Kick-start your industrial AI strategy for 2019

About Webinar

The potential of industrial artificial intelligence is yet unknown by companies. The transformational changes that this technology brings into business operations do not only ensure enhanced decision-making but also fosters new business opportunities. However, companies are still struggling to adopt AI into their business practices due to lack of:

• Awareness about AI capabilities
• An AI strategy
• Understanding of use cases to apply AI techniques

In this webinar, our subject matter experts will discuss the challenges faced by organizations while adopting industrial AI. We will also explain the importance of preliminary planning and data strategy to prioritize requirements and align expectations with business needs. This webinar will help you with in-depth understanding of implementing AI models and smart analytics. Our experts will further explain use cases for AI adoption.

This webinar is for you if:

• You not only want a competitive advantage but want to win against the competition
• You want to adopt a data-driven culture
• You want to strategize your AI planning and adoption

Key takeaways:

• Tips on how firms can set a solid foundation for innovation today to prepare for the industrial AI revolution in 2019
• Real world scenarios to plan implementation of AI in your business practices

Agenda

• Where industrial AI is and will be in 2019
• AI roadblocks and how to overcome them
• Kick-starting your AI journey
  • - fining data strategy
  • - Implementing AI models
  • - Integrating smart analytics into business processes
• AI-driven use cases to present to decision-makers
• Q & A

Questions & Answers

The following are the answers to the questions that were asked during the live webinar.

Question 1: We have a question from Matt who wants to know how AI can improve the performance of his sales teams

Answer 1: That’s quite a good question, because so far we have had questions where people wanted to know how AI can improve sales. But this is a bit interesting as Martin wants to understand how AI can improve the performance of his sales team.

Actually, there's no shortage of ways in which AI can improve the performance of sales teams.

In fact, from lead generation to predicting customer behavior to more effectively targeting customers to increasing customer engagement, AI's applications are vast and powerful.

By taking into account factors such as gender, age, location, demographics, and sentiments, they can predict what people need or are looking to buy.

With right data in place, I think your salespeople can use these insights to refine their sales pitches and convert more leads into customers and hence improve sales team performance.

Question 2: We have another question here: I want to improve customer engagement. How can AI help me? – this question is asked by Steve

Answer 2: Um… I would say AI is proven to make an immediate and quite tangible difference in customer relevance.

So Steve, you first need to look for channels in which AI can help determine the right message at the right time for any given customer. I would suggest either targeted marketing through AI or recommendation engines could also help in customer engagement.

However, if cost reduction is an important driver, which, always is, think about AI-powered chatbots to reduce mundane customer service tasks. This will help your employees to focus on the core business while chatbot does the talking for you.

Question 3: We have another question from Chris and the question is how do I find out if my data architecture is suitable for implementing AI?

Answer 3: It is true that organizations need effective data architecture. They need to build a robust data strategy and ensure they have the right architecture in place. In my experience, this is becoming a bigger problem as companies hold onto data - just in case, and look to activate it later.

With the rise of data lakes and other data repositories, companies are starting to learn about the importance of storing and managing data so that even if, not now, maybe in near future, at some level you’ll not require to revamp the entire data infrastructure.