AI Implementation: A Few Things Enterprises Must Know
The exceptional opportunity of Artificial intelligence (AI) makes it imperative for enterprises to plan for change. The technology promises to solve problems organizations could not before because it delivers benefits that no humans could legitimately perform.
AI offers the means to maintain optimum efficiency and proficiency to meet customer demands. CIOs, chief data officers (CDOs), application development leaders and enterprise architects, among others, must be willing to explore, experiment with, and implement, AI capabilities to pursue new value generating opportunities.
AI is becoming more common. In fact, by 2021, Gartner projects that 40 percent of new enterprise applications implemented by service providers will include AI technologies. Mike Rollings, research vice president at Gartner sheds light on the trends of shaping the future of AI and what businesses should consider in AI implementation to remain competitive in the market.
What are the key factors an enterprise should focus on when implementing AI?
AI is not defined by a single technology. Rather, it includes many areas of study and technologies behind capabilities, such as voice recognition, natural-language processing, image processing. These technologies and capabilities benefit from advances in algorithms, abundant computation power, and advanced analytical methods like machine learning and deep learning.
CDOs will immediately recognize that in order for AI to reach its full potential, they must develop greater organizational competency in data sciences and assure that data and analytics can be relied upon for various insights. This includes involvement in the assessment of frameworks, software and services claiming AI capabilities. They will also need to work with application development leaders to enable applications that can change behavior based on the flow of data and events.
Which industry do you anticipate will see the most uptake in AI in the near future?
Right now, the industry that is the most excited about AI implementation is the financial services sector. CDOs in this industry are dealing with a very large amount of data in the form of financial transactions that must be analyzed for fraud, or customer behaviors that provide insight into what type of financial advice would be most beneficial. Another industry is healthcare where insights generated from machine learning are improving discovery, diagnosis, care delivery and patient engagement.
How will AI impact the talent needs of an organization?
For AI to be effective within an organization, CDOs must help establish a data-driven culture – information as a second language, if you will. They may also be faced with impacts in the areas of talent sourcing; skills development and training; organizational structure; analytical methodologies; analytical tools; data acquisition and monetization; algorithm acquisition/creation; analytical modeling; analytical model training and maintenance; and process adaptation.
They may also need to create a skilled team of data scientists, data engineers, statisticians and domain experts who can manage the complexity of data, analytical methods and iteratively improve machine learning results associated with AI, as well as help apply it with workers, customers, and constituents.
The skills gap can be massive based on the aspirations of the enterprise and most likely ways AI will be used. To avoid the pitfalls of the skills gap, CDOs should work with their HR partners to develop recruiting plans and workforce enablement plans that include investing into their existing employees to develop both their creative and analytical thinking skills as AI implementation requires both.
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