Almost 90% of all executives say that artificial intelligence (AI) presents an opportunity. However, only 18% have tried to use the technology to generate revenue, according to research from MIT Sloan Management Review.
Gartner even goes as far as saying that enterprises do not achieve maximum leverage from AI investments despite increased spending.
Why? Like with any IT investment, deployment can be challenging if you don’t have the right resources.
There’s also a significant amount of hype around the technology. Companies become excited about the promise of a more intelligent company. However, they get so lost in the tech chatter, they lose sight of the big picture.
“For AI to transcend hype, businesses need to learn to use it for competitive advantage,” explains Mike Deittrick, President of the AI & Analytics Group and Chief Marketing Officer at DMI. “If you can do that, then AI will be a crucial part of how you conduct business and operate moving forward.”
Executive leaders must become keen and discerning creators of AI investment strategies to obtain optimum value from AI initiatives. Start by identifying the barriers you need to overcome and form a comprehensive digital business strategy to help AI quantifiably improve your organization.
In a 2020 report, Ericsson Industry Lab categorizes the top ten challenges surrounding AI adoption into three distinct types: technology, people/culture and organization. At the organizational level, enterprises’ main concern was a lack of skilled employees for AI or advanced analytics-related tasks.
Your AI solution needs to go beyond a promising demo. You have to bridge the gap between your AI and your organization’s use case. The more niche and customized your use cases are, the more challenging it will be.
Your AI may require specific training to adapt to your use cases. Production-grade AI, for example, requires a dedicated team of machine learning experts, training specialists and data scientists.
Scale, dimension and reach across the enterprise are the real return on investment in AI, according to Gartner. AI that is detached from enterprise performance outcomes is unlikely to provide material consequences. That’s why, on average, only 53% of AI projects make it from pilot to production. And those that do often incur significant unexpected maintenance costs.
Avoid these pitfalls by establishing realistic expectations and knowing your limitations. Anticipate the cost of what it will take to make AI possible — including budget for engineers, training specialists and other key personnel. Close skills gaps by setting up training workshops and making it easy for employees to educate themselves on AI, machine learning and analytics.
Finally, determine what adoption will mean from a mission-critical standpoint. For instance, what inefficiencies are you looking to solve? How will data analysis enhance the business and the humans who operate within that business? What’s at stake if your AI fails? Can you afford for it to fail?
“If you haven’t asked yourself these questions, you’re not going to get the most out of AI,” says Deittrick. “It’s just not the technology you buy. It’s the understanding of what it can do and the people who understand how to make it do that for you.”
“It’s the application,” adds Jesse Humphrey, VP of Marketing at DMI. “It’s more about the value that comes from applying machine learning and artificial intelligence to your business problems and less about how you’re building and maintaining those tools.”
“It’s the same way DMI approaches its offerings,” Humphrey continues. “We solve business problems first, and apply technology second.”
In the Ericsson report, employees also reported that one of their biggest technological obstacles was their data not being structured enough to enable AI or advanced analytics. Many organizations struggle with data quality and providing accurate inputs for reliable outputs. If the foundation of your AI and predictive analytics is unreliable, the inaccurate output of one model can transmit it to the next and have a cumulative effect.
Focusing on data quality will help you activate the potential of your AI. You’ll be able to create predictive models based on reliable insight. Implementing methods to clean and dedupe your data enables you to work with trustworthy information. We also recommend setting up proper data management policies, improving the usability of certain tools and improving access to data sources.
These initiatives will help you work with and continuously optimize your AI programs to make strategic decisions you can trust and move your organization forward.
Beyond technology and use-cases, the greatest AI challenges organizations face relate to their people and company culture. The following employee concerns were rated #1, #2, and #8 overall on the 2020 Ericsson report:
“Technology is built for humans, and not the other way around,” says Deittrick. “Most of what we do still requires humans to make judgement calls, and AI isn’t built to do that. AI is meant to enhance your processes, not replace your people. It minimizes human error and increases efficiency.”
Business process automation technologies, for example, can organize, classify, and process hundreds of emails. Chatbots and intelligent agents enable round-the-clock support.
Gartner estimates that, in 2021 alone, AI augmentation will create $2.9 trillion of business value and 6.2 billion hours of worker productivity globally.
“There’s hype around AI at large, but the idea that AI can enhance human-beings isn’t just a fad. It’s entirely possible. You can use artificial intelligence and deep learning to enhance humans and make them better at what they do and more efficient.”
— Mike Deittrick, DMI
“Augmented intelligence is all about people taking advantage of AI,” according to Svetlana Sicular, Research Vice President at Gartner. “As AI technology evolves, the combined human and AI capabilities that augmented intelligence allows will deliver the greatest benefits to enterprises.”
AI can be instrumental in blazing a trail for the future. However, AI can’t replace the ability to think creatively, have empathy and feelings, and build relationships. When humans and AI technology can work together, you can dramatically improve efficiency and satisfy your employees and your customers.
Whether you’re just beginning your AI journey or have a current project in-flight, you need to listen to the data and assess the possibilities where AI could drive value and hone in on the use case that offers the strongest ROI. Start with asking yourself, “Could I be more efficient? If so, where?”
Let DMI help you optimize the potential of your AI technology. Our strategy and consulting services draw from our experience with operationalizing machine learning to meet customers’ business objectives. Whether it’s about in-flight AI/ML projects that are hitting some bumps, or you need to kick start a new initiative, we’re able to work seamlessly with your platforms and technologies of choice. Contact us today to learn more.