Analytics

Most companies still don’t understand big data and AI — and their potential?

18th May `18, 01:15 PM in Analytics

For today’s business, leveraging the power of big data isn’t a nice option — it’s a clear necessity….

Nefa Berberovic
Nefa Berberovic Contributor
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For today’s business, leveraging the power of big data isn’t a nice option — it’s a clear necessity. For nearly every industry, from communications to energy, architecture to real estate, the power of big data to provide intelligent insight can’t be overstated. The fine-grained detail and big picture are both visible at this level, both captured by careful analysis of huge datasets.

But make no mistake: huge isn’t an exaggeration. When a dataset reaches into billions of points of information, it simply exceeds human capacities. Even an expansive team of analysts can’t deal with data that large. That’s where cutting-edge artificial intelligence (AI) and machine learning come in. Driven by processing power that can handle datasets of that size, the most advanced AI can deliver the goods for 21st-century business.

For example, ride-sharing services like Uber need some way to manage demand and make sure that their customers don’t wait too long for a ride they’ve hailed. And by using machine learning to predict the details of demand, they’ve made massive gains in efficiency. For instance, Uber Eats, Uber’s food-delivery service, adopted AI to improve its customer satisfaction. As Danny Lang, the head of machine learning at Uber, explained, they moved from “a finite approach — where you compute the time using the distance between you and the restaurant, the average speed and the time to prepare the meal — to taking the delivery times for thousands and thousands of meals and basing the prediction on that. Overnight, that improved our estimates by 26 percent.”

The popularity of AI and big data stall as they expand

That kind of wizardry has been an easy sell to companies already occupying a high-tech niche. But as AI begins to mature, really coming into its own, its developers are finding it increasingly difficult to impress less tech-oriented companies and more conservative industries. As Joe McKendrick writes for Forbes, “To be sure, there is no shortage of excitement around the possibilities AI and machine learning bring to enterprises. But most organizations are still tepid about embracing these approaches in a big way.” In fact, in industries like real estate, the major players are holding off on AI until it can demonstrate clearer return on investment. “The real estate industry is conservative, technology-averse, and not prepared to take technology on board as a product,” cautions Paulo Scarpelini Neto, a real estate tech entrepreneur.

Indeed, a new Imprev Thought Leader Survey revealed that a solid majority of decision-makers in real estate were giving AI a pass, despite expressing interest in the power of big data. As they report, “Don’t hold your breath for widespread adoption of Artificial Intelligence (AI), Augmented Reality (AR) and Virtual Reality (VR) 3D tours in the next five years. Real estate execs expressed their doubts by giving these emerging technology [sic] lukewarm ratings. In fact, AI was ranked highest among the emerging technology that executives were ‘least likely’ to invest in, followed by AR and VR.”

That may put the breaks on the market penetration of AI, and with it, greater adoption of big data strategies in business. But we think that’s simply wrong.

But this is because AI is poorly understood

The power of big data and AI is too good to miss, and especially for conservative industries like real estate, architecture, and retail, big data offers revolutionary gains. Want to know more about your customers and their preferences, allowing you to personalise offers to them in real time? Big data is the answer. Need to design a building that can accomodate a long list of competing demands? Big data is the answer. Having trouble matching prospective buyers with properties or figuring out who’s really looking from the merely curious? Big data is the answer.

This isn’t hype — big data really can deliver.

But the problem McKendrick and Neto point to isn’t an issue with the tech or the data. Instead, because AI is so new and so high-tech, it’s poorly understood by most companies and their leaders. It’s understanding that’s at issue, not the utility of big data and AI.

In fact, as Kriti Sharma reports for Business Insider, “43% in the United States and 46% of respondents in the United Kingdom admitted that they have ‘no idea what AI is all about’”. That’s understandable — just a few years ago, this tech wasn’t available; it takes time to adjust and adapt. Moreover, as Michael Chui, James Manyika, and Mehdi Miremadi explain for McKinsey, “It is hard to reach a leading edge that’s always advancing.”

In plain English, the tech has outpaced understanding. And to make sense of it, business leaders “need to understand not just where AI can boost innovation, insight, and decision making; lead to revenue growth; and capture of [sic] efficiencies—but also where AI can’t yet provide value”, they insist. “What’s more, they must appreciate the relationship and distinctions between technical constraints and organizational ones, such as cultural barriers; a dearth of personnel capable of building business-ready, AI-powered applications; and the ‘last mile’ challenge of embedding AI in products and processes.”

AI explained, very briefly

Most people don’t get AI and big data. They don’t understand what it is and what it can do, nor do they have a sense of its limitations. Let’s go over those quickly.

What makes artificial intelligence and machine learning unique is that it learns to do something without being specifically programmed to do so. Using sophisticated neural networks that mimic the way human beings think and learn, AI uses carefully labelled data to teach itself. Consider facial recognition. In this application, a neural network might be shown millions of pictures of faces, each expressing a carefully identified emotion. By learning to associate the identified feelings with ‘maps’ of each face — the positions of the corners of the eyebrows, the distance between the edges of the mouth and the nose, etc. — a machine learning system actually…well…learns. After a while, it can look at unlabelled faces and have a very, very good sense of what that person is feeling.

It takes an enormous amount of data to get this process started, and there are technical challenges. Bias in the algorithms or the data can cause problems later, and sometimes, the AI learns to do what it does without us knowing exactly how, a problem known as the ‘black box’. And once it’s learned to recognise emotion, for instance, it can’t then automatically apply what it has learned to do something else with faces, like recognise gender. Its learning is often poorly generalisable, or in other words, limited to a very specific task.

But as AI advances, so too does its promise. And we predict that as businesses recognise the added value of big data — and the AI that manages it — they won’t be able to say no. And the undeniable benefits of AI are simply amazing, even in the most conservative industries.

AI in retail

These are real obstacles, fundamental challenges to the application of AI. But none of these are unworkable issues. And especially for routine tasks like tech support, in which the vast majority of calls relate to lost passwords, or customer service, in which the vast majority of tasks don’t demand the skills of a living, breathing human being, intelligent chatbots are revolutionising how we do business. For instance, Mai-Hanh Nguyen reports for Business Insider that “60% of US consumers have not completed an intended purchase based on poor customer service experience”. That’s a number that should terrify anyone who sells anything, not just the high-tech giants. But with intelligent chatbots that have learned to recognise emotional states and respond appropriately, your business can field top-notch customer service, 24 hours a day, 7 days a week. Take a look at IBM’s “Tone Analyzer”, for instance.

AI in real estate

Don’t underestimate the power of intelligent chatbots in real estate, either. Whether it’s fielding calls from clients, answering simple email queries, or organising showings, bots can free up valuable time for agents. And with emerging AI in the guise of smart speakers, some revolutionary changes are on the horizon for realty.

But where big data and AI really shine in real estate is in matching buyers to properties they’ll love. By using massive datasets, artificial intelligence can do some pretty amazing things. Already in 2016, simple bots demonstrated that they were better than human agents at predicting buyer preferences. With the more advanced machine learning and better data available now, AI offers realtors a ground-breaking new approach to marketing homes.

AI in architecture

Though traditionally slow to embrace change, the architecture industry will soon give AI more than a passing glance. Generative design is an exciting new approach in architecture. And by tasking AI with drawing up plans, and giving it a sense of what you need a building to do, it can run thousands upon thousands of designs to see which permutations give the best results. For instance, when Autodesk wanted to build a new headquarters, they realised that weighing competing design goals one against another at this scale was Herculean, so they handed the project over to AI. As Danil Nagy, a designer and senior research scientist for the company, explains, “The starting point for our use of generative design was trying to determine which aspects of the architectural design process were the most complex and difficult for humans to think through, and figuring out how to get a computer to work them out for us … It’s all about isolating those very tricky practical issues, and then using a computer to automate the development of solutions for those problems.”

Don’t listen to the naysayers

If anything’s clear, it’s that big data and AI are here to stay. They’re not just flashy tech and empty hype, and as these examples suggest, even industries reluctant to adopt new tech will soon happily embrace the awesome power they offer.

It’s understanding — not utility — that’s lacking.

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