Seven Questions to Ask Before Implementing AI in Your Enterprise
Artificial intelligence is the talk of the digital town, and probably will be for many more years to come. Due to the surge in AI’s popularity across several industries, many businesses are eagerly investing in this technology. While artificial intelligence is undoubtedly transforming the way we do business, it’s crucial to carefully consider a few questions before implementing AI and adopting this technology for your company or project.
Is There Enough High-Quality Data?
Without data and specifically, high-quality data, your AI investment is useless. It’s essentially like purchasing an expensive car with an incredibly powerful motor without any access to a fuel source.
Artificial intelligence algorithms need vast amounts of high-quality data to deliver valuable insights. AI such as machine learning requires diverse data to build its intelligence and continuously learn.
Before investing in artificial intelligence, check whether your company has access to a sufficient amount of high-quality data sets. Access to data allows your business to leverage the full potential of artificial intelligence.
Are Your Data Scientists Encouraging AI?
It’s no secret that data science is the solid foundation upon which your AI must be built upon. Without a solid data infrastructure, your AI integration will struggle to achieve its full potential. Before implementing artificial intelligence, it’s a smart idea to consult your data science team.
Since they know the state of your company’s data better than anyone, they also know whether your company is ready to adopt artificial intelligence. Prior to investing in AI, ask your chief data officer and the rest of your data science team if your data infrastructure can handle artificial intelligence.
Do All Employees Know What To Expect From AI?
Before undertaking any costly investment, it’s important to confirm that everyone on your team is on the same page. The adoption and implementation of artificial intelligence requires the involvement of various stakeholders.
From your data science to operations teams, everyone should have a clear idea of how this new technology fits into their roles. To reduce the adjustment and learning period, educate yourself and the rest of your team about the AI’s capabilities beforehand.
There are plenty of online courses and other resources available to familiarize your employees with artificial intelligence. Taking the time and effort to prepare your team for the impact of AI allows for a smoother integration.
Is Collaboration Possible?
When thinking about implementing AI successfully, your company will need a lot of cross-interdisciplinary collaboration. If your company integrates machine learning, your data scientists must continuously collaborate with the machine learning operations team.
What Issues Is Artificial Intelligence Intended To Solve?
Before you purchase the solution, it’s a good idea to revisit the problem first. If your company has specific issues that require a complex solution, determine whether artificial intelligence is the most suitable solution.
Re-examining existing problems also identifies the areas where artificial intelligence could offer your business the most value. Recognizing trouble areas before adopting AI enables you to choose the right AI capabilities to invest in.
For example, if the issue is customer retention, machine learning is a great solution. Machine learning can provide valuable insights to better personalize the customer experience and produce a customer churn prediction model. This model helps your business predict which customers are most at risk of ‘churning’ or taking their business elsewhere.
Can Your Company Sustain AI After Deployment?
After successfully integrating artificial intelligence into several of your company’s processes, a strategy to maintain it is vital to its longevity. Deploying your AI is only the first step. Unless the right strategies are in place, your company will be left with a costly investment without reaping its benefits.
Some strategies to maintain a machine learning model include:
- Monitoring your machine learning model constantly and feeding it with the most relevant and updated data.
- Developing the pipelines using principles that handle real-time data accurately.
Can Your Company Sustain Maintenance Costs After AI Deployment?
Machine learning solutions are far from cheap. Not only are they expensive to implement, but they are also just as costly to maintain. Before adopting a machine learning model, ensure that your enterprise has the financial resources to sustain it.
Conclusion on Implementing AI
As more and more companies invest in artificial intelligence, it’s important to consider the questions above to determine if this technology is the right solution. Instead of simply jumping on the AI bandwagon, take the time to determine if this technology is practical for your enterprise.
Read more data science articles on OpenDataScience.com, including tutorials and guides from beginner to advanced levels! Subscribe to our weekly newsletter here and receive the latest news every Thursday. You can also get data science training on-demand wherever you are with our Ai+ Training platform.