Questions we hear most.
Answers to what product leaders typically want to know before they reach out. If yours isn't here, just ask.
What exactly does Colab do?
Colab helps product teams get to market faster and build better products by strengthening their capabilities, redesigning their product development lifecycle (PDLC), and embedding AI-native ways of working. We work across three areas: capability building (training & coaching), AI tool enablement, and systems & playbooks (workflow design, product ops, transformation delivery).
How is this different from a standard training course or consultancy?
We're not a one-off training provider or a traditional consultancy. We partner with your team over 3–6 months, combining live workshops, discovery, hands-on AI workflow design, and embedded coaching. The goal is lasting behaviour change — not just awareness. We also bring tool partnerships (Miro, Productboard, Pendo) to activate workflows inside the tools your team already uses.
What does a typical engagement look like?
Most engagements follow four stages: Diagnose — AI & product maturity assessment, skills audit, exec roadmap; Accelerate — live workshops and AI workflow training grounded in your real work; Embed — apply new practices inside a live pilot team; Transform — full operating model: structure, hiring, tooling, and culture. Engagements typically run 6 weeks to 6 months depending on scope. We prove it works at the team level before scaling.
How long does it take to see results?
Clients typically see early signals within the first 6 weeks. Measurable outcomes we've tracked include: 28% faster discovery cycles (EROAD); 56% increase in AI confidence in product workflows (Wood Mackenzie); 33% improvement in commercial acumen and product strategy communication; discovery confidence 4.8 → 8.2 out of 10 (EROAD); stakeholder influence 5.6 → 8.5 out of 10 (EROAD).
How do you baseline and track before vs. after?
We run a 30+ dimension skills assessment at the start and end of every programme — covering discovery, commercial thinking, stakeholder influence, AI confidence, and more. Participants self-rate on the same instrument both times, giving a direct before/after comparison. We pair this with midpoint champion 1:1s and end-of-programme reviews for qualitative signal.
What's the minimum team size?
Our programmes are designed for a minimum of 8–12 people. For larger orgs (50–150 people), we run multiple cohorts in parallel or sequence.
Is delivery in-person or remote?
Both. We recommend an in-person kickoff — which consistently drives higher engagement and connection — with the remainder delivered virtually. We operate globally across APAC, UK, and North America.
Do you work with our existing tools, or do we need to change our stack?
We're tool-agnostic and adapt to your existing stack. We have deep partnerships with Miro, Productboard, and Pendo, and can activate AI workflows inside those tools — but we work with whatever your team uses (Jira, Confluence, Figma, Claude, etc.).
We already have AI tools — why do we need Colab?
Having tools and using them well are very different things. Our data shows 91% of product teams report some AI integration, but only 6% are scaling it across departments. The gap isn't tooling — it's the operating model, workflows, and PM capability to use AI systematically. We close that gap.
How do you approach AI adoption for product teams?
We start with the PDLC, not the tools. You can't accelerate a process that doesn't exist — so we map how the team actually works first, then identify which steps need humans, which can be automated, and where AI adds the most leverage. The highest-value starting points we see consistently: discovery synthesis, stakeholder communication, and weekly reporting. We build shared AI workflows (gems, agents, prompt libraries) around those steps so the whole team benefits — not just the individuals who've figured it out themselves.
Do you have a framework for automation vs. agentic workflows?
Simple rule: if you can write a complete flowchart for it today, automate it. If it depends on judgment or unstructured inputs, consider agentic. If it's too complex, keep a human in the loop. For most product orgs, the highest-value starting point is agentic support for discovery synthesis and stakeholder comms, with automation handling operational plumbing underneath.
Our engineering team is already ahead on AI. Is this just for PMs?
This is one of the most common situations we see. Engineering adopts Cursor and Claude Code; PMs fall behind. Our programmes are designed specifically to close that gap — helping PMs and product leaders operate at the same pace as engineering, and redesign the PDLC so the whole team moves faster together.
We've tried training before and it didn't stick. What's different?
Most training fails because it's disconnected from real work. We use your actual products, roadmaps, and challenges as the content — not generic case studies. We run discovery sessions with each participant before the programme starts, and embed coaching and office hours throughout.
How do you engage and motivate people at different skill levels?
We run discovery before the programme starts to understand where each person is — their confidence, frustrations, and what they want to improve. Sessions are built around participants' real work, which keeps motivation intrinsic. We also create visibility moments — presenting back to the team or leadership — which accelerates both learning and buy-in.
How do you handle people who aren't motivated or resistant to change?
Resistance usually signals something — unmet expectations, low psychological safety, or a mismatch with what they think they need. We get curious first, not pushy. We pick it up through champion conversations and session dynamics, then work with the sponsor to address it directly. In our experience, once resistant team members see peers gaining real confidence, the pull effect does more than any push we could apply.
How do you scale what worked in the pilot across the rest of the organisation?
We codify before we scale — documenting what made it work, what's essential to replicate, and what was specific to that team's context. We use a pull model rather than a top-down mandate: let other teams opt in once they've seen the results. We embed a champion from the pilot team into each new team to carry the practices, with the same success metrics throughout so you're comparing apples to apples.
Should we hire someone internally or bring in a partner like Colab?
A full-time hire makes sense when you have a stable, large team and a clear ongoing programme to run. The challenge is that a single person rarely has the breadth a transformation requires — and they lack external perspective. A partner is typically the right move when you're in a build or shift phase. Most clients do both: we build the foundation, they hire someone to maintain it once it's working.
Can you work with teams across multiple regions or time zones?
Yes. We've run programmes across UK, APAC, North America, and Europe simultaneously — including split APAC/EMEA session slots.
How do we get started?
The best first step is a 30-minute discovery call to understand your team's current state and goals. From there, we typically recommend starting with our PDLC + AI Readiness Diagnostic — a 3–4 week engagement that gives you a clear picture of where your team is and a 90-day uplift roadmap.
We'd like to partner with you.
The fastest way to know where to start is a 30-minute conversation. We'll listen, share what we'd typically recommend, and tell you honestly if we're the right partner.