While many companies eagerly declare themselves "AI-first" to signal innovation and readiness for the future, the reality of such a transformation often brings both tangible benefits and significant challenges. For some, the shift has enabled rapid scaling, increased automation and operational leverage. For others, public backlash, concerns over quality or job security, and cultural misalignment have triggered reputational and internal turbulence.
We've previously explored what it means when companies position themselves as AI-first and examined how industry leaders like Google, NVIDIA, and Shopify have embraced this transformation. But declaring yourself "AI-first" is one thing; executing it without destroying trust is another entirely.
One of the most visible examples is Duolingo. In 2025, the company announced it would pivot to an "AI-first" model, including replacing contract workers where AI could handle tasks. The backlash was swift and vocal: users accused the company of putting AI ahead of people, and many threatened to cancel their subscriptions.
In response, Duolingo's co-founder clarified that AI was meant to "accelerate what we do, at the same or better level of quality," and that the shift would not involve mass layoffs of full-time employees.
This mix of opportunity and risk raises an important question for any company or advisor considering the "AI-first" path: how to get on this journey without losing trust, quality, or people. That's what we explore below.
An "AI-first" company treats AI not as a bolt-on tool, but as the central nervous system of the organization, shaping decisions, workflows, and team structure. In practice, this means the shift isn't limited to product or R&D but spans every function: marketing, HR, operations, content, support.
AI underpins how resources are allocated, how work is prioritized, and even how teams are built. As seen with Duolingo's hiring policy, some companies implement rules like "if a team can automate a function, then hiring is stalled." The expectation is that AI becomes part of everyday workflows, replacing or augmenting many manual or repetitive tasks, enabling scalability and operational efficiency.
Hence, "AI-first" speaks to a company-wide orientation and ambition.
In contrast, "AI-native" refers to products or services that are designed from inception around AI. AI isn't just a component; it's the core value proposition. Remove AI, and the product loses its purpose. These products rely on AI for their primary functionality, often leveraging generative models, ML, or automation to deliver their value. For example, certain generative AI tools, recommendation engines, or automation platforms can be considered "AI-native products."
A company can be both "AI-first" and build "AI-native" products: i.e., reorganize around AI while delivering products whose core value is AI-powered. But the reverse isn't necessarily true. A startup might launch with an AI-native product without overhauling its entire operations or culture.
The distinction is important because "AI-first" implies internal transformation, governance, new workflows, cultural and organizational shifts, while "AI-native" implies product-level innovation, not necessarily deep changes in company structure. Understanding this difference clarifies which challenges you may face: building AI-native products mostly tests your engineering and design capabilities; becoming AI-first tests your culture, governance, hiring, change management, and stakeholder trust.
When effectively implemented, an AI-first strategy can bring structural advantages beyond mere automation or cost-cutting. Key potential benefits include:
The case of Duolingo illustrates some of the risks and pitfalls of rushing into "AI-first" without careful planning, communication, and human-centered oversight.
The company announced that it would "gradually stop using contractors for work AI can handle," and that hiring would only resume if teams couldn't automate their work. The announcement triggered widespread backlash: many users and content creators viewed this as AI replacing humans. One comment stated: "AI-first means people last."
Many threatened to cancel subscriptions. Some social media channels of the company reportedly removed content, a sign of internal turbulence and reputational damage. In response, leadership attempted to clarify, stating AI would "accelerate" work rather than replace people and promised training and advisory support for teams to adapt.
Messaging and transparency failures: The initial memo framed AI adoption strongly around replacing contract work, understandably raising fears about job losses and quality drop.
If a company decides to go down the AI-first path, here are key guidelines, grounded in lessons from real-world successes and failures, to maximize benefits and minimize risks.
Be explicit: is it about embedding AI in workflows, automating operational tasks, building AI-native products, or all of the above? Communicate internally and externally what will change, what stays human-driven, and why. Too many companies rush to declare themselves AI-first without defining what that actually means for their specific business. This vagueness leads to confusion among employees, mixed messages to customers, and ultimately, failed implementations.
For functions requiring human sensibility (content, quality assurance, customer trust, ethics), keep humans in the loop. Combine AI efficiency with human oversight. The goal is to free people from repetitive work so they can focus on the nuanced, creative, and relationship-driven tasks that AI can't handle well. This approach not only maintains quality but also helps ease employee concerns about their roles.
Track user feedback, quality metrics, and employee sentiment. Allow the shift to be iterative. Learn from missteps and course-correct, rather than treating AI adoption as a one-time switch. Your first implementation won't be perfect. That's expected. What matters is that you're listening to both your internal teams and your customers, measuring the impact, and adjusting course when something isn't working. This iterative approach prevents small problems from becoming PR disasters.
Be upfront about what's changing: content generation, hiring practices, automation, and why. Offer training and adaptation support for employees; be human-centric rather than purely cost-driven. Duolingo's backlash wasn't just about the AI decision itself but also about how that decision was communicated. When stakeholders feel blindsided or sense that profit is being prioritized over people and quality, trust erodes fast. Clear, honest communication from the start can prevent much of that damage.
In user-facing products, especially in sensitive domains (like education, o health), quality, authenticity, and trust matter more than just scaling fast. Ensure AI-driven changes preserve or improve what users value most about the product. If your customers love your product because of its human touch, cultural authenticity, or personalized approach, replacing those elements with AI might save costs in the short term but destroy what made your product valuable in the first place.
Becoming AI-first is a transformation, not just a technical upgrade. Invest in data pipelines, governance, ethical AI guidelines, and continuous upskilling. Treat AI as infrastructure, not a feature, and build the organization accordingly. This means thinking about data quality, model monitoring, ethical use policies, employee training programs, and governance structures that ensure AI is used responsibly. These aren't sexy investments, but they're the foundation that prevents AI implementations from collapsing under their own weight.
Before you declare your company AI-first, use this checklist to honestly assess whether you have the foundation in place to succeed:
The AI-first journey is complex, but it doesn't have to be chaotic. The difference between companies that successfully transform and those that face backlash often comes down to strategy, communication, and having the right partner.
At NineTwoThree, we've helped companies across industries implement AI strategies that deliver measurable ROI without sacrificing what makes them valuable to their customers. We understand that becoming AI-first isn't just about the technologym but about your people, your culture, and your users' trust.
Whether you're just exploring what AI-first could mean for your business or you're ready to begin your transformation, we're here to help you do it right.
Let's talk about your AI transformation and build a strategy that delivers real value without the backlash.
While many companies eagerly declare themselves "AI-first" to signal innovation and readiness for the future, the reality of such a transformation often brings both tangible benefits and significant challenges. For some, the shift has enabled rapid scaling, increased automation and operational leverage. For others, public backlash, concerns over quality or job security, and cultural misalignment have triggered reputational and internal turbulence.
We've previously explored what it means when companies position themselves as AI-first and examined how industry leaders like Google, NVIDIA, and Shopify have embraced this transformation. But declaring yourself "AI-first" is one thing; executing it without destroying trust is another entirely.
One of the most visible examples is Duolingo. In 2025, the company announced it would pivot to an "AI-first" model, including replacing contract workers where AI could handle tasks. The backlash was swift and vocal: users accused the company of putting AI ahead of people, and many threatened to cancel their subscriptions.
In response, Duolingo's co-founder clarified that AI was meant to "accelerate what we do, at the same or better level of quality," and that the shift would not involve mass layoffs of full-time employees.
This mix of opportunity and risk raises an important question for any company or advisor considering the "AI-first" path: how to get on this journey without losing trust, quality, or people. That's what we explore below.
An "AI-first" company treats AI not as a bolt-on tool, but as the central nervous system of the organization, shaping decisions, workflows, and team structure. In practice, this means the shift isn't limited to product or R&D but spans every function: marketing, HR, operations, content, support.
AI underpins how resources are allocated, how work is prioritized, and even how teams are built. As seen with Duolingo's hiring policy, some companies implement rules like "if a team can automate a function, then hiring is stalled." The expectation is that AI becomes part of everyday workflows, replacing or augmenting many manual or repetitive tasks, enabling scalability and operational efficiency.
Hence, "AI-first" speaks to a company-wide orientation and ambition.
In contrast, "AI-native" refers to products or services that are designed from inception around AI. AI isn't just a component; it's the core value proposition. Remove AI, and the product loses its purpose. These products rely on AI for their primary functionality, often leveraging generative models, ML, or automation to deliver their value. For example, certain generative AI tools, recommendation engines, or automation platforms can be considered "AI-native products."
A company can be both "AI-first" and build "AI-native" products: i.e., reorganize around AI while delivering products whose core value is AI-powered. But the reverse isn't necessarily true. A startup might launch with an AI-native product without overhauling its entire operations or culture.
The distinction is important because "AI-first" implies internal transformation, governance, new workflows, cultural and organizational shifts, while "AI-native" implies product-level innovation, not necessarily deep changes in company structure. Understanding this difference clarifies which challenges you may face: building AI-native products mostly tests your engineering and design capabilities; becoming AI-first tests your culture, governance, hiring, change management, and stakeholder trust.
When effectively implemented, an AI-first strategy can bring structural advantages beyond mere automation or cost-cutting. Key potential benefits include:
The case of Duolingo illustrates some of the risks and pitfalls of rushing into "AI-first" without careful planning, communication, and human-centered oversight.
The company announced that it would "gradually stop using contractors for work AI can handle," and that hiring would only resume if teams couldn't automate their work. The announcement triggered widespread backlash: many users and content creators viewed this as AI replacing humans. One comment stated: "AI-first means people last."
Many threatened to cancel subscriptions. Some social media channels of the company reportedly removed content, a sign of internal turbulence and reputational damage. In response, leadership attempted to clarify, stating AI would "accelerate" work rather than replace people and promised training and advisory support for teams to adapt.
Messaging and transparency failures: The initial memo framed AI adoption strongly around replacing contract work, understandably raising fears about job losses and quality drop.
If a company decides to go down the AI-first path, here are key guidelines, grounded in lessons from real-world successes and failures, to maximize benefits and minimize risks.
Be explicit: is it about embedding AI in workflows, automating operational tasks, building AI-native products, or all of the above? Communicate internally and externally what will change, what stays human-driven, and why. Too many companies rush to declare themselves AI-first without defining what that actually means for their specific business. This vagueness leads to confusion among employees, mixed messages to customers, and ultimately, failed implementations.
For functions requiring human sensibility (content, quality assurance, customer trust, ethics), keep humans in the loop. Combine AI efficiency with human oversight. The goal is to free people from repetitive work so they can focus on the nuanced, creative, and relationship-driven tasks that AI can't handle well. This approach not only maintains quality but also helps ease employee concerns about their roles.
Track user feedback, quality metrics, and employee sentiment. Allow the shift to be iterative. Learn from missteps and course-correct, rather than treating AI adoption as a one-time switch. Your first implementation won't be perfect. That's expected. What matters is that you're listening to both your internal teams and your customers, measuring the impact, and adjusting course when something isn't working. This iterative approach prevents small problems from becoming PR disasters.
Be upfront about what's changing: content generation, hiring practices, automation, and why. Offer training and adaptation support for employees; be human-centric rather than purely cost-driven. Duolingo's backlash wasn't just about the AI decision itself but also about how that decision was communicated. When stakeholders feel blindsided or sense that profit is being prioritized over people and quality, trust erodes fast. Clear, honest communication from the start can prevent much of that damage.
In user-facing products, especially in sensitive domains (like education, o health), quality, authenticity, and trust matter more than just scaling fast. Ensure AI-driven changes preserve or improve what users value most about the product. If your customers love your product because of its human touch, cultural authenticity, or personalized approach, replacing those elements with AI might save costs in the short term but destroy what made your product valuable in the first place.
Becoming AI-first is a transformation, not just a technical upgrade. Invest in data pipelines, governance, ethical AI guidelines, and continuous upskilling. Treat AI as infrastructure, not a feature, and build the organization accordingly. This means thinking about data quality, model monitoring, ethical use policies, employee training programs, and governance structures that ensure AI is used responsibly. These aren't sexy investments, but they're the foundation that prevents AI implementations from collapsing under their own weight.
Before you declare your company AI-first, use this checklist to honestly assess whether you have the foundation in place to succeed:
The AI-first journey is complex, but it doesn't have to be chaotic. The difference between companies that successfully transform and those that face backlash often comes down to strategy, communication, and having the right partner.
At NineTwoThree, we've helped companies across industries implement AI strategies that deliver measurable ROI without sacrificing what makes them valuable to their customers. We understand that becoming AI-first isn't just about the technologym but about your people, your culture, and your users' trust.
Whether you're just exploring what AI-first could mean for your business or you're ready to begin your transformation, we're here to help you do it right.
Let's talk about your AI transformation and build a strategy that delivers real value without the backlash.
