Ep. 20 - The Key to Implementing AI in Your Organization: Be Agile with Steve Brown


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What does it take to implement artificial intelligence to create business momentum? Steve Brown, Director of Einstein Analytics Specialists at Salesforce, says being agile is the key.

With experience in startups as well as F100 companies, Steve has been on the forefront of operationalizing new technologies to help build and grow businesses for much of his career. Now at Salesforce, he’s at the tip of spear of not only bringing their technology to market but also helping to socialize and evangelize the the benefits of Artificial Intelligence (AI).


What Inhibits the Implementation of AI Throughout an Organization? The Walled Garden Approach and Fear

Artificial intelligence (and there is now a trend to just called Augmented Intelligence) is really about helping us do our jobs better, Steve tells us.

And at Salesforce, Steve indicates that AI isn’t this science project in a lab. Rather, it’s about “infusing intelligence for every interaction that an organization is having with their customers.”

However, scaling AI can’t be left to a small group of data scientists. You have to enable those with an understanding of that business data with the power to explore it and draw valuable insights from it.

“They're trying to build up this walled garden of data science expertise, and they don't realize that there is an opportunity to really scale AI throughout the organization.”

And of course, there is fear when it comes to potentially implementing AI. It’s that fear coming into play because you don’t understand it. And if you don’t understand it, how do you get started? And even once you get started, how you do know you won’t go down the wrong path?

Be Agile and Bridge the Gap Between the Business and Data Scientists to Create Momentum

Steve recommends taking an agile approach – that involves starting small, testing, and learning along the way. You also need to get the broader organization involved so that you are expanding that circle wider than a small group of data scientists.

But it also requires communication and goal alignment between the data scientists and business analysts to ensure that traction. And at Salesforce, it’s about helping companies bridge that gap, so everyone can truly benefit from the data.

Key Takeaways

  • To scale AI throughout an organization, it can’t be left to a small group of data scientists to create that world-changing algorithm. You have to have a broader set of people involved in the process 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 AI implementation, not the data scientists. Data scientists are isolated from the business problems and don’t have the type of context that the business side has.

  • Develop a common language between the technical and business sides so both can understand and align to common objectives and goals in order to drive towards business results.

  • Implementing AI isn’t going to be this big bang, one and done approach. Start small, testing and learning along the way.

    • By adopting this learning mindset and exposing the organization to its capabilities, it’ll help push the fear of the unknown out.

    • Begin with a use case that you think would benefit from AI, and connect that use case to the data.

  • It’s important to empower those familiar with the business data in finding ways to explore insights that help address a specific objective or goal. And they don’t need to know data science. They just need the tools to help them do the discovery and exploration.

    • With Salesforce Einstein, you can create that common language by letting a business analyst see the deeper correlation in the data that they may not have been able to see before. It helps them test and learn too while also enabling them to get a little closer to explaining what they may need from the data scientist which will guide them more effectively.

    • It’s ensuring you can now enable the business side to operationalize the insights. And at the end of the day it’s about putting insights from those models at the point of engagement with the customer.

  • Don't be intimidated by AI. While it seems complex and mysterious, the best way to conquer the fear is just think “what if...” and get started. With resources like Einstein Trailhead that demystify how AI is transforming CRM, you’ll be able to start down this journey of seeing what it could do for your business.




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