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Enterprise artificial intelligence still waiting to exhale

Enterprise artificial intelligence is poised to change how we work and live. But it still awaits broader adoption. 

For many in technology, it seems as though there has never been a more exciting time to be in IT. Technology infrastructure now pairs efficiency and innovation to create new business value.

Nowhere does this marriage seem more potent than in the realm of artificial intelligence (AI) and machine learning (ML). AI- and ML-enabled technologies are poised to change every facet of human existence through newfound efficiencies as well as innovative developments.

Machine learning algorithms have already delivered relevant suggestions to shoppers online, optimized supply chain logistics and automatically generated news content. In healthcare, deep learning algorithms now provide early detection of diseases like diabetes and cancer.

“AI has the power to dramatically improve the way we work and live,” said Fei-Fei Li, chief scientist of AI at Google Cloud, at the Google Cloud Next 2018 conference in San Francisco. “AI is empowerment.”

Democratizing enterprise artificial intelligence

So far, however, only about 20% of AI-aware companies currently use AI technology in a core business process or at scale, according to a McKinsey report on enterprise artificial intelligence.

One reason for incremental enterprise artificial intelligence adoption is that AI is still complex; it remains mostly the province of data scientists and companies with the resources to invest in it. Experts including Li say that enterprise artificial intelligence must be democratized to realize its full potential.

“As the technology reaches more people, its impact becomes more profound,” Li said at Google Cloud Next in 2017. “This is why the next step for AI must be democratization: lowering the barriers to entry and making it available to the largest possible community of developers, users and enterprises.”

In her Google Cloud Next keynote in 2017, Li identified four areas for democratization of enterprise artificial intelligence:

  1. Democratizing computing. AI requires enormous computing resources to work, which aren’t always readily available to enterprises. A deep learning algorithm can easily boast tens of millions of parameters and billions of connections.
  2. Democratizing algorithms. AI is still quite complex. Not every company has the resources in-house to adopt and foster enterprise artificial intelligence. For developers not ready to build their own models, application programming interfaces (APIs) can open up these capabilities to companies. APIs are “like a switch that activates an intelligent component in an app,” Li said, to undertake tasks such as understanding speech, identifying objects in photos or parsing natural language.
  3. Democratizing data. To learn natively and develop insight, AI models require a huge amount of data. Access to substantial data sets can be a steep barrier to researchers. “We need a more scalable and effective way to democratize data,” Li said.
  4. Democratizing talent and expertise. While some companies have invested in AI workforces, not all have data scientists. Companies need to be able to develop AI capabilities in-house but also call on partnerships to solve complex and industry-specific AI challenges.

AI in the real world: Contact center AI

Still, AI is becoming more accessible, and APIs have reduced the barriers to entry of enterprise artificial intelligence.

With APIs, AI capabilities can be realized in just a few lines of code, Li said. The Bloomberg news service uses Google’s translation API, for example, to deliver news to a global audience in seconds. This translation can happen rapidly, and without developers having to invest hours in writing code.

In retail, AI and ML have already begun to have an impact on customer experience through what Li referred to as “contact center AI.” One online retailer now uses “blended AI” to improve customer experience: The approach to customer service joins virtual agents (via chatbots) with human contact center agents as engagements become more complex.

At Google Cloud Next 2018, the online retailer demoed this scenario with a virtual agent that helps a customer through the process of returning an item, and then seamlessly passes the transaction to a human being for product recommendations and an ultimate sale.

“I think we’re all excited about the possibility of spending less time on hold,” Li said. But, she emphasized, “It’s more than that: Contact center AI elevates human talent. It makes simple tasks faster while making human interactions more meaningful,” she said. AI enables humans to augment human knowledge and tasks with technology.

Enterprise AI brings optimism, apprehension

Many workers, of course, are apprehensive about the prospects for AI to replace them in the workforce. According to Workforce Institute data, while 82% see the opportunity for enterprise AI to improve their jobs, 34% expressed concern that AI could someday replace them altogether.

This mixed picture of optimism and apprehension captures where enterprise AI is on its journey toward greater adoption. Even the McKinsey Global Institute offers a vague portrait of the future workforce under AI.

“Machines will be able to carry out more of the tasks done by humans, complement the work that humans do, and even perform some tasks that go beyond what humans can do,” wrote McKinsey in its report “AI, automation and the future of work.” The McKinsey report when on to say, “As a result, some occupations will decline, others will grow, and many more will change.”

Despite this cautious view, those immersed in the industry remain more than optimistic about the business potential of enterprise artificial intelligence.

“I’ve witnessed my field grow from a lofty but academic pursuit to the biggest driver of . . . change,” Li said. “Rather than replacing human skills, AI’s greatest potential lies in enhancing humanity.”

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Lauren Horwitz

Lauren Horwitz is the managing editor of Cisco.com, where she covers the IT infrastructure market and develops content strategy. Previously, Horwitz was a senior executive editor in the Business Applications and Architecture group at TechTarget;, a senior editor at Cutter Consortium, an IT research firm; and an editor at the American Prospect, a political journal. She has received awards from American Society of Business Publication Editors (ASBPE), a min Best of the Web award and the Kimmerling Prize for best graduate paper for her editing work on the journal article "The Fluid Jurisprudence of Israel's Emergency Powers.”