You’ve spent two decades across media agencies, creative agencies, and platform-side roles. How has that breadth shaped the way you think about marketing leadership as CMO at boost.ai?

It’s made me very allergic to siloed thinking. Agencies teach you storytelling and persuasion. Platform roles teach you product truth and scale. When you’ve lived on both sides, you learn marketing can’t just be the “loudspeaker” – it has to be the bridge between what a product genuinely does and what the market genuinely needs.

As CMO, my job isn’t just awareness. It’s alignment. Narrative has to connect product reality, customer problems, sales conversations, and category dynamics. My background means I’m comfortable in brand, demand, and product conversations – and that’s critical in AI, where credibility matters more than noise.

What drew you to boost.ai, and what convinced you that conversational AI was at a tipping point for enterprise adoption?

Two things: product maturity and market pressure.

Conversational AI has been “almost there” for years. From my own experience, it often looked promising in demos but struggled in real-world complexity. What I saw at boost.ai was different. This wasn’t a vision of what could be – it was production-grade performance in complex, regulated environments. That got my attention.

What genuinely excited me, though, was the customers. I’ve worked at major platforms with world-class brands, and getting strong public advocacy is rare. At boost.ai, there were multiple enterprise customers openly sharing measurable success, often proactively. That level of validation told me this wasn’t experimentation – it has proven impact.

That’s a very different category from novelty AI, and gives a foundation for my role to succeed.

On a personal level, it’s also rare to step into a role where you can truly shape and define a brand at a category-defining moment.

At the same time, the market forces are undeniable. Enterprises, particularly in financial services and other regulated sectors, are under intense pressure from cost, rising customer expectations, and the need to scale digital service. They can’t hire their way out of the problem. AI shifted from being a future innovation topic to an operational necessity. That’s when you know a market has reached its tipping point.

Where do traditional chatbots most commonly break down from a CX standpoint, and how does boost.ai avoid those failure points?

Most traditional chatbots tend to break down in three key areas. The first is understanding. They can be brittle, struggling with even slightly unexpected phrasing and quickly failing when conversations move beyond tightly scripted inputs. The second is depth. Many bots can handle basic FAQs but fall apart when asked to support real service journeys that involve context, nuance, or multiple steps.

The third challenge is trust. When these systems make mistakes, they often do so with misplaced confidence, which frustrates customers quickly and erodes belief in the channel altogether.

boost.ai was built to manage real service complexity, not just surface-level interactions. That means stronger language understanding, structured conversational design, and the governance needed for enterprise use. The goal isn’t to imitate humans – it’s to deliver reliable, consistent help at scale.

Trust is as central as speed to AI adoption today. How does boost.ai design conversational experiences that feel both efficient and safe?

We think about trust on three interconnected levels. First, there’s customer trust, which comes from delivering clear and predictable experiences. That means providing reliable answers, avoiding hallucinations, and recognizing when a conversation should be handed off to a human. Consistency and transparency are what make customers comfortable engaging with AI in the first place.

Then there’s enterprise trust. Organisations, particularly in regulated industries, need control, compliance, and visibility into how AI is operating. You can’t deploy systems you can’t govern. Confidence at this level comes from strong oversight, clear guardrails, and the ability to understand and manage how decisions are made.

Finally, there’s brand trust. A conversational assistant isn’t just a tool – it represents the company. It has to reflect the organization’s tone, policies, and risk posture in every interaction. Efficiency may be what initially captures attention, but trust is what ultimately makes AI viable and sustainable in production environments.

Conversational AI is often overpromised. How do you market boost.ai’s capabilities without feeding unrealistic expectations?

We try to be very specific about where AI works well and where it doesn’t. That’s actually more powerful than broad claims.

Instead of “AI transforms everything,” we talk about measurable outcomes in defined use cases: containment, resolution rates, CSAT, operational impact. Enterprise buyers are sophisticated – they’re not looking for magic, they’re looking for reliability.

Credibility is a long game. Especially in AI.

How do you help customers shift their mindset from cost reduction to experience transformation when adopting conversational AI?

Cost reduction often gets these projects approved, but experience is what makes them strategic. We help organizations see that conversational AI isn’t simply about deflection – it’s the emergence of a new service channel. One that’s always on, instant, and consistent. When designed well, it removes the friction customers have quietly learned to tolerate. That doesn’t just improve efficiency; it reshapes how people perceive the brand.

The conversation moves from “How many contacts can we avoid?” to “How do we serve millions of everyday micro-moments better?” That shift is where real transformation happens.

We brought this to life through our recent Do Better campaign. The films highlight the small but familiar frustrations people experience with poor conversational AI – the wrong name, endless loops, talk to a human, where’s my package? – moments we’ve all lived through. Each one ends not just with a criticism, but with an ambition: to do better, and to show that better is possible.

Ultimately, we’re trying to tell a bigger, more human story. Expectations have changed. Conversational AI is no longer just an operational tool – it’s a frontline brand experience. Get it right, and you build trust and loyalty over time. Get it wrong, and you create a pattern of frustration people don’t forget.

How do you foster collaboration between marketing, product, and engineering to tell a coherent story to the market?

You can’t tell a strong market story if marketing sits downstream from product decisions. The narrative has to be built alongside the product, not layered on afterward. That requires real alignment on what we are uniquely built to solve, where we deliberately choose not to compete, and the proof points that come from real-world deployments rather than theory.

The most effective messaging doesn’t come from a workshop or a clever line on a slide. It comes from listening closely to product teams, understanding the realities customers face, and translating that truth into language the market can recognize and trust.

What are you most focused on evolving next – boost.ai’s narrative, its reach, or its role in the broader AI ecosystem?

Narrative and role in the ecosystem go hand in hand.

Conversational AI is moving from being seen as a feature to being understood as infrastructure for digital service. I’m focused on positioning boost.ai in that shift – not just as a vendor, but as a foundational layer in how enterprises orchestrate and deliver customer interactions in an AI-first world.

Reach follows when the narrative is clear and credible, and we continue to tell our story in interesting and confident creative ways.