How To Solve Problems With AI [theCUBE Interview] 

What is trustworthy AI? How are companies using AI? What are some of the top concerns about AI and privacy today? 

Pandata CEO and AI Strategist, Cal Al-Dhubaib, recently joined Jonathan Seckler of Dell Technologies and Dave Vellante of theCUBE to answer these questions and more on AI, privacy, and top challenges companies face today. 

Here are some of the top takeaways from the interview.   

1. AI Leaders vs. AI Novices 

From healthcare to fintech to retail, we’ve seen companies emerge as leaders in AI and others that are still in the early stages of AI adoption. 

What sets these AI leaders apart from the AI novices? 

As Cal mentions in the interview, AI leaders are ones that are not only leveraging the power of AI, but also have a policy in place to manage the risks and ethical implications of using data for machine learning.  

Proactively considering the impact of AI on your company, your stakeholders, and all others involved is a non-negotiable attribute of an AI leader—especially for organizations looking to scale with AI.   

2. How Companies Solve Problems With AI 

There are a number of ways companies across all industries are leveraging AI to improve their processes and augment human abilities. For example, a Fortune 500 energy company used AI to sift through billions of cybersecurity events and sort the top 100 for humans to evaluate for anomalies. 

To determine which problems or tasks are suitable for AI intervention, look for the following opportunities: 

• Are there any repetitive tasks where humans are starting to act more like machines instead of humans? 
• Could certain areas of decision making become easier if AI were involved (think: detecting anomalies among billions of events)? 

These areas are perfect starting points for a strategic AI conversation. 

3. Top AI Concerns for Organizations

Between the complexity of machine learning and well-known consequences of poorly executed AI, it’s reasonable that organizations have concerns when it comes to adopting AI. 

Through his years of working with AI-ready leaders, Cal has noticed two common concerns across industries: 

1. The company’s data isn’t clean enough or of high enough quality for AI. 
2. The company doesn’t have the data science expertise or bandwidth to monitor AI for bias.

Luckily, it is easier now than ever to get started with AI. Experimenting with existing data and pulling in third-party datasets enable organizations to test and improve the quality of their data. Companies also have a number of toolkits at their disposal, specifically designed to profile, manage, and mitigate the risk of AI. 

Click here to view the entire on-demand interview with Cal, Jonathan, and Dave. 

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Contributor: Nicole Ponstingle McCaffrey is the COO at Pandata.