From The Terminator to Blade Runner, pop culture has always leaned towards a chilling depiction of artificial intelligence (AI) and our future with AI at the helm. Recent headlines about Facebook panicking because their AI bots developed a language of their own have us hitting the alarm button once again. Should we really feel unsettled with an AI future?
News flash: that future is here. If you ask Siri, the helpful assistant who magically lives inside your phone, to read text messages and emails to you, find the nearest pizza place or call your mother for you, then you’ve made AI a part of your everyday life. Even current weather forecasting systems, spam filtering programs, and Google’s search engine – among so many other practical applications – are AI-powered. Now, artificial intelligence doesn’t seem that alarming, right?
What Is Artificial Intelligence?
AI refers to machine intelligence or a machine’s ability to replicate the cognitive functions of a human being. It has the ability to learn and solve problems. In computer science, these machines are aptly called “intelligent agents” or bots.
Not all AI are alike. In fact, what is considered artificial intelligence has shifted as the technology develops. Today, there are three recognized levels in the AI spectrum, all of which we can experience today.
Assisted intelligence – This refers to the automation of basic tasks. Examples include machines in assembly lines.
Augmented intelligence – There is a give and take with augmented intelligence. An AI learns from human input. We, in turn, can make more accurate decisions based on AI information. As Anand Rao of PricewaterhouseCoopers (PwC) Data & Analytics puts it: “There is symmetry with augmented intelligence.”
Autonomous intelligence – This is AI with humans out of the loop. Think self-driving cars and autonomous robots.
Deep Learning
It is actually just in recent years when a good number of scientists and innovators began to devote their work to artificial intelligence. Technology has finally caught up with faster and more powerful GPUs. Industry observers tack this resurgence to 2015, when fast and powerful parallel processing became accessible. This is also around the birth of the so-called Big Data movement, when it became possible to store and analyze infinite amounts of data.
Thus, we reach today, the era of Deep Learning. Deep learning pertains to the use of artificial neural networks (ANNs) in order to facilitate learning at multiple layers. It is a part of machine learning based on how data is presented, instead of task-based algorithms.
Deep learning has led the way in revolutionizing analytics and enabling practical applications of AI.
We see it in something as basic as automatic photo-tagging on Facebook, a process developed by Yann LeCun for the company in 2013. Blippar, on the other hand, has come out with an augmented reality application that employs deep learning in real-time object recognition in 2015.
You can look forward to driverless cars and so much more. In the same we, we can expect AI to be applied further in business, particularly in decision-making.
Artificial Intelligence in Business
According to Dr. John Kelly III, IBM Senior Vice President for Research and Solutions: “The success of cognitive computing will not be measured by Turing tests or a computer’s ability to mimic humans. It will be measured in more practical ways, like return on investment, new market opportunities, diseases cured and lives saved.”
Yes, AI technology isn’t the end but only a means towards effectiveness and efficiency, improved innovative capabilities, and better opportunities. And, we’ve seen this in several industries that have begun to adopt AI into their operations.
According to a survey by Tech Pro Research, up to 24 percent of businesses currently implement or plan on using artificial intelligence. Stand-outs are in the health, financial services and automotive sectors.
In financial services, PwC has put together massive amounts of data from the US Census Bureau, US financial data, and other public licensed sources to create $ecure, a large-scale model of 320 million US consumers’ financial decisions. The model is designed to help financial services companies map buyer personas, simulate “future selves” and anticipate customer behavior. It has enabled these financial services companies in validating real-time business decisions within seconds.
The automotive industry, on the other hand, has developed several AI applications, from vehicle design to marketing and sales decision-making support. For instance, artificial intelligence has led to the design of smarter (even driverless) cars, equipped with multiple sensors that learn and identify patterns. This is put to use through add-on safe-drive features that warn drivers of possible collisions and lane departures.
Like in the financial services sector, AI is used to develop a model of the automobile ecosystem. Here, you have bots that map the decisions made from automotive players, such as car buyers and manufacturers, and transportation services providers. This has helped companies predict the adoption of electric and driverless vehicles, and the implementation of non-restrictive pricing schemes that work on their target market. It has also helped them make better advertising decisions.
The key here is how artificial intelligence systems are able to run more than 200,000 GTM (go-to-market) scenarios, instead of just a typical handful. What you get is optimized scenarios that maximize revenues.
It’s a similar case in the fields of retail, marketing and sales. According to Adobe Marketing Cloud Product Manager, John Bates: “For retail companies that want to compete and differentiate their sales from competitors, retail is a hotbed of analytics and machine learning.” AI application development has provided marketers with new and more reliable tools in market forecasting, process automation and decision-making.
AI and Business Decisions
Prior to the resurgence of AI and its eventual commercial application, executives have had to rely on inconsistent and incomplete data. With artificial intelligence, they have data-based models and simulations to turn to.
According to PwC’s Rao, limitless outcome modeling is one of the breakthroughs in today’s AI systems. He reiterates: “There’s an immense opportunity to use AI in all kinds of decision making”
Today’s AI systems start from zero and feed on a regular diet of big data. This is augmented intelligence in action, which eventually provides executives with sophisticated models as basis for their decision-making.
There are several AI applications that enhance decision-making capacities. Here are some of them:
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Marketing Decision-Making with AI
There are many complexities to each marketing decision. One has to know and understand customer needs and desires, and align products to these needs and desires. Likewise, having a good grasp of changing consumer behavior is crucial to making the best marketing decisions, in the short- and long-run.
AI modeling and simulation techniques enable reliable insight into your buyer personas. These techniques can be used to predict consumer behavior. Through a Decision Support System, your artificial intelligence system is able to support decisions through real-time and up-to-date data gathering, forecasting, and trend analysis.
Customer Relationship Management (CRM)
Artificial intelligence within CRM systems enable its many automated functions, such as contact management, data recording and analyses and lead ranking. AI’s buyer persona modeling can also provide you with a prediction of a customer’s lifetime value. Sales and marketing teams can work more efficiently through these features.
Recommendation System
Recommendation systems were first implemented in music content sites. This has since been extended to different industries. The AI system learns a user’s content preferences and pushes content that fit those preferences. This can help you reduce bounce rate. Likewise, you can use the information learned by your AI to craft better-targeted content.
Expert System
Artificial intelligence has tried to replicate the knowledge and reasoning methodologies of experts through Expert System, a type of problem-solving software. Expert systems, such as MARKEX (for marketing), apply expert thinking processes to provided data. Output includes assessment and recommendations for your specific problem.
Automation Efficiency and AI
The automation efficiency lent by artificial intelligence to today’s business processes has gone beyond the assembly lines of the past. In several business functions, such as marketing and distribution, AI has been able to hasten processes and provide decision-makers with reliable insight.
In marketing, for instance, the automation of market segmentation and campaign management has enabled more efficient decision-making and quick action. You get invaluable insight on your customers, which can help you enhance your interactions with them. Marketing automation is one of the main features of a good CRM application.
Distribution automation with the help of AI has also been a key advantage of several retailers. Through AI-supported monitoring and control, retailers can accurately predict and respond to product demand.
An example is the online retail giant, Amazon. In 2012, it acquired Kiva Systems, which developed warehouse robots. Since its implementation, Kiva robots have been tasked with product monitoring and replenishment, and order fulfillment. They can even do the lifting for you. That’s a big jump in Amazon efficiency, compared to the time when humans had to do the grunt work.
Social Computing
Social computing helps marketing professionals understand the social dynamics and behaviors of a target market. Through AI, they can simulate, analyze and eventually predict consumer behavior. These AI applications can also be used to understand and data-mine online social media networks.
Opinion Mining
Opinion mining is a kind of data mining that searches the web for opinions and feelings. It is a way for marketers to know more about how their products are received by their target audience. Manual mining and analyses require long hours. AI has helped shorten this through reliable search and analyses functions.
This form of AI is often used by search engines, which regularly rank people’s interests in specific web pages, websites and products. These bots employ different algorithms to get to a target’s HITS and PageRank, among other online scoring systems. Here, hyperlink-based AI is employed, wherein bots seek out clusters of linked pages and see these as a group sharing a common interest.
The Future of Business Decision-Making With AI
With no Terminator or Replicant looming in the periphery, there really is no danger to artificial intelligence, only potential. Arguably, there shouldn’t even be the more practical scare of losing people’s jobs to machines. Experts say that AI can actually enhance people’s jobs and allows them to work more efficiently.
And surely, this rings true with respect to decision-making. When decision-makers and business executives have reliable data analyses, recommendations and follow-ups through artificial intelligence systems, they can make better choices for their business and employees. You don’t just enhance the work of individual team members. AI also improves the competitive standing of the business.
The gap lies in developing artificial intelligence systems that could deal with the enormous amount of data currently available. According to Gartner, a marketing research organization, today’s data is set to balloon to up to 800% by 2020. With this, you get about 80% of unstructured data, made up of images, emails, audio clips and the like. At this point, there is nothing – neither human nor artificial intelligence – that can sift through this amount of data, in order to make it usable for business.
According to IBM’s Dr. Kelly: ““This data represents the most abundant, valuable and complex raw material in the world. And until now, we have not had the means to mine it.” He believes that it is companies involved in genomics and oil that will find the means to mine this resource.
He delves further on the future of AI and analytics: “In the end, all technology revolutions are propelled not just by discovery, but also by business and societal need. We pursue these new possibilities not because we can, but because we must.”
