Turbocharge productivity and efficiency with AI
By Susanne Hupfer | 4 minute read | January 6, 2017
Artificial intelligence (AI) is rapidly moving from “nice to have” into “must have” territory for organizations. In fact, nearly six in ten AI early adopters think it will be a necessary element to remain competitive within the next few years. Two-thirds say AI is very important to their organization’s strategy and success.
AI can be a powerful tool for getting people the right information at the right time for decision making. By augmenting human intelligence and improving planning, AI has the potential to transform workplace productivity.
AI systems can rapidly ingest vast amounts of structured and unstructured data—including text, images, audio, video—and give it meaning by creating models of entities and concepts and the relationships among them. They generate hypotheses, suggest answers to questions, and provide recommendations and predictions. And they can discover and present these insights within seconds.
So far, satisfaction on the front lines is high: 62 percent of early adopters report that outcomes from their AI initiatives exceed their expectations.
Here are four ways in which AI is boosting productivity and efficiency for organizations.
Making search more intelligent
Some companies have struggled to provide search platforms that can access information across multiple databases, documentation formats, fragmented customer histories, and extensive product catalogs. The result is frustrating, time-consuming searches as employees and customers try to hunt down the information they need from myriad sources.
AI-enabled search platforms have the ability to interpret data in a variety of formats across a company’s varied data repositories—including unstructured documents like PDF files, emails, and industry-specific formats such as engineering specs and drawings. This smarter, more unified search can help provide a more rapid and comprehensive view of a topic for customers or employees. Forty-two percent of early adopters report their AI initiatives are enabling faster response to customer and market needs.
Enhancing customer care
According to an IBM analysis, roughly half of the 270 billion customer service calls that are made annually go unresolved. Increasing that poor resolution rate represents a huge opportunity for companies to improve their relationships with customers.
Some companies allow customers to pose questions in natural language in a variety of convenient ways—whether by text message, instant message, web chat, or mobile apps—AI capabilities are then used to interpret these natural language questions efficiently and present evidence-backed answers or recommendations.
In other cases, when customers reach out to call centers with questions, representatives consult an AI platform behind the scenes—loaded up with product information, catalogs, manuals, technical support data, terms and conditions, and call center logs, as well as relevant public information. Rapidly armed with the right answers, the reps can quickly deliver accurate information.
Any time customers get their questions answered efficiently and with minimum wait and frustration, it’s a big win for the customer experience. Indeed, half of early adopters say their AI efforts are already resulting in improved customer service.
It’s not just customers that seek answers to their questions. Employees with specialized knowledge—whether IT, legal, marketing, or other particular expertise—may find themselves barraged with requests for information from other parts of their business. While the knowledge transfer is necessary and useful, these interruptions can impact the steady production of specialists’ core work. Sometimes, branch offices may need to consult experts in the main office—a process which can create a workflow bottleneck before answers get funneled back to where they’re needed.
AI technologies can help scale organizational expertise, cut down on interruptions, and improve workflow. Instead of bothering specialists over and over again, an organization can deploy AI technologies that allow employees to ask questions in natural language and get suggested answers back from comprehensive repositories of institutional knowledge. Instead of waiting days for answers from an expert, workers can get their answers back in seconds. Half of early adopters say that they’re already improving workplace productivity and efficiency—for example, by leveraging organizational wisdom—through their AI initiatives.
Proactively predicting issues
In any industry that relies on extensive equipment or large infrastructure—such as aircraft fleets, electrical grids or pipelines, ports or plants—problems with facilities or machinery can cause lost revenue and customer dissatisfaction.
Some companies are turning to AI solutions to help keep their operations running smoothly. For instance, they can use a AI platform to combine real-time and historical views of their operations. When combined with IoT and cloud-based analysis of sensor data, these systems can provide real-time monitoring of operations and equipment. Predictive analytics can alert management to upcoming maintenance issues before they become critical. And if they’re integrated with the company’s supply chain, these systems may even proactively order parts. Forty-six percent of early adopters say that their AI projects are leading to improved decision making and planning
Learn more about the business benefits of AI
You can learn more about how AI systems are being used to augment organizational expertise, improve workflow and response times, and provide predictive insights. But the business benefits of AI don’t end with productivity and efficiency gains. The broader picture includes a range of business outcomes, including increasing customer engagement, improving customer service, and even reinventing risk management.
Learn more about how you can unlock productivity gains with AI:
- Rapidly build a cognitive search and content analytics engine and add it to existing applications
- Enhance information retrieval with machine learning
- Add a natural language interface to your application to automate interactions with your end users
- Provide a conversational, personalized self-service experience to your customers
Susanne Hupfer, Ph.D., is a Senior Consultant and a lead analyst for the IBM Cognitive Study.