Although artificial intelligence (AI) may seem a subject only discussed in recent years, the term had been first introduced in 1956 by Nathaniël Rochester, designer of the IBM 701 and Claude Shannon, founder of information theory.
Throughout the decades, several advancements were made that fuelled further investments in Ai research such as the Eliza computer program and General Problem Solver Program.
The downside to these programs was that they assumed that human intelligence could be formalized in “if-then” statements. The same was case with IBM’s deep blue chess program, that although was able to beat the world champion Gary Kasparov, could not perform seemingly simple tasks such as distinguishing an apple from a car.
A true AI on the other hand can interpret external data correctly, learn from the data and use that information to achieve specific goals and task through flexible adaptation.
The Turing Test
Over the years, it became apparent that it would take much longer for a computer program to pass the Turing test, which deems a machine intelligent when a human is unable to distinguish interactions between another human and a machine.
Moreover, the lack of computing power at the time stagnated research in other statistical methods aimed at achieving true AI, like research into artificial neural networks, which aims to replicate the process of neurons in the human brain.
AI as part of Digital Transformation
Today, however, artificial neural networks and Deep Learning form the basis of most applications we recognize as AI. Image recognition used by Facebook, smart speakers and self-driving cars are all the result of advancements made in the last few decades.
Recent technological advancements have reignited public interest in AI and many expect it to transform our lives and businesses in unprecedented ways. However, the topic of AI is still surrounded with a lot mystery and often mixed with unrealistic expectations and claims.
This leaves businesses unsure as to how to implement and use AI in their current and future operations and what place it has within the realm of digital transformation.
In this article we want to offer businesses a realistic view on AI and mention the prerequisites needed to successfully implement AI and conclude with an actual real-world example of how AI can make a difference in your business, today.
Things to consider before implementing AI
The first key point is to remember that the application of AI is usually not used in isolation but is rather one of the components of a business’s digital transformation strategy, consisting of several other complementary technologies alongside exiting IT.
A telecom company, for example, can use advanced data analytics in combination with IoT and AI to automate and improve customer satisfaction and service interactions to reduce costs and increase revenue.
Moreover, just like any other technological innovation, successful AI implementation not only depends on the technological skills and knowledge, but also on organizational capabilities and characteristics such as business agility, leadership, investment, patience, partners, culture, and employee engagement.
Another factor to AI success is the availability, amount, and access to accurate and timely data. Data forms the foundation of AI and is required for machine learning techniques to generate value in the form of usable insights.
It is also important to start your AI project in a small scale first and integrate it in your current business and processes. By testing and learning from the process, it can then be applied in a wider scale. What also separates successful businesses using AI is the way in which they handle their core business processes in terms of data management.
In other words, the degree to which they have digitized their operations and allowed it to connect and integrate enterprise wide to capture value and generate knowledge. As knowledge on AI is scarce, we also encourage businesses to think about whom to partner with in support of their AI or digital transformation success.
A few AI use cases
The adoption, benefits and use cases of AI are not limited to a particular industry, but will be widespread across all of the traditional industrial sectors- energy, transportation, telecommunications, healthcare, financial services, manufacturing, mining, logistics, construction, government and all the other underlying sub industries.
With the vast amounts of data available to telecom companies; the use and value of image detection, natural language processing and video analytics are starting to become apparent. For example, image and video detection are used to analyze when and what type of video is being streamed.
This data can then be used to deliver customer notifications when a favorite show, episode or team is playing. Information on engagement can then also be tracked and sold back to advertisers to adjust, optimize, or deliver new content.
In today’s service economy, customer expectations are higher than ever before. To meet those expectations, organizations need to be flexible and have rapid response times and ensure first time fixes.
In order to ensure optimal service delivery and customer satisfaction, service companies can use AI for predictive maintenance & service, field service optimization and planning & scheduling.
At Eqeep, we’ve been thinking about AI for a long time, and instead of fueling the hype concerning AI and creating unrealistic expectations, we offer real and practical solutions to make companies better and more efficient, today.
Eqeep as your partner in Digital Transformation
We help companies increase their competitive advantage, by analyzing, organizing, and simplifying their value chain through digital transformation. With our customer and solution focused approach we enable our customers to achieve their business goals. Contact us to explore how our AI capabilities can benefit your operations.