The Building Blocks of an AI Strategy
Organizations need to transition from opportunistic and tactical AI decision-making to a more strategic orientation.
The AI & Machine Learning Imperative
Brought to you byAWS
As 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.
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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.