How AI-driven customer centricity can transform your engagement strategy

Remember when brands first jumped on social media in the mid-2000s? Platforms like Instagram and YouTube gave businesses direct access to their target audiences – and it felt like a revolution. Although the channels changed, the general approach didn’t. Most engagement still relied on broad messages and basic targeting.

Over time, digital platforms gave companies access to huge amounts of data. The potential was there to make every interaction feel personal, but the reality often fell short. Without the right tools, even the most data-rich strategies remained reactive and disconnected.

Now, artificial intelligence is changing the game. With AI, we can finally move beyond static segments and delayed responses. We can understand what customers want in the moment, tailor content dynamically, and deliver value in real time. It’s customer centricity – but powered by systems that actually keep up.

What does AI-driven customer centricity actually mean (beyond personalisation)?

For years, customer centricity has largely meant building segments and sending tailored messages (usually a pasted first name at the beginning) to each one. But AI opens the door to something more dynamic – something closer to a true, adaptive conversation.

AI-driven customer centricity is built on a continuously learning system that interprets behaviour, identifies intent, and delivers content or support in real time. It pulls in data from multiple touchpoints – CRM, web activity, emails, content interactions – and uses that to anticipate needs, rather than simply react.

The result is not just personalisation, but predictive contextualisation: knowing not just who someone is, but what they’re trying to achieve – and how to help them do it.

What AI-driven engagement looks like in practice

AI-powered engagement isn’t a futuristic idea – it’s already reshaping how brands interact with customers across digital channels. From personalised emails to predictive web content and real-time ad targeting, AI can help businesses create experiences that are more timely, relevant, and intuitive.

Benefit Cosmetics used AI to personalise email marketing, triggering the next message based on customer behaviour. This approach led to a 50% increase in click-through rates and a 40% lift in revenue. Similarly, HP Tronic, a consumer electronics leader in Central Europe, used AI to personalise website content for new visitors, boosting conversion rates by 136%. And HMV, the British entertainment retailer, leveraged AI to personalise ad targeting and achieved a 14% week-over-week revenue increase in their campaigns.

While these examples come from B2C, the same principles apply – and are perhaps even more impactful – in complex, regulated industries like pharma.

Imagine an HCP receives an email about a new therapy. She clicks through to a landing page – not a static one-size-fits-all page, but one that dynamically adapts to her specialty, region, and previous engagement history. Or consider a patient support website where content adjusts based on the visitor’s diagnosis stage, language preferences, or device – with AI-powered translation ensuring cultural relevance and regulatory compliance. Or a CRM system that suggests a next-best-action for a sales rep based on recent webinar attendance, digital interactions, or prior outreach – helping the rep meet the HCP exactly where they are in their decision-making journey.

These are not hypothetical scenarios. With the right AI infrastructure, data foundation, and content strategy in place, they’re already being deployed, helping brands move from static campaigns to dynamic, customer-centric experiences that evolve in real time.

The 4 pillars of AI-driven customer centricity

So, what does it actually take to become truly customer-centric with the help of AI?

Predictive intelligence: AI systems analyse historical and behavioural data to forecast what customers will need next. This allows brands to anticipate and act, instead of waiting to respond.
Real time adaptation: Journeys are no longer linear. AI enables marketing content, messaging, and channel strategies to adapt on the fly, based on how customers behave and engage in the moment.
Intent recognition: AI helps interpret not just what a customer does, but why. This deeper insight into intent leads to more relevant, timely, and respectful engagement.
Feedback-loop optimisation: Every interaction feeds the system. The more customers engage, the smarter the system becomes – continually optimising messaging, timing, and content based on real performance data.

Together, these pillars create an engine of relevance; one that scales without losing its human touch.

Strategic shifts AI demands

To fully leverage AI for customer engagement, brands need to rethink how they approach strategy, execution, and collaboration.

From campaigns to conversations: Campaigns are planned. Conversations evolve. AI enables marketers to respond in real time to what customers say and do.
From channels to journeys: Instead of optimising channel by channel, marketers must orchestrate seamless experiences that move with the customer across platforms.
From execution to experience: Marketing is no longer just about delivering messages – it’s about creating experiences that feel coherent, personal, and intuitive.

In short, AI requires marketers to stop thinking in fixed outputs and start thinking in adaptable systems.

Building the foundation for AI-driven centricity

AI is only as good as the ecosystem it operates in. Here’s what that ecosystem needs:

Clean, connected data: Without unified, well-structured data, AI can’t function effectively. Disconnected datasets lead to fragmented experiences.
Integrated tech stack: AI must integrate with the tools marketers already use – CRM, CMS, campaign and content experience platforms, and analytics tools – to be actionable.
Cross-team collaboration: AI breaks down silos between marketing, content, analytics, and IT. Success depends on collaboration, not just implementation.
Cultural buy-in: Perhaps most importantly, teams must be open to testing, learning, and changing how they work. AI-driven strategies require a shift in mindset, not just tools.

Measuring the impact

Measuring AI-driven engagement requires a shift in both mindset and metrics. Traditional KPIs – like open rates, impressions, and basic click-throughs – can no longer tell the full story. As marketing becomes more dynamic and personalised, so too must our measurement frameworks.

Leading organisations recommend measurement models that are tied to business outcomes, not just media performance. AI maturity is built on outcome-based KPIs like revenue contribution, campaign ROI, content reuse, and customer lifetime value.

To achieve this, companies need an integrated view of the customer across all touchpoints, from email clicks and website visits to CRM actions and offline interactions. This unified data foundation enables predictive modelling, journey tracking, and real-time performance insights.

New metrics are brought into play, like:

Journey completion: measuring how effectively a user moves through stages, not just clicks on isolated assets.
Time to value: how quickly a customer gets what they came for, whether that’s information, support, or a product.
Personalisation accuracy: assessing how closely the experience aligns with user intent and needs.
Engagement depth: understanding not just that a user interacted, but how meaningfully they engaged.

Why traditional engagement strategies are falling short

Across industries, many customer engagement strategies still rely on static content calendars, outdated segments, and disconnected tools. They often work on assumptions, not real-time insights.

Let’s take a look at pharma.

While 82% of pharma executives say they’re satisfied with their engagement strategies, only 28% of healthcare professionals feel those strategies actually meet their needs. This isn’t just a communication gap; it’s a strategic disconnect. Most HCPs expect tailored, timely, and relevant interactions across channels. But in many cases, pharma teams are still relying on limited outreach methods like email and in-person rep visits.

As one omnichannel leader recently put it after attending Reuters Pharma 2025: “We only have the rep and email.” Let that sink in.

This example underscores a broader truth: many companies talk about omnichannel and AI, but they’re still operating on legacy systems and limited infrastructure. Their customers – especially digital-native Millennials and Gen Z – have moved on.

Navigating challenges along the way

Of course, AI isn’t without its challenges, and acknowledging them is key to building strategies that last. One of the most pressing concerns is data privacy and trust. This is especially critical in regulated industries, where customer data must be handled with transparency, consent, and strong ethical safeguards.

Another essential factor is human oversight. While AI can enhance decision-making, it still relies on human judgment to ensure communication remains empathetic, contextually appropriate, and aligned with brand values.

And finally, there’s change management. Successfully adopting AI requires more than implementation, it depends on training, internal advocacy, and a willingness to evolve existing processes. These hurdles are real, but far from insurmountable. With thoughtful planning and long-term commitment, they can become stepping stones to more intelligent and impactful engagement.

AI as the enabler, not the end goal

AI won’t make your engagement strategy customer-centric by default. It will only amplify what you’ve already built – whether that’s relevant and responsive, or clunky and disconnected.

True transformation happens when AI is paired with a deep understanding of customer needs, clean data, agile teams, and a shared commitment to relevance.

In sectors like pharma, the need for this transformation is clear. Customers are already digital-first, omnichannel, and highly selective. The brands that succeed won’t be the ones with the most tech, but the ones who know how to use it to listen better, respond faster, and engage smarter.

About Viseven

Viseven is a global MarTech company specializing in digital content solutions for the Life Sciences and Pharma industries. With over 15 years of expertise, Viseven empowers pharmaceutical companies and their production agencies with AI-driven content management and automation solutions.

Our flagship eWizard Platform streamlines content planning, creation, distribution, and management—enhancing efficiency, reducing operational costs, and accelerating brand time-to-market. Designed for omnichannel and multichannel engagement, eWizard optimizes campaign management, data collection, and performance analysis, ensuring continuous message improvement for Brand Managers and Content Operations teams.

Visit us at viseven.com or follow us on social media: LinkedIn