AI and Machine Learning: What it is and How to Use it to Create Business Momentum

Artificial Intelligence (AI), and an application of AI, machine learning, are no longer buzzwords that captivate executives and marketers with mystery and business potential. They are being applied across industries to sell better products, enhance customer service experiences, and target advertisements at a granular level, and businesses are seeing results.

For business leaders, AI and machine learning are are opportunities to optimize business operations in numerous, if not all, verticals. It’s a powerful force in driving business momentum -- and therefore a popular topic on our Strategic Momentum Podcast.

Through interviews with industry experts Steve Brown, Director of Einstein Analytics Specialists at Salesforce, and Jason Hunter, Data Science business lead for CapTech Consulting, I’ve gathered takeaways that can help any business leader get started with AI and machine learning. Here’s what you need to know to overcome common misconceptions and successfully implement machine learning processes for your business.  

How are companies using AI and Machine Learning?

Companies large and small in nearly every industry are using AI and machine learning. They can be applied in a number of use cases to gather intelligence and generate automated responses. Here are some ways industry applications:

  • Retail & eCommerce: Identification and discovery of related customer interests to create location-specific product offers.

  • Manufacturing: Optimization of business workflows based on employee and internal operations data.

  • Travel & media: Real-time advertising; companies can instantaneously determine the ad to show a consumer.

  • Healthcare: Identification of risk factors and physician fraud in healthcare industry.

What do you need to implement AI?

Let’s bust the main AI myth: you don’t need a team of data scientists to create the perfect AI algorithms. In fact, even if you do have a team of the best data scientists in the world, you’re not likely to get the results you’re after. Why? Because you need to pair data mining expertise with pure business strategy -- and oftentimes companies don’t do enough to fill that gap.  

To leverage your organization’s data, there needs to be a holistic understanding from both the technical side and the business side of how the specific business functions, where data comes from for that business, and the statistical processes that can be used to create an effective model.

Before you can start with any implementation, you need a strategy. Here’s what Jason suggests for documenting and creating your top-down data strategy:

  1. Identify all of the business functions that need machine learning or data science.

  2. Determine the high-level data needs of those business functions themselves.

  3. Link the need to metrics and look at the relationship between all of those items – you'll immediately see where areas of overlap exist in reporting and data needs.

  4. Once you document these relationships in a systematic and networked fashion, you can go bottom-up and start connecting the right data sources and relevant applications with those needs.

Listen to: Machine Learning: How To Implement It & Drive Business Momentum

How to do you implement it?

The key is to be agile, says Steve. With AI (and machine learning), you have to start small and test and learn. Companies fail when they try to execute massive systems across multiple functions all at once.

To ensure alignment between data experts and business leaders, collaboration across teams must be standardized and systematized from the very beginning. As Steve tells us, “There are just too many data-driven questions that an organization has to solve than there are data scientists to solve them. So there needs to be this mechanism of empowering others within the organization.”

The business side needs to lead the implementation, not the data scientists. Start with a hypothesis for a particular use case -- ex. customer service FAQs. Use that use case to build a system to execute against your hypothesis and then evaluate both the result and the process of achieving that result. Then, iterate, optimize, and try again with more use cases, cultivating human learning alongside the machine learning.

In implementing AI, it’s about creating a common language that empowers anyone who works with or depends on business data to explore insights that help address a particular objective.

Listen to: The Key To Implementing AI In Your Organization: Be Agile -  with Steve Brown

What resources are out there?

Salesforce’s Einstein Analytics created Einstein Trailhead for businesses getting started with or wanting to hone skills in AI. Trailhead demystifies how machine learning is transforming CRM, and with it, you’ll be able to start down this journey of seeing what AI could do for your business.

Other resources include:

With the breadth of resources out there, getting started with AI may be the easiest part. What’s most critical is having a top-down strategy put in place, an agile mindset, realistic short-term goals, and cross-functional buy-in to make this, or really any tech process, work for you.