As AI capabilities rapidly mature, the question of how to leverage AI and how to transition from experimentation to implementation is a struggle for many executives interested in gaining the competitive advantage of Artificial Intelligence. 

The organizations that have been more successful in scaling AI for their needs are financially out-performing organizations who haven’t. But bridging the AI skills gap, while critical to realizing the true value of AI, is not easy. In a study done by IBM and Oxford Economics, they found that 63% of over 5,000 global executive respondents see skills as the top barrier to AI success. This same study explores the transformation of how companies views of AI have changed over the course of two years (2016-2018). These are the differences the discovered.

Less Experimentation, More Implementation

82% of enterprises are at least considering AI adoption now. By comparing recent data with the 2016 data, they found 33% more organizations are beyond the AI testing and implementation stage. Executives have shifted their attention from worrying about whether to adopt AI (availability of technology) to struggling with how to adopt AI (skills and data).

AI adoption is higher and will probably accelerate faster in more digitized industries like financial services, where 16% of companies are already operating or optimizing AI systems, but also in industries like automotive and healthcare. This might reflect a continued optimism in the value AI can deliver.

Less Cost-Savings, More Customer Satisfaction

Executives continue to rank customer satisfaction and retention as primary objectives of their AI investments, significantly above cost considerations. Obviously that doesn’t mean cost isn’t important. AI projects have a built-in cost reduction element, but many C-suite executives are placing greater emphasis on customer experience (68%) than traditional products and services (19%). 

Among a number of innovators surveyed in 2017, AI’s impact on the customer experience outranked any other business model component including cost, organizational structure or capital investment. Enhanced customer experience often relies on a company’s customer-facing support, where AI-enabled virtual assistants can augment existing expertise to deliver answers to customers’ questions more quickly, accurately and cost effectively. No offense humans. 

Less About Technology Availability, More About Data Capabilities

Availability of AI technology is far less concerning for executives than it was two years ago. Only 29% of respondents from the 2018 survey cited it as a potential barrier, compared to 46% in 2016, when the availability of technology was a top factor. Recent studies point to the accelerating growth of data as executives’ primary challenge.

So what is needed to optimize the value of AI? From a data strategy perspective, a robust but  flexible foundation is critical, as well as an organizational structure supported by governance and policy that adheres to common standards. Recent research indicates that 65% of outperformers capture, manage and access business, technology and operational information on key corporate data with a high degree of consistency across the organization.

Infrastructure needs to be nimble enough to respond to new market dynamics, customer demands, strategic initiatives and user needs. Because AI and its decisions are grounded in data, the ability to recognize contextual data quality is crucial for successful operational execution. Organizations must foster a culture that embraces using data differently, which means open collaboration across business units, functions and IT.

Less About Labor Productivity, More About Talent Development

AI has significant potential to dramatically increase the productivity of workers. And higher labor productivity can translate into proportionately increased labor income. But as with the introduction of any new technology, change can initially be disruptive, even if the end result is positive. 

Skills now reflect the biggest concern executives have about deploying AI. 63% of executives cite the availability of skilled resources or technical expertise as the biggest barrier to implementing AI. As the demand for data scientists and other AI experts increases, employee retention risks also rise. Startups are aggressively poaching AI talent from universities and established corporations. But many organizations will also need to make more with what they already have. Without a more sustained focus on developing the skills required, AI initiatives face a higher risk of delay between proof of concept and implementation.

In conclusion…

There are still significant challenges to face when transitioning from experimentation to implementation of AI technology into your organizational structure. It’s clear that embracing the next stage of the AI journey requires an enterprise-wide commitment. Anything less risks organizations remaining mired in the hype of the previous few years, and missing the opportunity to realize the full potential of enterprise-grade AI.

Earlier this year we introduced enterprise AI servers from IBM, the IBM Power Systems LC921 and LC922, as well as what IBM considers their training server for AI; IBM Power System AC922. Check out the blog post here.