Nand Kishor Contributor

Nand Kishor is the Product Manager of House of Bots. After finishing his studies in computer science, he ideated & re-launched Real Estate Business Intelligence Tool, where he created one of the leading Business Intelligence Tool for property price analysis in 2012. He also writes, research and sharing knowledge about Artificial Intelligence (AI), Machine Learning (ML), Data Science, Big Data, Python Language etc... ...

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Nand Kishor is the Product Manager of House of Bots. After finishing his studies in computer science, he ideated & re-launched Real Estate Business Intelligence Tool, where he created one of the leading Business Intelligence Tool for property price analysis in 2012. He also writes, research and sharing knowledge about Artificial Intelligence (AI), Machine Learning (ML), Data Science, Big Data, Python Language etc...

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By Nand Kishor |Email | Sep 25, 2017 | 9930 Views

It's clear artificial intelligence (AI) is 2017's hype, with vendors AI-washing their offerings and analysts talking up a storm about it at any and all confabs. But, cutting through the hype, there actually appears to be a "there" there. There is movement, with some base-level implementations and experimentation underway. One recent study makes the case that AI is already making a dent in things, helping organizations to better package and sell their goods and services. There is even early evidence that AI may even ultimately serve to be a job generator, not a job killer.

Let's do away with the preconceived notions about AI one at a time, and dive into some of the takeaways of a recent survey of 993 business leaders, conducted by Anne-Laure Thieullent and Ron Tolido, both with Capgemini.

The survey is full of supporting data, but one number really stands out in the compilation of findings. That is, Harley Davidson, the motorcycle maker, saw a 2,930% increase in sales leads using an AI tool over three months. The Capgemini report authors cited a Harvard Business Review article, which described the company's employment of an AI system, called Albert, which employed AI to help generate leads and analyze marketing campaign variables.

As originally reported in HBR, "once it determined what was working and what wasn't, Albert scaled the campaigns, autonomously allocating resources from channel to channel, making content recommendations, and so on. For example, when it discovered that ads with the word 'call' - such as, 'Don't miss out on a pre-owned Harley with a great price! Call now!' - performed 447% better than ads containing the word 'buy,' such as, 'Buy a pre-owned Harley from our store now!' Albert immediately changed 'buy' to 'call' in all ads across all relevant channels."

As Harley Davidson is more associated with the wild side of life, it seems only appropriate that they produce such a wild ride from their AI work to date. For most other companies, AI seems to be delivering at more down-to-earth levels. Three in four organizations implementing AI increased sales of new products and services by more than 10%, the Capgemini survey finds. The survey finds 79% of organizations implementing AI generate new insights and better analysis. In addition, 78% of organizations implementing AI increased operational efficiency by more than 10%. Another 75% of organizations using AI also reportedly enhanced customer satisfaction by more than 10%.

The research shows that "organizations are using AI to influence sales in a variety of ways, from supporting new products to generating leads," said Thieullent and Tolido. They provide a working picture of what goes into AI: encompassing "a range of technologies that learn over time as they are exposed to more data," including speech recognition, natural language processing, semantic technology, biometrics, machine and deep learning, swarm intelligence, and chatbots or voice bots.

AI seems to be boosting innovation as well, and not necessarily putting people out of jobs. A total of 74%, for example, believe that AI is making their organizations more creative. AI is creating new job roles in many organizations. Four out of five executives in the Capgemini survey say AI has already created new job roles, mainly at the senior level -- at the grade of manager or above. In fact, a majority of organizations (63%) have not seen AI produce a negative effect on jobs. Among organizations that have implemented AI at scale, more than three in four say AI is creating new job roles.

There are challenges to making AI function well for organizations, however. In the survey, 64% of executives said they were encountering a lack of appropriate skills and talent to help make AI happen. Another 63% were concerned about cybersecurity and data privacy concerns. Employee job-loss fears were also in the top three, cited by 61%, as well as 57% citing a related issue, resistance to change.

Interestingly, a majority of executives, 57%, also agree that their organizations "firmly believe that human judgments are superior to machine judgments." We'll see how that one plays out in the years to come.

The rules for achieving a successful AI project within the enterprise actually bear a striking resemblance to technology engagements of the past - launching successful pilots to illustrate the worth of the technology, then selling the organization on its virtues. Thieullent and Tolido make the following recommendations for successfully pursuing an AI strategy:

Pinpoint use cases in the "sweet spot of high benefit and low complexity." Harley-Davidson accomplished its exponential surge in sales leads this way - by targeting key areas and focusing on them like a laser beam. Start with use cases that are "not too complex to implement - to avoid the risk of failure or suboptimal results," the Capgemini authors state, and seek those that will show quick paybacks. Those that have been in the IT world for some time will recognize this as the "low-hanging fruit" part of the playbook for gaining traction with new technologies.

Identify leaders and proponents. Ideally, this would be a CXO who reports to the CEO, Thieullent and Tolido recommend. However, AI leaders are still few and far between, they add - "only about a third (37%) of organizations implementing AI have a dedicated AI head or lead in their firm."

Establish AI governance and roadmap. Having a "central governing body for AI implementation increases benefits in multiple areas," Thieullent and Tolido state. At this point, only about 37% of organizations implementing AI have a central governance team.

Win over employee trust and support. This is a biggie, since AI now has a horrible reputation, deserved or not, as a job-killer. This was seen as a concern in 61% of the organizations in the Capgemini survey. "Leaders openly communicate with employees and involving them at each step in the journey. They demonstrate how AI will augment employees' work and how training and other programs will increase their comfort level with the technology."

The Capgemini report quotes Michael Natusch, Global Head of AI at Prudential: "We are running a training program for employees from all BUs to learn Alexa programming skills. The primary objective is not to develop AI solutions, but we are trying to increase the level of confidence that our colleagues have with AI. We hope to build an understanding of what those things can, and cannot do, as both of them are obviously equally important."

Prepare AI skills. There simply aren't enough people well-versed enough in AI who can walk through the door and start making things happen. These skills need to be built from within. "Building a team of AI specialists who can conceptualize AI use cases, code, and implement them, is vital. Nearly two-thirds of organizations (64%) consider the lack of skills to be the biggest challenge to AI implementation." Consider the implications of poor skills development, they warn: "Insufficient or irrelevant data jeopardizes the accuracy of AI applications, rendering them unreliable and unusable."

Pursue rapid experimentation. Thieullent and Tolido recommend that a concerted AI effort start with "experimenting with pilots and launching them on selected use cases." Big data analytics platforms are great tools to "speed up AI experimentation," they add.

Source: Forbes