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AI 360: Accelerating AI in the enterprise

The race to the tipping point is on

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Outpacing change

In some respects, artificial intelligence (AI) is old news. In the consumer market, conversational AI – think of Siri or Alexa – allows you to ask machines to complete tasks for you and is commonplace in many homes.

In the enterprise, the story is a little different. As more people experience the benefits of AI in their personal lives, they expect the same in their business interactions too. But, while many business leaders understand the importance of AI and are keen to explore its possibilities, the consumer market is setting the standard, and enterprises risk disruption if they cannot do the same.

Photography is a prime example of the rate of technological change. Progress was slow and incremental over centuries, and then exponential and dramatic all at once. Many of the established companies that led the industry's advancements were not prepared for the tipping point of digital photography. It eliminated some of the traditional market leaders, proving that as consumer expectations continually evolve, even businesses at the front of the field can't afford to stand still.

When it comes to AI for the enterprise, a similar tipping point is looming. "Our prediction is that by 2025, companies that are AI leaders will be 10 times more efficient and will have twice the market share of companies that have not adopted AI," says Vikram Mahidhar, global business leader, AI solutions, at Genpact.

Some organizations are already making headway. AI-enabled businesses are using the technology to make informed decisions and predictions, spot risks and opportunities before their competitors, and anticipate customer needs to boost satisfaction, loyalty, and revenues. For businesses only just beginning to explore AI, developing and implementing their strategies at speed is essential.

As we look at how different industries are using AI, it's clear there are still significant barriers to overcome. However, there is hope for a new approach that allows businesses to realize the benefits of AI faster than ever before.

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"Our prediction is that by 2025, companies that are AI leaders will have twice the market share of companies that have not adopted AI."

Slow progress is no progress

For businesses to accelerate AI, it must be a core part of their overarching strategies. And it is for a growing number of companies, according to Genpact research. Earlier this year, as part of our AI 360 series, we launched a survey of more than 500 senior executives and 4,000 consumers and workers. The research revealed that within large companies, a quarter of senior executives say they plan to fundamentally reimagine their businesses with AI by the end of 2021 (compared to 14% in 2017). An additional 54% say they will use AI to transform processes (up from 41%).

Despite the increase in adoption, the benefits of AI are not being realized as quickly as expected. In our research, respondents identified a number of barriers to AI adoption. The most challenging was a lack of clarity on where to use AI effectively.

This means businesses are plowing ahead without a clear vision, and, when they hit a roadblock, their AI program stalls. And, even if they find ways to overcome the roadblocks in the short term, it's unlikely their AI strategy will follow a connected approach that unites the business in the long term.

Disrupt or be disrupted

Banking is a prime example of the need for accelerated AI. Our AI 360 research shows that 97% of banks have implemented some form of AI. But we're at a point where industries can no longer afford for AI to be on the fringes of an organization. It needs to be at its core – as the neural wiring, stitching the organization together. AI's potential to help banks grow revenue, manage risk, enhance customer experiences, drive innovation, and lower cost is too large to ignore.

It's not just banks that need to act. Every business is at risk of becoming an AI laggard as expectations for personalized, seamless user experiences carry over from the consumer to the business world. Enterprises must move from cumbersome legacy systems to more agile, digital technologies to meet their organizational goals and remain competitive.

As an example, AI gives consumer-goods companies an opportunity to get closer to their end customers. At present, retail giants control this relationship. If consumer-goods firms don't attempt to go direct to customers, profits are at risk. Using AI, they can take back control by interpreting buying signals, spotting trends, and nimbly adapting products and promotions to customer needs. The longer they take to explore AI, the longer retail giants will hold the upper hand and the more time competitors will have to close the gap first.

To truly realize digital transformation, AI must be a fast-tracked priority for every enterprise. So, without time on your side, how can you accelerate your AI strategy without compromising on quality?

Fast-track AI. Go modular.

For many established enterprises, accelerating AI can feel impossible. With so many legacy, complex, and often disparate systems and processes in place, where do you begin? The answer – stop getting overwhelmed by the big picture and start thinking about modularity.

Consider modular computers. You can separate interconnected components for repairs or upgrades to take full advantage of the latest developments. Built in a similar way, cloud applications have interchangeable building blocks that businesses can optimize separately. This architecture lets a company scale and refine its applications at speed – not to mention boost innovation.

"Stop getting overwhelmed by the big picture and start thinking about modularity."

Sadly, this approach cannot be taken with AI. Until now. With prior training, pre-programmed solutions can plug and play into a company's core business processes – such as assessing a mortgage application or managing invoice exceptions – to improve experiences, accuracy, and efficiency at previously impossible speed. This eliminates the need for customized machine learning and iterations, shortening the path to digital transformation.

Context is king for true transformation

But, let's not forget, you must always consider AI in the context of your industry. Paul Roma is the former CEO and current chair of the life sciences advisory council at Ciox Health, the US healthcare market leader for clinical information exchange. Having led a major digital transformation strategy at the company, Roma says, "80% of the value of machine learning comes at the industry level, only 20% comes from the science itself. The heart of the matter is how to apply it to the industry, to the domain, and augment how humans are making decisions."

To Roma's point, an AI solution that monitors risk in the pharmaceutical industry is different from an AI solution that monitors risk in a lending portfolio. Both can use the same core AI platform but are trained with different datasets, semantics, and processes. Only when combined with the relevant context that people with deep industry and process knowledge bring can AI applications deliver value.

The path to game-changing results

It's this combination of accelerated AI and industry expertise that will separate winners from losers. “Leaders will come down the AI path faster and solve the problems that laggards are trying to solve," says Genpact chief digital officer, Sanjay Srivastava. "If you can adopt an accelerated approach to AI with pretrained solutions, then you will see the benefits and keep yourself ahead of the marketplace, rather than being left behind."

Forward-thinking organizations are already seeing the impact of pretrained AI solutions backed by industry expertise. In the insurance industry, thanks to pretrained AI, a US company can now process hundreds of thousands of claims each week, which speeds up turnaround time to days from weeks, while improving decision accuracy. And, given the scale of fraud against the industry (estimated to be more than $30 billion every year in the US alone), AI can have a big impact on insurers' profitability and reduce customer churn from increased premiums. By analyzing data from a wide range of sources, including previous claims, customer information, and social media, AI can build predictive models to identify, score, and prioritize possible cases of fraud.

While AI adoption in the enterprise is currently a marathon, adoption in the consumer market is a sprint. Fortunately, using pretrained AI solutions, there's an opportunity to catch up. It's an accelerated approach to AI, relying on pretrained solutions backed by industry expertise, that will enable the most forward-thinking enterprises to move faster with AI and close the gap.

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