From Kickoff to Launch in 90 Days: The Real Story Behind Fast Betty Implementations

As I hit the nine-month mark on the Betty Customer Onboarding team, it felt like the right time to step back and look across what we’ve learned from the roughly 70 customer launches and in-progress implementations that I’ve directly worked on. The pattern is clear: moving from contract to a live, member-ready AI assistant doesn’t have to take “forever.” With a little preparation and the right cadence, teams can consistently launch in under 100 days—and some have gone from kickoff to live in under 50!

If you’ve spent time in the association world (or any membership-driven org), the skepticism is understandable. New technology projects collide with board calendars, competing priorities, and real constraints. Add AI—an assistant that needs to read and reliably use a large share of your content—and the default assumption is that the timeline will balloon.

But when we reviewed emails, project plans, meeting transcripts, and post-launch outcomes, the teams that shipped quickly weren’t bigger, better-funded, or more technical. They did a few simple things exceptionally well—and avoided a handful of predictable traps.

What the fastest launches did differently

Across the fastest-moving teams, the same habits show up again and again:

1) They used soft launches to build momentum
They didn’t wait for a “perfect” day-one rollout. They launched to a small group first, placed the widget live without a full marketing push, or started with a subset of content while the remaining sources were being prepared.

2) They kept testing windows short—and scheduled
The best teams treated testing like a sprint. Testers were prepped and committed to specific, time-boxed sessions (often 30–60 minutes) within a tight window (commonly a week, sometimes just a few days, for everyone to complete that round of testing).

3) They ran parallel work streams
Content collation, ingestion, testing, and launch/marketing prep happened at the same time. No single thread became the bottleneck that stopped everything else.

4) They had an “AI-ready” mindset
They understood a key truth: AI doesn’t need to be perfect to be valuable on day one. The fastest teams were willing to launch, learn from real usage, and improve continuously.

5) They made content easier for AI to access—early
Teams that moved quickly prioritized making content available in ways that require minimal manual intervention (modern CMS patterns help a lot here).

Why projects slow down (and how to prevent it)

The “slow” implementations usually slowed down for a small set of reasons—not because AI is inherently complex, but because the inputs and workflows weren’t ready.

Extended testing rounds
When feedback cycles stretch into multiple weeks per round, momentum disappears and decisions stall.

Critical content that isn’t able or ready for ingestion
The most common offenders: disconnected vendors & inaccessible data sources, large sets of scanned or image-only PDFs, or scattered and out-of-date content where teams were expecting the ingestion process to also “clean up” pre-existing content issues.

Vague feedback (eg. “This doesn’t seem right”)
When testers don’t provide substantive feedback with URLs and/or expected answers, teams end up in feedback follow-up loops. The fastest teams use structured feedback responses that align with our distributed best practices and guidance (URL of correct content, example answer, desired behavior, etc).

Coordination gaps
Missing decision-makers, unclear owners for content areas, or the wrong people attending key meetings can add weeks to project due to missed communications and delays.

The simplest way to summarize the difference: fast-launch teams involved the right team members and didn’t wait for perfection or 100% data coverage. They started with a core group and pushed forward with what could be made available first, then iterated.

What we changed on our side to make launches faster

A “fast launch” requires coordination and shared efforts, but so far we’ve mostly discussed what our customers have done — it’s also on us to set the stage and guide customer teams to reduce any friction and improve clarity on the goal and how to achieve it. We have learned and adapted along the way, and here are a few of the most impactful adjustments we’ve made:

A stronger ‘quick start’ with the Onboarding Sheet
We now use early responses from the sales process and early questionnaire to start the dialogue before the content mapping call—making sure the right people are invited and that prep work is identified early. That alone has saved days (and in some cases weeks).

Earlier, more explicit ingestion and data decisions
We added clearer pointers on ingestion methods so your team can come to the first working session ready to recommend the best path—because no one understands your content landscape better than you do.

Earlier testing via temporary content paths
When the ideal automated method isn’t immediately available, we often start with an interim (but equivalent) static content set so testing can begin while the long-term connection is built. Earlier testing -> Earlier adjustments -> Earlier launch.

Marketing prep as a true parallel stream
We encourage customers to bring marketing in early—naming, branding, mascot design, launch messaging—so the “go live” moment isn’t delayed by internal approvals and creative cycles. We actively support this parallel stream during the project with meetings focused specifically on providing customer teams tools, examples and guidance.

Better tester enablement
Testing doesn’t require a heavy UAT machine—but it does require coaching. We now provide clearer guidance, including Best Practices and examples, on how testers should “train” Betty through feedback so cycles stay short and specific.

Three common bottlenecks (and the fix that keeps projects moving)

1) Protected-content bottleneck
Sometimes APIs don’t expose access-controlled metadata, so member-only content can’t be separated from public content without some (light, but important) API technical work. The fix: start with public content immediately, then swap in protected feeds once the API is ready—turning a dead stop into measurable progress within days.

2) Non-text content pretending to be “ready”
If a feed is actually images of text (or an image-based PDF viewer), ingestion becomes conversion work. The fix: gate-check text readiness upfront. If it’s image-only, convert it or use the original text source as primary.

3) The feedback vacuum
The longest cycles often come from non-specific feedback and over-extended testing cycles. Part of what sets Betty apart is how she learns from feedback. While she knows all of your content, she needs guidance to understand and align with the nuances of your organization, and this is why detailed feedback is critical. The fix: We strongly recommend structured testing timeframes and guided inputs (URL, expected answer, desired behavior) in accordance with our Best practices and guidance.

What this looks like in the real world (anonymized)

A medical organization that chose launch confidence over pre-launch paralysis
They set a soft-launch date early, requested embed code for their website immediately, and had a web partner build a custom API feed while branding and navigation were still being finalized. Small issues were handled iteratively after targeted go-live.

A content provider that set the “speed run” benchmark
They maintained momentum with clear, concise feedback and parallel work streams (uploads, whitelisting, content mapping, marketing prep), so backend details didn’t block the front-end experience.

Fire protection experts who delivered early value—and polished later
They launched aggressively with a clear content path, iterated quickly on style requirements, and avoided stalling even through a key personnel change because documentation and continuity were in place.

A lean, practical launch checklist for the first 30 days

If your goal is a real launch in under 100 days, the first month matters most. Here’s what to lock in early:

Week 1: Align and remove ambiguity

• Name an internal owner (and a backup).
• Confirm which content is public vs. protected (and how access is determined).
• Identify “text readiness” issues now (scanned PDFs, image viewers, etc.).
• Pick a soft-launch target date.

Week 2-3: Start ingestion and book testing

• Begin with the fastest available content path (even if it’s interim).
• Schedule a short, specific testing window with 3–5+ testers.
• Share a structured feedback template before anyone starts testing.

Months 2-3: Test, tune, and prepare launch assets

• Run a tight testing sprint and iterate quickly on the highest-impact gaps.
• Begin marketing prep in parallel: naming, personality, announcements, FAQs.
• Confirm what “good enough to soft launch” means for your organization.

This is the core idea: momentum is created by decisions, not by waiting.

Why this matters

If you’re evaluating AI assistants but worried the implementation burden will outweigh the value, here’s the reality we see every week: with a named owner, a clear cadence, short feedback loops, and parallel work streams, you can ship a branded, production-ready assistant in under 100 days.

What Betty will do for your organization

• Understand both public and member-protected content
• Respect your access controls
• Answer in your voice and cite authoritative sources
• Integrate with your existing website and stay in sync as content changes

Getting started

Do two things this week:

1. Name a project owner & separately a content champion that knows where the content is and how to get to it.
2. Book a 30-minute discovery call to map your content landscape and pick a soft-launch target.