According to industry research firm TechEmergence, Artificial Intelligence (AI) technology will have the single most radical, transformational impact on business and society. AI is driving the fourth industrial revolution or 4IR—the newest industrial era, which is characterized by emerging technological disruption and increasingly blurred lines between the physical and digital worlds. Organizations are rapidly exploring the business gains that can be made by investing in machine learning (ML), a form of AI, including improvements in automation, predictive analytics, and customer service via intelligent chatbots.
In this environment, organizations across all sectors are asking the question: Should we invest now in AI, or take a wait-and-see approach? With companies already investing tens of billions of dollars globally in AI and ML technologies—and leading-edge companies seeing massive improvements in operational efficiency, customer experience, and innovation—it’s imperative to make AI a part of your organization’s strategy to stay competitive. At a recent AI conference Appen attended, one presenter claimed, “If you haven’t yet invested in machine learning … you’re already 12 months behind.”
This year’s Constellation Research AI Study found that 60% of C-level executives plan to increase their AI investment in 2018 by more than 50% compared to last year. And the research group expects overall AI budgets to continue to rise by more than 50% annually over the next four years as companies gain significant returns on their investments in machine learning and AI.
While some of the larger global tech firms have taken the lead investing in visible consumer-facing AI applications (such as smartphone voice assistants or photo library management with image recognition), leading companies in other industries such as automotive, healthcare, and manufacturing are also getting in on the act. AI examples in these sectors, respectively, include the development of self-driving vehicles, more intelligent patient diagnosis, and the predictive maintenance of heavy machinery.
Even if your company hasn’t yet adopted machine learning and AI, it’s certainly not too late. Investing in machine learning is economically advantageous, and doing it sooner rather than later will help your organization secure a stronger competitive position. Let’s look deeper at some of the research and thinking on this topic.
Why should our company invest in AI?
The short answer as to why firms should invest in AI is: It’s profitable to do so. A few weeks back, we blogged about learnings from McKinsey’s April report, “Notes from the AI Frontier, Insights from Hundreds of Use Cases,” where its consultants analyzed over 400 use cases to assess how machine learning and a subset of ML called deep learning were helping to solve a range of business problems. The paper examined the broader value of machine learning, reporting that a company could expect to gain returns between 1 and 9 percent of its overall revenue from applying AI. That translates to trillions of dollars of potential impact across the business sector.
Naturally, every organization is different. An AI investment strategy must therefore be customized to advance each firm’s overall business strategy. That’s the principal reason to invest in any technology—machine learning included—to progress the wider strategic vision.
You can start by asking questions like:
- What product development feedback loops could we shorten using machine learning?
- Could we derive greater value and become more efficient by better analyzing our data stores?
- Is there predictive modelling we could be doing to help us focus on certain markets over others?
We’ve previously blogged about finding good use cases to experiment and get started with machine learning. In short, start by identifying a business problem you want to solve, and putting the right data strategy in place up front. You can also research what companies in your industry are doing in the space, attend forums, and talk to analysts. Ultimately, investing in AI requires the right levels of initial strategic planning. Partnering with tech vendors, specialists, and service providers can help once you identify the framework or business issue you wish to pursue. The right partner can also help you define your strategy. Appen, in particular, has helped companies define their data strategies in this space for more than 20 years.
Why should we invest now?
The time imperative for investing in machine learning sooner rather than later comes down to competitive advantage. As Harvard Business Review pointed out in May, the predictive capabilities of AI are getting cheaper and more efficient, so businesses will need to figure out how to take advantage of them to stay competitive.
A second HBR report from March discusses getting value from machine learning, highlighting the importance of correctly understanding how to apply it. It’s important to place machine learning at the center of the business to create new revenue streams, re-imagine products, and increase operational efficiencies. The next wave of machine learning, HBR claims, will involve data-driven predictive decision making across all aspects of the business—a core capability that will help firms provide better customer experiences, streamline operational efficiency, and outpace competitors.
In January, a McKinsey article, “Artificial Intelligence: The time to act is now” described the rush of recent advancements in AI—namely improvements in hardware, better algorithms, and larger stores of data. This confluence of factors means the error rate for machines is now similar to or better than a range of human cognitive functions. In other words, the moment is now—not at some point in the future.
The report concludes: “Our most important takeaway is that companies need to act quickly. Those that make big bets now and overhaul their traditional strategies will emerge as the winners.
“If companies wait two to three years to establish an AI strategy and place their bets, we believe they are not likely to regain momentum in this rapidly evolving market. Most businesses know the value at stake and are willing to forge ahead, but they lack a strong strategy … The key question is which players will take this direction before the window of opportunity closes.”
Are you embarking on a machine learning initiative? Check out Appen’s solutions page to learn how we help organizations improve their machine learning-based solutions with our data services. We can help you get the high-quality training data you need to accelerate and scale your machine learning program.