How Artificial Intelligence can deliver Real Value to Customers
Early adopters who have invested billions into AI are now beginning to reap a variety of benefits; companies new to the space can learn a great deal from them in regards to adoption strategy, integration, etc. At Jinigram, we offer our carefully designed "Max Impact" framework to help you navigate the AI landscape, and to see where your business can best benefit from AI.
After decades of Hollywood extravagance, promise and frustration, artificial intelligence (AI) is finally beginning to deliver tangible benefits to early-adopting businesses. Retailers on the digital cutting-edge rely on AI-powered bots to manage their warehouses—and even to order stock automatically when inventory levels are exhausted. Automakers utilize the technology in self-driving cars while Utilities use AI to calculate the demand for electricity.
A convergence of improvements is driving this sea change in AI development. Computer power is growing, algorithms and AI models are becoming more sophisticated, and, most important of all, the world is producing once-unimaginable volumes of the fuel that powers AI—data. Billions of gigabytes every day, collected by networked devices ranging from web browsers to turbine sensors.
The entrepreneurial activity unleashed by these developments attracted three times as much investment in 2016—between $26 billion and $39 billion—as it did in 2013. The significant part of the interest in AI comprises of internal R&D spending by large, money-rich digital-native organizations like Amazon, Baidu, and Google.
For the majority of that investment, much of the AI adoption outside of the tech space is at an early, preliminary stage. Barely any organizations have deployed it at scale. A survey of over 3,000 AI-aware organizations around the globe found that early AI adopters tend to be companies who rigorously plan for innovation. They were found to have the following attitudes towards AI; they deploy AI across innovative tech groups, utilize AI in the core part of their value chain, implement AI to increase revenue stream and also diminish costs, and have full support from the senior leadership. Organizations that have not yet adopted AI technology at scale, or in an integral part of their business, are uncertain of a business case for AI or of the returns they can expect on investing in AI.
In any case, early evidence proposes that there is an amazing business case, and that AI can deliver a genuine incentive to organizations willing to utilize it across functions and within internal departments. Early AI adopters that combine powerful digital capability with proactive strategies have higher profit margins and expect the performance gap with different firms to widen in the following three years.
This adoption pattern is widening the gap between innovative early adopters and not-so-tech-savvy companies. Sectors such as high tech and telecoms or financial services are likewise driving AI adoption and have the most ambitious plans for innovating using AI. These pioneers use multiple technologies across functions or deploy AI at the center of their business. Automakers, for example, use AI to improve their operations as well as develop self-driving vehicles, while financial-services companies use it in customer-experience functions. As these firms expand AI adoption and acquire more data, stragglers will find it harder to catch up.
Governments should also keep up with tech advancements; by shaping regulations that help to promote fair business practices without inhibiting innovation, and proactively highlight the occupations that are most likely to be automated and ensure that retraining programs will be provided to people whose jobs may be succeeded by AI-powered automation. These individuals must acquire the skills that work with, not against, machines. These skills include the significant role of knowledgeable employees in training AI systems on the boundaries of their role and everything that it entails. This "knowledge-dump" to the AI engine will help individually trained systems to link together to complete various tasks in tandem with other functions of the business it’s learning from.
Organizations are already planning for innovation in AI, but not everyone sees the immediate advantage of investment. Companies based in the United States received 66 percent of all external investments into AI companies in 2016; China was second, at 17 percent, and is growing fast. The two nations have built AI "ecosystems"— groups of entrepreneurs, investors, and AI adopters—and have issued national strategic plans in the past year-and-a-half with critical AI steps, occasionally backed up by billions of dollars of AI-funding ambitions. South Korea and the United Kingdom have begun similar crucial plans. Other nations that want to become key players in AI would be wise to imitate these changemakers.
Significant gains from AI-implementation are achievable for many organizations; this implies accelerating the journey towards digital-transformation. AI wouldn't allow organizations to miss the opportunity of getting the basics right. They should get the proper digital assets and skills in place to have the capacity to organize for AI innovation efficiently.