Is there a recipe for AI success?

Here s a brief excerpt from Deloitte’s State of AI in the Enterprise survey (2nd edition) that provides an invaluable update on latest developments in the field of artificial intelligence (AI). To read the complete article, check out other resources, sign-up for email alerts, or learn more about the firm, please click here.

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According to our second annual AI survey, 82% of companies are already seeing positive returns from their investments in AI. But behind this rosy picture lurk growing cyber, legal, and ethical risks—and an uncertain employment future for human workers.

Learn more from US business leaders about where the money is going, how the technology is being deployed, where the risks lie, and how the workforce may be impacted.

FOR the second straight year, Deloitte surveyed executives knowledgeable about cognitive technologies and artificial intelligence, representing companies that are testing and implementing them today. We found that these early adopters remain bullish on cognitive technologies’ value. As in last year’s survey, the level of support for AI is truly extraordinary. Our analysis uncovered three main findings:

  1. Early adopters are ramping up their AI investments, launching more initiatives, and getting positive returns. Cloud-based cognitive services are increasing adoption by reducing the investment and expertise required to get started.
  2. Companies should improve risk and change management. This includes reducing cybersecurity vulnerabilities—which can slow or even stop AI initiatives—and managing ethical risks. Project selection and managing return on investment are also critical.
  3. Early adopters need the right mix of talent—not just technical skills—to accelerate their progress. They are short of AI researchers and programmers but also need business leaders who can select the best use cases. To garner this talent, they are training their current workforce, but many feel the need to replace existing workers with new people. Early adopters also may need a strategic approach to talent that automates what machines do best, while still capitalizing on human judgment and creativity.

These findings illustrate that cognitive technologies hold enticing promise, some of which is being fulfilled today. However, AI technologies may deliver their best returns when companies balance excitement over their potential with the ability to execute.

Activity, investment, and positive results

A year later, and the thrill isn’t gone. In Deloitte’s 2017 cognitive survey, we were struck by early adopters’ enthusiasm for cognitive technologies.That excitement owed much to the returns they said cognitive technologies were generating: 83 percent stated they were seeing either “moderate” or “substantial” benefits. Respondents also said they expected that cognitive technologies would change both their companies and their industries rapidly. In 2018, respondents remain enthusiastic about the value cognitive technologies bring. Their companies are investing in foundational cognitive capabilities, and using them with more skill.

Compared with their counterparts in typical companies, our early-adopter respondents have high—and growing—penetration rates of key cognitive technologies:

  • Machine learning is the ability of statistical models to develop capabilities and improve their performance over time without the need to follow explicitly programmed instructions. Most cognitive technologies are based on machine learning and its more complex progeny, deep learning. That includes computer vision and natural language processing (NLP). Machine-learning adoption was already high at 58 percent in 2017, and it grew by 5 percentage points in 2018.
  • Deep learning is a complex form of machine learning involving neural networks, with many layers of abstract variables. Deep learning models are excellent for image and speech recognition but are difficult or impossible for humans to interpret. New technologies are making it easier for companies to launch deep-learning projects, and adoption is increasing. Among our respondents, 50 percent said they use deep learning, a 16 point increase from 2017—the largest jump among all cognitive technologies.
  • Natural language processing is the ability to extract or generate meaning and intent from text in a readable, stylistically natural, and grammatically correct form. NLP powers the voice-based interface for virtual assistants and chatbots, and the technology is increasingly used to query data sets as well. Sixty-two percent of respondents have adopted NLP, up from 53 percent last year.
  • Computer vision is the ability to extract meaning and intent out of visual elements, whether characters (in the case of document digitization) or the categorization of content in images such as faces, objects, scenes, and activities. The technology behind facial recognition—computer vision—is a part of consumers’ everyday lives. For example, some mobile phones permit their owners to log in simply by looking at them, via facial recognition. Computer vision technology “drives” driverless cars and animates cashier-less Amazon Go stores. Computer vision has also gone mainstream with our survey respondents, 57 percent of whom say their company uses it today.

What’s behind the growth of cognitive technologies among early adopters, especially the popularity of sophisticated technologies such as deep learning? One answer is investment. Thirty-seven percent of respondents say their companies have invested US$5 million or more in cognitive technologies. Another reason is that companies have more ways to acquire cognitive capabilities, and they are taking advantage. Nearly 60 percent are taking what is perhaps the easiest path: using enterprise software with AI “baked in” (see figure 1).

More respondents gain cognitive capabilities through enterprise software, such as CRM or ERP systems, than by any other method. These systems have the advantage of access to immense data sets (often their own customers’ data), and can often be used “out of the box” by employees with no specialized knowledge.

The cognitive tools available through enterprise software are often focused on specific, job-related tasks. While this can make them less flexible, they may be impactful nonetheless. For example, Salesforce Einstein can help sales reps determine which leads are most likely to convert to sales, and the optimal time of day to contact those prospects. Moreover, vendors continually develop advanced tools, which are gradually integrated into the software. Salesforce recently developed an advanced NLP model for handling multiple use cases that typically require different models.

The “easy path” will likely become even more attractive as software vendors and cloud providers develop AI offerings tailored to business functions. Google recently announced a set of prepackaged AI services aimed at contact centers and HR departments. SAP’s AI capabilities, which it collectively calls “Leonardo Machine Learning,” also include specific solutions such as cash management in finance, video analysis in brand management, and trouble ticket analysis in customer service. The need for companies to develop bespoke cognitive initiatives will likely decline as similar services enter the market.

Off-the-shelf can go only so far, however. Many companies will likely need to develop customized solutions to meet their lofty expectations for cognitive technologies. Here, too, there are tools to accelerate adoption. Many of the big cloud providers offer AI through an as-a-service model: Instead of having to build their own infrastructure and train algorithms, companies can tap into the technologies they need right away, and pay only for what they use. According to a recent Deloitte study, 39 percent of companies prefer to acquire advanced technologies such as AI through cloud-based services, versus 15 percent that prefer an on-premise solution.13 Indeed, the appeal of the AI-as-a-service model is reflected in its annual global growth rate, which is estimated at a remarkable 48.2 percent.14

Cloud-based deep-learning services can give companies access to immense—and previously costly—computing power necessary to extract insights from unstructured data. They can also manage large data sets and accelerate app development with pretrained models.

While there are myriad ways for companies to access ready-made AI or develop their own, many also seek outside expertise. Fifty-three percent of respondents codevelop cognitive technologies with partners, and nearly 40 percent use crowdsourcing communities such as GitHub.

Through cloud services and enterprise software, companies can try cognitive technologies and even deploy them widely, with low initial cost and minimal risk. The growing number of cloud-based options may explain the spike in pilots and implementations between 2017 and 2018. Fifty-five percent of executives say their companies have launched six or more pilots (up from 35 percent in 2017), and nearly the same percentage (58 percent) claim that they have undertaken six or more full implementations (up from 32 percent).

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Here is a direct link to the complete article.

Jeff Loucks is the executive director of Deloitte’s Center for Technology, Media & Telecommunications, Deloitte Services LP. He is based in Columbus, Ohio.

Tom Davenport is an independent senior advisor to Deloitte Analytics, Deloitte Consulting LLP. He is based in Arlington, Virginia.

David Schatsky analyzes emerging technology and business trends for Deloitte’s leaders and clients. He is based in New York.

 

 

 

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