Driving business value with responsible AI

By now, most people have interacted, often unknowingly, with artificial intelligence (AI) through increasingly ubiquitous chatbots or smart products and devices. Across industries, there is a host of far more intriguing applications of AI emerging in fields such as healthcare, manufacturing, insurance, and professional services. These use cases demonstrate that, far from replacing human intelligence, AI – when employed responsibly – is unleashing human expertise and creativity in ways that deliver tremendous value to the individual, the enterprise and even the community.

How much value? Gartner projects the global business value derived from AI will reach $3.9tn by 2022, through improved customer experience, new revenue and cost reduction. Gartner predicts that decision automation—harnessing unstructured data to make sense of ambiguity—will be a key driver of this trend, growing from 2% of AI-derived value in 2018 to 16% by 2022.

In the digital economy, data-led decision-making can be a strategic differentiator for enterprises to create value. AI can dramatically improve and accelerate this essential function by automating tasks that are complex or time-consuming for humans to perform independently—such as synthesizing large volumes of seemingly disconnected data to derive insights. The result is a human-machine collaboration that unlocks capabilities that would be impossible for either humans or machines to achieve alone.

Where human-machine collaboration takes a major leap forward

There are a growing number of examples of how AI is making an impact across almost every industry. Let’s look at a few.

Drug discovery offers tremendous opportunities for AI-enablement. With the rise of precision medicine, pharmaceutical R&D is increasingly focused on data-intensive research techniques. Using AI to process scientific repositories, clinical data, genomics data and population health information to unlock key insights can help researchers form a better understanding of therapeutic pathways. This accelerates time-to-market for drug discovery and launch, benefiting both the research organization and society.

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Machine learning, AI and other cognitive capabilities are also opening the doors to new levels of skills and learning opportunities. Online courses offered by academies use AI for automatic enrollments, assignment management, performance evaluation, and even customized learning programs, offering access to a much higher level of education to previously underserved segments of society.

In the financial sector, AI is increasingly used to safeguard against fraud and identity theft. By analyzing vast volumes of clients’ historical spend data, purchase patterns, and location, cognitive technologies can identify and trigger alerts for transactions that seem out of order.

As data scientists evolve complex algorithms to handle new or previously unknown threats and challenges, it will be up to enterprises to harness these technologies to their advantage – not only to drive business growth and realize new efficiencies but also to prevent crime and unethical practices.

From preventing unconscious bias to actively identifying drug trafficking and arms sales, these are not “what if” scenarios; they are actual uses of AI being leveraged in forward-looking organizations today.

Choosing where AI fits

Contrary to popular belief, this does not mean AI is a panacea for all business problems. The key to realizing the full value of human-machine synergy is applying AI to the right business challenges. For enterprises that are looking to make initial investments in AI, what are the keys to realizing short-term business value?

First, look to apply AI to business issues or processes that are data-intensive. This may seem obvious, but organizations find this daunting because they are often ill-prepared to analyze data-intensive problem sets.

Second, look for scenarios where high-quality data is available. In this case, the popular expression, “garbage in; garbage out” rings true. If clean, formatted data is not available or relevant to solving a specific business need, AI will fail to deliver the anticipated value.

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Finally, consider business problems that involve predictive patterns and predefined outcomes. With AI, companies can accelerate decision-making and response time for recurring scenarios by enabling a higher degree of automation across standard business processes.

And, while fully autonomous, AI-driven decisions may be appropriate for some business models (consider the example of Netflix or Pandora autonomously serving up content according to predictive personalization), higher-order business functions that involve human decision-making are where AI really comes into its own by expanding enterprise value creation.

Reshaping the enterprise

As more companies recognize and embrace the potential of responsible AI to magnify value creation, we will see a gradual reshaping of the enterprise itself. Companies that build their businesses on an AI-powered foundation will transform in much the same way the Internet and mobile computing transformed business models a decade ago.

In fact, we recommend businesses take a “machine first” approach to reshaping their entire business around digital technology. This implies not only eliminating unnecessary manual work so that employees can take on new, more interesting and imaginative roles but also using AI to make swift unprecedented changes to business models and key business processes—marketing and selling, product development, production and distribution, order fulfilment, talent management, and much more.

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For the workforce, responsible AI will create entirely new roles for people. In this new role, people will serve as “guardians of the outcomes” that machines help produce.

AI is no longer the future. It is the here and now, creating a new vision of machine-human collaboration and taking value creation to new heights. The companies that are responsibly building that AI-fueled foundation today will have a major advantage tomorrow.

Written by PR Krishnan, head of enterprise intelligent automation, Tata Consultancy Services

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