What sets AI visionaries apart from the pack?

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Enterprises are no longer struggling to decide if, but when and how, to apply artificial intelligence in their operations. Businesses are constantly finding new ways to leverage AI, raising adoption rates and comfort levels with the technology.

This is what the second edition of our global AI research confirms, according to views from senior executives, employees and consumers.

A quarter of the senior executives in the study say that their companies plan to fundamentally reimagine their businesses with AI in the next three years—these organizations are the “visionaries” that are taking bolder steps. What’s also significant is that more workers see career opportunities from AI than feel it threatens their current job. And more than half of consumers say that AI is making their lives better.

Among the companies currently adopting AI, we find different tiers of maturity and ways of measuring success. There are organizations in an exploratory stage—testing, piloting, and discovering new possibilities. They measure success in the number of experiments completed and how successful they were.

Then there are companies that have moved passed experimentation and are industrializing AI in their businesses. They measure success in terms of economic results from AI.

The visionaries are leveraging the data that sits in the ecosystems around them—and are applying AI on top of the data as a neural wiring that allows them to harvest value from the data and turn it into predictive insights that in turn helping organizations get ahead of risk and grasp opportunities.

While we find growing acceptance of AI across the board, our study also points to some lingering concerns, namely in mitigating bias, the need to connect data across ecosystems, and tackling the talent gap. Addressing these disconnects also separates the visionaries from the rest of the pack.

Combatting AI bias

In the study, 78 percent of consumers say they this it is important that companies take active measures to reduce AI bias. On the enterprise side, 95 percent of senior executives say their companies are already trying to mitigate bias, yet only 34 percent are doing so holistically with comprehensive internal frameworks. So, the questions on the table are: how do we approach AI bias? And how do we set up a governance mechanism that is both comprehensive and rigorous?

Combatting AI bias requires breaking it down to the source of the problem. One large driver is the lack of diversity with comprehensive datasets. Teams working with AI, i.e., the people building the algorithms, also need to come from diverse backgrounds to help guard against developing code with unintended bias. And there are the actual areas of application; it is important to be very specific and sure that we are using AI in the right ways and for the right use cases.

Organizations must map these areas into a control framework with a governance mechanism to evaluate and control the rollout of AI. Such a governance framework is critical to delivering AI safely and ethically.

Connecting ecosystems

Another challenge for companies to is how to break down silos so they can leverage data across the enterprise and best take advantage of AI’s benefits. The ability to work seamlessly across internal teams, and externally with partners, customers, and even competitors, in connected ecosystems, is foundational to any business.

The visionaries are taking more advantage of this, as they strongly agree that they can easily share data across all departments (63 percent vs. 41 percent of other respondents), also report a slight edge with AI helping them collaborate across departments and functions (36 percent vs. 33 percent).

Successful organizations can bring in the relevant external data that exists from surrounding ecosystems to complement the internal data they gather from their operations, combined with their domain and process knowledge. The ability to harmonize and harvest the data from this collective intelligence allows successful enterprises to use AI to drive a sense of shared, collaborative performance.

Nurturing “bilingual” talent

Companies need the business and industry knowledge of experienced workers to help with goal orientation and adding context to AI’s algorithms. At the same time, they also need people who understand technology. As a result, there is a growing demand for workers who have both industry domain and technical AI expertise. This “bilingual” talent is, however, short in supply.

To fill this talent gap, companies can look to their own employees. Enterprises must invest in reskilling and provide the opportunity to acquire new knowledge in working with these technologies.

Among the senior executives in our study, 86 percent believe that their employees will be very comfortable in using AI in the work they do. More than half (53 percent) state that they have already started the journey of reskilling their workforces. And 80 percent of workers are willing to learn new skills to take advantage of AI in their current job. Yet only 35 percent of workers say reskilling options are available at their companies, and just 21 percent of these respondents say they have participated in that training.

Employees want training, executives say they are providing it, but workers haven’t experienced it. In 2019, a goal for companies is to work harder to bridge this divide. There is a real opportunity to use reskilling as a competitive advantage and apply it to create more adaptive workforces.

But beyond educational courses, successful reskilling also requires a complete culture change and rethinking of a company’s infrastructure. In a way, workers need to actually unlearn their ways of working to start considering what a new process might look like within the context of digital transformation through AI. A culture of innovation is where bilingual talent can really emerge and thrive.

Broader AI adoption is inevitable. Companies are implementing and moving forward with it at a steady but growing pace.

What will distinguish the next generation of businesses that will lead from the rest of the pack is they are moving now to build AI into the neural wiring of their organizations, impacting processes and operations, products and services, employee and customer experiences, and ability to adapt. They are leveraging internal and external data to inform their decisions, and developing practical, comprehensive frameworks for governance and to address AI bias.

Moreover, these leaders have cultures of change and innovation, where bilingual workers can think outside of the box about how to create better processes at the intersection of AI and domain.

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Artificial intelligence Data strategy Machine learning