Artificial intelligence (AI) is the new black, the shiny new being, the answer to all marketer's prayers, and the end of creativity. The recent emergence of artificial intelligence from the halls of mysterious and back-to-back data science academies was driven by stories of drones, robots, and driverless cars by tech giants such as Amazon. Google and Tesla. But the noise goes beyond everyday reality.

Amnesty International has a 50-year history of development in mathematics, computer science, experimentation and thought. It is not an overnight sensation. What makes it exciting is the convergence of large data sets, improved systems and software, faster and stronger processing capabilities, and a growing cadre of data scientists eager to exploit a wide range of applications. The everyday uses of artificial intelligence and machine learning will make a greater difference in consumers 'and brands' lives compared to cheerful applications in journalism.

So consider this reality AI check:

Big data is messy . We create data and link large data sets at extraordinary rates, which double every year. The growth of mobile media, social networks, applications, automated personal assistants, wearable devices, electronic medical records, cars and self-reporting devices, and the Internet of Things to Come (IoT) creates enormous opportunities and challenges. In most cases, there is significant and long work to align, normalize, fill and communicate various data long before starting any analysis.

Collecting, storing, filtering and linking these bits and bytes to anyone is difficult and intrusive. Gathering the so-called "golden record" requires great computing power, a strong platform, mysterious logic or deep learning to connect disparate pieces of data and protect appropriate privacy. It also requires great skill in modeling and a cadre of data scientists able to see the forest instead of the trees.

One to one is still ambitious. The dream of individual personal communication is on the horizon but still ambitious. Pulsating factors are the need to develop common protocols for resolving identity, protecting privacy, understanding individual feelings and permissions, identifying reflection points, and a detailed drawing of how individuals and individual consumers move across time and space on their journey from the need for brand preference.

Using AI, we are at an early stage of testing and learning led by companies in the financial services, communications and retail sectors.

People Award predictive analytics. We trained Amazon to anticipate personalized recommendations. We grew up online with the idea "If you like this, you might like it." As a result, we expect our preferred brands to know us and use the data we share responsibly, knowingly and without knowing, to make our lives easier, more comfortable and better. For consumers, predictive analytics works if the content is personal, useful, and valuable. Anything less than that is SPAM.

But making realistic, practical data-based forecasts is still more than science. Humans are creatures of habit with some expected patterns of interest and behavior. But we are not necessarily rational, often inconsistent, and quick to change our minds or change the course of our work and the characteristic in general. AI, using deep learning techniques where the algorithm trains itself, can go some way to understanding this data by monitoring procedures over time, aligning behaviors with observable standards and assessing anomalies.

Spread platform. It seems that every technology company is now in the AI ​​space making all kinds of claims. With over 3,500 Martech offers plus a myriad of legacy systems installed, it's no wonder that marketers are disorganized and IT men have been deprived. A recent Conductor survey revealed that 38 percent of marketers surveyed use 6-10 of Martech solutions and another 20 percent use 10-20 of solutions. Combining a coherent IT landscape in the service of marketing objectives, constraining the boundaries of legacy systems and existing software licenses while processing big data sets is not heartless. In some cases, AI needs to work around proven technology platforms.

Artificial intelligence is valuable and sophisticated. It is not a silver bullet. It requires a mixture of skilled data scientists and a powerful contemporary platform oriented from a customer-centric perspective and test-and-learn mindset. AI works this way, and it will provide consumers with much greater value than drones or robots.


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