Ticket triage · Production

TomTom's Generative AI Journey: Hub-and-Spoke Innovation for Location Technology

The problem

TomTom needed to stay competitive by adapting its location technology for AI use cases externally and streamlining internal operations, while managing GenAI risks such as hallucinations and potential confidentiality breaches—without substantial additional investment or a large influx of new hires.

Workflow diagram · grounded in source
1
Spoke team identifies opportunity
Trigger
Local teams with domain knowledge identify opportunities for GenAI applications within their domain.
▾ source quote
“Spokes, local teams with considerable domain knowledge and business understanding, are equipped with an understanding of GenAI's potential. This positions them to identify opportunities for GenAI applications within their domain, and they retain ownership of these solutions following their creation”
2
Hub and spoke frame problem
Human review
The hub and spoke teams work together to frame the problem, outline anticipated outcomes and impact, and design the solution.
▾ source quote
“the hub and spoke teams work together to frame the problem, outline anticipated outcomes and impact, and design the solution”
3
Joint POC development
Output
Both teams allocate a small amount of resources to jointly develop a proof-of-concept solution.
▾ source quote
“Both would allocate a small amount of resources to jointly develop a proof-of-concept (POC) solution - typically a task for 1-2 people over a few weeks”
4
Ongoing iteration and feedback
Feedback loop
Close collaboration continues for a few months for ongoing iteration and feedback, resulting in a seed product or process.
▾ source quote
“This close collaboration continues for a few months for ongoing iteration and feedback, resulting in a "seed" product or process”
5
Spoke team assumes full ownership
Output
Once the product matures, the spoke team takes over full product ownership and maintenance.
▾ source quote
“Once the product matures, the hub team provides consultative support as necessary, while the spoke team takes over full product ownership and maintenance”
Reported outcome

TomTom launched a ChatGPT location plugin, the Tommy in-car AI assistant, and multiple internal GenAI applications simplifying mapmaking and location technology development—all without significantly increasing budgets or expanding team size.

Reported metrics
Task performance improvement (cited external research)30-60%
Development time reductionfrom quarters to mere weeks
Innovation budget and headcount changewithout significantly increasing budgets or expanding team size
Reported stack
Azure OpenAIGitHub CopilotChattyMicrosoft 365 CoPilotBentoMLAzure MLChatGPTSalesforceWorkday
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Source
https://engineering.tomtom.com/GenAI-journey/
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Frequently asked questions

What did this team achieve with this AI workflow?

TomTom launched a ChatGPT location plugin, the Tommy in-car AI assistant, and multiple internal GenAI applications simplifying mapmaking and location technology development—all without significantly increasing budgets…

What tools did this team use?

Azure OpenAI, GitHub Copilot, Chatty, Microsoft 365 CoPilot, BentoML, Azure ML, ChatGPT, Salesforce, Workday.

What results were reported?

Task performance improvement (cited external research): 30-60%; Development time reduction: from quarters to mere weeks; Innovation budget and headcount change: without significantly increasing budgets or expanding team size (source-reported, not independently verified).

How is this ticket triage AI workflow structured?

Spoke team identifies opportunity → Hub and spoke frame problem → Joint POC development → Ongoing iteration and feedback → Spoke team assumes full ownership.

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