Lisa Knowles had never spoken to the customer. She'd never been in a meeting with them. But when she walked into a room in Japan as the only non-Japanese person at the table, she knew their goals, their competitive pressures, and their pain points — and she'd prepared the entire presentation in Japanese. It took her about an hour.
That's not a party trick. It's what happens when you build AI workflows on top of the customer data your teams are already collecting — and it's the kind of thing Lisa does every day as Strategic Program Manager at Miro.
In this episode of How to Love a Customer, our CEO & Co-Founder Mikhail Dubov sits down with Lisa Knowles to explore how Miro is using its own platform — and a culture of experimentation — to transform how go-to-market teams understand and serve their customers.
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🔑 Key Takeaways from the Episode
1. One AI Workflow, One Hour, One Transformed Customer Meeting
When Lisa needed to prepare for a meeting with a long-standing Japanese customer she'd never met, she turned to an AI workflow she'd built inside Miro. It pulled together every CSM note, scoping document, annual report, and competitor signal into a single briefing. Then she used it to generate tailored recommendations and output the slides in Japanese.
The customer was so impressed that the engagement changed shape entirely. What started as a general AI transformation conversation became a deep strategic partnership.
"I heard after that they were very impressed that someone who had never even spoken with them before could come into that meeting and be so prescriptive with recommendations." — Lisa Knowles
2. Customer Centricity Is Infrastructure, Not a Poster on the Wall
Lisa was direct about this: you can't just declare yourself customer-centric and expect it to happen. It has to be built into how teams work — the systems they use, the processes they follow, the expectations leadership sets. At Miro, one of the core values is "practice empathy to gain insight," and that's backed up by actual tooling and workflows, not just words.
"We can't just say we're customer centric and then all of a sudden we're customer centric. You have to have it from a leadership level — and now we're also implementing processes, ways of working around being customer centric." — Lisa Knowles
3. "The Canvas Is the Prompt" — Miro's Transparency-First AI Philosophy
Unlike chat-based AI tools where outputs appear from a black box, Miro's approach puts everything on the canvas. You can see inputs, trace how outputs were generated, and check the prompts behind any result. For Lisa, who comes from a data and analytics background, this matters enormously — it keeps the human in the loop and ensures evidence is visible at every stage.
"With Miro, the canvas is the prompt. So you can see everything, you can see what's going on, where the information is coming from." — Lisa Knowles
4. AI Flows — The Feature Customers Can't Stop Requesting
The standout AI feature Lisa highlighted is AI Flows: a visual way to build multi-step AI workflows directly on the canvas. Instead of chatting with a model, you connect inputs — sticky notes, product briefs, customer feedback — and watch the AI process them through defined stages: summarising, ideating, then outputting documents, emails, or images all in one step.
Since launching in beta at Miro's annual Canvas event, demand has been so strong that it's driving new teams onto the platform entirely.
5. Saving CSMs 4–6 Hours Per Engagement
Lisa's team found that customer success managers were already doing deep research before every engagement — often on their own time, after hours. The AI-powered briefing workflows didn't replace that instinct; they accelerated it. CSMs now save four to six hours per engagement and can reinvest that time in actual customer-facing conversations.
"They were always doing that research, even if it was after work and in their own time, because they wanted to go in and ensure that they were building the right solution and partnership with this customer." — Lisa Knowles
The metric Lisa's team optimises for? More customer-facing time. The less time teams spend assembling information, the more time they have to ask better questions and consult on the platform's best uses.
6. Everyone Should Be an Analyst — With the Right Guardrails
Lisa advocates for a model where data and analytics teams serve up the right tools so that everyone across the organisation can self-serve insights. But she's clear-eyed about the risk: AI can get things subtly wrong, and without domain expertise, those errors go unnoticed. The answer isn't to restrict access — it's to build systems that make validation easy and to keep subject matter experts close to the data pipelines.
"Everyone is an analyst and the data and analytics people are serving up the right tools to make them self-serve and to help them become analysts." — Lisa Knowles
💡 Why This Episode Matters
Miro's story is a useful case study for any CX or insights team navigating the AI transition. Lisa doesn't talk about AI in the abstract — she talks about specific workflows, measurable time savings, and the cultural infrastructure that makes adoption stick. The takeaway for CX leaders: technology only works when it's embedded into how people already want to work, backed by leadership, and aimed squarely at giving teams more time with their customers.
💬 Hot Takes
🎯 CX Buzzword That Should Retire: "Alignment" — because it focuses on where you are now instead of where you can go together.
🛒 Brand Shout-Out: Costco — for caring more about customer loyalty than any individual product, and using returns as data rather than losses.
✈️ Standout CX Moment: A handwritten note from Delta flight crew waiting on her economy seat as a SkyMiles member — proof that small, personal touches land harder than upgrades.
🚩 What Software Still Gets Wrong: Claiming customer centricity without building the systems, processes, and leadership behaviours to actually deliver it.
🎧 Listen to How to Love a Customer
If you work in CX, Product, UX, or Insights, this podcast is for you — filled with real customer stories, practical frameworks, and lessons you can apply tomorrow.
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Episode Notes
00:00 – Meet Lisa Knowles, Strategic Program Manager at Miro
From customer insights manager to strategic program manager — how AI transformation reshaped the role.
03:00 – What Miro Is Today: The Innovation Platform
Beyond the infinite whiteboard: brainstorming, ideation, reviews, and structured outputs for the full innovation lifecycle.
05:00 – AI at Miro: Philosophy and Approach
A culture of experimentation, safe adoption, and empowering every team to leverage AI tools.
07:30 – "The Canvas Is the Prompt"
Miro's transparency-first AI philosophy: visible inputs, traceable outputs, and humans in the loop.
09:30 – AI Flows: Miro's Breakout Feature
Visual AI workflow builder that takes multiple inputs and processes them through defined stages on the canvas.
11:30 – Challenges for Software Companies in the AI Era
Showing the art of the possible, bridging the gap between awareness and adoption, and partnering closely with customers.
15:00 – The Japanese Customer Story
How an AI-powered briefing workflow turned a first meeting into a strategic partnership — with slides in Japanese.
20:00 – Saving CSMs 4–6 Hours Per Engagement
Accelerating research so teams spend more time with customers, not preparing for them.
21:30 – Building Customer Centricity Top-Down
Why values, systems, and leadership behaviour all have to align for customer centricity to be real.
27:00 – Setting Up an Insights Function
Start with one person who listens well, build processes around evidence-based decisions, and scale from there.
29:30 – Everyone Should Be an Analyst
The right model: data teams serve up tools; everyone self-serves — with guardrails.
35:00 – Hot Takes Round
CX buzzwords, Costco love, Delta's handwritten notes, and what software still gets wrong about customer experience.
