The debate as to whether artificial intelligence (AI) will take over the world has well and truly reached its conclusion: it very much will and is already doing so.

Machine learning and AI have quietly been running the show from behind the scenes for some time. Just think of product recommendation engines, playlist algorithms, and hyper-personalisation. But with the recent launches of consumer-facing AI products such as ChatGPT and Midjourney and the pending releases of AI browser and search solutions from both Microsoft and Google, 2023 will be remembered as the year AI came to the masses. Nothing will ever be the same again.

Many business leaders expect these technologies to bring about a scale of change as big as the birth of the internet itself. They believe machine learning and AI are set to shake up every department of every business in the land. It’s hard to disagree when you consider the impact that AI has made in customer services and commerce, with chatbots and more recently, even more holistically, in content creation.

So, what does all this mean for the marketer and how can you get yourselves into good shape to embrace andcapitalise on these new capabilities?

The first thing to point out is that AI is not a golden bullet. Marketers can’t just point it at the business and, as ifby magic, become better at serving your customers, sell more products, improve your efficiency and becomemore profitable. Whilst all these are possible with a solid implementation of AI tooling, there is some groundwork to do first.

There are some key enablers to getting AI-ready which need to be in place:

1. Assess the impact and opportunities for AI in your business

Do your research. AI can help all departments work better. Put together a team with representatives from your different team disciplines and investigate what AI-based technologies are out there and what impact and opportunities they could bring. Ensure this is documented and distributed organisation-wide to build awareness and fuel healthy debate. AI can help the marketer in lots of ways.

For example, it can be used for sentiment analysis based on data from social media so you can better understand the perception of your brand, product or service in the market. Or it can help you make better decisions and deliver better-performing content through predictive analytics. AI can also take the pain out of routine tasks like lead scoring, product merchandising, or sending communications, allowing you to focus on more strategic activities.

2. Prepare your CX strategy

Before diving headfirst into AI, it’s important to take a step back and consider it as another tool in your customer experience toolkit. Focus on the needs of the user and the business first, then consider how AI can help deliver on these. AI should not be treated in a silo; it should perform a specific role within a wider CX strategy.

For example, you might be a publisher and want to ensure you provide the most relevant content for your audience. In which case, think about how AI could be used to create news stories tailored to individual reader interests or consumption habits.

3. Structure your data

AI technologies utilise data to learn from and train their models so the more data you can provide in a usable format the better the responses and predictions will be. Good data starts with a data strategy that sets out the sources of the data, the management processes and how it will be governed in a compliant manner. The data model and design define how it is organised, structured and made available to other systems.

4. Ready your content

When using AI to consume/serve your content it is important that the content is modelled and tagged effectively and made available via APIs. This is achievable using both a headless CMS and modular DXPs that offer content APIs. If using AI to deliver personalised user experiences, have channel-specific templates ready to consume this content, such as web components, email templates and banners.

5. Ensure your Solution Architecture is compatible

Technology is the enabler for AI so it’s critical to have the right stack in place. Typically, this includes an API-first CMS, DAM, CDP, Multichannel, Marketing Hub and an appropriate AI engine, plus a cloud platform to provide data storage and high-performance computation. Commission a Solution Architect familiar with these products to conduct an audit and gap analysis on your current stack and identify any barriers to getting started.