AI Adoption Program: 4,000+ hours unlocked, €50K+ efficiency gains

The problem:

Backbone runs an integrated platform that covers visuals, branding, listing content, and documents — and their business depends on moving fast and staying consistent at scale. Internally, that means many teams constantly doing variations of the same high-leverage work:

• •researching accounts/markets and prioritizing efforts
• •creating content and messaging
• •outbound and follow-up coordination
• •analyzing performance and bottlenecks

They had dozens of AI-worthy opportunities — but adoption was messy:

• •teams tried different tools independently
• •duplicated work across teams
• •workflows stayed local instead of becoming shared systems
• •no consistent way to prioritize, scope, and govern what gets built
• •weak incentives/accountability to push adoption into core operations

Their current approach:

• •bottom-up experimentation with tools and prompts
• •ad hoc workflows living in personal setups
• •enablement wasn't connected to day-to-day execution

The consequences:

• •wasted time + duplicated effort
• •promising prototypes that didn't survive beyond one team
• •inconsistent standards (maintenance, ROI framing, governance)
• •no clear roadmap for what to scale across the organization

The goal wasn't "run AI training." The goal was to make Backbone AI-native: shared language, practical building capability, incentives, and a repeatable system for selecting and scaling what works.

1. Full-day workshop: capabilities + building muscle (40 people)

We ran a hands-on workshop with 40 Backbone team members, focused on what matters in their environment (fast execution, consistency, scale):

• •Where agents fit in Backbone's context: research, content creation, outreach, browsing, analysis
• •How to identify bottlenecks and shortlist high-leverage workflows
• •How to scope and de-risk (inputs/outputs, data access, ROI, edge cases, maintenance)
• •How to build quickly using no/low-code tools like Dust and n8n
• •How to turn prototypes into a roadmap (quick wins vs foundational systems)

This wasn't theory — teams built real prototypes so they could execute immediately.

2. Adoption contest: incentives + accountability (8 teams)

To convert training into adoption, we launched an internal contest:

• •8 teams participated
• •each team picked a workflow, built an agent, tested it, and proved value
• •the contest created momentum, visibility, and healthy pressure to ship
• •it surfaced internal "AI champions" naturally — the people who build and drive adoption

Examples of workflows built:

• •Sales copilot to research leads and prepare account context
• •Inbound qualification to respond faster and route prospects correctly
• •Additional research/outreach/analysis automations across teams

3. Demo day + jury: select what gets scaled (and why)

Teams presented their projects and were evaluated against a shared rubric:

• •ROI / measurable impact
• •maintenance burden and ownership
• •governance / risk (data handling, failure modes)
• •ease of use and adoption likelihood
• •reach across teams
• •integration complexity

This turned "cool demos" into an execution pipeline: what gets productized, funded, and scaled.