Artificial intelligence is a popular topic in boardrooms and C-suites today. As AI becomes one of the most-discussed opportunities, businesses are making significant investments in technologies designed to innovate new business models and improve business processes.
Yet if history proves anything, many companies will swing and miss at this latest business strategy. Why? From the dot-com boom and bust in the late 1990s and early 200s to the collective reluctance of cloud computing in the last decade, businesses have missed obvious opportunities for growth.
Researchers at the University of Pennsylvania’s Wharton School and Google fear that companies are already missing out. Their work leads to a key question regarding artificial intelligence: Will corporate inroads fail?
The authors write in Harvard Business Review that some early AI failures are leading to companies retreating from investing in the technology. These retreats, are irrational, they write, given the potential AI provides.
Companies are not leveraging the early investments they’ve made in data and analytics. A report in the technology news by the McKinsey Global Institute shows that industries such as manufacturing, health care, and government have been particularly inefficient.
The major barriers to using artificial intelligence and other innovative technologies include:
- Siloed data stored in legacy systems or across company divisions or agencies
- Poor interoperability
- Ineffective data sharing
- Lack of talent
Initial failures or difficult barriers can lead to retrenchment, loss of funding or canceling of projects.
Instead, the Harvard Business Review authors suggest that companies take a portfolio approach to AI projects. With a mix of projects, leaders can create some quick wins while working on some longer-term projects that collectively drive innovation and transformation.
Quick-win projects could leverage recent advances in speech, vision and language technology, for example, or use smart tools to schedule complex meetings. While these smaller projects may not be transformational, they will help employees and customers in ways that will lead to internal advocacy.
These smaller projects also help companies get a better handle on the collection and analysis of more data points and how to use this data for more ambitious AI projects.
Long-term projects can include the re-thinking of end-to-end processes, more workflow automation, and leveraging the power of the Internet of Things.
Companies that are aggressive with AI are already seeing those approaches pay off. A study by Vanson Bourne for Infosys surveyed 1,600 businesses worldwide. It showed that early adopters in AI are already reaping rewards.
Its key findings:
- Businesses expect more AI growth by 2020. Those that have planned for or have implemented AI technologies expect a 39 percent revenue increase and 37 percent cost reduction.
- Eighty percent of companies whose workers are being displaced by AI are investing in programs to redeploy or retrain affected employees to retain them in the organization.
- Surveyed companies invested on average $6.7 million in AI in 2016, mostly in big data automation (65 percent) and predictive or prescriptive analytics (54 percent).
Survey respondents acknowledge that barriers remain, including ethical considerations and a lack of understanding among suppliers and customers. But the bullish attitude suggests these early adopters are poised to reap future rewards.