Product leaders often measure success in terms of retention, revenue, and growth. In this episode of Product Momentum, Nesrine Changuel explains that the most successful products achieve those outcomes by creating meaningful emotional connections with users. Nesrine’s experience includes leadership roles at Skype, Spotify, Google Meet, and Google Chrome. But these days, she is perhaps best known for her book, Product Delight.

Nesrine will explore these and other topics at the ITX Product + Design Conference, June 24-25 in Rochester NY. On conference Day 1, she will conduct a workshop that delves into her Product Delight framework, guiding attendees on how to design products that users will not only use – but will come to love, remember, and recommend to others.

During this conversation, Nesrine discusses what she has learned from product teams since the book’s release and explains why delight is not simply a design tactic, but a business strategy that transcends industries.

Product Delight Extends Beyond Design

One of Nesrine’s biggest discoveries during her global book tour was the widespread misunderstanding of what product delight actually means. Many practitioners associate delight with visual design, animations, or clever interface surprises. This view is too narrow, she says. Nesrine defines delight as the overall feeling users experience while interacting with a product.

“Product delight is the entire experience,” she adds. “It’s actually the feeling that the user enjoys while using the product.” It extends well beyond aesthetics to focus on creating experiences that improve how users feel while accomplishing their goals.

Emotional Connection Creates Measurable Outcomes

Among Nesrine’s key challenges was helping product and business leaders recognize the fact that user emotions directly influence business performance. Emotional design has long been familiar to designers and marketers, but many product organizations still prioritize operational metrics above customer sentiment.

Nesrine was able to connect the dots between delight and business performance by demonstrating that “the delight itself can actually double retention, referral and revenues.” By framing emotional connection through the lens of retention, referrals, and revenue growth, she demonstrated that delight is not a soft concept. Rather, it is a practical approach that can strengthen both customer loyalty and business results.

Adding Delight to Your AI Product – Emphasize the Human Aspect

On conference day 2 – Keynote Day – attendees will be treated to brand new content from Nesrine: how to add delight into your AI product.

“I’ve been very quiet about AI over the last couple of months,” Nesrine shared. “I’ve always believed in the humanization of product and the human aspect, but now the product is the conversation. So how can we add delight in these kinds of experiences? That’s something new that I’m going to be talking about” at the ITX Product + Design Conference. As product teams continue navigating AI-driven technological change, core product principles remain unchanged: products that make people feel understood, respected, and supported are more likely to earn user trust, loyalty, advocacy – along with long-term success.

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Mike Belsito has spent years at the center of the product management community. As the founder of Product Collective, a leader at Mind the Product, and now Head of Product Evangelism at Pendo, Mike has built a career around learning from product professionals and sharing those insights with the broader industry.

In this episode of Product Momentum, Mike joins Sean and Dan for a discussion that is absolutely top of mind for today’s product leaders today: while artificial intelligence is transforming how products are built, timeless skills such as curiosity, judgment, and taste remain essential. In fact, he argues, these capabilities will become even more valuable as technology accelerates the pace of product development.

Navigating AI Through Human-Centered Product Skills

In doing research for a new book, Mike engaged many product leaders who had experienced previous periods of technological disruption – e.g., the rise of the internet, telecommunications, and mobile computing. Now dealing with AI-driven opportunities and uncertainties, many leaders point to the same enduring qualities that helped them and their teams adapt during earlier transitions.

Rather than focusing solely on new technologies, they emphasized the importance of human-centered skills that guide decision-making and product strategy. “It’s kind of relying on the same timeless characteristics that we’ve always thought were important,” Mike says. “And even today, we still think are important, which are things like curiosity, judgment, taste.”

Balancing Output and Outcomes

Our conversation with Mike also explored a growing tension within product organizations – a theme also covered in recent Product Momentum episodes. As AI enables teams to create more content, code, and functionality faster than ever before, Mike cautions against using increased output” as a measure of success.

Product teams have spent years shifting their focus from user satisfaction to delivery metrics to business outcomes, Mike continues. “That mindset remains critical, even as AI changes workflows. But how do we make sure that it’s not just about the output – that we’re actually building the right things?” For product managers, designers, and engineers, the challenge is ensuring that speed does not come at the expense of delivering business value.

Curiosity as a Practiced Skill

Among Mike’s more surprising research discoveries was how often leaders highlighted curiosity as a skill that can be developed intentionally. Rather than viewing curiosity as an innate personality trait, many described it as a practice that strengthens through deliberate effort. It’s an insight that brings important implications for today’s product teams.

Learning, questioning assumptions, and seeking new perspectives become competitive advantages in times like these when the technology landscape evolves so quickly, Mike adds. “I wasn’t thinking of curiosity as a practice or as a muscle to be flexed.”

As AI continues to reshape product development, Mike offers a practical perspective for product leaders. Technology will continue to evolve, he says, but the ability to ask thoughtful questions, exercise sound judgment, and focus on meaningful outcomes remains fundamental. Those timeless capabilities may ultimately determine which teams are best equipped to thrive in an increasingly AI-driven future.

Want to hear more from Mike Belsito? Be sure to join us as he emcees the 2026 ITX Product + Design Conference, June 24 & 25 in Rochester, NY – for the fifth consecutive year!

“I’m honored to return as emcee for the fifth year in a row. This event continues to stand out because of the incredible community it brings together and the energy in the room each time we gather. I’m proud to be part of something that keeps growing in impact and connection.” – Mike Belsito

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Prerna Singh helps organizations build better products and stronger communities. As the founder of Scrappy to Scale Advisors and the former VP of Product and Design at Meetup, she has guided startups and mission-driven organizations through rapid change, customer discovery, and product strategy.

In today’s episode, Prerna explains why human connection and disciplined product thinking matter more than ever during the AI boom. While AI may accelerate product work, she says, successful teams avoid the must stay grounded in curiosity, customer insight, and authentic community building.

Sense of Community Addresses the ‘Isolation Problem’

What started as a casual gathering for fractional product leaders, Prerna’s Product Breakfasts quickly evolved into a broader support system for people navigating uncertainty and AI-driven change and the professional isolation that often comes with it. Many product professionals, she says, now feel overwhelmed by the pace of technological advancement and the pressure to keep pace.

“I don’t think there’s any catching up,” Prerna adds. “‘Catching up’ implies that there’s an end goal to this. And there isn’t. So that’s where I think Breakfast is the evolution of people coming together to share what they know and helping reduce that anxiety that isn’t just a knowledge gap. It’s also an isolation problem.”

First Principles, Supported by Human Interaction

Product professionals need environments where they can safely discuss their own vulnerabilities. The ability to openly admit uncertainty about AI and its impact, to exchange ideas, and learn together is the hallmark of authentic, in-person interaction.

“The IRL connection isn’t going anywhere,” Prerna continues. “We need that human-to-human interaction to have an outlet for where those vulnerabilities are gonna go. Otherwise, they’re just contained within us and we’re just spiraling in our own heads.”

Avoiding the Trap Starts with Better Discovery

Prerna’s extensive background in user research informs her belief in the importance of first principles in product management. AI tools, she says, make it deceptively easy to jump directly into solutioning without fully understanding the customer’s needs and the business’ problems. In her fractional product manager role, Prerna listens “for the thing that clients return to when they stop performing.”

“‘We need AI’ is a common mantra,” she says. “But what’s interesting for me is the kernel of truth that frames that statement. And it’s not what they want. It’s what they can’t circle back to – like there’s a hidden customer insight that we’ve maybe navigated around.”

Lean into Discovery, Prerna concludes. Product teams must remain disciplined about validating assumptions, conducting research, and identifying the real customer need before building anything. “Avoid the trap of jumping into solutioning.”

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Adam Creeger is the CTO of Slate and creator of iLoom (pronounced “il-LOOM”). His leadership experience at Meta, Greenhouse, and Frame.io not only informs Slate’s transformation into an AI-native organization, but also shapes the way AI influences product strategy, engineering workflows, and operational models.

Throughout his conversation with Sean and Dan, Adam argues that becoming AI native is not about layering AI features onto existing products. Instead, it requires companies to rethink how software is designed, built, and operated – from the ground up. His perspective offers a practical framework for product leaders navigating AI-driven transformation.

Here’s what else we learned:

‘AI Native’ Requires Organizational Reinvention

AI native organizations are willing to rethink every layer of their business, Adam says. Rather than adding AI features superficially, AI native organizations redesign workflows, team structures, and customer experiences around AI capabilities. He emphasized that AI transformation changes not only products, but also how people contribute inside organizations.

“To be AI native requires this deep exercise in re-imagination and not just imagination,” Adam continues. “In an AI native company – from the day-to-day operations to the ‘who does what’ – the roles and the owners of things are going to look very different.”

AI is expanding participation across teams, enabling designers, support teams, and non-engineers to contribute directly to product delivery. That shift signals a major change for modern software organizations.

AI and the Future of the Software Development Life Cycle (SDLC)

Our conversation then turned to an exploration of how AI is already changing the traditional software development lifecycle. Years ago, Agile development emerged because humans had historically struggled to fully reason through complex systems before implementation.

“I’ve realized that Agile was really a mitigation of a few things, mostly that we humans are limited in our abilities to reason through abstract concepts,” Adam says. “So when we thought about a software project, we didn’t have the ability to see around corners and understand the problems we’d face – until it was real, until you really started playing with it.

Turns out that many of those challenges are very solvable by AI, allowing us to go much deeper into the problem space without ever writing a line of code. In addition, AI-assisted planning allows teams to revisit some waterfall-style thinking, but with dramatically faster iteration and validation cycles.

Product Managers’ New Role: Communicate Context

Importantly, AI is actually elevating the role of product managers, Adam offers. Rather than acting primarily as tactical decision-makers, product leaders can (and should) focus on providing context that enables teams to make informed decisions independently.

“More than ever, the product manager has become a role about providing context,” he adds. “PMs should be elevated to a much more strategic role, understanding the long-term vision and helping to translate that to engineers.”

Adam also feels that PMs should be using AI to communicate ideas about the product vision much more effectively. That evolution creates a faster and more collaborative product environment. Teams can evaluate real implementations earlier, gather customer feedback sooner, and align around outcomes instead of specifications alone.

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Welcome to TiPS – the Topics in Product Series – a new podcast format powered by ITX and the team at Product Momentum. The TiPS mission is to engage the same important product space issues that you confront every day – but this time through the experiences of ITX product managers, UX researchers and designers, engineers, security analysts, and the rest of the team.

In this inaugural TiPS episode, Dan Sharp is joined by Sean Murray and Andrew Knoblauch to reflect on a recent Product Leaders Breakfast, hosted by Prerna Singh. Together, they draw on insights from event attendees to discuss how AI is being applied inside real organizations.

The central theme was clear: successful AI adoption depends less on hype and more on first principles and core product skills that drive disciplined product thinking, incremental progress, and strong decision-making.

Here’s what we learned:

Top-Down ‘Do AI’ Directive Is the Wrong Reason for Integrating AI

The integration of AI into software development is no longer the proverbial “hammer in search of a nail.” The days of doing AI for AI’s sake are behind us. Today’s product leaders focus on making incremental improvements tied to bona fide business problems.

As Sean points out, our response to the ‘do AI’ directive should be: “’Where do you want to see improvement? What outcomes are you looking for?’ I think back to our conversation with Teresa Torres, about applying best practices in the initiation and discovery phases of the SDLC so that when we actually get into building something, it’s gonna have some sort of relevant business value.” It’s a more grounded approach that reflects a broader industry need to align AI efforts with tangible outcomes..

Building Stakeholder Trust Through Incremental Change

Trust emerged as a critical factor in AI adoption, but not only in the technical sense. Instead, as attendees discussed, trust is built gradually through careful implementation and organizational alignment. Andrew explains that product teams build trust not by tackling the biggest, riskiest challenge – but by prioritizing low- to medium-risk opportunities while involving stakeholders early, especially those in Legal and Compliance.

“This idea of building trust among others in your organization.” Andrew continues. “We do this every day with our clients and with our own teammates. We learn about people’s concerns, what they care about.” The conversation reinforces the idea that AI should be introduced as a collaborator within workflows, not as a replacement for human judgment.

Decision Quality as the True Differentiator

One of the key threads weaving through our conversation was a return to foundational product principles – specifically, the importance of decision-making. While AI fluency is valuable, it does not replace the need for strong judgment and clear thinking. Teams that succeed will be those that consistently make informed, high-quality decisions, Sean says. “The biggest differentiator moving forward is gonna be decision quality…your ability to consistently make good decisions.” In this context, AI becomes an enabler, not the driver, of product success.

The conversation at the Product Leaders Breakfast (hosted by Prerna Singh) reinforces a familiar but essential message for all product leaders. AI does not replace core product skills; it amplifies them. Teams that stay focused on problem definition, stakeholder alignment, and disciplined execution will be best positioned to realize its full potential.

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John Haggerty brings more than 25 years of product leadership experience at companies like Datasite, Prodege, and Highway.ai. As co-founder and CEO of BiasHawk, John leverages his expertise in product management, behavioral psychology, and AI to develop an AI-powered platform that acts like a behavioral clinical psychologist to diagnose cognitive bias and heuristics in other AI models.

In this episode of Product Momentum, John joins Sean and Dan to explore how AI is reshaping product work while also introducing new risks. John’s message is clear: as AI accelerates execution, product leaders must confront the invisible risks that come with AI and double down on critical thinking, context, and judgment to deliver quality decisionmaking.

AI as an Accelerator, Not a Replacement

AI is dramatically compressing the time required to execute product work. Tasks that once took months can now be completed in hours. As we discover every day, speed does not eliminate the need for thoughtful product management. John argues that it merely shifts where product managers can and should focus their energy.

“As AI expedites the execution process,” John says, “it also allows us to automate the areas of our work where we really need to be involved in cognitive thinking, reasoning, and creativity.”

The Hidden Risk: Bias in AI Decision-Making

Large language models inherit the same cognitive biases found in human thinking, John adds. These biases influence not just outputs, but the reasoning behind decisions we make.

“It’s not what the decision is or what the output is, it’s more about how the AI model arrived at it.” This distinction is critical for product teams. Without understanding how AI arrives at conclusions, teams risk introducing flawed logic into their products, especially in high-stakes areas like hiring, healthcare, and financial management.

Monitoring AI: A New Responsibility for Product Teams

To address these challenges, John launched BiasHawk – an AI platform designed to monitor and evaluate AI systems for cognitive bias. The goal is not just testing outputs, but continuously assessing decision quality over time.

“We all understand that these systems are designed to evolve. They’re designed to change. They’re designed to drift. But who’s monitoring that to make sure that decision quality stays where it’s supposed to be.” As AI continues to evolve, the role of the product manager becomes even more critical — not less so. Execution may be faster, but judgment, context, and ethical responsibility remain firmly within our human domain.

John Haggerty, in his own words:

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Margie Agin is a seasoned go-to-market advisor for B2B technology scale-ups. She brings deep expertise across digital marketing, IT, and cybersecurity. As Founder and Chief Strategist of Centerboard Marketing as well as a former leader at companies like Cisco and Blackboard, she has built a career translating complex technical products into effective market strategies. In this episode (which marks her second visit to Product Momentum), Margie’s message is clear: go-to-market (GTM) is not a one-time event or a siloed function – it is an ongoing, cross-functional system that must connect product teams and broader business goals.

GTM: A Shared, Continuous Responsibility

It’s time to redefine go-to-market as a shared, continuous responsibility across teams, Margie says. Product managers in particular often feel disconnected because their fellow stakeholders in the organization misunderstand go-to-market as either a launch event or solely a sales function. Margie reframes GTM as “a coordinated cross-functional engine that spans product, marketing, sales, customer success, and even finance.”  It’s a perspective that challenges product teams to actively engage in downstream outcomes and collaborate beyond traditional boundaries.

Business Context Drives Product Contribution

Fundamental to making this critical connection between product team and business outcomes is embracing the product’s fit within the broader business and portfolio strategy. Margie reiterates a message shared by recent guests that product managers need to look beyond their individual product scope and consider how their work contributes to company-wide goals like growth, positioning, and revenue. “Think about your product within the context of the business and how it fits into the whole portfolio,” Margie urges.

Know Your Targets: Clarity of Audience and Signals Improves Outcomes

Rather than trying to boil the ocean by targeting broad customer segments, teams should focus on specific attributes and behaviors that indicate a strong fit. Defining a precise ideal customer profile and identifying meaningful signals of readiness bring a level of clarity to your message that enables more effective messaging, prioritization, and sales efficiency. “It [your target] can’t just be like, everybody that has money,” Margie says. “It has to be somebody with a defined problem and defined attributes – beyond just industry or size of company.” For product leaders, this reinforces the need to deeply understand customer context and bring that insight into go-to-market planning.

In the Age of AI, a Strong Point of View Still Matters

Finally, even as AI accelerates execution, it does not – indeed, can not – replace the original thinking and nuanced messaging. Teams must still define what makes their product unique and why it matters. AI can enhance delivery, Margie adds, but it cannot generate true insight or perspective. “The difficult part is always what the difficult part has always been, which is figuring out what you have to contribute to the conversation that is unique.”

Margie Agin, in her own words:

[04:23] When I think of go-to-market, I think one of the most important aspects is that it is connected across different teams.

[08:22] Go-to-market is all about connecting the strategy to the execution to make sure everyone is on point with the strategy.

[08:53] Product teams need to think about how their product fits into the context of the organization’s whole portfolio.

[11:30] As a company matures, its go-to-market strategy lands in one of three buckets: problem-market fit, product-market fit, and platform-market fit.

[19:29] We can’t try to boil the ocean and sell to everybody, right? Target customers can’t be ‘everybody who has money.’ Customers have to have a defined problem and some defined attributes, beyond just industry or size of company.

[23:58] That type of deep, nuanced thinking…that human work…I don’t think at this point, is something that is solved by AI.

[26:40] AI can execute a lot of work on your behalf, but only you know what ultimately you want the result to be.

Andrew Knoblauch leads Sales, Partnerships, and Acquisitions at ITX. He believes the best technology partnerships start with genuine relationships, and that understanding a business deeply is what turns a software engagement into lasting value. Andrew connects organizations with technologists and product leaders while remaining invested in delivering strong business outcomes.

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Product Momentum welcomes Rich Mironov back to the pod to help us drill down to the bottom line – literally. Rich is a Silicon Valley veteran and longtime product management advisor. He’s spent decades helping C-suite executives and product leaders connect their work to business outcomes. In this episode, Rich reinforces a single, powerful theme: product managers must translate their ideas into clear financial impact. It’s not enough to build great features – success comes from telling compelling “money stories” that resonate with executives and drive decisions.

Here’s what we learned:

Why Product Leaders Must Speak the Language of Money

Rich makes no bones about the yawning communication gap between product teams and executives. Product managers focus on features, processes, and operating models – while executives focus on revenue and outcomes. As he explains, “Any sentence that comes out of the mouth of a product leader that doesn’t have a currency symbol in it is one that the rest of the executive team can’t hear and doesn’t care about.”

As he reframes the role of product leadership, Rich explains that it’s not just about building the right thing – it’s about articulating how that thing makes money.

The Power of Simple, Structured “Money Stories”

At the core of Rich’s approach lies simplicity. He advocates for building “money stories” using just three numbers – two we know, one we estimate – to quantify potential impact. “A money story has no more than three numbers in it…two of the numbers you know, and one of them you’re going to reach into the air and make up or estimate.”

This framework isn’t about precision, Rich explains. It’s about enabling better conversations. By introducing even rough estimates, product leaders can engage sales, marketing, and executives in meaningful dialogue. The goal is to create alignment around opportunity size and business value, and shift the focus from abstract ideas to tangible outcomes.

From Features to Impact: Driving Better Product Decisions

On the pod, we’ve been talking a lot lately about “impact.” Rich’s approach, covered concisely in his recent book Money Stories, also highlights a critical shift in mindset: product managers must own the financial performance of their products. Without that, they risk being sidelined from strategic decisions. “If you can’t vaguely explain how the thing you do makes money, you’re just a cog in the process.”

This means knowing basics about your product – core metrics like units sold, pricing, and revenue – and using them to guide decisions. It also means prioritizing revenue-generating opportunities over less impactful work and being cautious with cost-saving narratives that may have real human consequences.

Bottom line: product leaders who connect their work to measurable outcomes are the ones who influence strategy, secure investment, and drive meaningful results.

Rich Mironov, in his own words:

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In this episode of Product Momentum, we’re joined by Bansi Mehta, founder and CEO of Koru UX Design, an enterprise healthcare UX agency supporting some of the US’s largest healthcare technology companies. We discussed the busy intersection of artificial intelligence, product management, and UX Design. Bansi’s Sense – Shape – Steer framework helps guide UX design teams as they integrate AI into their products – and avoid the trap of AI’s drive toward mediocrity that limits individual creativity and expertise.

Here’s what we learned:

Avoiding the Trap: AI Solutions’ Race to Mediocrity

AI’s ability to rapidly generate hi-fi prototypes and voluminous content brings great benefit, but also significant risk. The risk manifests in mediocrity – i.e., solutions that drive to the mean. This sense of “good enough” stifles designer creativity and diminishes the quality – the Delight – of the final product.

“The speed of AI makes it easier than ever to churn screens,” Bansi says. “But it’s designed to deliver to that average mean that allows us to say, ‘that works, that makes sense.’ And that’s really the trap….these days, there’s less patience in the industry for discovery and research.”

Introducing the Sense – Shape – Steer Framework

To combat this new reality, Bansi developed the Sense – Shape – Steer framework to help teams navigate the complexity of building AI-powered products.

Broader Insights, Future Outlook

The conversation underscores the notion that while AI accelerates development and content generation, it also requires subject matter experts in UX and Product to demonstrate greater vigilance than ever to maintain quality and relevance. The Sense – Shape – Steer framework calls on product teams to think first about user needs before considering whether and how to integrate AI.

Our episode with Bansi Mehta feels like the capstone conversation to recent episodes with Nesrine Changuel, Teresa Torres, and Oji Udezue, where we examined bringing Delight to the user experience, re-engaging Discovery in the development process, and adjusting to the Speed of today’s AI-driven development.

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Keith Lucas is a startup advisor, author, and leader who specializes in building high-impact teams. In this episode with Product Momentum, Keith delivers a master class on leadership, team building, culture, values, and motivation. Our conversation is especially relevant in the context of transitioning from a technical individual contributor to product team leader in high-tech organizations.

Here’s what we learned:

IC to Team Lead: Navigating the Mindset Shift

The transition from hands-on IC to leader of a highly technical team requires a mindset shift from “me to we.” The transition requires an adjustment of priorities from solely outcomes-based to team health, inspiration, and mental well-being.

“A simple trick I use, when I wake up every day my first thoughts are, is the team on a good path? Are they unblocked? Are they inspired and mentally healthy? Are they all in a good place to have impact? Knowing those things helps reduce friction on the team and increases the odds of our success.”

Two ‘New Leader Archetypes’ – Overcoming Team Dysfunction

Keith discusses two types of leaders who struggle in their new roles. The first is the hands-on leader who has fallen into the oxymoronic trap of trying to “micromanage at scale.”  The other is the visionary talent-oriented leader whose eagerness to succeed leads to the team’s being focused on too many things. The hands-on person is just trying to get stuff done by being effective, efficient, Keith says, while the talent person is committed to autonomy and building a team that scales.

The goal is to put both of those value sets together. For the hands-on leader, that means creating regular touch points with your team. For the talent-oriented leader, it’s about closing loops while showing the team how to go from vision to delivering real outcomes. “In both cases,” Keith adds, “use a regular cadence for when you get together to talk about progress, challenges, and course correction.” This approach creates the right kind of trust — a trust in the system that you have opportunities to contribute in a healthy way.

Value-Based Culture: The Foundation of Decisionmaking

Keith thinks about culture as “the team’s operating system.” And the foundation of that operating system is the team’s values — i.e., their standards of behavior.

“The team’s values are really the foundation of the operating system,” Keith says. “If the system is to be reinforced, then decisions about who gets hired, promoted, and retained must be informed by those values.” If that’s not happening, Keith adds, you end up with a culture that may be codified, but never truly realized.

Here’s some more key takeaways:

As the author of Impact: How to Inspire, Align, and Amplify Innovative Teams, Keith Lucas distills years of experience at the intersection of data, storytelling, and strategy into a practical framework that helps leaders move from player/coach to true team builder while avoiding common scaling pitfalls like diminishing impact, productivity loss, culture dilution, and disempowerment. 

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