When it comes to the adoption of artificial intelligence in business, there is a big gap between vision and reality. While nearly half of all CIOs plan to deploy AI, only 4 percent have actually gotten there, according to Gartner’s 2018 CIO Agenda Survey.
What’s holding people back? In many cases, people aren’t quite sure how to get started. Where do you start? What data do you need? What IT infrastructure? How deeply should you dive in?
For organizations facing questions like these, practical advice can be found in two recent white papers from the consulting firm Moor Insights & Strategy (MI&S). In this blog post, we will walk through some of the advice gleaned from these papers.
Start from where you are, not where you want to be.
Starting the journey to an AI-enabled enterprise with a moonshot approach rarely ends well, according to MI&S. Instead, the firm advises organizations to start with machine learning (ML) extensions to existing applications, and then build AI value-added services and products based on deep learning (DL) techniques, where appropriate.
“While the technology can seem daunting, a stepwise, practical approach to embracing Artificial Intelligence in the enterprise does not have to be intimidating,” MI&S says. “Solid ROIs can be achieved with relatively straight-forward extensions to existing Big Data infrastructure already in place in most enterprises.”
Start small, and focus on business value.
In starting down the path to AI, MI&S suggests that organizations keep the old mantra of “crawl, walk, run” in mind. Take time to learn about:
- The problems in your organization that are most appropriate to solve with ML
- The shape of your organization’s data
- The projects that your team can handle
“We recommend starting small, perhaps extending Big Data analytics to include one or two Machine Learning capabilities where the ROI is relatively easy to achieve and measure,” MI&S says. “Subsequent projects can then harness Deep Learning for voice, image, or text processing for smart offerings or smart operations.”
Put the right team in place.
Talent, skills, and resources are critical success factors in AI projects. MI&S recommends that organizations provide their internal teams with training for SPARK MLlib or SciKit-Learn if they are not already familiar with these tools.
“This training is critical for projects that build on existing Big Data infrastructure with classical Machine Learning,” MI&S says.
Technology and services partners are critical part of an AI team. With that thought in mind, MI&S examined the technologies and assistance that are available from Dell Technologies to enable successful AI projects. The firm concluded that Dell Technologies offers an impressive portfolio of products and services for AI, including HPC and new Ready Solutions for AI, all of which are born from direct experience with customers.
“We believe the new Ready Solutions for AI are a cut above the competition with integrated hardware and software stacks to speed time-to-solution,” MI&S says. “Thus, Dell Technologies offers what many vendors put on the shelf from today’s technologies and invests in new technologies and writing software. The company also provides integration that we believe will keep them in front as the AI market continues to evolve.”
To learn more, get the full story in these papers from Moor Insights & Strategy:
 Gartner, “Gartner Says Nearly Half of CIOs Are Planning to Deploy Artificial Intelligence,” February 13, 2018.