Client Ask: Write a blog on Artificial Intelligence for all platforms

Simply put, artificial intelligence (AI) drives transformational business value that can’t be ignored. In fact, as more companies look for ways to add value to their products and services, investing in AI is becoming the norm in industries across the globe. Research from Capgemini predicts that “leveraging AI to solve business problems across the enterprise brings more than 90% of the value, but companies are only spending 30% of their budgets in this area.” We observe that the ambiguity regarding ways to get a positive Return on Investment (ROI) with AI projects is one of the main reasons why companies are resistant to spend their budgets. While embarking on AI can be exciting, the goal should always be to have successful AI ROI. As highlighted in the book, Competing in the Age of AI, AI leaders enjoy better financial performance with 3-year average gross margin of 55%. So, what can you do to ensure that you’re one of the AI leaders and you’re not wasting your investment by adding AI in your day-to-day operations? In this blog, we’ll take a look at some of the things that can impact AI ROI.

‍1. AI Vision aligned with strategy: In our ROI of AI Webinar, Steve Meier, Head of Growth at KUNGFU.AI, examines the financial performance of AI Leaders. Here, he uses case studies and figures to illustrate how companies with a clear vision, like Amazon, are succeeding and seeing incredible returns with AI. While your company may lack the manpower and resources of an industry-leader like Amazon, defining your business needs and adopting new technology is essential to your success. We highly recommend that you do some research to decide the role you want AI to play in your company. Ask yourself the following questions before you get started.

1. How can we use AI to significantly improve our growth trajectory?
2. How can AI be used to significantly reduce our major cost drivers?
3. How will implementing AI impact my staff?
4. How will we measure success and how long is the trial period?

While you may feel like this initial phase is slowing down your AI implementation, it is essential to set a foundation where your expectations are clear. Without this, you won’t be able to define success or failure. Additionally, this could prove to be a major time saver that helps you avoid poor investment decisions and dead ends to your adoption. Don’t waste your time or money.

‍2. Rich data equals ROI on AI Bad data is never a good thing especially with AI which learns from data. Not only does it lead to biases and inaccuracies that can set your business back but it may also be limiting your ability to capitalize on the full potential of AI for your business. Another limitation may be using the same old data. Oftentimes, you need to use rich new data sources to drive the significant impact and differentiation that’s possible with AI. With the right data, you will realize ROI from your AI projects. In one of the takeaways from the webinar, Meier states, “The number one problem that we often see is just having bad data. Either not having enough information, not having enough of the right information or not having information in the proper order to leverage advanced machine learning models”.

3. Have a measurable definition of success As you embark on this new AI journey, you’ll find that you’re encouraged to think about the variety of technologies available. However, resist the urge to start with technology and focus on defining how AI can positively impact your business. From there, define specific use cases and then identify the best technology that can power those use cases. For some businesses that will mean an evolution from the current technology architecture. For other businesses that will mean using new innovative technologies. In any case, make sure that your technology selection is open and doesn’t box you into a technology silo. It is also vital to pick realistic performance goals when it comes to your business and the software that you’re using. Optimizing around business and technical metrics is essential and often overlooked when seeking strong ROI. Make sure you don’t make this mistake.

‍4. Align use cases with goals It’s important that you select a use case that makes sense for your business and aligns with your business goals. Unclear use cases continue to be one of the reasons why AI investment and adoption continues to be a topic of debate. If you’re modeling your AI implementation after a use case that aligns with your business goals, make sure that you understand and document it fully. According to a survey conducted by Narrative Science, 20% of businesses said a lack of clarity about AI’s value proposition prevented them from adopting it into their business. As you take on your upcoming AI projects, make sure that any use cases you consider are clear, concise and narrow. All AI today is ANI (artificial narrow intelligence). Consequently, we should treat it as such. The more well-defined a problem is, the easier it is to solve and automate.

5. Prepare team for culture change Culture change and expertise play a major role in successful AI implementation.  In the in-depth webinar, Meier pinpoints cultural readiness (how much your team is prepared and excited to adapt to change) and digital literacy (how comfortable they are with new software and programs), as two major points. By fostering an environment that values data, innovation and change, you’ll be doing your part to create a culture that welcomes AI implementation. If your team is resistant, try positioning yourself as a part of the market that needs to change and adapt for survival. As Meier points out, businesses that make changes to many processes are 6X more likely to realize significant financial benefits than those making no or small changes to a few business processes.