As organizations embark on the next phase of their digital transformation (DX) journeys, artificial intelligence (AI) frequently makes its way into the conversation—yet oftentimes, it never becomes more than just part of the dialogue. So, what gives? Why are business leaders continually overlooking solutions that can help take their digital initiatives to the next level?
Part of the answer is that definitions of AI are often cloudy, confusing, and filled with technical jargon, leaving many executives unsure how or where to begin strategizing and leveraging its capabilities. Another reason is that AI expenses can run the gamut depending on the capabilities being harnessed.
At a high level, Gartner defines AI as the application of “advanced analysis and logic-based techniques, including machine learning, to interpret events, support and automate decisions, and take action.” In other words, AI can empower companies to innovate through better use of data. While still technically in its early stages, AI is not a new concept and there are several low-hanging-fruit capabilities that companies can consider adopting as part of their digital strategy. It’s best to adapt and adopt these more approachable AI features as soon as possible, as Gartner predicts that in the next four years, 69% of what a manager currently does will be automated.
That said, undertaking new AI capabilities should be approached with caution. If they haven’t been thoroughly vetted by technology and data leaders, these initiatives can do more harm than good. Consider the case of the collaboration between the MD Anderson Cancer Center and IBM’s Watson. In 2013, they teamed up to launch an ambitious project aimed at diagnosing and recommending treatments for certain cancers. However, four years and more than $62 million later, the project was abandoned after it was determined that it was not ready for use on patients.
On the other hand, Watson has appeared to have found its AI sweet spot by tackling everyday business problems. According to The New York Times, Watson is now a “collection of software tools that companies use to build A.I.-based applications — ones that mainly streamline and automate basic tasks in areas like accounting, payments, technology operations, marketing and customer service. It is workhorse artificial intelligence, and that is true of most A.I. in business today.” While Watson may seem like an extreme example, it’s evidence that AI solutions are continuously improving, and adopting less costly yet highly functional capabilities can bring numerous returns, especially in key business areas such as marketing, sales, and customer service. Here are three of the biggest benefits businesses can reap from the most basic AI offerings.
Improved Customer Experience (CX)
It’s no secret that customer expectations are higher than ever. According to McKinsey, the Covid-19 pandemic caused consumers to completely rethink their purchasing habits. “Three-quarters of U.S. consumers changed something significant about the way they shop, such as trying out new brands, retailers, or shopping methods. Brand loyalty is increasingly elusive as more than 80% of consumers intend to continue this experimentation.” Thankfully, AI can help companies gain a competitive edge and foster brand loyalty by quickly collecting and assessing the most relevant data points to provide personalized, authentic customer experiences. Real-time insights allow brands to make informed decisions and create highly personalized experiences to enhance the customer journey, eliminate pain points, and increase sales.
Furthermore, features such as chatbots, digital assistants, and messaging apps can ease customer concerns through instant communication. In fact, according to recent research from MIT Technology Review, chatbots are currently the most widely used application among organizations because of their swift ability to provide customer service for minor incidents, create personalized offers, and share helpful information. In situations that are more complex, chatbots can easily connect a customer with a human representative.
There’s long been a misconception that AI will replace the need for human involvement, but this is a skewed outlook. Rather than a replacement for a human worker, AI takes care of mundane, repetitive, technical tasks, allowing employees to focus their efforts on more critical work. According to an Accenture report on AI, this increased productivity can lead to a profit increase of nearly 60% (from $17 to $27 for every $100 of revenue) for the wholesale and retail sector. AI has also made it far easier to track physical inventory, in some cases cutting down a month-long task to just 24 hours.
The same Accenture report shows that AI has the potential to boost rates of profitability by an average of 38% and “lead to an economic boost of $14 trillion across 16 industries in 12 economies by 2035.” But tracking this profitability isn’t easy, as AI can fall into a grey area in terms of ROI. The best way to assess revenue generated through AI is to look at how it has improved specific areas of the business. For example, a manufacturing company might reduce time on the assembly line through a program that instantly detects when to ramp up or slow down production. Profits can be reaped through increased operational efficiency, risk mitigation, and customer growth and retention.
Before adopting any new AI capability, it’s imperative to assess how it will fit into the overarching DX strategy, the likelihood of it benefiting the business, and how it will be leveraged to prove ROI. It’s also crucial to remember that AI is not another tool or a temporary solution, but rather a set of processes designed to improve what already exists (make sure you understand your data before diving in). Above all, involve the technology team from the get-go and ensure that all key decision makers are equipped with a robust understanding of how the AI strategy works and how it will affect the business. As Gartner puts it, “Even if your current AI strategy is ‘no AI,’ this should be a conscious decision based on research and consideration.”