Artificial Intelligence and Business Software in 2017
Why talk about Artificial Intelligence in business?
• According to angel.co, over 50% of the 2,200+ AI start-ups emerged in the last two years.
• Three major enterprises are well-positioned to deploy and monetize new AI-based services. (Salesforce, Adobe, and ServiceNow)
• 4 out of every 5 IT leaders are currently invested in Artificial Intelligence.
• According to Tractica, AI revenue will grow from $1.4B last year to $59.8B by 2025 in the World Markets.
What kind of AI is used in business?
The most prevalent area of AI used in business today is Machine Learning, followed by Deep Learning.
Machine Learning (ML) will generate the most revenue and is currently pulling the most venture capital investments in all areas of AI. ML has raised $3.5B from over 400 companies.
ML consists of a wide range of libraries and frameworks that perform a variety of tasks when implemented correctly. When ML is seen in software, it allows applications to make decisions and calculated predictions based entirely on data. An example of this is Salesforce Einstein, which makes predictions on what actions have the highest likelihood of converting a lead. The algorithm learns by supervised or reinforced learning, based on the data sets given to it. There are several different types of algorithms, some of which are association rule learning, clustering, decision tree learning, and Bayesian networks. Connecting these algorithm types with data sets requires a high level of engineering skill.
Deep Learning differs from ML specifically because it uses artificial neural networks to make decisions, and does not require extensive human training. Artificial neural networks allow elaborate algorithms to make decisions in a similar way as the human brain. However, these decisions are made on a smaller scale because replicating the neural networks in the human brain is currently impossible. Deep Learning consists of three subcategories; image recognition (computer vision), voice recognition, and natural language processing (NLP). Image recognition allows applications to learn images pixel by pixel. An example of the image recognition algorithm implemented in software is Amazon’s price comparing tool in its mobile version, where users can take a picture of an item to compare its market prices. NLP is unique because of its ability to take in the human language in its natural form, thereby enabling the AI to understand simple commands via speech by the user. Each of these subcategories use artificial neural networks for a greater level of learning.
How are Machine Learning and Deep Learning currently seen in business software?
• Email Marketing – Predicts the best time to send an email for increased open rates
• Content Marketing/Sales – Recommends products to boost sales
• Hyper-personalization – Segments market based on recent actions, frequency of engagement, and monetary function
• Sales Lifecycle Marketing – Predictive analytics enable salesman to send contextual emails and empowers marketers to build stronger campaigns
• Content Marketing – Automatically generate new content
• CX – Deploy chat bots to interact with users
• CX – AI enables social media platforms to customize the user’s news feeds
• Sales – Predictive intelligence determines which leads are ideal to convert
• SEO – AI changes the ranking factors from query to query
• Marketing – Machine Learning targets users most likely to click on an advertisement
• Sales – Alerts users when contact may be considering a rival service
• Content Marketing – Suggests keywords to use while writing blog or email
• CX – Changes website design based on customer data
It’s clear that AI empowers businesses across all departments by providing a prediction, or point solution to an element of the business, acting like the perfect cog in the machine.
What’s next for AI in business?
AI, although very new, has made significant breakthroughs in business software. However, for an application that leverages AI to help business, there needs to be a high level of domain expertise. AI relies on the data we feed it. So whether its Machine Learning or Deep Learning, businesses should be able to identify fundamental data sets that will be used to train the algorithms. Even a Deep Learning algorithm needs to be trained on how to properly train itself. Therefore, the engineer must have a clear understanding of what output is being searched for by the company. It’s easy to get caught up in cool AI applications instead of focusing on business results.
AI will grow as the understanding of its position in business grows. Ask yourself, “What AI capabilities can help streamline my core business operations?”.
What does it take for AI to be successful in business?
• Make your data as accessible as possible.
AI requires a ton of data sets to optimally deliver results. If there is a disconnect anywhere in your data storage, address the issue right away. For AI to work, you need to have your data straight.
• Equip your IT environment with the right resources.
Having cloud-based applications ensure developers have the tools to explore the latest AI capabilities.
• Focus on the result.
Hone into the capabilities that will help achieve the result your business is aiming for instead of spreading your focus on the breadth of AI’s functionality, a common mistake when launching an AI project.
• Set up a new training program for employees.
Employees using AI-based applications to gather predictive analysis are going to have a different work flow, new tools, and a new set of expectations.
• Stay on top of AI developments.
Data scientists and engineers need to constantly evaluate new technologies and how they can leverage them to help achieve the company’s goals. With the current rapid growth of AI in business software, it’s very easy to fall behind the curve.