When an inbound engine is already performing well, AI creates an opportunity to extend its reach further. For our portfolio company Webexpenses, they had a strong flow of inbound demand and an established sales approach. Meaning they had the foundations to explore how AI could complement human follow-up. In turn? This could create personalised experience across a broader range of prospective customers. In short – the opportunity was not to replace what was working, but to build on it.
The opportunity
Webexpenses is a long-established expense management platform, trusted by finance teams to control spend, automate reimbursement and reduce the administrative burden of manual claims. Its marketing generates a healthy flow of inbound enquiries from organisations actively looking to solve a problem Webexpenses addresses well.
Those enquiries naturally span businesses of different sizes, sectors and requirements. Webexpenses identified an opportunity to use AI to create more tailored journeys across that audience, while allowing its sales team to continue focusing human attention where it adds the greatest value.
The ambition was clear from the get go: use AI to complement an already successful inbound model, and while doing so – create an additional route for engaging prospective customers at scale.
Why the approach required careful consideration
Unsurprisingly, personalisation at scale is harder than it first appears. Of course traditional automated sequences can increase reach, but Webexpenses wanted to go one step further – using the information available about each prospect to make communication genuinely relevant to their individual context.
Several things needed to work together:
- Better context: Understanding characteristics such as industry, company size and role at the point of enquiry
- Meaningful personalisation: Creating communication shaped by that context rather than simply inserting standard fields into templates
- Brand consistency: Ensuring every interaction maintained the credibility and tone customers expect from Webexpenses
- Internal capability: Building an approach the Webexpenses team could own, operate and continue developing
Combined, this made the opportunity about more than automation – it was about exploring how AI could create a richer and more scalable customer journey whilst doing so.
The AI Sherpa approach
Tenzing worked closely with the Head of Marketing at Webexpenses, and his talented team to develop two connected capabilities.
- AI-led triage. Enquiries are enriched as they enter the system, providing greater context around factors such as company size, industry and role. This information helps determine the most appropriate customer journey – including where immediate human engagement can add the most value.
- Hyper-personalised sequencing. For prospects entering an automated journey, communication can be generated using the context available for each contact rather than relying solely on standardised templates.
This enables messaging to reflect factors such as the recipient’s role, industry and business context while still operating at scale.
The journey progressively introduces richer content, including personalised messaging, AI-generated video and interactive product experiences – giving prospective customers different ways to engage with Webexpenses. Crucially, the approach was built alongside the Webexpenses team, enabling them to better understand, operate and extend the capability themselves.
But what changed, and how fast?
Early results have been encouraging. Most notably, engagement held across the sequence rather than falling away as the emails progressed – a pattern that runs counter to the typical drop-off seen across automated email journeys. This sustained engagement is an important early signal. Reason being, it suggests the personalisation was creating enough relevance to keep prospective customers engaged beyond the initial interaction.
Unsubscribe rates also remained low – this provided further indication that greater personalisation was indeed adding relevance, and most importantly without compromising the customer experience.
The approach has given Webexpenses an additional way to engage inbound demand, complementing its existing sales activity with a personalised journey that can operate at scale. It has also created a richer set of engagement signals. Behaviour throughout the journey can provide additional context around prospect interest and help inform where subsequent human engagement may be most valuable.
A deeper shift
Beyond the initial use case, the engagement has created a set of reusable AI-enabled capabilities.
Webexpenses now has experience combining enriched customer data, intelligent routing, personalised content and AI-generated video within its go-to-market activity. These capabilities have potential applications across other stages of the customer journey, ranging from prospect engagement through to onboarding and account management. And just as importantly, the Webexpenses team has developed the capability to take the approach further itself.
The team appointed an internal AI champion during the engagement and has already begun applying elements of the approach in other areas.
What this enables:
- More personalised engagement across a broader prospective customer base
- Faster experimentation and iteration using real engagement signals
- A repeatable approach that can be extended to future opportunities
AI in the funnel – what this demonstrates exactly
The Webexpenses engagement highlights a broader opportunity for businesses with established go-to-market engines. Reminder – AI does not have to replace existing sales and marketing processes to create value. It can sit alongside them – extending personalisation, increasing the range of customer journeys available and helping teams direct human attention where it can have the greatest impact.
For organisations already generating strong inbound demand, this creates an opportunity to extract even greater value from an existing strength. The Webexpenses work is an early example of that shift: taking a successful inbound engine and adding a new layer of intelligent, scalable personalisation around it.
About Tenzing’s AI Sherpa Programme
Tenzing’s AI Sherpa Programme combines AI-assisted prototyping, product strategy, and hands-on delivery to help organisation’s move from concept to validated product direction – quickly, and without premature engineering risk.
It is designed for situations where the opportunity is clear, but the optimal path forward needs to be proven.
By replacing speculative development with rapid validation, Tenzing’s AI Sherpas enable teams to commit resources with confidence – and focus investment where it will deliver the greatest return.
