3 Biggest Challenges in Adopting AI: Skills, Fear of the Unknown and Data Quality

If you're reading this article, then it's likely that you've been hearing a lot about AI and its potential benefits. You might know some of the basics but are still unsure how to get started in an AI project. Don't worry - we'll be going over three ways for overcoming challenges with adopting AI: skills, fear of the unknown, and data quality.

The three biggest challenges that are faced when adopting AI are skills, fear of the unknown and data quality. There is a lot of hype around artificial intelligence and it can be difficult to discern what AI really does. The first challenge in adopting AI is skills. Business leaders acknowledge that there will be a change in required skill sets for workers who use AI as their primary job function. This leads into our second top challenge: fear of the unknown. A large percentage of CEO's don't fully understanding the benefits and uses for AI in the their eco-systems because they don't want to take on risk or experiment with new technology before seeing results from others. The third challenge of artificial intelligence is the full data scope, or how large your AI has to be in order for it to get enough information on what needs to happen.

It's not easy getting around these obstacles but they're worth overcoming because once successful at AI our companies can improve drastically, create efficiencies, cost savings and gain significant competitive advantage. Keep in mind that YOUR NEXT SUCCESS STORY MAY BE HIDING IN YOUR DATA and to access it you need AI. Practically what does that mean? Check out some of these use cases as an example:

• AI for Medical can and is already improving the early detection of diseases including cancer.
• AI for Predictive Maintenance by leveraging data so that you can identify when and where an issue might occur.?Minimize downtime, as well as prevent device failures.
• AI for Marketing and Sales: Attribution AI is?used to attribute credits to touchpoints leading to conversion events. Implementing AI by marketers to help quantify the marketing impact of each individual marketing touchpoint across customer journeys. In Sales, AI can improve forecasting, pipeline analysis and deal intelligence.
• AI for Finance by streamlining and optimizing processes ranging from credit decisions to quantitative trading and financial risk management.
• AI for Logistics by optimizing shipping container routing.

While the thought of implementing an AI project in your company or augmenting your current AI strategy within your company may seem daunting, it doesn't have to be. Some AI companies offer the ability to work hand in hand with your team, bringing you their experience and handing over know how in the process. They will set up , track and maintain your AI initiatives without needing you to "re-invent the wheel" and figure things out yourself from scratch. Whether you're moving in the direction of adopting AI for your company or already have AI initiatives in place my advise is start with seeking experienced professionals that can lead AI/ML projects for you, help you set up your own CoE, define an AI strategy, and audit your AI models. I would be happy to advise on any of these if you're after clear deliverables and better outcomes for your company.