Presented by Lucidworks
Employees have a full spectrum of content and data, but it’s easy to get lost in unproductive, dead-end hunts. Join this VB Live event to learn how AI-powered smart search boosts efficient data discovery and insights that deliver real-world, high-value solutions for complex problems of every size.
Digital transformation means pivoting to become more efficient, data-driven, and nimble. Traditional enterprise search is anything but.
To do their jobs, enterprise employees need to tap into a huge amount of content and data available both inside and outside the company, and the tools they’re handed aren’t up to the job, says Simon Taylor, vice president worldwide channels & alliances at Lucidworks.
“The volume of data in those silos has created a form of disconnection, both from the employee to the information and the employee to the application, which ultimately drives down productivity,” Taylor says. “Also, if you have too much information, you get information overload.”
A lot of employees today are disengaged at work because they can’t get to the information that makes their role or purpose more relevant, he explains. Their search results aren’t relevant enough to their specific role, or surfaced to them in relevant contexts, and that’s why employees, particularly millennials, get frustrated in their workplace, he adds. They’re very used to tools they use externally, whether it’s social media or browsing or searching.
“The workplace is having to transform the way it does things, to provide the same type of demand level tools, relevancy level tools, insight-driven tools, to make employees more productive,” Taylor explains.
He warns that if you don’t have the right solutions in place, or even the right insight tools to drive productivity from employees, you’ll make bad decisions, and the company as a whole is going to spend more money on operationalizing things that fundamentally should be a lot easier — and that’s where you get a strong business case for search.
Two types of AI-powered search differentiate businesses today. In curated search you give the AI framework direction about what you need to learn, and then ask it to surface insight that’s relevant to your personal journey in order to make more informed decisions about how to use that information.
On the flip side of that is deep learning, which auto-curates the most relevant topics and classifications and categories, or the ontology, from the data, and then presents it as a corpus of information with recommendations about how to best use it.
In a business context, both curated and uncurated search is essential, and can drive value in three areas. The first is making access to information more efficient, and delivering contextualized, relevant results quickly — for instance, use cases like HR portals.
With natural language processing and machine learning, knowledge management tools can serve responses through an automated communication platform like chatbots. It improves efficiency and it saves money too, by reducing the amount of operational resources required to answer rudimentary questions.
The third category of solution is finding opportunities to make money, or the data discovery area, which helps employees find ways to do things more quickly and actually drive more revenue.
He points to an oil and gas company that had spent money repeating the same research three times in a 10-year period because their ECM (enterprise content management) was not able to find and pull up the records for previous studies. This was to determine the viability of a new drilling site, and, of course, each time the study gave researchers the same result — the area they were exploring would not be a lucrative drill site, and they would not be getting back the time and the money they had wasted discovering that again.
AI-powered search broke down the company’s information silos so that they were able to access research that had been done across the business, and better decide where to focus the company’s assets and resources in order to get the most value.
“That business case, for them, represented multi-billion-dollar savings and revenue generation,” Taylor says. “It’s the real power of search, when you can deliver some significant business-changing direction.”
Transitioning to an AI-powered search tool isn’t really optional these days, he adds — your competitors are doing it to get to information in the most efficient way possible.
“It’s not a resource thing, where you can throw more people at it,” he says. “You have to become clever in the way that you apply technology and think smarter about the way you use it.”
To learn more about how search and machine learning can deliver both savings and revenue opportunities, a look into some of the most compelling business use cases, and more, don’t miss this VB Live event!
Don’t miss out!
- What operationalized AI means
- How search and machine learning align to drive efficiency and Opex (operational expenditure) savings
- How search and machine learning can create revenue opportunities
- Success factors for operationalized AI and top lessons learned
- Simon Taylor, Vice President Worldwide Channels & Alliances, Lucidworks
- JP Sherman, Enterprise Search & Findability Expert, Red Hat