“If someone is able to show me that what I think or do is not right, I will happily change, for I seek the truth, by which no one was ever truly harmed.” — Marcus Aurelius, Meditations, Book VI, 21
Everyone wants to use AI. But where, exactly, should you use it? That’s the practical question we hear from CEOs and product leaders on repeat. They’re excited to apply AI, but also cautious. They want to use it everywhere but know to prioritize impact over novelty.
One of the core ways Mainsail AI Labs is helping our portfolio companies understand and choose which problems to solve with AI is a process called Value Stream Mapping (VSM).
In this article, I’ll walk through two high-leverage examples of how we’ve seen VSM used in practice to make AI decisions:
- Accelerating engineering throughput by identifying the real point of constraint.
- Reducing churn by optimizing customer onboarding.
Both are levers that most software businesses can pull to streamline processes with AI, unlock efficiency, and deliver more tangible value to customers.
Seek First to Understand, Then to Improve: What is Value Stream Mapping?
Value stream mapping is a visual, measurable method for gaining visibility into how work moves through a business, where it slows down or breaks, and where it can be improved.
VSM involves mapping the full flow of that work from initial trigger to delivery to the customer, and every step in between. But it’s not just a diagram of boxes and arrows – rather, VSM captures real data:
- Cycle time
- Wait time
- Throughput
- Quality
VSM often reveals that, as software companies scale, delays aren’t due to code issues or individual efforts. Rather, the holdups are in the silent handoffs, unowned issue resolution, and misaligned assumptions. VSM codifies each of these and enables you to focus on reducing those bottlenecks.
Traditionally, VSM was used in manufacturing. But like many ideas that started on the factory floor, it’s even more powerful in software development, where complexity increases exponentially.
VSM can also be applied externally to optimize processes like customer onboarding, adoption, renewals, go-to-market workflows, and retention loops.
In all cases, the result is a better sense of what your problems and opportunities really look like before you jump into solving them.
As Epictetus said, “First say to yourself what you would be, and then do what you have to do.”
Example #1: AI in Engineering
The Challenge: Engineering teams often start with AI coding assistants to help their developers work faster. But coding isn’t always the root of inefficiency, and improving code output without solving other workflow issues can make the problem worse. For most teams, the real delays stem from waiting on code reviews, QA or test pipelines, or deployment approvals.
Consider this… making developers 50% faster doesn’t make customers 50% happier if the code sits waiting in QA. Instead, it increases the burden on QA and reduces the organization’s overall capacity.
And if QA cuts corners to catch up, you risk delivering buggy products. Faster code just pushes more work downstream into another process that’s already struggling to keep up.
You can only move as fast as your slowest step in the chain.
How Value Stream Mapping Can Help: By mapping your process from idea to production – including key data points like cycle and wait times – the bottlenecks become clear, and you can pinpoint where AI could be most impactful. In our experience, it’s almost always in the waits, review queues, QA delays, and deployment hurdles, not coding.
Here is a typical Value Stream Map for engineering:
What it reveals as opportunities for AI:
- Code Review Bottleneck: If peer reviews take too long, use automated AI-assisted code reviews to do the first pass immediately after the code is checked in.
- QA Bottleneck: If manual QA is the longest process in your entire value stream, leverage AI for automated test generation to cover common and time-consuming test plans.
Once these are fixed, AI coding assistants can finally deliver value through a streamlined process.
Example #2: AI in Customer Onboarding
Not every AI use case lives in engineering. One of the most overlooked opportunities for AI in software companies is customer onboarding, and VSM can help here, too.
When we mapped one company’s onboarding flow, we found significant friction points:
- Delays between signature and kickoff
- Handoff gaps between Sales and Customer Success
- Repetitive requests for the same customer data
- Delays in onboarding due to manual data imports
Some of these could be addressed with workflow optimization. However, several had clear paths for AI or automation:
- Intelligent routing of customer requests
- Automated documentation delivery
- Pattern recognition to flag at-risk accounts before they stall
- Automated data imports leveraging existing APIs
With improved visibility, even basic automation shortened time-to-value and boosted retention for this company. As with engineering, VSM revealed where the drag was and how AI could help alleviate it. Best of all, the team achieved this automation without having to write any new code—a win-win for the business.
Why VSM Matters to Your AI Strategy
At Mainsail AI Labs, we believe goal setting is a critical step toward strategic application of AI in any part of your organization. VSM not only helps identify exactly where to use AI in your process, but, with that knowledge, you can then set measurable goals and identify how AI can help you reach them.
Sample Goals for AI in Engineering:
- Double the number of pull requests per developer in each period.
- Increase code coverage for test cases to 90%.
- Cut review time by 50% between when code is completed and pushed to production.
- Ensure every feature is documented.
Sample Goals for AI in Customer Success:
- Cut onboarding time by 50%.
- Respond to inbound product help inquiries automatically 90% of the time.
- Automate material generation for quarterly reviews using customer usage data.
5 Steps to Start Using Value Stream Mapping
You don’t need a team of consultants to do this. Start small:
- Choose one workflow: Onboarding, deployment, or whatever is keeping you up at night.
- List each step and who owns it.
- Mark the delays, handoffs, waits, and rework.
- Estimate the time spent on each part.
- Look for friction, then ask: How can AI help?
Pro Tip: Engage the people doing the work. What they say versus what you assume happens may surprise you.
AI ROI Starts with Clarity
AI is powerful, but not everywhere and not all at once. If you want AI to accelerate your business, you need to understand your business. That starts with mapping how value flows through it and finding the points where it gets stuck.
Once you have that visibility, AI becomes a strategy, not just an experiment.
Marcus Aurelius wrote that he sought the truth, by which no one was ever harmed. Value Stream Mapping is simply a tool to find that truth in your engineering, product and other cycles, eliminating the guesswork and assumption that AI is a magic wand.
Need help finding your truth? Leverage Mainsail AI Labs to quickly and effectively uncover the best use cases for AI in your business.
