Organizations need to transition from opportunistic and tactical AI decision-making to a more strategic orientation.

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SwissCognitive, AI, Artificial Intelligence, Bots, CDO, CIO, CI, Cognitive Computing, Deep Learning, IoT, Machine Learning, NLP, Robot, Virtual reality, learningAs the popularity of artificial intelligence waxes and wanes, it feels like we are at a peak. Hardly a day goes by without an organization announcing “a pivot toward AI” or an aspiration to “become AI-driven.” Banks and fintechs are using facial recognition to support know-your-customer guidelines; marketing companies are deploying unsupervised learning to capture new consumer insights; and retailers are experimenting with AI-fueled sentiment analysis, natural language processing, and gamification.

A close examination of the activities undertaken by these organizations reveals that AI is mainly being used for tactical rather than strategic purposes — in fact, finding a cohesive long-term AI strategic vision is rare. Even in well-funded companies, AI capabilities are mostly siloed or unevenly distributed.

Organizations need to transition from opportunistic and tactical AI decision-making to a more strategic orientation. We propose an AI strategy built upon three pillars.

1. AI needs a robust and reliable technology infrastructure.
Given AI’s popularity, it is easy to forget that it is not a self-contained technology. Without the support of well-functioning data and infrastructure, it is useless. Stripped of the marketing hype, artificial intelligence is little more than an amalgamation of mathematical, statistical, and computer science techniques that rely heavily on a stable infrastructure and usable data.

This infrastructure must include support for the entire data value chain — from data capture to cleaning, storage, governance, security, analysis, and dissemination of results — all in close to real time. It is not surprising, then, that the AI infrastructure market is expected to grow from $14.6 billion in 2019 to $50.6 billion by 2025.

A good infrastructure allows for the establishment of feedback loops, whereby successes and failures can be quickly flagged, analyzed, and acted upon. For instance, when Ticketmaster wanted to tackle the growing problem of opportunists — people who buy event tickets ahead of genuine customers, only to resell them at a premium — it turned to machine learning algorithms. The company created a system that incorporated real-time ticket sales data along with a holistic view of buyer activity to reward legitimate customers with a smoother process and block out resellers. As the company soon realized, resellers adapted their strategies and tools in response to the new system. Ticketmaster then modified its infrastructure to include feedback loops, allowing its algorithms to keep up with the resellers’ evolving techniques.

2. New business models will bring the largest AI benefits
AI has the potential to offer new sources of revenue and profit, either through massive improvements over the current way of doing things or by enabling new processes that were not previously possible. But incremental thinking about how AI can be used will most likely lead to modest results. Significant benefits are unlikely to be achieved without a new business model mindset, or a so-called intelligence transformation.

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AI allows for improvements that far surpass human capabilities. For example, OrangeShark, a Singapore-based digital marketing startup, uses machine learning for programmatic advertising, thus automating the process of media selection, ad placement, click-through monitoring and conversions, and even minor ad copy changes. Because of the efficiency offered by its system, OrangeShark is able to offer a pay-for-performance business model, whereby clients only pay a percentage of the difference between customer acquisition costs from a standard advertising model and the OrangeShark model. By completely automating a previously semi-automated task, the company has created a new business model that makes monetization of massive efficiency gains possible.

At the other end of the spectrum, Affectiva, which calls itself an “emotion measurement” company, houses the world’s largest image database of sentiment-analyzed human faces. The company analyzes and classifies a range of human emotions using deep learning models that can then be made available to clients. Some applications study emotional responses to ad campaigns, while others help people relearn emotional responses after a stroke. Affectiva has built a business model based on providing intelligence as a service in an area where nonhuman intervention was previously impractical.

These examples merely scratch the surface of possible AI-enabled business models. We will soon have smart cameras that facilitate franchising contracts and employee compensation schemes. Machine learning on granular data will allow for customization of products and services across time. As these and similar developments open up new sources of revenue and profit, new business models should therefore be considered as a foundation of any AI strategy. […]

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