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Want to Get Started with Artificial Intelligence? Ask These Questions About Your Data

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Artificial intelligence has an almost infinite ability to process data in ways that create tremendous value for firms. But how do brands go from reading about AI to actually using it?

It starts with asking the right questions about your data. Data powers all artificial intelligence solutions. What AI can and can’t do for your brand depends on the type and amount of data you have.

For instance, natural language generation (NLG) turns spreadsheet numbers into narratives. Image recognition systems use photo datasets to identify your pictures. Prediction engines weigh thousands of data points to predict what products or content you’ll love (and buy). These technologies all use different types and quantities of data in different ways to achieve diverse goals.

Start with these questions to better understand what’s possible, determine which AI solutions are a good fit and improve the quality of discussions with AI providers. 

Questions to Ask

Do you have data?

Some AI technologies can only be used if you have access to data, either generated by a third-party (like a CRM system) or generated in-house. If you don’t have access to data, you’ll need to rule out any solution (like natural language generation) that requires it.

However, keep an open mind: plenty of AI solutions use their own datasets to achieve goals, so you can use them whether you have data or not.

What does your data look like?

Is your data structured or unstructured?  Structured data is ordered data displayed in columns and rows. Unstructured data is any data that is not organized in a specific way, such as Word documents, social media posts and emails. Which type of data you have determines what AI technologies can benefit your organization.

What stories can you tell with your data?

If you create content, consider the stories your data might be able to tell. Natural language generation (NLG) is an artificial intelligence technology that turns structured data into narratives. NLG takes numbers and tells stories about them based on rules that humans create.

Once an NLG system is “trained” to understand your data, it automatically generates content—in some cases, thousands of articles—from your numbers. For example, the Associated Press uses the technology to write its earnings reports and brands use NLG to write narratives about their Google Analytics data.

If you have structured data, NLG might be a good fit for you. Start asking yourself: What stories can your brand tell with its data? What types of content would you like to create at scale? What relationships do your numbers describe that could make compelling narratives?

How can you get more out of your data?

Your data might have untapped potential. Consider how it might be used to discover insights, predict outcomes, devise strategies, personalize content across channels and tell stories at scale. Discuss these possibilities with providers of AI tools. Often, what’s possible is surprising.

For instance, IBM’s Watson Analytics is a free tool to explore and discover insight from data with automatic data visualization and predictive analytics capabilities.

Do you need programming skills to manipulate, analyze or apply AI to your data?

Some AI solutions work out of the box. Others require experts and implementation periods. Some datasets need dedicated professionals to change, maintain, analyze or structure data. Some data can be manipulated by anyone with Excel. Your internal capabilities (or access to external talent) could determine whether or not certain AI solutions make sense for your brand.

Where are the biggest opportunities to implement artificial intelligence?

Evaluate repetitive, manual marketing tasks that might be intelligently automated. Candidates may include time-consuming tasks like:

  • Drafting, scheduling and publishing social media updates.
  • Writing and optimizing content.
  • Testing headlines, landing pages and ads.
  • Writing performance reports.
  • Predicting opens, clicks and conversions.
  • Reviewing analytics.
  • Personalizing and curating content.


The activities that consume the most time, energy and money are a good place to start.

What are the AI capabilities of your existing marketing technology?

Chances are you’re using some type of CMS, CRM, email, social or marketing automation platform. Your current solution, or a platform you’re considering, may have artificial intelligence capabilities. Some existing martech players have built AI into their solutions or plan to do so. (Salesforce is baking AI into each of its core products.)

Ask current providers if their solutions incorporate AI or will in the near future. You may be able to achieve your goals without undertaking a costly new technology project.

Who can help you make sense of how to get started with AI?

Providers of artificial intelligence technology are generally knowledgeable and happy to talk through your needs. Though make sure you’re clear on the questions in this post to foster a clear, productive discussion. Additionally, if you have questions about what’s possible with artificial intelligence and how to implement it, get in touch with us.

For more news, analysis and interviews on artificial intelligence in marketing, subscribe to the Marketing Artificial Intelligence Institute.

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