When a business operates at scale, the volume of opportunity tends to grow faster than the systems that support it. This is particularly true in enterprise and public sector markets, where each new opportunity arrives in the form of a detailed RFP, requiring speed, accuracy, and consistency. Like many organisations in this space, Totara works with a steady flow of RFPs as a core part of its commercial activity.
The Opportunity
Totara is a leading provider of enterprise learning platforms, supporting organisations to deliver and manage workplace learning at scale. With a well-established presence across enterprise and public sector markets, the bid team plays a central role in converting opportunity into revenue.
Each RFP typically includes between 100 and 300 questions, covering areas such as security, accessibility, hosting, and commercial terms. The answers are well understood within the business, built up over years of product development, customer delivery, and previous responses.
As is often the case in organisations with a long track record, this knowledge exists across a range of sources – past RFPs, product documentation, internal systems, and team expertise. Bringing that information together in a consistent, efficient way is a natural area of focus as volume increases.
In practice, responding to each RFP required a structured process of retrieving, adapting, and assembling existing answers into a coherent response. While effective, this approach meant that the time required scaled alongside demand, particularly for more detailed tenders.
Why the conventional route required careful consideration
At first glance, RFP response appears well suited to automation. However, in practice, the requirements are more exacting than they initially seem.
In environments like this, responses must do more than sound plausible – they need to be accurate, aligned with product reality, and traceable back to a clear source. This is particularly important when dealing with areas such as security, compliance, and contractual commitments.
Generic AI tools can generate responses quickly, but without grounding in company-specific knowledge, they risk introducing inconsistency or inaccuracy.
There were also important considerations around how any solution would operate within Totara’s existing structure. This included maintaining clear traceability for audit purposes, supporting regional separation as the business expands internationally, and ensuring that the system could recognise when human input was required.
Taken together, this meant that any approach needed to balance efficiency with control, and automation with accuracy.
The AI Sherpa approach
A central principle of Tenzing’s AI Sherpa Programme is to build from what already exists. Rather than asking AI to generate new answers, the focus is on enabling it to find, apply, and adapt the right ones.
Working closely with Totara’s bid team, the first step was to bring together existing answer libraries, product collateral, and security documentation into a structured, searchable knowledge base. This created a foundation from which responses could be generated consistently and reliably.
From there, the platform was designed to interpret incoming RFP questions, retrieve the most relevant content, and draft responses in Totara’s voice. Each answer is assigned a confidence level, allowing the team to focus attention where it adds the most value.
High-confidence responses can be reviewed quickly, while lower-confidence questions are routed to the appropriate specialist, with relevant context already in place. The result is a more efficient workflow that supports the team’s expertise, rather than replacing it.
What changed – and how fast
The impact was both immediate and measurable – a process that previously took several working days could now be completed in under an hour, with the platform drafting approximately 200 responses in 40 minutes.
Quality remained high throughout. Internal testing showed that the majority of responses required only light editorial refinement and could be clearly traced back to source documentation.
“It has dramatically decreased my workload. The answers are 95% there, which lets me polish and focus on the more complex, nuanced questions instead of starting from scratch.” – John McCann, Business Development, Totara
The shift was not only in speed, but in how the team’s time was used. Instead of focusing on retrieval and assembly, effort could be directed towards the areas where experience and judgement have the greatest impact – shaping responses, refining positioning, and strengthening how proposals are presented.
A deeper shift
Over time, the benefits extend beyond individual RFPs. The knowledge base becomes a living asset, improving continuously as new information is added and existing answers are refined. Updates made in one place carry through to future responses, reducing duplication and improving consistency.
This also has implications for how the team operates. New members can contribute more quickly, without needing to absorb years of historical context, and past responses can be viewed alongside outcomes to support ongoing improvement. What was previously distributed knowledge becomes a structured, scalable capability.
The pilot to international rollout
The platform was initially developed with Totara’s UK bid team, with international rollout considered from the outset. Each region operates within its own secure workspace, allowing for local ownership of content while maintaining a consistent overall approach.
“This is something we are planning to roll out to our American and Australian businesses.” – Ian Regan, Chief Commercial Officer, Totara
This model supports both regional flexibility and global consistency, enabling the approach to scale alongside the business.
The takeaway
While the engagement centres on AI, the broader takeaway is about how organisations apply what they already know. The answers were always there, however the difference is that they can now be accessed and used more effectively, and at greater speed.
For teams working with structured, repeatable content – whether in sales, compliance, or client reporting – the constraint is often not the creation of new material, but the ability to apply existing knowledge consistently. Addressing that constraint can have a meaningful impact on both efficiency and capability.
About Tenzing’s AI Sherpa Programme
Tenzing’s AI Sherpa Programme combines AI-assisted prototyping, product strategy, and hands-on delivery to help organisations 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 enables teams to commit resources with confidence – and focus investment where it will deliver the greatest return.
