second and third most critical elements in implementing AI are:

  • Ensuring good quality data
  • Knowing what problems you want to solve

Market-minded individuals call out the problems and goals, while your tools and data science teams reveal the markers you need to make the path a success


So, where do you start in the implementation process? Look to your data. Focus on the data you’re producing, the data you want to have, and why it will be beneficial once combined with machine learning algorithms. You’re collecting sales data, but how much do you know about what you sell? The more data attributes you know and collect about the products you’re selling, the better data you have to train the system. The quality of your insights directly relates to the quality, depth, and amount of your data.

When you think about the goals you want to establish for a new implementation, think about how you can add AI capabilities to your existing products and/or services. In order to even begin determining where AI will provide optimum efficiency, your company needs to align goals with specific situations in which AI could solve a business problem you’re facing or provide your company discovery, newfound insights that present new approaches and sometimes new questions.

Remember, you’re not going to achieve AI-enablement overnight, but you don’t need a massive budget to make it happen. Choose one problem you’d like to solve and one property to address. Gather all of your data points and let them reveal your current gaps, successes, and areas of opportunity for algorithms to do the work. Know what capabilities your platform(s) have and how to implement them. Track results! Learn from your new data points to further iterate on your AI achievements.