AI-Powered Commerce E-Book

Andy Pandharikar

• Herausgeber: Packt Publishing
• Kategorie: Wissenschaft und neue Technologien
• Sprache: Englisch

Seitenzahl: 347

Veröffentlichungsjahr: 2022

Beschreibung

Commerce.AI is a suite of artificial intelligence (AI) tools, trained on over a trillion data points, to help businesses build next-gen products and services. If you want to be the best business on the block, using AI is a must.
Developers and analysts working with AI will be able to put their knowledge to work with this practical guide. You'll begin by learning the core themes of new product and service innovation, including how to identify market opportunities, come up with ideas, and predict trends. With plenty of use cases as reference, you'll learn how to apply AI for innovation, both programmatically and with Commerce.AI. You'll also find out how to analyze product and service data with tools such as GPT-J, Python pandas, Prophet, and TextBlob. As you progress, you'll explore the evolution of commerce in AI, including how top businesses today are using AI. You'll learn how Commerce.AI merges machine learning, product expertise, and big data to help businesses make more accurate decisions. Finally, you'll use the Commerce.AI suite for product ideation and analyzing market trends.
By the end of this artificial intelligence book, you'll be able to strategize new product opportunities by using AI, and also have an understanding of how to use Commerce.AI for product ideation, trend analysis, and predictions.

Leseprobe

AI-Powered Commerce

Building the products and services of the future with Commerce.AI

Andy Pandharikar

Frederik Bussler

BIRMINGHAM—MUMBAI

AI-Powered Commerce

Copyright © 2021 Packt Publishing

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To our hard-working team, innovative customers, and supportive investors.

– Andy Pandharikar

To the reader, may you have as much fun reading this as I had writing it.

– Frederik Bussler

Contributors

About the authors

Andy Pandharikar is the CEO and founder of Commerce.AI. His previous start-up was acquired by the Flipkart group, which later was acquired by Walmart for $16B. Prior to that, Andy held various product and engineering positions at Cisco. He has an M.S. in management science and engineering from Stanford University and has an executive degree from Harvard Business School.

Frederik Bussler is a consultant and analyst, with experience across innovative AI platforms such as Commerce.AI, Obviously.AI, and Apteo, as well as investment offices such as Supercap Digital, Maven 11 Capital, and Invictus Capital. He has featured in Forbes, Yahoo, and other outlets, and has presented for audiences including IBM and Nikkei.

About the reviewer

Joey Bertschler is a brand and marketing expert who has worked with some of the world's most influential companies and thought leaders. His work is featured in Forbes, Entrepreneur, Hacker Noon, and many other outlets.

He has shared his journey in front of thousands at leading events such as Step Conference Dubai. Mr. Bertschler has worked internationally, from AI firms operating in Japan and India to pioneering sustainability ventures in Nigeria.

Table of Contents

Preface

Section 1:Benefits of AI-Powered Commerce

Chapter 1: Improving Market Opportunity Identification

Identifying market opportunities the traditional way

Big data challenges in market opportunity identification

Using AI for market opportunity identification

Exploring AI-powered market reports

Summary

Chapter 2: Creating Product Ideas

Understanding the pillars of AI

Language understanding

Visual understanding

Information extraction

Information organization

Creative AI

Why is product ideation so hard?

Using Commerce.AI for creative AI

Building product ideas

Selecting product ideas

Iterating product ideas

Summary

Chapter 3: Understanding How to Predict Industry-Wide Trends Using Big Data

Technical requirements

Why traditional forecasts fail

It's impossible to know everything about all products and services

Knowing how products perform today is not a good guide for predicting how they will perform tomorrow

The behaviors of many products are highly correlated and can be difficult to disentangle

Traditional models get overwhelmed by today's big data

The data itself keeps changing

Using big data to enable better forecasts

Understanding deep learning

Learning from examples

Demand forecasting with a practical example

Sentiment forecasting with a practical example

Gaining value from data-driven forecasts

Summary

Section 2:How Top Brands Use Artificial Intelligence

Chapter 4: Applying AI for Innovation – Luxury Goods Deep Dive

Technical requirements

Understanding the challenges of luxury brands

Brand management

Increasing competition

Social media management

Matching eccentric customer preferences

Understanding unique customer profiles

Understanding the data extraction process

Tumi uses AI for better marketing

Burberry uses AI to improve its clothes

Algorithmic couture

AI runways

RefaceAI

Zalando

Using Commerce.AI for luxury brands

Design and user research

Product development and marketing

Brand management

Trend analysis

Summary

Chapter 5: Applying AI for Innovation – Wireless Networking Deep Dive

Technical requirements

Understanding the challenges of wireless networking brands

Growth in traffic

Performance challenges

Increasing complexity

Sustainability

Becoming data-driven

5G

Analyzing product data for wireless networking brands

Analyzing wireless networking product review data

Using Commerce.AI for wireless networking brands

Enter data-driven solutions

Star ratings

Improving best sellers ranking

Time compiling weekly reports

Improving product sentiment

Improving product conversion

Search result ranking

Detail page glance views

Summary

Chapter 6: Applying AI for Innovation –Consumer Electronics Deep Dive

Understanding the challenges faced by consumer electronics brands

The needs of the connected consumer

A new reality of short-term attention span

Meeting the demands of the content consumer

The need to become data-driven

Emerging consumer electronics markets

Analyzing product data for consumer electronics brands

Key considerations in the data-driven product strategy

How to collect consumer data

How to integrate data into the product design

Using Commerce.AI for consumer electronics brands

Understanding product positioning

Analyzing the market with consumer electronics AI reports

How does Commerce.AI help with consumer electronics brand research?

Generating consumer electronics product ideas

Extracting insights from Shopify

Sharing insights on Slack

Summary

Chapter 7: Applying AI for Innovation – Restaurants Deep Dive

Understanding the challenges of restaurants

Profitability

Changing guest preferences

Creating profitable menus (and pricing)

Menu engineering

Maintaining online reviews and social media marketing

Analyzing product data for restaurants

Predicting how food items are likely to perform

Predicting how competitors will perform

Predicting customer needs based on previous purchases

New profile discovery

Using Commerce.AI for restaurants

Analyzing restaurant customer data

Mobile surveys

Gauging customer sentiment response based on marketing campaigns

Stay connected with your customers

Finding and predicting trends in the restaurant business

A case study – how a large French pizza chain used Commerce.AI

Summary

Chapter 8: Applying AI for Innovation – Consumer Goods Deep Dive

Technical requirements

Understanding the challenges facing consumer goods brands

Competitive consumer goods

Consumer goods market intelligence

Inventory management

Creating the right product mix

Creating consumer goods content at scale

Consumer goods review analysis

Analyzing product data for consumer goods brands

Consumer goods content generation

Analyze consumer goods reviews

Lead time analysis

Demand forecasting

Maintaining adequate cash flow

Analyzing the impact of discounts

Identifying seasonal trends

Social media analytics

Using Commerce.AI for consumer goods brands

Measuring product attributes and trends

Predicting revenue opportunity

Analyzing user personas and customer segments

Analyzing the customer journey

Generating consumer goods product ideas

Summary

Section 3:How to Use Commerce.AI for Product Ideation, Trend Analysis, and Predictions

Chapter 9: Delivering Insights with Product AI

Commerce.AI for product concept and development

Market research

Understanding demand

Product ideation

Product launch

How AI is changing product launches

Predicting demand from early signals

AI for the two types of product launches

Using AI for product launches—advantages and disadvantages

Product management

Tracking product wishes

Brand management

Using AI for consumer insights

Using AI for product tracking

Marketing and merchandising

Customer support

Summary

Chapter 10: Delivering Insights with Service AI

Empowering your front line

Better understanding customer affinities

Better understanding purchase reasons

Better understanding customer challenges

Turning your next interactions into great brand experiences

Managing your locations

Optimizing your branch

Optimizing your employees

Optimizing your service

Enhancing service offerings

Identifying growth areas

Leveraging AI for creating stronger service offerings

Identifying opportunities to boost customer loyalty

Finding new uses for your store

Getting a picture of bottlenecks before they escalate

Summary

Chapter 11: Delivering Insights with Market AI

Analyzing trends and white space discovery

Improving product idea generation with white spaces

The virtualization of everything (VE)

Augmented reality

E-commerce

The rise of social commerce

The rise of influencer marketing

The gamification of everything

The rise of the mass affluent

The rise of authenticity

Gen Z

Demand for sustainable products

Connecting market shifts to brands, products, and services

Gauging product shifts

Recognizing product risk areas

Product risk management with AI

Understand market DNA

Finding market DNA attributes

Finding user wishlists and emerging needs with AI

AI and consumer-generated content

Finding new use contexts with AI

Summary

Chapter 12: Delivering Insights with Voice Surveys

Engaging your customers with ease

Product feature prioritization

New service offering

Post-purchase survey

Hotel experience survey

Store experience survey

Post-call survey

Pricing survey

Improving your offerings

Deriving insights into your existing products

How to leverage survey feedback to understand your customers

How to act upon those insights

Coming up with new product ideas

Improving customer loyalty

What drives customer loyalty?

Summary

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Section 1:Benefits of AI-Powered Commerce

In the first section of this book, you will learn the benefits associated with AI-powered commerce and understand how to use AI systems for your own business.

This section comprises the following chapters:

Chapter 1, Improving Market Opportunity IdentificationChapter 2, Creating Product IdeasChapter 3, Understanding How to Predict Industry-Wide Trends Using Big Data

Chapter 1: Improving Market Opportunity Identification

Just a few years ago, every conversation about artificial intelligence (AI) seemed to end with an apocalyptic prediction. In 2014, Elon Musk said that, with AI, we are summoning the demon, while Stephen Hawking said that AI could spell the end of the human race. More recently, however, things have begun to change. AI has gone from being a scary black box to something people can use for a variety of use cases.

This shift is because these technologies are finally being explored at scale, including by product teams for market opportunity identification. AI hasn't always been used in the industry. In fact, it started out as a scientific curiosity. In the 1950s, computer scientist John McCarthy wanted to see whether it was possible to build machines that could learn how to do tasks such as play chess themselves. Today, AI is everywhere.

In this chapter, we'll explore how market opportunity identification can be improved with big data and AI, covering the following topics:

Identifying market opportunities the traditional wayBig data challenges in market opportunity identificationUsing AI for market opportunity identificationExploring AI-powered market reports

Market opportunity identification is important for a product team to identify an unmet need. It helps them find out how their product will stand out in the market and what they need to do in order to grow. They need to identify the competitive landscape, define the market opportunity, and use this to create a value proposition.

Furthermore, market opportunity identification sets the groundwork for later chapters, including creating product ideas and predicting future market trends.

Identifying market opportunities the traditional way

When it comes to commerce, venturing blindly into the unknown is a recipe for failure. Belief in a market opportunity is not enough – there needs to be hard data. Market opportunity identification is the process of acquiring and analyzing data, from any source, to understand the potential size of a market and the potential share of that market that you can capture.

Broadly speaking, the market should be considered in three segments:

External: External markets are those that are already established and they may be served by other companies.Internal: Internal markets are those that exist within the company but they may not be recognized as such yet.Potential: Potential markets are those that have not been identified yet.

Traditional market opportunity identification can be done in a number of ways:

Firstly, it can be done by surveying the consumers. Market surveys are one of the most effective ways to find out what customers want and how much they are willing to spend for it. This not only gives a company an idea of what to produce but also helps figure out how much money they will make from their products.Secondly, market opportunity identification can be done through brainstorming techniques such as the 5 Whys technique. The 5 Whys technique is simple and can be used as a brainstorming exercise by asking why? five times in response to a topic, problem, or issue.Thirdly, market opportunity identification can be done by analyzing internal data such as surveys and interviews that have already been done before. Often, large companies will have a lot of unorganized, unstructured information that is divided across departments and projects, from Google Surveys to JotForm to SurveyMonkey. When this data is analyzed, they may find opportunities they weren't aware of before.Finally, traditional market opportunity identification can be done by examining external data and data from social media platforms or competitors in the same industry. This includes sites such as LinkedIn or Facebook where both companies' and individuals' data can prove valuable.

One tool that is becoming very popular for market research is Google Trends, which allows people to examine the search volume in a particular area of interest over a certain period of time. For example, if you were interested in finding out about the popularity of rooftop gardens in Los Angeles, you could type into Google Trends Los Angeles Rooftop Gardens, and analyze search trends over time. Additionally, Google Trends lets you easily export this data for further analysis, whether it's to create visualizations or merge with additional data.

Once potential market opportunities have been identified, then the company should consider the decision to either pursue or ignore them. If the company chooses to pursue an external, internal, or potential market, then it needs to consider which approach is best.

For example, if an internal market is being considered for pursuit, it may be best for the company to create an incentive and offer it to its employees in order to get them involved with the new product.

If an external market is being considered for pursuit, it may be best to invest in advertising campaigns to make customers aware of the new product.

If a potential market is being considered for pursuit, it may be best for the company to invest in research and development in order to create a product that will appeal to this new market segment.

There is also a difference between customer-driven identification and opportunity-driven
identification. Customer-driven identification is where an organization determines a marketing need based on what customers want or need, whereas opportunity-driven identification is where an organization identifies areas of potential value based on strengths and weaknesses.

For example, if an organization is strong in manufacturing but weak in marketing, then opportunities for enhancement may be found in the marketing area that would not have been found had they done customer-driven identification.

While there are a number of benefits to traditional market opportunity identification, these methods fall short in the modern world, which is faced with big data challenges.

Big data challenges in market opportunity identification

Big data has become a buzzword in the product community.

Big data involves the speed at which data is generated, the amount of data that's generated, the types of questions that can be answered with it, and the number of sources it's coming from. In short, big data is about more than just size.

Big data describes the veritable explosion of data we're seeing from billions of people accessing the internet.

Product teams want to use big data to identify new market opportunities and new ways to target their customers. Yet, many companies are struggling with how to collect and analyze big data, particularly when the data is scattered across different sources.

Business executives are asking questions such as: How can we get useful information from all of this disparate data? How can we make better strategic decisions with our big data? How can we address the challenges of storing, organizing, and managing big data? And how can we increase the value of our big data?

Commonly, there are a few major challenges associated with managing big data for market opportunity identification:

The first challenge is that it's difficult to find relevant predictive patterns in a sea of unrelated variables. For example, using traditional business intelligence (BI) techniques such as data munging and manual data mining, it might take weeks or even months to discover a predictive pattern in a large database of customer survey results. Imagine that your dataset includes demographics, firmographics, psychographics, purchase data, reviews, and more. That's a lot of information to look through, and a lot of variables that might not seem relevant at first glance.A second challenge is that traditional BI tools are not designed for efficient discovery of predictive patterns when analyzing big data, which is increasing in volume at a faster pace than traditional databases are being updated. Not only does this make it difficult to keep up with the latest insights, but it also becomes more costly and time-consuming to build insights into existing systems.Another major challenge with big data is simply getting it in the first place.

Large companies such as Google and Amazon have access to tremendous amounts of computing power and virtually unlimited storage thanks to their substantial investments in hardware and software services, but for smaller organizations – even those that are eager to harness the potential of big data – the story is different.

Data is coming in faster than ever before. The explosion of data being generated has outpaced the capabilities of traditional database systems to keep up with growth, but setting up and maintaining systems such as AWS or GCP requires dedicated engineers, given the requirements for technical know-how on scaling, security, data pipelines, and more.

Therefore, the problem is often that businesses lack the necessary resources that would allow them to collect, aggregate, analyze, and interpret such volumes of information without investing large sums of money in server clusters or other specialized infrastructure.

With AI, companies can solve these challenges more easily, and analyze big data to improve market opportunity identification.

Using AI for market opportunity identification

The most important thing to remember about market opportunity identification with AI is that it is not about creating a magic wand that will instantly identify all of the major new market opportunities. That would be a data scientist's or software developer's dream. But in the world of marketing, where success depends on being fast, efficient, and smart, those are the characteristics of an outright nightmare.

Marketing and product development professionals are constantly under pressure to deliver new products and services to market – and be first to market with them. They need to do so in a cost-effective manner, without sacrificing quality, and often with limited resources.

AI offers speed, efficiency, and smartness. With models deployed on scalable servers, AI can scan a huge amount of data and identify patterns that humans wouldn't see. This means that millions of data points can be analyzed within hours or even minutes.

AI is being used in a number of verticals, such as autonomous vehicles, facial recognition, and fraud detection.

By using AI for market opportunity identification, marketers can free themselves from the burdens of manually reviewing and analyzing information on countless possible markets or new product categories. They can offload tasks such as data collection and the creation of new product ideas to machines.

The best way to use AI for market opportunity identification is by focusing on real problems that need real solutions. For example, marketers may want to analyze their existing customer base for potential new product categories they could enter into – or specific products they could create for those existing customers.

Another example is using AI to identify new geographic markets for existing brands – or even entirely new brands – by looking at various combinations of customer demographics, buying habits, lifestyle choices, and other criteria in each region of the world.

In either case, AI can assist marketers by helping them use data already in their possession more effectively than they could otherwise (by automating some data collection tasks and streamlining others). It can also help overcome some key barriers, including assessing whether any given potential market or product category is truly scalable.

An additional benefit of using AI for market opportunity identification is that you can also use it as a tool for demonstrating your commitment to innovation and differentiating your brand(s) from competitors' offerings – which can also boost your brand awareness, reputation, and overall value.

As we've explored, AI has a number of broad benefits for product teams. Let's dive into a specific use case: AI-powered market reports.

Exploring AI-powered market reports

AI has transformed machine learning, a branch of artificial intelligence that automates the identification of patterns in data. The big data analytics industry is abuzz with the possibilities of AI-generated market reports (www.commerce.ai/reports) in identifying opportunities and trends.

An AI engine can be used for a variety of purposes, including generating market reports to identify potential opportunities for improvement in marketing campaigns. The technology is ripe for marketing professionals to take advantage of, making it easier than ever to generate local or global reports using existing data.

Here are some of the benefits of using AI-generated market reports:

Automation: Automating repetitive tasks such as generating reports can help marketing professionals save valuable time and energy.Cost-effectiveness: Generating report templates could cost less, with AI used in the background to quickly adapt the template for local or global needs.Overall quality improvement: By using AI, marketers could have more accurate, precise, and complete information to use in their campaign planning efforts.Data tracking: Using machine learning algorithms to analyze large amounts of data could help marketers gain valuable insights into consumer preferences and buying trends.

Highlighting these benefits is not meant to imply that there are no drawbacks to using AI-generated market reports. Instead, it is meant to highlight several key benefits that can help you make a final decision about whether or not you should use this technology in your business operations moving forward.

With a new AI-generated market reports feature, Commerce.AI now delivers high-quality insights straight to the public.

Previously available exclusively to those with access to Commerce.AI's data engine, market reports are now available to all.

Market reports analysis is based on consumer feedback and offers valuable insight into how people consume products and services. With the press of a button or a simple search, anyone can now access market reports across 10,000 categories.

Commerce.AI's AI-generated reports are a concrete way to find your next product idea or seek a new customer base:

Figure 1.1 – A sample of Commerce.AI's AI-generated market reports

For example, searching for gaming keyboards will show us a high opportunity score, as this niche market category has grown tremendously during pandemic-fueled lockdowns. As Commerce.AI continues to grow, the quality of its insights will only improve and become more accurate as more product data related to new trends in the marketplace is analyzed.

Commerce AI's data engine has delivered over 140 million dollars in revenue through insights across market categories. The market reports are an incredible resource built around billions of unstructured product data points, from sources such as forums and blogs, surveys, videos, customer support tickets, CRMs, and more. They provide a glimpse into how people consume products and services, with the potential to help you find your next big idea.

When it comes to innovation and product development, market research is invaluable. It provides insights into how people consume products and services, which can be used to inform the next idea that will take on the world. Without using consumer feedback as a starting point for conceptualizing original ideas, you're limiting possible endeavors.

Commerce.AI's market research reports include a summary for each category, the fastest-growing brands, the bestseller, top-rated products, the number of products and reviews, and more. All this data is consolidated into a single value – the opportunity meter – showing the size of the market opportunity at hand.

AI calculates this by taking into account the average market size, the number of competitors in each category, and the potential for growth, creating a line of best fit between the data points:

Figure 1.2 – A sample AI-generated market report

Traditional market research is expensive, inaccessible, and confusing. Commerce.AI's new AI-generated market reports feature solves these problems as well as the issue of a limited time frame in exploring market opportunities.

Market reports are available without having to pay an exorbitant fee for access to data-driven consumer insights.

Commerce.AI's system relies on a variety of different types of product data points, including unstructured data, which makes up 95% of the web. These sources include Amazon, Walmart, Target, and other commerce sources that are crucial for product teams.

All of this, compiled together and analyzed with AI, produces marketplace insights providing valuable knowledge into how people consume products and services.

With Commerce.AI's new marketplace insights, you can be confident in the next product idea you're considering as well as your current markets. With data from 10,000 market categories scanned and reports available to all, there has never been this scale of access to high-quality insights.

Skip the expensive market research firms that don't have a clue about how people are spending money these days, and take advantage of the power of billions of data points analyzed for you with AI-generated market reports.

Of course, there are other outlets for market reports, from Gartner to Nielsen. However, without the power of AI, these traditional market reports can be subjected to biases, while missing out on analyzing billions of data points. Moreover, AI-generated reports are far more cost-effective, costing a fraction of their traditional counterparts.

For instance, Gartner's research subscription costs around $30,000 a year, since the overhead that goes into traditional reports is high. This is because it takes highly paid teams of analysts many hours to produce a report.

In contrast, AI-generated reports compile billions of data points across the web using natural language processing (NLP) software; as such, they take only minutes to generate and cost far less. NLP works by computationally analyzing patterns in language, which gets encoded as numerical token values. It's based on the idea that all language is structured around a core set of elements, such as words and phrases, and these can be combined in many different ways to express an enormous range of ideas in a vector space.

Billions of data points are boiled down into a single opportunity score for any given market, providing unparalleled accuracy and scale, without sacrificing clarity and ease of understanding. These data points provide organizations with unequaled insights on how to advance their strategy and prepare for expansion around the globe.

Summary

Clearly, AI has the potential to radically improve the quality and speed of market opportunity identification.

But it's not just about finding better ways to do what researchers have always done or even automating tasks. Instead, AI can help us see new opportunities that we never could have uncovered before.

In a traditional BI environment, analysts must sift through hours of data on thousands of companies in order to identify promising new targets.

With an AI solution built around unstructured data sources (such as text documents or images), this task becomes straightforward; an AI system can simply scan millions of documents for identifying keywords or patterns and then provide recommended matches with commercial significance.

Commerce.AI is one of many advanced technologies available to product teams. It uses a range of machine learning techniques to find insights based on structured and unstructured data.

Understanding market opportunity identification is important for product teams to lay the groundwork for the product development process. Traditional methods have been in place for generations, and are still relevant today, while AI-based methods can give teams a competitive advantage. This understanding is crucial for the upcoming chapters, as product development should be based on a deep understanding of the market. Moving on from this understanding, we'll dive deeper into topics such as building, selecting, and iterating product ideas.

In the next chapter, we'll explore how the product creation process can be improved with AI.

Chapter 2: Creating Product Ideas

Coming up with great product ideas isn't easy. It's both an art and a science, and those with the ability to come up with great ideas are remembered in history, from people such as Steve Jobs to Mark Zuckerberg.

In this chapter, we'll explore the five pillars of AI, which are driving new and innovative ways to create product ideas: language understanding, visual understanding, information extraction, information organization, and creative AI. Understanding the pillars of AI will help you become more strategic about how you plan, manage, and invest in your AI projects.

For instance, some product teams might like to use AI to generate new product designs, which would focus on the pillar of visual understanding, while others might like to use AI to understand desired customer features that will lay the framework for new products, which would involve pillars such as language understanding and information extraction.

After covering that, we'll cover building, selecting, and iterating product ideas. Finally, you'll learn how to use Commerce.AI to improve the product ideation process and take advantage of billions of data points to gain a competitive advantage.

In this chapter, we will cover the following topics:

Understanding the pillars of AIWhy is product ideation so hard?Using Commerce.AI for creative AIBuilding product ideasSelecting product ideasIterating product ideas

Product ideation is crucial to business success. In the past, companies have failed to create products customers wanted because they didn't know what the customer wanted until after the product was built. But that approach is now increasingly obsolete. The internet has given companies unprecedented access to customer data and insights about their customers at any given time, allowing them to build better products faster than ever before, aided by the power of AI.

Understanding the pillars of AI

These are the five pillars of AI, which lay the groundwork for using AI for product innovation:

Language understandingVisual understandingInformation extractionInformation organizationCreative AI

When you combine the first four pillars with creativity, you get what's called creative AI. In other words, the first four pillars are needed to create the data structure that fuels a gamut of creative use cases.

Creative AI is an advanced form of artificial intelligence that can solve problems previously thought impossible for machines, whether that's designing wholly new products or coming up with truly innovative ideas, such as how Google used AI to design rounded and organic computer chips much faster than human engineers, or how designers at Autodesk use AI to design skeletal scaffolds that are lighter, stronger, and more efficient than regular designs. In this section, we'll explore the five pillars of AI, and how they tie into product creation, in greater detail.

Language understanding

Language understanding is the ability to read users' minds.

One of the most important pillars of AI innovation is language understanding, which allows machines to interpret human text and reasoning, and then return a response that the user can understand. The ability to do this is often referred to as natural language processing (NLP). NLP has been around for decades, but just recently it has witnessed significant advances through deep learning algorithms.

In contrast to traditional machine learning methods, deep learning can identify patterns in data using neural networks. This approach makes use of large amounts of datasets and produces accurate results at higher speeds than previous methods. Deep learning can not only predict future outcomes but also perceive the user's intent or state of mind from their voice or writing.

For example, if you ask a Google Home device about the weather in San Francisco tomorrow, it might respond with It will be sunny with a high of 82 degrees Fahrenheit. This is due to deep learning technology interpreting your spoken words as requests for information, and providing you with what it believes you want based on its vast knowledge base.

The point here is that today's modern NLP technology enables machines to understand our intentions better than ever before, which means we can effectively build conversational
agents easier for a variety of tasks (for example, when scheduling a meeting). The good news is that because modern NLP technology isn't very complex, compared to other AI technologies such as image recognition, we don't have any shortage of examples where it could benefit our lives.

One way language understanding helps product teams is by generating new product ideas from existing ones. For example, say you have an existing product idea for a new hard hat for construction workers. You could use language understanding to automatically extend that idea into a smart hard hat that monitors the worker's location and warns them about their proximity to dangerous objects. At a high level, this is like autocomplete on steroids. We'll explore how this works in the Transformers section.