In a recent global survey of 700 leaders across industries and departments, 95% of respondents believed embedding AI into the operations, products, and services of their company would be a benefit. Compare this with the number of C-suite respondents who said they have actually implemented AI solutions across their organization: 6%.
Clearly, there’s a disconnect.
The same survey found one of the biggest reasons is how hard it is to find and implement AI-ready technology stacks — two big pieces being a cloud-based infrastructure and robust data management. Even in a world of open-source machine learning models and increasingly standardized data, implementing AI requires groundwork. For the most part.
Sitecore AI — Image Similarity offers one counter example. As you upload images and videos, it auto-tags them all. It can then identify similar images for use in your campaigns, and even generate video transcripts when needed. All of this is out-of-the-box with Sitecore’s DAM. While this highlights one way AI can be used to automate routine tasks without requiring massive changes, it’s a simple use case, and it only scratches the surface of Sitecore AI’s capabilities.
As AI and machine learning (AI/ML) technologies continue to advance, costs will decrease and use cases will multiply. Those who take advantage of these changes are likely to leapfrog ahead of those who don’t. This is why forward-thinking marketing leaders and martech champions are laying the groundwork for AI and machine learning today.
Fortunately, for those who have already embraced headless, some of that groundwork has been laid. Depending on your headless solution, it may be a lot of it.
Are headless and AI compatible?
A few years back, Forrester analyst Mark Grannan suggested that headless and AI are in competition, at least when it comes to what gets prioritized in the digital experience stack. On the one side, you have headless, which focuses on agility and flexibility for developers. On the other, you have AI/ML, which require unified data, and this requires standardized systems that enable analysis.
At the time, Grannan’s analysis may have been correct. But when we dig a bit deeper today, we can see that, even if they once were, the two aren’t antithetical today. The right headless architecture can actually empower AI. Let’s see how.
How headless enables AI/ML
Think back to the old days when customers primarily engaged on your website — it didn’t matter if your front-end was tethered to your back end. But when customers engage across channels, you need a way to deliver seamless and consistent content-driven experiences to them on each one, and headless simplifies this. With headless, you can create content and deliver it to a landing page and a kiosk, for example. By powering omnichannel delivery, headless goes a long way toward solving the omnichannel challenge.
Then there’s personalization, both on websites and across channels, which headless also supports. By enabling a modular content strategy, headless empowers marketers to mix and match content to present unique experiences to various users based on their in-the-moment needs and desires.
But while traditional headless approaches offered the freedom of delivery that powered omnichannel and personalization, they restricted the data capture that leads to insight. In response to this issue, companies like Sitecore developed headless solutions that enabled both omnichannel delivery and data capture.
Thus, the right headless platform will actually support data capture, whether on your sites or across your channels, which is the first key step to implementing AI.
The next key step is ensuring the data you collect can be put to use. A Harvard Business Review article references a common trope that only 20% of data scientists’ time is actually spent analyzing data, while 80% is spent finding, cleaning, and aggregating it.
But the article also pointed out that this is changing. And this is in large part because of innovative technologies like customer data platforms (CDPs).
By linking every interaction on the channels belonging to your website, including CRM, social media, e-commerce platforms, and apps, a CDP acts as a central repository for your martech stack. Not only does a CDP gather omnichannel data, but it also merges it with email addresses, telephone numbers, and other personal information, drastically improving the accuracy of individual profiles. And the best CDPs don't just merge clean data — they find ways to activate it through decisioning and personalization.
Designed for integrations from the bottom up
And this brings us back to headless. We saw how headless increases the flexibility of front-end delivery. This flexibility doesn’t end with front-end systems. It also extends to the back end. With a headless (micro-services and API-first) architecture, expanding your back end becomes much, much easier.
As we saw above, collecting, aggregating, and cleaning data are all essential prerequisites for AI. Connecting a headless digital experience platform — whether a composable or all-in-one solution — that’s able to collect data from all channels with a CDP takes care of these prerequisites, and a headless architecture increases the ease of doing this exponentially. The next challenge is connecting the AI/ML tools that can analyze it all and offer insight.
If you have a DXP with built-in AI/ML, like the latest version of Sitecore Experience Platform™, you’ve solved the problem already. But even then you may want a third-party solution, such as Microsoft Precept for running AI technologies at the edge. Whatever your AI/ML aspirations, a headless architecture, and specifically one that is or is headed toward MACH, goes a long way toward preparing for it.