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Most pharma companies calling themselves omnichannel are, in practice, still multichannel. That’s not a provocation for the sake of it — it’s the starting premise of the latest episode of Pharma Talks, and it sets the tone for a conversation that spends more time on organizational discipline than on any specific piece of technology.
Host Nataliya Andreychuk sat down with Matteo Colonna, a pharma marketing leader with an unusual background: he started in theoretical physics before spending years inside two major pharma companies, and now teaches future pharma leaders at Radar Academy in Milan. As Nataliya notes in opening the episode, it’s an unexpected combination — physics, omnichannel strategy, and hands-on pharma marketing experience — but one that, in her words, “can deliver a real result, with some unexpected moves and decisions.”
What follows is a wide-ranging discussion about AI in pharma: where it genuinely moves the needle, where it’s oversold, and why — according to Matteo — almost none of it works without first fixing how people and functions work together.
Matteo opens with a principle he returns to throughout the conversation: don’t start with the tool.
“The way I approach this is not starting from technology. You firstly identify what the business needs, and then work back with the technology. It has to help us achieve our goals and deliver value to our customers.”
That sequencing matters more now than it did a few years ago, Nataliya points out, because the inputs pharma marketers have access to have changed completely. Where the industry once spent millions on formal market research, social media listening tools now surface customer behavior, preferences, and interests continuously and cheaply. In theory, pharma is better equipped than ever to understand what its customers want to hear.
In practice, she argues, “the devil is in the details” — and the gap sits in execution. People, process, and technology are the well-worn milestones everyone in pharma already recognizes. What’s harder is pinpointing exactly where, inside a given organization, that gap actually lives.
Matteo agrees that most of the industry is still early in this journey. “This is the very beginning,” he says. “After the pandemic, companies really started investing in omnichannel solutions, and we still have to stay on top of those things.” His view is that the fix isn’t procedural — it’s cultural. Companies need a clear roadmap for delivering relevant, targeted experiences, but that roadmap has to cover skill sets, ways of working, and whether teams are genuinely operating cross-functionally toward the same objectives, not just the technology layer underneath it.
Underneath all of that sits a simpler idea: the customer doesn’t experience your org chart. “The customer is the same,” Matteo says. “We want to engage our customers with perceiving our company as the same, even though touch points are delivered by reps.” That means omnichannel execution has to be prepared strategically first, with processes and systems built to support consistent delivery — not layered on after the fact.
Nataliya pushes on this directly: is siloed, fragmented data — separate systems, no unified view of the HCP — actually solvable, or has it become pharma’s standing excuse for moving slowly?
Matteo doesn’t dodge the framing. “This is the kind of provocation that I like,” he says, before conceding the point: disconnected systems and organizational silos are “a common issue” that most companies are still working through, even the ones further along in their journey. But he resists treating it as an intractable, industry-wide condition. Players like Amazon and Google have already set the customer experience standard that HCPs now bring to every interaction, pharma included — and closing that expectation gap is exactly where competitive advantage will be won, not lost.
His advice for teams stuck waiting on the “perfect” data infrastructure is pragmatic rather than aspirational: start with what you already have.
“We can really start working on the data we have today, generate insights — there are things we can start now, and plan proper solutions to engage customers the way they want to be engaged.”
Nataliya frames this as the difference between real digital transformation and the version that lives only in strategy decks:
“Start with what you have. Use the signals you can already see. That is the practical version of digital transformation — not the beautiful slide version. Tomorrow it will be too late. Start now.”
The conversation’s sharpest turn comes when it moves to AI in omnichannel marketing specifically. Matteo’s first instinct is to slow the enthusiasm down. “Everybody wants to try it, of course,” he says, describing a common pattern: companies chasing “the latest fancy system” mainly because a competitor has it, without first defining the problem it’s meant to solve.
His rule for AI investment is the same discipline he applies to omnichannel generally: identify the business problem before touching the technology. “The companies that can start from the business problem are the ones who do better,” he says — and that includes being rigorous about how value gets measured, not just for AI but for any technology layered into a project. Leadership, he notes, is increasingly asking exactly that question: what real value does this deliver for the business and the customer, and do our people actually understand how to use it? Without that understanding, even a well-chosen AI investment becomes a risk.
Where Matteo does see AI delivering measurable value, the use cases are specific rather than sweeping:
The common thread, he stresses, is starting from an understanding of the company’s actual needs and goals rather than chasing a single flashy use case with a custom-built solution.
But Matteo is just as direct about where expectations have run ahead of reality. “There is a mismatch about AI,” he says. Too many organizations expect it to fully solve their problems — “we can sit down and relax” — when in fact “human is still critical, in AI especially.” Final decision-making, he argues, has to remain with people: human-in-the-loop is “still relevant,” even as some tasks are fully automatable.
Beneath that sits a more structural problem: data quality. “You have to be sure your system captures data the right way and provides quality insights,” Matteo says. “Otherwise, it’s garbage in and garbage out.” Data needs to be clean and integrated across sources before any meaningful AI layer can sit on top of it — something he calls “very critical for pharma companies” specifically, given the compliance stakes involved.
The other unresolved piece is governance. Who is accountable when the AI is wrong? Matteo argues this question — along with clear change management and a genuine mindset shift toward having “AI in DNA” — has to be addressed before, not after, deployment. Nataliya sharpens the point further, noting that being an “AI-first” organization doesn’t offer an escape from that accountability question. “That is the question nobody is asking loudly enough yet,” she says.
Zooming out, Nataliya asks Matteo what’s actually changed in HCPs expectations from pharma, having seen the shift both from inside pharma companies and now from the outside, training the next generation of leaders.
The clearest signal, in his view, followed the pandemic: access to HCPs has narrowed, and the time they’re willing to give pharma reps has shrunk with it. Part of that is a deliberate shift in what HCPs want from the interaction — moving away from basic product information and toward value-based insight that’s genuinely useful to their practice. HCPs, Matteo notes, are already sourcing product and clinical information elsewhere: research he cites suggests roughly 70% of HCPs across Europe are already using tools like ChatGPT to do their own research.
That shift changes what reps, MSLs, and digital engagement channels are actually for. The value pharma can add is moving away from being an information source and toward becoming what Matteo calls a long-term partner — helping guide HCPs toward the best specific treatment for the patient, rather than simply delivering medical content.
Making that real, in his view, requires several things pharma organizations still under-invest in:
Asked for the single thing pharma most consistently gets wrong in trying to make this transformation real, Matteo doesn’t hesitate: starting with the tool rather than the need.
“One of the biggest mistakes companies can make is just saying, ‘I want these specific tools, this AI, this CRM,’ because it’s nice. This is not the correct way to do it.”
Only once the vision and strategy are clear should governance, roles, and objectives be defined around the technology chosen to support them.
Nataliya connects this back to a familiar industry statistic: around 80% of pilots never scale beyond the pilot stage. The opportunity AI creates, in her framing, isn’t just efficiency — it’s the freed-up capacity that comes from automating routine tasks, which teams can then redirect toward bigger, non-routine priorities. Closing that loop, so the learning from a pilot compounds into something sustained rather than stalling out, is, in her words, “the difference between a transformation that sustains and one that stalls after the pilot.”
To close, Nataliya puts Matteo through a quick-fire round: a series of pointed, sometimes deliberately provocative statements about pharma, AI, and omnichannel — one sentence, no hedging.
Taken together, Matteo’s answers land in one place: the obstacles pharma keeps citing — technology, data, channels — are rarely the real ones. Almost every “hot” answer points back to people, coordination, and how the organization chooses to work.
Closing the episode, Nataliya sums up what stood out: Matteo consistently returned to business need before technology, and to people before process — the change management, the cross-functional coordination, the ways of working that either make a transformation stick or quietly kill it.
Her takeaway is a useful filter for any pharma organization currently mid-transformation: the companies that will win this decade won’t be the ones with the most sophisticated omnichannel AI stack. They’ll be the ones that connected that technology investment to a genuine shift in how they serve the HCP — and built the internal discipline to sustain that shift well beyond the pilot stage.
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