CIOs will be familiar with some of the AI hype over the years. From the 1980s Lisp machines to Deep Blue in 1997 to the debut of IBM’s Watson in 2010s, AI in various forms has been around for a long time. But commercial uses of AI are in very specialized industry-specific applications such as actuarial forecasts and medical diagnosis — making CIOs understandably cautious about promoting AI’s potential business value.
“Savvy CIOs are experimenting jointly with business peers to discover top use cases and ROI for AI to evaluate its potential to disrupt markets and remake existing business models,” says Janelle B. Hill, vice president and distinguished analyst.
Read More: Steer Clear of the Hype: 5 AI Myths
Here are four key insights for CIOs to know before they start a successful AI journey.
- Digital business is accelerating interest in AI at a pace that has left many CIOs hurrying to build an AI strategy and investment plan appropriate for their enterprise.
Over the last few years, the pace of innovation in AI technologies has been staggering, predominantly coming from small vendors. CIOs are in the perfect position to educate the company’s CEO and board about recent developments in AI and illustrate how AI might influence their business and their competitive landscape. By following this approach, CIOs can potentially flip the traditional engagement model between IT and the business, influencing business strategy at the outset, rather than simply developing implementation projects that follow up on the executive team’s decisions.
- Although many core AI technologies are proven, the market for solutions using those technologies overall is in its infancy, such that CIOs should expect rapid product and solution change.
Some industries have utilized AI to great success. In healthcare, for example, thanks to “computer-assisted diagnosis,” a computer was able to spot 52% of breast cancers based on mammography scans up to one year before the women were officially diagnosed. But there are limits to AI solutions, especially if there isn’t enough data available or if it’s of poor quality. By jump starting innovation, CIOs in combination with business peers can jointly figure out how to best use AI technologies in their industry. Companies that commit to promoting experimentation across the organization can encourage their employees to interact with low cost AI products — Alexa, Cortana, a drone, a wearable, and so on. CIOs can then actively monitor the market for emerging solutions that build on lessons learned from the experiments.
- Deep learning, natural-language processing and computer vision are leading areas of rapid technology advancement, and are the areas where CIOs need to build knowledge, expertise and skills.
Capabilities like voice recognition, natural-language processing (NLP), image processing and others benefit from advances in big data processing and advanced analytical methods such as machine learning and deep learning. And while most organizations may not pursue these leading-edge uses of AI, it will play an increasingly important role in the top three business objectives often cited by CEOs — greater customer intimacy, increasing competitive advantage and improving efficiency. As a result, companies will have to monitor emerging AI solutions to build out a business case and to identify the limitations in current-generation technologies so they can understand the complexity of skills needed to fill talent gaps.
- Market conditions for commercial success with AI technology are well-aligned, making AI safe enough for CIOs to investigate, experiment with and strategize about potential application domains.
Recent breakthroughs in machine learning, big data, computer vision and speech recognition are increasing the commercial potential of AI. But AI requires new skills and a new way of thinking about problems. CIOs must ensure that IT owns the strategy and governance of AI solutions. Although pilot AI experiments can start with quite a small investment, for full production rollout the biggest area of investment is building and retaining the necessary talent. These skills include technical knowledge in specific AI technologies, data science, maintaining quality data, problem domain expertise, and skills to monitor, maintain and govern the environment.