Speaker 1: Welcome to the deep dive. Today. We're really digging into
Speaker 1: something fascinating the art, maybe the science of AI product development. Right,
Speaker 1: We've looked at some great material from an expert in
Speaker 1: the field, focusing on how you actually build AI that
Speaker 1: delivers real business value. And our mission for you listening
Speaker 1: is really to cuf through that complexity, give you the
Speaker 1: tools to approach AI projects with let's say, ambition and clarity.
Speaker 2: Yeah, and what's so interesting is that AI, Well, it
Speaker 2: feels a bit like science fiction sometimes, doesn't it.
Speaker 1: It really does.
Speaker 2: But the actual building blocks, they're becoming more and more accessible,
Speaker 2: almost plug and play in some cases that there's a catch.
Speaker 2: There's always a catch despite the promise, there's still a
Speaker 2: lot of complexity, a lot of uncertainty.
Speaker 1: So what we want to do today is show you
Speaker 1: how to maybe sidestep the common pitfalls.
Speaker 2: Exactly learn on the job, but in a structured way.
Speaker 2: We're drawing directly from the experiences laid out in the
Speaker 2: material we reviewed.
Speaker 1: Okay, so let's untack this first big question, why even
Speaker 1: bother with an AI development project?
Speaker 2: Right? The fundamental why.
Speaker 1: The sources we looked at make a pretty bold claim here.
Speaker 1: They say if your business offers digital products or services, well,
Speaker 1: AI isn't just an option. It can enhance or even
Speaker 1: totally transform what you do.
Speaker 2: It's a big statement, but think about the scope refuining marketing,
Speaker 2: automating customer support.
Speaker 1: Adding smart search features, building completely new disruptive products.
Speaker 2: Maybe exactly. The potential is huge.
Speaker 1: But and this is a big butt. It feels like
Speaker 1: we hear about AI projects going sideways pretty often. Why
Speaker 1: is that?
Speaker 2: That's the crucial follow up question, isn't it? And the
Speaker 2: sources definitely highlight some critical mistakes, like what Well, a
Speaker 2: really common one is simply using AI for the sake
Speaker 2: of AI, you know, building a cool tech solution that
Speaker 2: doesn't actually solve a real problem.
Speaker 1: Right the solution looking for a.
Speaker 2: Precisely the material gives this example of financial analytics dashboard
Speaker 2: already crowded with info. Okay, and the team decides haylo
Speaker 2: out of chatbot. Why is because you know, jenai hype
Speaker 2: is everywhere?
Speaker 1: Ah okay? And how did that turn out? Because this
Speaker 1: is where it gets.
Speaker 2: Interesting, right, it gets very interesting. The sources point out
Speaker 2: that this team ended up creating more errors than actual insights. Ouch. Yeah,
Speaker 2: the chatbot just couldn't handle what users threw at it.
Speaker 2: Requests were too complex, sometimes way off topic. People even
Speaker 2: tried asking for investment advice it wasn't supposed to.
Speaker 1: Give, so that really highlights some core problems.
Speaker 2: Then, Absolutely, things like misaligned data the user needs just
Speaker 2: weren't connected to the data the model was trained on.
Speaker 1: Makes sense, and a lack.
Speaker 2: Of guidance in the UI. You know, a simple chat
Speaker 2: box invites basically anything, but the model couldn't cope.
Speaker 1: And user expectations probably didn't help.
Speaker 2: No, definitely not overblown user expectations fueled by you know,
Speaker 2: maybe flashy marketing images that promised more than the AI
Speaker 2: could deliver.
Speaker 1: So connecting this back, the big takeaway is you absolutely
Speaker 1: need a clear opportunity, a real problem to.
Speaker 2: Solve you got it, have to justifies the investment. The
Speaker 2: sources propose this mental model for AI systems three core parts,
Speaker 2: which are data intelligence that includes the AI models themselves,
Speaker 2: and the user experience.
Speaker 1: Okay, data intelligence UX.
Speaker 2: And all of that is constrained by and this is
Speaker 2: crucial AI governance requirements. Can't forget those guardrails.
Speaker 1: Right, governance is key. So using that model, how do
Speaker 1: we actually find those good opportunities those high impact areas.
Speaker 2: It's really about spotting where AI can significantly improve things
Speaker 2: for the user. Let's take a music streaming app. Okay,
Speaker 2: users often get stuck in a rut, right, yeah, struggle
Speaker 2: to find new music.
Speaker 1: Yeah, it happens to me all the time.
Speaker 2: So AI powered recommendations there that could be a total
Speaker 2: game changer. Boost engagement, help discovery.
Speaker 1: Makes perfect sense. And the sources mentioned several types of
Speaker 1: benefits AI can bring beyond just you know, shiny new features.
Speaker 2: Oh yeah, definitely. It's not just about innovation. First up is.
Speaker 1: Automation, okay, reducing manual work.
Speaker 2: Cutting the cost of manual processors. Yeah, But the sources
Speaker 2: emphasize this AI cost equation. It's not just building and
Speaker 2: running the AI.
Speaker 1: What else is in there?
Speaker 2: You have to factor in the cost often overlooked of
Speaker 2: finding and fixing the mistakes the AI makesh okay, and
Speaker 2: the risk of mistakes you don't catch that could be significant.
Speaker 1: That's a really important point about hidden costs. What's next
Speaker 1: after automation.
Speaker 2: Then there's improvement and augmentation. This is where AI supports
Speaker 2: human creativity, doesn't replace it.
Speaker 1: Interesting like a copilot kind of Yeah, Yeah, The example
Speaker 1: given is Miro, the whiteboarding software.
Speaker 2: They use AI for brainstorming, diagramming, summarizing ideas. It plays
Speaker 2: to both human strengths and AI strengths. Nice mortalization, adapting
Speaker 2: the product to what each user needs, like that music
Speaker 2: app learning your taste over.
Speaker 1: Time, tailoring playlists to my mood. I like that.
Speaker 2: Exactly, But you need to be realistic. Good personalization it needs.
Speaker 1: A lot of data, and bad personal is you can.
Speaker 2: Really push users away. Privacy concerns too.
Speaker 1: Obviously true, Okay, any others.
Speaker 2: One more convenience, just making things easier, reducing friction. Think
Speaker 2: of an AI search that anticipates what you're looking for.
Speaker 1: Get see the answer faster. Yeah, I can see the
Speaker 1: value there.
Speaker 2: Absolutely.
Speaker 1: So. The sources also talk about two different ways to
Speaker 1: actually launch these AI products, two strategic approaches.
Speaker 2: That's right. It really depends on the situation. The stakes.
Speaker 2: First is the careful approach, think ready, aim.
Speaker 1: Fire, so lots of planning upfront.
Speaker 2: Loads of it SORO research validation before you commit to
Speaker 2: significant development. This is for when the cost of failure
Speaker 2: is really high, like healthcare. Perfect example, health tech company
Speaker 2: building a diagnostic feature, you'd need to rigorously check, user impact, feasibility,
Speaker 2: business value regulations, everything you can't afford to get it wrong.
Speaker 1: Makes sense.
Speaker 2: And the other approach, that's the fast approach, ready fire aim.
Speaker 1: So build something quickly and see what happens.
Speaker 2: Pretty much, get a prototype out there fast, get real
Speaker 2: world feedback almost immediately, like.
Speaker 1: The music recommendation feature we talked about.
Speaker 2: Exactly like that. Yeah, speed is key, Failing fast is
Speaker 2: okay if the initial costs are low and maybe the
Speaker 2: market's crowded. Yeah, you learn by doing.
Speaker 1: And that fits well with how AI models are developed right,
Speaker 1: iteratively perfectly.
Speaker 2: It lets you reduce that uncertainty step by step based
Speaker 2: on how real users interact with it.
Speaker 1: Okay, great, so we know YAI, how to find opportunities,
Speaker 1: different ways to launch. Let's get into the actual building blocks.
Speaker 1: The sources call it the AI solution space.
Speaker 2: That's the term they use yet data, intelligence and user experience.
Speaker 1: Let's start with data, the fuel for the AI.
Speaker 2: Absolutely described as the fuel. It's the raw material for training,
Speaker 2: for fine tuning, for evaluating everything.
Speaker 1: And it comes in different forms the.
Speaker 2: Forms textual data, needing NLP, visual data, auditory data, sensorimotor
Speaker 2: data for robotics, self driving cars, even computer code itself
Speaker 2: like for get Up Copilot.
Speaker 1: Wow. Okay, broad range, very.
Speaker 2: Broad but the absolute crucial point, and the sources hammer
Speaker 2: this home. Data quality directly drives output.
Speaker 1: Quality, garbage and garbage out.
Speaker 2: Essentially, that's the classic saying, and it's never been truer
Speaker 2: than with AI. The quality of your data dictates the
Speaker 2: value you can provide. Period, got it?
Speaker 1: And how does the AI actually learn from this data?
Speaker 1: The sources mention different types of learning, right.
Speaker 2: They distinguish between unsupervised learning and supervised learning.
Speaker 1: Okay, what's the difference.
Speaker 2: Unsupervised learning is about exploring the data, finding hidden patterns
Speaker 2: structures like clustering users into segments based on their behavior
Speaker 2: without knowing the segments.
Speaker 1: Beforehand, so letting the data speak for itself in a way.
Speaker 2: Yes, it helps uncover insights you might not expect. Then
Speaker 2: supervised learning is different. You train the model with labeled data.
Speaker 1: So you tell it what the right answer is, for example, exactly.
Speaker 2: You give it examples with known outcomes, so it learns
Speaker 2: to classify new unseen data, think predicting customer churn or
Speaker 2: segmenting users based on predefined criteria.
Speaker 1: Okay, unsupervised for exploring supervised for predicting or classifying based
Speaker 1: on known labels makes sense.
Speaker 2: And underlying all this data use, of course, are ethics,
Speaker 2: user privacy, data minimization, consent. These are non.
Speaker 1: Negotiable absolutely so data is the fuel. What about the
Speaker 1: intelligence part of that model?
Speaker 2: Right, the AI itself. The sources break this down into
Speaker 2: three main types. First, predictive AI. Sometime it's called analytical AI.
Speaker 1: What does that do?
Speaker 2: It focuses on well defined tasks analyzing data to make
Speaker 2: predictions or solve pretty clear.
Speaker 1: Problems like sentiment analysis.
Speaker 2: Perfect example, taking unstructured text like product reviews and turning
Speaker 2: it into a structured output like a numeric sentiment score.
Speaker 1: But humans still need to figure out why people feel
Speaker 1: that way exactly.
Speaker 2: The AI gives you the what, but humans often need
Speaker 2: to interpret the why and decide what to do about it.
Speaker 1: Okay. Type number two.
Speaker 2: Generative AI or GENAI. This is what everyone's.
Speaker 1: Talking about, creating new content.
Speaker 2: Precisely text, images, video code, even things like chemical structures
Speaker 2: for drug discovery. It's reshaping whole industries.
Speaker 1: How does it work? Fundamentally?
Speaker 2: It essentially combines existing information in new, often unexpected ways.
Speaker 2: It's learned patterns from vast data sets and can generate
Speaker 2: novel outputs based on prompts. It can even do surprisingly
Speaker 2: well on things like you know, standardized tests.
Speaker 1: Impressive and the third.
Speaker 2: Type AGENTIC AI, This is maybe the next frontier. This
Speaker 2: is where AI doesn't just predict or generate.
Speaker 1: It acts acts how so it changes.
Speaker 2: The state of the world. These agents combine language models
Speaker 2: with external tools think APIs, databases, functions.
Speaker 1: So they can interact with other systems.
Speaker 2: Yes, and they can reason, plan and learn from those interactions.
Speaker 2: The example given is like a product management agent doing
Speaker 2: what imagine it updating a project roadmap, automatically analyzing transcripts
Speaker 2: from sales, calls for insights, even drafting communications to stakeholders,
Speaker 2: all autonomously.
Speaker 1: Wow. That's that's a significant step up.
Speaker 2: It really is. It implies a much higher degree of autonomy.
Speaker 1: Okay, so we have data and these three types of
Speaker 1: intelligence predictive, generative AGENTIC. What about the third piece, user experience?
Speaker 1: How is UX different for AI?
Speaker 2: That's a great question, because it is different traditional software.
Speaker 2: It's deterministic, you click a button, the same thing happens
Speaker 2: every time, right. Predictable AI not so much. It's inherently uncertain.
Speaker 2: It can make mistakes, it can hallucinate, make things up.
Speaker 1: So the UI has to account for that uncertainty exactly.
Speaker 2: It needs to be designed with unpredictability and potential failure
Speaker 2: baked in. How do you do that well? The sources
Speaker 2: suggests things like showing confidence scores, like next to AI
Speaker 2: generated text it might say high confidence or medium confidence.
Speaker 1: So the user knows how much to trust it precisely.
Speaker 2: It helps calibray trust. Another idea is footprints, ways to
Speaker 2: trace the AI's steps. How did he get from the
Speaker 2: prompt to this result? That builds transparence.
Speaker 1: That transparency seems key. The source has had a good example,
Speaker 1: didn't they from a sustainability reporting app.
Speaker 2: Oh yeah, that was a great one. When it is
Speaker 2: generating a draft report instead of just a generic spinner, Yeah,
Speaker 2: it shows a progress window explaining what the AI is doing,
Speaker 2: analyzing financial data, checking regulatory guidelines.
Speaker 1: Et cetera, keeping the user informed.
Speaker 2: Exactly, and then the draft report itself. It uses those
Speaker 2: confidence scores color coded green for high confidence, yellow for HM.
Speaker 1: Maybe double check this bit that really empowers the user,
Speaker 1: doesn't it. It shows them where their expertise is needed.
Speaker 2: It absolutely does. It's about collaboration, not just automation.
Speaker 1: And there was this idea of rethinking friction. Sometimes making
Speaker 1: things slightly harder is good.
Speaker 2: Yeah, fascinating concept. Instead of making everything seamless. Sometimes you
Speaker 2: introduce intentional disruptions like what like maybe the AI flags
Speaker 2: its own potential errors, calls it a self critique, or
Speaker 2: it poses challenge questions back to the user, prompting deeper thought.
Speaker 1: Why would you do that?
Speaker 2: It can active the user, make them more engaged, and
Speaker 2: fight against that tendency to just blindly trust the AI,
Speaker 2: what they call automation bias.
Speaker 1: Interesting, So deliberately slowing things down sometimes to improve the outcome.
Speaker 2: Exactly counterintuitive but potentially very effective.
Speaker 1: Okay, so you've got your data, your intelligence type, your
Speaker 1: UX designed for uncertainty. Now how do you make the
Speaker 1: AI model itself smarter, more specific to your needs?
Speaker 2: Right? Customization? The sources detail three main ways to customize
Speaker 2: language models kind of increasing in technical complexity.
Speaker 1: What's the starting point?
Speaker 2: Prompt engineering? This is your entry point, the most accessible way.
Speaker 2: It's all about crafting effective instructions.
Speaker 1: It's telling the model what you want more clearly.
Speaker 2: Pretty much, you guide the model without having to actually
Speaker 2: change the model itself. Simple tweaks in the prompt specifying
Speaker 2: the tone, the format, the desired output.
Speaker 1: Style like write in an authoritative, professional.
Speaker 2: Tone exactly or use a friendly, approachable voice. Small changes
Speaker 2: can make a huge difference to the quality.
Speaker 1: And there are different techniques within prompt engineering.
Speaker 2: Oh yes, there's basic zero shot prompting just input output,
Speaker 2: then FEU shot where you give it a couple of
Speaker 2: examples to learn from by.
Speaker 1: Analogy, learning by example, right, and.
Speaker 2: Then more advanced stuff like guiding its reasoning step by step,
Speaker 2: asking it to think out loud, or having it generate
Speaker 2: multiple answers and then kind of vote on.
Speaker 1: The best one, asking it to critique itself.
Speaker 2: Even yeah, that reflection technique, asking the LM to review
Speaker 2: and improve its own output. Lots you can do just
Speaker 2: with prompts.
Speaker 1: But prompts have limits. I assume they.
Speaker 2: Definitely do, especially when you need the AI to use specific,
Speaker 2: up to date or proprietary information. That's where the second
Speaker 2: technique comes in. Retrieval augmented generation.
Speaker 1: Or RAG ray. Okay, what does that do?
Speaker 2: This is really powerful. Our RAGE lets the AI dynamically
Speaker 2: retrieve relevant information from external sources.
Speaker 1: Like your company's internal database or recent articles.
Speaker 2: Exactly, internal databases, articles, meeting notes, whatever you connect it to.
Speaker 2: It pulls that relevant info in at the time of
Speaker 2: the request and weaves it into the LM's response.
Speaker 1: Ah, so it's not just relying on its initial training data.
Speaker 2: Precisely. This is crucial for factual accuracy, especially with rapidly
Speaker 2: changing information, and for tailoring responses using your own private data.
Speaker 1: Like that content generation app example, pulling in client case studies.
Speaker 2: Perfect example, And importantly, RA significantly reduces those hallucinations.
Speaker 1: Because it's grounding its answers in real retrieved information.
Speaker 2: You got it. It has specific texts to base its
Speaker 2: answer on, rather than just making things up based on
Speaker 2: its general training.
Speaker 1: Okay, so prompt engineering first, then RA for external knowledge.
Speaker 1: What if that's still not enough, then.
Speaker 2: You move to the third most involved technique, fine tuning.
Speaker 1: Fine tuning the model itself.
Speaker 2: Yes, this is for when the base LM just fundamentally
Speaker 2: lacks intrinsic understanding of your domain and brand voice.
Speaker 1: Like the example the content app struggling with really niche
Speaker 1: B to B sauce topics exactly.
Speaker 2: Prompting and roged might help, but maybe the core model
Speaker 2: just doesn't get the nuance, the specific terminology, the required style.
Speaker 1: So fine tuning trains it on that specific stuff.
Speaker 2: Right. You take the base model and further train it
Speaker 2: on your own proprietary data, your past successful content. Your
Speaker 2: style guides your specific.
Speaker 1: Jargon, so it really internalizes your specific domain and voice.
Speaker 2: Deeply internalize it. It moves beyond just retrieving facts. It
Speaker 2: learns the style, the nuance, the perspective. There's also instruction
Speaker 2: fine tuning, which is interesting.
Speaker 1: How does that work?
Speaker 2: The model adapts more dynamically based on direct user feedback
Speaker 2: and edits. If a user corrects an output, the model
Speaker 2: learns from that specific instruction for future tasks, makes it
Speaker 2: more responsive.
Speaker 1: Okay, three levels prompting our rag fine tuning a clear
Speaker 1: progression in complexity and capability.
Speaker 2: That's a good way to put it.
Speaker 1: Now, we've talked a lot about the tech, but building
Speaker 1: these products, it's not just code and models, is it.
Speaker 1: It's about people?
Speaker 2: Oh? Absolutely, central. The sources really emphasize this. AI teams
Speaker 2: are almost by definition diverse and interdisciplinary.
Speaker 1: You need lots of different skills.
Speaker 2: You do software engineers, data scientists, UX designers, crucially, domain
Speaker 2: experts who actually understand the area the AI is working in.
Speaker 1: And the product manager what's their role in this mix?
Speaker 2: The PM becomes almost an educator and a translator. They
Speaker 2: have to bridge these different worldviews, different technical languages, different.
Speaker 1: Goals, balancing the tech possibilities with the user needs and
Speaker 1: the business.
Speaker 2: Goals, and reconciling priorities between all these different stakeholders. It's
Speaker 2: a complex coordination role, especially.
Speaker 1: As we said, with all the uncertainty involved, how do
Speaker 1: you manage that the potential for failure.
Speaker 2: This brings us back squarely to AI governance. The sources
Speaker 2: spend a lot of time on this, and rightly so
Speaker 2: it's paramount.
Speaker 1: What does governance cover specifically in AI?
Speaker 2: Several critical areas. First, security, protecting.
Speaker 1: The whole system against what kind of threats, things.
Speaker 2: Like data poisoning at attackers feeding bad examples to mess
Speaker 2: up the training data, data exiltration and leakage sensitive information
Speaker 2: getting out maybe through prompts or model outputs insecure output handling.
Speaker 2: Imagine the AI generating harmful code like a delete database
Speaker 2: query when it should have done a select and even
Speaker 2: model theft attackers trying to steal or replicate your valuable
Speaker 2: AI model.
Speaker 1: So security is multifaceted.
Speaker 2: What else under governance privacy by design, building privacy and
Speaker 2: from the start, not bolting it on later.
Speaker 1: How do you do that?
Speaker 2: Proactive risk assessments, making privacy the default setting, ensuring end
Speaker 2: to end security for data, and being transparent with users
Speaker 2: about how their data is used.
Speaker 1: Transparency again seems vital it is.
Speaker 2: Then there's mitigating bias. This is huge. AI models can
Speaker 2: easily pick up and even amplify existing societal biases present
Speaker 2: in their training data.
Speaker 1: So how do you fight that?
Speaker 2: Strategies include things like data audits, carefully examining your training
Speaker 2: data for imbalances, maybe using tools like fair learn and
Speaker 2: algorithmic bias mitigation techniques, trying to make the model itself fairer,
Speaker 2: and explaining its decisions.
Speaker 1: Explaining decisions That sounds like the next part exactly.
Speaker 2: Transparency and accountability, providing explainability, understanding how the AI reached
Speaker 2: a decision, and interpretability making the outputs understandable and actionable.
Speaker 1: For humans and keeping humans involved.
Speaker 2: Absolutely, establishing human in the loop or human on the
Speaker 2: loop processes, especially for high risk decisions, ensuring a human
Speaker 2: can always intervene, review, or override the AI.
Speaker 1: So governance is really about building trust and safety around
Speaker 1: the AI.
Speaker 2: That's a perfect summary. It's the essential foundation.
Speaker 1: Okay. So with all that complexity, the tech, the teams,
Speaker 1: the governance, how do you effectively communicate the value of
Speaker 1: what you're building, especially to stakeholders who might not be
Speaker 1: AI experts.
Speaker 2: Great question, The sources stress being concrete. Articulate the value
Speaker 2: in tangible terms.
Speaker 1: So instead of just saying it boosts sufficiency.
Speaker 2: Quantify it for efficiency and productivity. Show the time saved.
Speaker 2: The example they used was brilliant. Our platform automates data aggregation,
Speaker 2: saving your team eighteen point three to three hours.
Speaker 1: Per week, okay, specific and.
Speaker 2: Then translate that into cost savings. That's equivalent to nearly
Speaker 2: forty seven, six and fifty eight dollars annually. Numbers make
Speaker 2: the impact real.
Speaker 1: That definitely makes it easier to understand the ROI. What
Speaker 1: about communicating well the inevitable failures?
Speaker 2: Ah? Yes, communicating about failure. This is critical for managing
Speaker 2: expectations and building trust.
Speaker 1: So don't hide the mistakes.
Speaker 2: Absolutely not be realistic, the sources say. Instead of pretending
Speaker 2: engineers can eliminate all errors, you need to be upfront
Speaker 2: about the types of mistakes.
Speaker 1: The AI will make like what kinds of mistakes.
Speaker 2: They list common ones, false positives, predicting something that doesn't happen,
Speaker 2: like a sales spike, false negatives, missing something important like
Speaker 2: the impact of a flash sale, right, ambiguity or misinterpretation,
Speaker 2: just misunderstanding the input, bias in the outputs due to
Speaker 2: biased data and of course hallucinations the AI making things up.
Speaker 1: So you name the potential problems.
Speaker 2: You name them, you're transparent about maybe how often they occur,
Speaker 2: and crucially, you explain how you're working to continuously improve
Speaker 2: the system.
Speaker 1: So it reframes mistakes not as disasters, but as part
Speaker 1: of the ongoing learning process exactly.
Speaker 2: It builds trust, manages expectations, and can even enlist users
Speaker 2: in helping to improve the AI collaboratively.
Speaker 1: That's a really insightful way to handle it. Okay, we've
Speaker 1: covered a lot of ground here. Looking back over this
Speaker 1: deep dive, what really stands out to you?
Speaker 2: For me, I think it's that successful AI product development
Speaker 2: is this constant interplay, this dance, as you called it,
Speaker 2: between ambition and clarity. It's about having big ideas but
Speaker 2: also having a systematic way to tackle all the inherent
Speaker 2: uncertainty whether that's through really good data practices, careful model selection,
Speaker 2: designing that user experience thoughtfully for.
Speaker 1: Uncertain robust govern in someplace is exactly.
Speaker 2: It confirms the journey isn't really about achieving perfection right
Speaker 2: out of the gate. It's about that continuous improvement, that
Speaker 2: iterative learning, getting better step by step.
Speaker 1: I agree. My main takeaway is maybe connecting that back
Speaker 1: to trust. By understanding all these nuances we discussed avoiding
Speaker 1: the AI for AI's sake trap, knowing when to use
Speaker 1: RAG versus fine tuning, being transparent about failures, You're not
Speaker 1: just building a product.
Speaker 2: You're building trust with your users, with your stakeholders. That
Speaker 2: feels fundamental, It really does.
Speaker 1: Which leaves us and you listening with maybe a final
Speaker 1: thought to ponder.
Speaker 2: Yeah, I question, maybe in what part of your own
Speaker 2: work or even your life, could you apply this kind
Speaker 2: of systematic deep dive approach to understanding and maybe leveraging AI.
Speaker 1: And if you did that deep dive, what unexpected insights,
Speaker 1: what little nuggets might you uncover along the way