Applied AI – Time to lead from the front | HCL Blogs

Applied AI – Time to lead from the front

Applied AI – Time to lead from the front
November 19, 2019

They call it Kiva robots or warehouse bots. Or, at times, just smart machines. It’s an entire cohort of robots that operates inside an Amazon warehouse on the outskirts of Robbinsville, New Jersey. This warehouse is a dizzying hive of activities, with machines and humans working in carefully concerted harmony. Besides highlighting the advanced warehousing capabilities of Amazon, the fulfillment center also serves as an example of how soon technology will start assisting humans in complex manual tasks.

Not flying cars or autonomous supercomputers. The future is applied AI and it's now. Here’s how it’s changing the face of businesses and factories as we know it.

As applied artificial intelligence (AI) applications, like Amazon’s Kiva, become more pervasive across sectors, the time has come for industry leaders to consider the implications of AI applications, like Amazon’s Kiva, on both top and bottom lines of businesses. Research reveals that nearly 85% of business executives believe that AI will enable their companies to gain a competitive advantage. Yet, only one out of twenty companies have rigorously implemented AI technology in their processes and products.

How Applied AI is Changing Businesses – Minus the Hype

There are countless new ways through which applied AI is yielding unprecedented results for companies. Most of them are modest and unlike anything exorbitant that theoretical AI often propagates. Advancements in concurrent technologies like cloud computing and machine learning (ML) is propelling AI research across industries, unlocking real-world use cases that were once thought insurmountable.

At the ground level, applied AI is helping companies to:

Gain sustainable competitive advantage

To build a sustainable cost advantage through digital transformation, businesses must adopt a systematic automation strategy that involves intelligent automation leveraging AI and robotic process automation (RPA). This comprehensive approach can enable many progressive enterprises to extend automation’s impact beyond cost reduction to enhance competitiveness, drive increased revenues, and improve the overall employee and customer experience. Such strategies have already started opening endless possibilities in sectors like farming and agriculture.

For instance, John Deere, an American manufacturer of agricultural, construction, and forestry machinery unveiled a new series of harvesting equipment last year. The system comes embedded with a Combine Advisor System, which has seven smart features that help operators of combine harvesters to automate and optimize threshing without manual intervention.

The system uses a new application that allows operators to set the initial threshing parameters. Also, a pair of cameras at tailing elevators and the clean grain allows operators to inspect what is going on inside the machine. The system provides real-time updates on the quality of grain going into the tank. Additionally, by using the images and combining the data generated from a series of sensors, the machine’s internal computer (ActiveYield) can automatically adjust performance to enhance the threshing outcome.

Empower New Business Models

The automotive industry serves as a classic example of the current AI revolution. Cars have had inbuilt sensors for decades now, but they have been primarily used to provide real-time data about fuel levels, speed, tire pressure, and so on. With the advent of AI and smart sensor technologies like the Internet of Things (IoT), connected cars have become an intrinsic part of the wider ecosystem. Combining its data with other data sets helps in the detection of traffic patterns and accidents, intelligent navigation, and predictive maintenance.

AI not only enables car manufacturers to innovate and upgrade product lines but also allows them to analyze usage patterns, optimize product performance, and coordinate usage and maintenance across a wider customer base. Results? Improved aftermarket services, higher customer satisfaction, and increased profit margins.

A similar progression is evident in other domains, for instance, healthcare. The use cases of AI in this sector are just as varied as in sectors like manufacturing and automotive. Iqvia, an American company specializing in clinical research and health information technologies, for example, has come up with a new AI safety platform that reduces Pharmacovigilance costs and complexity . Built as a cloud-based solution on the Salesforce architecture, the solution will leverage optical character recognition (OCR), NLP, and ML to help organizations to simplify healthcare regulatory reporting.

Simplify Operations, Drive Revenue, and Improve Margins

Following the example of automotive manufacturers selling performance and capacity, business leaders should also explore ways through which AI can drive revenue and improve profit margins. This seems more pertinent today as an increasing number of companies are trying to harness the power of AI to transform their processes.

Munich-based startup 4tiitoo, for example, is using AI and eye-tracking to develop an intelligent, hands-free supply chain. The 4tittoo NUIA (Natural User Interaction to all Applications) platform makes hands-free control convenient and easy, just like using language and gestures. Such innovations can be easily adapted to any Enterprise Resource Planning (ERP) landscape.

Until recently, warehousing and inventory data processing were largely done manually. With applied AI technologies like smart eye tracking, facility managers and warehouse operators can perform routine warehousing operations like signing clearance documents and processing checklists completely hands-free.

Progressively, these AI innovations will lead organizations towards an Autonomous Enterprise to yield the best outcome based on factory conditions, market demands for product mix, forecasts, environmental conditions (IBM’s acquisition of the weather channel should give us something to think about), among others.

Opportunities Across Domains

Generative design using applied AI is another area that promises endless opportunities for companies to improve their bottom lines. As computers become more powerful and algorithms become cleverer, AI-based design applications are now capable of generating design solutions rather than simply evaluating designs proposed by humans.

Generative AI-based design applications can yield unprecedented results in automotive design and fields that are often marred by conflicting requirements. In fact, generative AI-based design can also transform plant design and maintenance.

Recently, a designer and Fulbright fellow, Stanislas Chaillou created a project at Harvard leveraging ML to explore the future of generative design and architectural styles. He used Generative Adversarial Neural Networks (GANs) to design model floor plans on the basis of several architectural styles and space mechanics.

Going forward, as AI research and innovation increase, the world will see use cases grow in variation and scale. According to the new IDC Spending Guide, the global spending on cognitive and AI systems will reach a whopping USD 77.6 billion in 2022. The growing acceptance of AI technology across sectors hints at new revenue opportunities for players.