Our on-demand webinar, “Machine Learning: How Modern Executives Make it a Safe Bet,” explains how you can get great ROI from machine learning (ML).
Many executives we interact with are eager to talk about machine learning (ML), and with good reason.
McKinsey has determined that ML is the electronic brain behind more than 95% of modern artificial intelligence (AI) solutions today. With AI’s global market impact expected to be between $3.5 to $6 trillion, the stakes are huge.
In fact, about 30% of companies today have at least one AI or ML initiative in place, with about a 60% growth rate.
ML is here to stay. We advise clients and potential clients that their competitors are already using it to trim costs and erode their market position. ML is something that you can make happen within your organization, or something that will happen to your organization.
Unfortunately, many organizations have trouble getting started because they can’t get past several longstanding misconceptions about ML.
In our webinar, we walk through many of those misconceptions and how you can overcome them in your organization. Then we talk about implementing an ML initiative that will have low project costs and great ROI.
In general, you can think about ML’s financial impact in terms of either expense reduction or revenue increase, or (ideally) both. ML applications can drive costs out of inefficient processes while helping you improve sales and create great customer experiences. The tactics for accomplishing these goals are virtually limitless.
However, ML comes down to some five simple, basic imperatives when it comes to controlling costs and increasing revenue for ROI. In this webinar, we describe how to:
- Identify your champions
- Find your opportunities
- Operationalize your model
- Focus on Business Processes
- Hyperautomate (combine ML with robotic process automation)
ML is not going anywhere, and it’s going to be very important for most companies. You need to start thinking about it just like you think about development teams for your applications and data.
So, how do you get started implementing machine learning into your organization? And once started, how do you achieve ROI?