Sanjay Dhawan is the CEO of SymphonyAI, a leader in high-value enterprise AI SaaS for strategic industries.
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What I call “AI-augmented workers” are building a new wave of productivity gains in enterprises.
The initial use cases are starting to become evident, but one theme unites them. Namely, marrying the benefits of human judgment and domain expertise with AI software that can process massive amounts of data and find new insights or predictions in that data. This combination—the things people are good at plus the things AI is good at—is the magic formula that generates business intelligence, eliminates rote work and helps workers focus on making and executing the best decisions quickly.
Where AI Fits In The Modern Workplace
Of course, enterprises need to invest resources in discovering, refining and implementing this formula. The more complicated the use case, the more difficult that process can be. Leaders need to consider what’s needed from their people and technology, including security and data privacy. We cannot assume frontline workers will embrace AI easily. Training and process adaptations are important, and some employees will need to be convinced why they should welcome—and trust—AI insights.
These factors are true for any productivity innovation. But they’re surmountable, especially if businesses use the lessons from early successful examples of AI helping employees make better decisions and drive better outcomes.
One example is the auto industry, which helped pioneer AI integration into legacy technologies while ensuring safety and security and keeping costs low. For example, conversational AI has assisted drivers for years, offering travel routes and directions, maintenance reminders, and other information with voice commands.
Chatbots or recommendation engines harness AI, too—these tools act like a support counselor who provides advice on steps to take but within strict and narrow possibilities. These technologies were a giant leap forward and delivered remarkable value, but they are no longer cutting edge.
However, more recent innovative applications of AI in passenger cars and trucks might be precursors of how AI is poised to transform how many other businesses work. How people interact with technologies in their vehicles is a real-life bellwether of how they might use similar technologies in offices, on shop floors and in other contexts.
The best AI allows cars to sense other vehicles on the road, alert drivers to needed adjustments and decide when to ring alarm bells to warn the driver or act unliterally to avoid an accident. AI is moving from blunt suggestions to specific, even insightful, advice and, in certain circumstances, explicit directives or control.
Similar applications of AI are coming to support workers in ways that will dramatically benefit them and their companies. Consider, for example, a factory floor worker who could receive instructions for a manufacturing process via AI-driven software in a hands-free smart device like Google Glass. Or a monitoring system that can not only sense errors or malfunctions but predict them in time for a technician to intervene.
Between Human And AI
This approach is familiar: an “open loop” process where the system offers recommendations but the human operator decides what to do. A “closed loop” automates that step, removing the human operator from the decision. That step has been seen as invasive, where the operator has essential experience and training. But many simple tasks can be automated to allow humans to focus on higher-value work. And human error is also a cause of errors, malfunctions and stoppages. More intelligent systems not only provide workers with guidance but also help reduce errors now, too.
An AI-augmented enterprise worker using smart glasses, phones or tablets might receive instructions for repairing a piece of equipment via their devices in the typical open-loop scenario. But their devices might also instantaneously order replacement parts as soon as their AI knows the nature of the malfunction. They might instantly notify a supervisor or other colleague who might be tracking this particular piece of troublesome equipment. Or the AI might trigger a warning for a worker not to touch malfunctioning equipment due to a probable safety hazard.
The predictions about autonomous machines seizing control are off base. AI isn’t going to be telling enterprise workers what to do all the time in the same way that owners of cars with drive assist can’t let go of the wheel and let the AI take over.
Instead, AI will help a new generation of workers make better decisions more easily and quickly as enterprises, with their partners, create specific solutions for different industries to address unique problems. The most dedicated and tuned AI will also be the best option for enterprises that want to hit the ground running with AI-assisted insights rather than spend resources on expensive systems and building from scratch. Today, out-of-the-box AI platforms fill that need.
While platform-based AI can sound appealing, because it means an enterprise can build to fit exactly its custom requirements, the reality has been starkly different. High costs, long timelines, and custom service deployments are often the norm. Much in the same way that custom software development has been replaced by SaaS applications in the enterprise, AI adoption is headed in the same direction, for the same speed, efficiency and time-to-value benefits.
For these reasons, specific use cases offer the best examples of AI’s potential. Today, retailers use computer vision to help improve the in-store experience for shoppers. AI computer vision via shelf cameras and robots assists stores with managing product availability and inventory, cutting out-of-stock times and ensuring planogram compliance, pricing and promotions. Combining real-time computer vision data with large contextual data sets further improves forecasting reliability and accuracy. Similar advances have occurred in IT service management and workflow, where AI can improve services exponentially by improving customer service and helping technicians better diagnose issues.
Businesses that start thinking today about how AI might augment their workers are the ones that are likely to capture exponential increases in productivity sooner. Once they get started, they might be able to come up with some ideas on their drive home.
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