How to deliver AI personalisation in 2025

AI personalisation is the process of using artificial intelligence (AI) to deliver unique experiences to customers based on their preferences, habits and past interactions.

What was once a subtle business objective is now a consumer expectation. Eighty-one per cent of customers prefer companies that can deliver personalised experiences. Previously, this was a tough task. However, AI models can now analyse massive datasets in real time, giving businesses the tools to meet customers where they stand and deliver tailored messaging at scale.

But it isn’t all plain sailing. In our State of the AI Connected Customer report, we found that only 49% of people feel brands use their information in a beneficial way. Trust is also a concern, with 71% of customers becoming increasingly protective of their personal information.

So, while personalisation is still a powerful growth lever, there’s a balance to be had. In this guide, we’ll take a look at how it all works, explore real-world use cases, and discuss how to walk the tightrope between AI-driven personalised experiences, transparency, and trust.

Fifteen years ago, personalisation might’ve consisted of a recipient’s name tacked onto a bulk outbound email. Customer expectations have come a long way since then, as has the technology we can use to create tailored experiences.

Artificial intelligence makes it possible to deliver personalisation at scale in less time. Let’s kick things off by taking a quick look at how it works:

1. Data analysis and integration - First, businesses unify real-time data from across the organisation into a single profile. This acts as the foundation for the AI and ML analysis.
2. Pattern recognition - Next, the AI model uses the unified data to discover patterns and segment audiences based on their characteristics.
3. Personalised interactions - Generative AI can now begin to tailor content, offers, and communications dynamically based on audience traits, behaviours, and preferences.
4. Continuous learning - The AI model consistently improves through a continuous feedback loop. The more information it gathers, the more accurate it becomes.

For instance, an outdoorswear brand might unify data from its ecommerce site, point-of-sale systems, mobile app, and email marketing solution. AI and ML can then detect patterns and group customers into segments, such as those who shop for winter gear in early autumn, helping the brand deliver tailored offers and recommendations earlier than competitors.

This essentially turns segmentation from a static tool into a predictive forecasting solution, giving brands the chance to stay ahead of customer needs. We’ll dive deeper into how to implement this within your own business later in this article. But for now, let’s take a closer look at how AI in personalisation looks in the real world.

L’Oréal has been disrupting and innovating within the beauty industry for more than 100 years, and a big part of its success is owed to its commitment to celebrating individuality.

As part of this commitment, L’Oréal needed a way to continue delivering ultra-personalised experiences to its customers in an increasingly fast-moving and digitally-focused market.

Here’s how Salesforce Customer 360 helped them achieve this goal.

L’Oréal has been accumulating valuable data for more than a century, but having information scattered across systems made it difficult for service teams to deliver personalised care.

The first transformation began with L’Oréal unifying its data from more than 200 direct-to-consumer websites with the help of Service Cloud. This combined real-time and historical websites, SMS, chat, and email insights to create a singular view of every customer.

Now, rather than searching through disparate systems to gather information, L’Oréal’s agents can see all consumer information (like purchase histories, order statuses and activity updates) in real time from a single dashboard, laying the groundwork for personalisation. This shift has also improved agent satisfaction scores by up to 70%.

With their data foundation in place, L’Oréal leveraged Salesforce’s Commerce AI solution to uncover trends and make predictive product recommendations based on browsing and purchase histories. For instance, if you were to add lightweight sunscreen to your cart, our AI solution might recommend SPF lip balms or moisturisers at checkout.

This AI process has allowed L’Oréal to better personalise its marketing and engage customers while freeing up more time for teams to focus on big-picture initiatives. The result? Salesforce now generates 15% to 20% of sales for one of L’Oréal’s major B2C brands.

With the help of Marketing Cloud and Commerce Cloud, L’Oréal now orchestrates seamless omnichannel personalisation for every customer. From personalised product recommendations to beauty advice and special offers, AI helps the brand distribute the perfect tailored message on the right channel at the ideal time based on individual customer journeys.

Salesforce AI also powers some of L’Oréal’s most innovative beauty tech initiatives. These include the Instant Skin Reader on Kiehl's website (which analyses your face to provide the right product regimen for healthy skin), the Virtual Try On tool (that lets you ‘try on’ products wherever you are), and virtual beauty consultations through live chat features.

L’Oréal’s story showcases how AI in personalisation drives results. Now, let’s take a moment to distil the core benefits down for customers and businesses.

Here’s how AI-driven personalisation benefits your customers:

• Engagement - AI personalises content and communications to align with your customers’ interests, keeping them engaged for longer.
• Customer satisfaction - Tailored experiences equal smoother customer interactions, reducing friction while showing your brand truly understands your customer’s needs.
• Easier product discovery - AI-powered product recommendation engines make it easier for customers to find products they want faster, improving convenience.
• Stronger relationships - Ultimately, personalised communications foster trust and loyalty, making customers feel heard and valued by brands.

Despite these advantages, only 39% of customers feel comfortable with AI understanding their needs, even though half recognise its ability to improve products and speed up services (State of the AI Connected Customer report). It’s clear businesses still have work to do if they want to build trust and deliver experiences that are transparent and respectful of customer privacy.

Next, let’s touch on the advantages for businesses:

• Conversion rates - Delivering the right message at the right time increases the likelihood that customers will buy from your brand, boosting conversion rates.
• Brand loyalty - Stronger relationships with customers mean your audience is more likely to stick with your brand, supporting loyalty and retention.
• Scalability - AI personalisation allows brands to tailor messages at scale faster than ever before, freeing up time for high-value tasks by reducing manual effort.
• Data-led insights - The data gathered from AI personalisation can also feed into broader strategy, helping businesses make smarter decisions.
• Optimised marketing - Personalisation makes it more likely your marketing will hit the mark consistently, improving success metrics while minimising wasted ad spend.
• Increased revenue - Ultimately, customised experiences drive more sales, higher order value, and a greater customer lifetime value.

With customers and brands both standing to gain so many benefits, it’s little surprise AI personalisation strategies have become a top priority for businesses in Australia.

AI personalisation is changing how organisations operate in almost all sectors. From retail and healthcare to marketing and banking, here’s a closer look at how different industries are putting AI plus data into practice.

Retailers and ecommerce stores are using AI to create tailored shopping experiences and recommend products based on past browsing behaviours, such as an online grocery store with a “what you may have missed” section at checkout based on a customer’s past orders.

We’re also seeing how AI models can be tied to dynamic pricing, adjusting costs based on customer demand. Salesforce Commerce Cloud, paired with Salesforce AI, can help you identify buying trends and predict demand, supporting more accurate stock planning and targeted promotions.

In the marketing space, AI-powered chatbots can help customers get fast answers to common queries – for example, troubleshooting a product issue without waiting in a queue. Behind the scenes, tools like Service Cloud and Agentforce equip service teams with predictive analytics, which give agents real-time insights into customer needs and preferences.

All of this saves time for reps while helping resolve cases faster, boosting both customer and agent satisfaction.

Banks are using AI to offer hyper-personalised financial advice, such as recommending investment opportunities based on risk appetite or offering budgeting tips. Some financial institutions are also setting up AI credit scoring solutions to explore alternative ways to grant customers loans, supporting financial inclusion.

Financial Services Cloud is purpose-built to help banks and lenders achieve AI transformation. Along with the customer-facing benefits, our solution can handle digital labour, such as automating financial reports, giving more time for teams to focus on high-value tasks.

In healthcare, providers can use AI to build personalised treatment plans based on patient data. For instance, a clinic might use AI to analyse a patient’s medical history and predict which follow-up tests are most relevant.

As ever, AI also takes care of busywork like sending out appointment notifications or follow-up reminders for those living with chronic conditions, freeing up time for staff to tend to patients’ care.

It’d be impossible to discuss the implications of AI for personalised outreach without talking about ethics, transparency, and trust.

Customers want tailored experiences and understand how AI can help organisations reach that level of personalisation. However, they’re also understandably wary about losing their jobs to AI , copyright infringements and intellectual property theft , and they’re concerned about how their data is being used. Tellingly, 64% of customers feel companies are reckless with their data, and only 42% trust businesses to use AI ethically (State of the AI Connected Customer report), meaning the onus is on organisations to prove they are trustworthy.

Responsible and ethical use of AI within your organisation (especially in lieu of clear governmental oversight, which is still catching up to this new technology) plays a major role in establishing customer trust.

Transparency and explainable AI also play key roles. Seventy-three per cent of customers have concerns over unethical AI use, and 71% believe human validation is important (State of the AI Connected Customer report). Maintaining strong data governance and being able to clearly communicate how and why data is collected will help brands strike the balance of integrity and innovation.

But let’s reframe things. AI ethics and trust aren’t just about avoiding reputational damage. They can actively be a competitive advantage. By respecting privacy and proving you’re using AI responsibly, you’ll put your business in good standing to earn deeper loyalty, making customers more confident in sharing their data in the future. It’s a mutually beneficial cycle.

So, how can you put AI-powered personalisation into practice? We’ve laid out our recommended implementation approach into four core steps.

Great AI personalisation begins with great data. Start by unifying all of your information, like website metrics, email interactions, and CRM data under one roof. Platforms like Data Cloud can help with this by bringing data together into a single, unified customer profile.

This data needs to be real-time. Using historical information may lead to irrelevant predictions, like sending a discount for a product a customer has already bought. Instead, choose info like browsing behaviour, email engagement, and loyalty card data to get current insights. This will ensure you can deliver the right messaging at the perfect moment.

Once you’ve unified your data, you can bring AI into the equation. This typically involves machine learning (ML) algorithms analysing all of the data to find trends and patterns that humans often miss. Think of things like:

• Products that are often purchased together
• What times customers are most likely to browse
• What drives repeat purchases
• Average order value
• Preferred communication channels
• Customer lifetime value predictions

This analysis lets the AI model segment audiences based on granular traits. For instance, rather than being limited to broad categories like age or location, you could pinpoint a segment based on their lifecycle stage, preferences, predicted actions, or even purchase frequency.

Need some help with this? Salesforce’s suite of AI tools can help you uncover insights from your customer 360 data and turn them into actionable insights that enable truly personalised campaigns at scale.

After segmentation is complete, leverage generative AI to create dynamic content and communications for each individual. Now, instead of generic messages, customers see recommendations, offers, and information that aligns with their unique preferences.

For instance, a retailer might use AI to create a customised email chain for a customer who recently purchased a pair of shoes. This could include personalised recommendations for shoe care products or limited-time discounts on similar items they’ve shown interest in, all delivered at the right time based on predicted customer engagement.

The key here is to choose a tool that can convert your AI data into real-world use cases. Solutions like Salesforce Marketing Cloud take your unified real-time data and use it to deliver the right message at the right place at the right time, helping your business create seamless experiences for every customer across every channel.

AI personalisation is a continuous process. Once customers begin to interact with content and communications, your algorithm will collect new data and refine its models. This effectively creates a constant feedback loop, improving relevance and accuracy over time.

As one example, a streaming service that uses AI to recommend shows becomes more precise over time as it learns more about a user’s watch habits. The more shows and movies the recommendation engine has to learn from, the better its future recommendations.

Throughout this process, businesses need to measure their success to ensure the process is working as intended. Tools like CRM analytics allow marketers to track and visualise trends in real time to see what’s working and what isn’t.

Lastly, let’s finish up with some best practices to ensure your AI digital transformation goes smoothly:

• Make data quality a priority - AI personalisation relies on rock-solid data. Ensure it's accurate and up-to-date. You should also implement strict governance policies to make sure information is protected and compliant with regulations.
• Be honest and transparent - Your business may be confident in AI, but that doesn’t mean your customers are. Be clear about how you’re using data, why you use AI, and the benefits it serves. This transparency will build trust and encourage future engagement.
• Regularly retrain your models - Your AI solutions need to evolve over time. As customer behaviour changes or your business goals shift, retrain and refine your models to keep them accurate, relevant and fair.
• Don’t omit human oversight - No matter how much faith you place in your AI models, there’s always a need to keep humans in the loop for critical decisions. This maintains integrity and can help you catch errors early before they scale.
• Align personalisation with your goals - What are your goals? Do you want to improve retention, increase conversion rates, or improve satisfaction? Keep your strategy in mind as you get started with AI to maintain focus on your broader objectives.

Over the next decade, the businesses that balance innovation and personalisation with ethics and trust will come out on top. Put customers at the centre, maintain transparency, keep AI responsible, and you’ll be in the right position to capitalise on the opportunities available to your business.