Artificial Intelligence (AI) is one of the most popular business technologies today. This is in part, thanks to a diverse set of applications and new ways that simplify its use. AI helps businesses to make smarter, faster, and more informed decisions by collecting and processing data. These platforms use machine learning to organize and understand large datasets. And with advanced tools, can build predictive models and generate forecasts based on this information.
Everything from facial recognition to predictive text and chatbots uses AI frameworks created by data scientists. That broad potential makes AI a likely tool for many businesses, but not all applications are right for dipping a business toe in the water.
So, many companies are left wondering how to integrate AI into their business. Determining how to use AI involves understanding the opportunities for positive ROI and the team and training needed.
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3 considerations before adopting AI
To ensure AI is a useful part of your digital transformation, your organization will need to answer core questions about how to use it. This starts with a fundamental understanding of the technology and what it can do. This then requires you to identify the right people for development and budgeting.
How can you get familiar with AI?
One of the first steps to integrating AI into your business is understanding how it works, what it can do, and where that will help your business. Thankfully, there are many online courses and free options that can provide a solid foundational understanding of AI and its capabilities. Here are just four for you to try:
- Udacity’s Intro to AI free course
- edX’s online AI course
- MonkeyLearn’s Gentle Guide to Machine Learning
- Stanford University’s Artificial Intelligence: Principles and Techniques
Knowing the functional capabilities of AI tools helps as well. Understanding robotic process automation (RPA) can help you identify where AI might be useful.
What problems do you want AI to solve?
Like most other business investments, AI should come with a specific goal or target. Getting the best return involves you asking: What specifically do you need AI to do?
Ask if the problems you’re trying to address need data as part of their solutions and if you can gather that data. Review general services and look for case studies. Look for successes you can emulate, such as reducing customer service burdens or assisting with decision-making.
Depending on your industry, integrating AI can help solve a range of issues and improve bottom lines. If AI can address your problem, you’ll next want to identify the people in your organization who can help you with it.
Who are your evangelists?
Who within the organization can you partner with to help choose and implement an AI solution?
Tech evangelists can help you build significant company interest in a specific technology or application. These team members are already interested in AI and likely thinking about how it can help you improve operations. They don’t have to be data scientists, just interested and willing to help you test a solution.
If AI is going to support their area of operations, reinforce that you want AI to support and augment their work, not replace any staff. By creating and leveraging these partnerships, you’ll be able to generate greater buy-in from the organization as a whole.
6 leading methods to integrate AI into your business
It’s time to choose an AI tool once you’ve got your understanding of AI, have identified problems to tackle, and finalized internal teams to help. Pilot projects are a safe way to test different options and see if they’ll achieve the benefits you need. Plus they’ll help you learn the infrastructure or software you need to make the most of them.
When considering your first pilot project, six machine learning tools can make a worthwhile investment by controlling costs and potentially yielding substantial returns.
1. Big data synthesizers
Big Data is part of most companies’ introduction to artificial intelligence. That’s because AI acts as a data synthesizer that reviews the data you currently have and tries to add context relevant to your goals. Banks and credit card companies use these tools to look for trends in the products customers want as well as the fraud that bad actors try to commit.
Data synthesizers look at the information you have to identify patterns and opportunities. Then, you can take related actions such as sending follow-up sales emails to leads it identifies in your current CRM. Then you can tell the model if those actions generated the results you wanted. As you repeat this process, the AI learns to better spot trends or identify anomalies in the data you give it.
2. Chatbots
Chatbots allow you to deliver customer service and even sales messages 24/7 to people who visit your website and other channels. You can train AI chatbots to not only provide answers to frequent questions. They’ll learn the different ways customers ask related questions. Initially, they’ll turn your FAQ into a script to address customer needs. They also can grow to understand customers who may be ready to buy and flag that for your sales team.
Facebook Messenger chatbots are becoming smart sales tools that make specific product recommendations. LEGO tried one for its 2017 year-end holiday season and was able to answer customer questions and generate a nearly 2X higher average order value from customers that used the bot.
3. Audience-building smart algorithms
You can turn existing customer data into a blueprint for new audiences to target with your sales and marketing messages. These lookalike audiences are often a reliable tactic because they resemble current customers who buy from you.
AI models using advanced machine learning help companies build lookalike audiences by analyzing a wide range of characteristics of your current audience. These tools can identify trends among customers with high lifetime values or among those who purchased from you within the past month. You give the AI tool a specific segment, and it works to highlight patterns your teams can use.
A substantial benefit of AI is that it can process much more information than humans can in a short amount of time, and you can often incorporate data from other software or paid sources.
4. Smarter A/B testing (optimize ad timing, copy, placement, and creative based on machine learning)
AI can provide better insight into the work you’re already doing with your data. One common area is A/B testing, especially for your marketing. Supplying AI tools with data from your A/B tests can help you define your target customers better.
For example, tests may show that more people overall clicked on advertisement A. However, the AI dives deeper to find that people clicking on B were more likely to make expensive purchases. So, ad B better qualifies leads for long-term revenue.
Machine learning can use datasets from many campaigns to recommend the customer segments you should target. AI can automate performance analysis and even adjust based on what’s performing well. Advanced models can help you optimize a wide range of campaign elements, including copy and image selection, placement on a page, creative themes, and ad timing.
5. Programmatic ad buying
Programmatic advertising is an automated process of buying digital ads through large exchanges. These systems need to automate ad purchases in real-time when someone loads a page, performs a search, or takes another action.
Predictive intelligence tools are becoming common in these ad buys because they continually refine keyword targets, how much you bid (to avoid over- or under-bidding), the targets you use based on click-throughs, the time an ad is displayed, and more. AI makes it easier to buy the right ads and to tailor-make them for each audience member. Predictive analytics not only improve targeting but also make it more affordable to run your campaigns.
6. Listening tools
When someone responds to your customer service with “oh, great,” the context of the interaction informs the tone we read in to determine if that’s a thankful response or one dripping with sarcasm.
AI is learning how to make those distinctions, too, by understanding semantic relationships. Social listening tools are designed to help companies understand how people feel about them, not just the words customers use. Allowing AI to process customer service interactions, social media posts, and online reviews can paint an accurate picture of your reputation.
Listening tools help companies understand where products and services need improvement. They also help identify common complaints and what people like most about you. It’s incredibly useful information for addressing what you offer today and building a roadmap for future products and services.
