If you’re not sure what to make of the data around AI’s profitability potential, it’s understandable. Accenture projects AI could increase business profitability by an average of 38 percent by 2035. In 2019, 90 percent of surveyed executives said AI represents a revenue opportunity. And yet, just 10 percent of businesses reported significant financial benefits when using AI in a 2020 MIT study.
The simple fact is that effectively deploying AI is not simple—but when done right, it truly can yield financial benefits. Check out how those odds of profitability changed in the MIT study depending on its deployment:
- 20% achieved financial benefits when covering the AI basics of “having the right data, technology, and talent, organized around a corporate strategy”
- 39% achieved financial benefits with the basics, plus embedding company-specific AI solutions into their operations
- 73% achieved financial benefits when doing all that, plus engaging in organizational learning with the AI
AI does have the potential to boost profitability. But organizations have to go all-in on the technology to realize it. And because that takes time, taking a wait-and-see approach is a major missed opportunity. Here’s why taking a comprehensive approach to AI—and starting sooner than later—are so critical to achieving the best outcomes.
“AI has the potential to produce real, significant financial benefits. But organizations have to go all-in on the technology to realize them.”
The power of (lots and lots of) data
I used to focus solely on how AI was advancing at such a rapid pace, particularly in workplace automation. But it’s not advancements to the technology itself that matter as much as each business’s advancements in data gathering. The more data you have to train and refine AI, the better your algorithms will perform.
Companies build their data sets in different ways, depending on their goals. If operational efficiency is what you’d like to tackle with AI, the data you need comes from your employees. Companies can track everything from workflow progress through productivity software to individual users’ keyboard strokes, mouse movements, and clicks. This information is key to understanding which processes and tasks are ripe for automation. And with so many employees now working remotely using a variety of productivity tools, the opportunities to gather that data have risen. Over 90 percent of senior leaders have maintained or increased their use of employee analytics since the start of the pandemic, according to Gartner.
Data is becoming a valuable business commodity. The businesses that best understand and leverage their data to power AI solutions in the workplace will find efficiencies, profitability, and a competitive advantage over those left behind in the data race. But success is all about the size and quality of your data sets, so there’s no time to lose getting started.
The critical role of organizational learning
As we’ve learned, all of that data is merely part of the AI basics. And the fundamentals alone still leave companies with just a 20 percent chance of achieving financial benefits. Building custom AI solutions for your own company’s needs and capabilities is clearly another big part of this equation, but I want to focus on the big fish: organizational learning.
There are two main categories of AI: AI with supervised learning and AI with unsupervised learning. For supervised learning, the coaches (i.e., human software engineers) show the software data, for example, a series of images, until the program learns to identify such images itself. With unsupervised learning, the coach provides large data sets, and the software figures out patterns and correlations on its own.
Recent MIT research revealed the critical role of supervised learning—organizational learning, as they call it. The companies that achieve significant financial benefits with AI, they report, “intentionally change processes, broadly and deeply.”
“The companies that achieve significant financial benefits with AI “intentionally change processes, broadly and deeply.”
That’s no small deal. Their analysis revealed three specific characteristics of companies that found AI success through organizational learning:
- Humans and machines engage in systematic and continuous learning
- Humans and machines have multiple ways to interact
- The companies “change to learn, and learn to change”
These are significant efforts. These companies have gone in on AI big time. But the data shows that not only is organizational learning the best way to pursue financial benefits through AI, it’s the only way to push your company’s odds of success with the technology higher than a coin flip.
Now’s the time to get started with AI
We cannot avoid AI, nor should any business delay leveraging it another day, or else risk being at a competitive disadvantage. It’s clear that AI is the path to success in the future of business. And that future must begin now.
Businesses need robust data sets to create the most advanced AI that will create efficiencies for them. They also need the right strategy, talent, custom AI solutions, and more. And they need to build a framework for organizational learning. None of these things happen overnight or without serious commitment, planning, and development. Start engaging with AI now, and your business will be just beginning to build its way toward the financial benefits needed to compete in the future.