Experts are already predicting that artificial intelligence will take over every interaction and application, especially for enterprises. The only question is how quickly this enterprise AI revolution will happen and how much ROI will enterprises see.

Unfortunately, many IT departments are held back by outdated beliefs about the role that artificial intelligence and machine learning can make on their bottom line today. Many companies fall back on leaving behind the cutting-edge and emerging technologies to the companies in Silicon Valley and startups, but IT leaders at every company need to start looking at AI. The hypnotism these technologies appear to have performed on the media only makes the problem worse; the use cases that grab headlines, such as the AI winning chess games, appears to have little value for the IT departments grappling with digital strategy today.

We’re breaking down these myths to show how enterprise AI and machine learning are having an impact today in the workplace.

 

Myth #1: We don’t need machine learning or enterprise AI.

Your customers are not inundating your support team with calls asking for machine learning and the intranet is simply not a high priority right now. 

What many IT departments are hearing, however, is about a variety of problems in their workplace collaboration, customer service and website intelligence. However those teams just haven’t yet recognized these as use cases for enterprise AI. For example, many companies have deployed enterprise search to connect and search their various information sources in the intranet. Adoption of the intranet is low because users still struggle to find the right information.

In fact, on average, it takes up to eight searches to find the right document, according to SearchYourCloud. Fixing this problem is a massive manual effort on the part of IT: analyzing queries and click-through rates, then going in and manually tuning the system for relevance. With AI-Powered Search, this is automated. Machine learning automatically tune the system for relevance based on the behavior of users.

 

Myth #2: There isn’t a tangible benefit to machine learning for the enterprise yet.

It’s tempting to look at the AI use cases grabbing headlines and conclude that AI and machine learning are not ready to provide the immediate returns that enterprises require. Learning chess, diagnosing diseases; these are very specific use cases that require significant time to tune, even years potentially, and financial investment. It’s easy to conclude that without a team of machine learning specialists and years to wait, this technology is not within reach.

The use cases not grabbing headlines are often those that do have an impact, such as the acquisitions of relatively unknown or just recently created AI companies by large companies. Machine learning plays a crucial role in helping our customers’ businesses to scale.

We started with a very specific problem that we wanted to solve for our customers. After deploying cloud and usage analytics in 2014, we saw how successful our customers were with our product, but tuning search results for relevance required constant attention. When our customers added more users, more content and more connectors, you can imagine how it would be easy to get out of hand.

Machine learning solves this issue of scalability for our customers. We have automated the resource-intensive work of analyzing and applying insights from that analysis to tune the search engine. For example, during a crucial period of expansion to new markets, one Fortune 50 Healthcare company deployed AI-powered search onto their contact center knowledge base and decreased the amount of time for new hires to become proficient with highly technical and complex support calls from two years to two months. The rapid upskilling also enabled them to hire candidates with customer service backgrounds, rather than highly sought after medical technology degree-holders, lowering the cost and time to fill positions.

Another  Fortune 50 Healthcare company deployed AI-Powered Search onto their knowledge management for their research and development organization – and cut research time by as much as 160 hours, in one instance. The company had solved the issue of relevance and employees were able to more effectively collaborate. The increase in productivity translated to $6 million saved, with an additional $1.75 million saved in reduced labor requirements.

 

Myth #3 I don’t think my CEO is interested in enterprise AI.

If this is true, your CEO is in the minority. According to research from PwC, 72 percent of business execs believe AI will be the business advantage of the future. It may just be a matter of aligning the priorities of the CEO with problems that AI can solve.

Additional research from PwC indicates that the skills shortage may be the best opportunity; about 77 percent of CEOs reported that they are concerned the skills shortage will impair growth. According to the Society for Human Resource Management, in 2016 the median tenure of workers ages 55 to 64, at 10.1 years, was more than three times that of workers ages 25 to 34 (2.8 years).

Without a process in place to actively capture that knowledge and expertise, the knowledge is lost and new employees take longer to become proficient, creating major barriers to business growth.

AI-powered search can play a role in both keeping employees engaged and capturing the knowledge of those who leave. In a 2015 survey from Chief Learning Officer, 85 percent of respondents said they use search at least once a week for workplace learning. Companies who solely focus on formal training methods are losing out on this opportunity to upskill existing employees on the job, affecting the time-to-proficiency for the employee, as well as their likelihood to stay with the company. The Saratoga Institute estimates that the average company loses about $1 million for every 10 professional employees who leave.

To survive in the digital era, IT leaders need to embrace enterprise AI and educate their colleagues on the advancements that AI can bring to their workplace. Innovative and competitive business leaders have already invested in artificial intelligence and are seeing a real impact for their business. IT leaders need to invest now to compete in the next decade, a priority that their CEO is most likely are considering. One of the most effective places to start with enterprise AI is using AI-powered search to increase your relevance maturity, or ability to deliver the right insights to the right person at the right time.

 

Find out more about relevance maturity and AI-powered search by downloading the Coveo Relevance Maturity Model whitepaper

About Gauthier Robe

As VP of Coveo’s Platform line of business, Gauthier Robe oversees the company’s Intelligent Search Platform and roadmap, including Coveo Cloud, announced in June 2015. Gauthier is passionate about using technology to improve customers’ and people’s lives. He has over a decade of international experience in the high-tech industry and deep knowledge of Cloud Computing, electronics, IoT, and product management. Prior to Coveo, Gauthier worked for Amazon Web Services and held various positions in high-tech consulting firms, helping customers envision the future and achieve its potential. Gauthier resides in the Boston area and has an engineering degree from UCL & MIT. In his spare time, Gauthier enjoys tinkering with new technologies and connected devices.

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