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3 days ago
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The corporate world is witnessing a fundamental shift in decision-making that I call the “Why-Not-AI?” test. This test reverses the traditional burden of proof from justifying automation to justifying human intervention. It treats AI not as an occasional productivity tool but as the default solution for operational challenges.
Recent executive memos from Shopify and Duolingo exemplify this transformation. In April, Shopify CEO Tobi Lütke directed employees to explore AI-based solutions before requesting additional personnel resources. Three weeks later, Duolingo co-founder Luis von Ahn announced the company’s transition to an AI-first approach, evaluating employee performance partly based on AI proficiency.
Different industries, but a similar message: We’re moving from the experimental phase of generative AI to an operational phase where AI implementation decisions precede, not follow, human resource allocation. This shift represents a historical breakthrough alongside transformations like the emergence of the Internet, the smartphone revolution, and cloud computing.
This evolution toward AI integration has become increasingly apparent in my work developing my graduate information systems analysis course at the Texas A&M International University Sanchez School of Business. As I prepare my students for careers in an AI-augmented business ecosystem, I emphasize that practical analysis still requires human judgment, the critical assessment of AI-generated options, the contextual understanding of organizational needs, and the ability to translate business goals into implementable solutions.
Rather than promising AI will solve everything, I teach students to leverage these tools as productivity multipliers that handle routine tasks while freeing human expertise for higher-value work. This complementary approach to human-AI collaboration is what I’ve formalized as the “Why-Not-AI?” test, a conceptual approach for systematically evaluating where AI adds genuine value in cross-border business processes.
Corporate Implementation: AI Integration Strategies
Shopify and Duolingo illustrate two distinct approaches to AI integration that will likely become models for businesses across sectors.
Shopify’s approach represents a resource-allocation strategy. By requiring teams to demonstrate why AI cannot perform a task before requesting human resources, Lütke has fundamentally altered the company’s decision-making framework. This shifts the burden of proof from justifying automation to justifying human intervention, a subtle but profound reversal that transforms how managers approach staffing decisions. The goal isn’t just cost reduction but optimizing human talent for what humans do best: creative problem-solving, strategic thinking, and relationship building.
Duolingo, meanwhile, has adopted a production-process strategy. Rather than focusing primarily on staffing decisions, von Ahn’s approach reimagines how the company’s core product, educational content, is created and delivered. By integrating AI into content generation, Duolingo can scale its offerings exponentially without proportional increases in human effort. This strategy impacts everything from course development to user interaction, creating a fundamentally different production model than was possible before.
What’s interesting is how both approaches, despite their differences, converge on a similar perspective: AI implementation decisions should precede, not follow, human resource allocation. Both CEOs view AI not as a tool to be deployed after workflows are established but as the foundation upon which workflows should be built. This represents a decisive shift from viewing AI as an occasional productivity enhancer to seeing it as the default solution for operational challenges.
Both models can be useful for cross-border businesses. Shopify’s resource-allocation approach may be particularly relevant for logistics and supply chain operations where repetitive tasks consume significant human resources. Duolingo’s production-process model, meanwhile, offers insights for businesses that need to scale operations across languages and regulatory environments without proportional staff increases.
The Evolving AI-Centric Workforce
When I first discussed the concept of an AI-centric workforce in 2023, I proposed three categories to describe professional engagement with AI: AI Producers (technical specialists), AI Practitioners (professionals incorporating AI tools into workflows), and AI-aware individuals (those with basic understanding of AI capabilities).
Two years later, workplace dynamics have fundamentally changed. The memos from Shopify and Duolingo reveal not just an evolution within these categories but a complete reconfiguration of expectations. Companies are no longer satisfied with employees who are merely “AI-aware.” This tier is disappearing entirely. Instead, a new baseline is emerging that I call “AI-fluent professionals,” those who use AI reflexively in their daily work.
At Shopify, Lütke has established what he terms a fundamental expectation that all employees integrate AI into their workflows. Using AI is no longer optional or experimental but a baseline requirement for performance evaluation. Similarly, Duolingo’s approach under von Ahn focuses on making AI proficiency a criterion in hiring and performance reviews, expecting employees to leverage AI’s capabilities while applying human judgment.
Interestingly, both cases demonstrate how what once would have been considered specialized ‘AI Practitioner’ skills are now becoming standard expectations across organizations. The framework is evolving from specialized technical roles like AI Producers and Practitioners toward a new baseline where all professionals are expected to be AI-fluent, using these tools reflexively in their daily work.
In cross-border logistics and trade, this evolution carries particular significance. Documentation specialists who once focused on manually processing forms can evolve into exception handlers who use AI to process routine documents while applying their expertise to complex cases. Trade compliance officers can shift from manual classification research to supervising AI-augmented classification systems, focusing their expertise on novel products, regulatory changes, or even tariff compliance.
The path from “AI-aware” to “AI-fluent” extends beyond individual career evolution, representing a fundamental shift in how value is created in international business operations. What distinguishes this transition from previous technological shifts is its unprecedented pace and universality. Unlike the gradual adoption of computers or mobile technology, AI fluency has become a baseline expectation virtually overnight.
For border economies, where technological adaptation has traditionally followed a more measured pace, this acceleration presents both opportunity and challenge. Cross-border businesses face a stark choice: develop organizational AI fluency rapidly or risk becoming obsolete as competitors, clients, and partners advance.
Implications for Stakeholders
The “Why-Not-AI?” test carries profound implications for various stakeholders across the cross-border ecosystem.
For individual workers, this shift fundamentally redefines professional competitiveness in cross-border industries. The distinction between being merely “AI-aware” and becoming fully “AI-fluent” becomes critical to career viability, not just advancement.
Those who fail to develop reflexive AI usage habits will increasingly find themselves at a competitive disadvantage. This doesn’t require becoming technical specialists. It means developing the ability to effectively prompt, evaluate, and refine AI outputs within one’s domain of expertise. For cross-border professionals, this includes the ability to use AI to work on language differences, regulatory complexities, and documentation requirements that have traditionally created inefficiencies in international trade.
For business leaders, this transformation represents a strategic inflection point rather than merely an opportunity for operational efficiency. The competitive advantage gained by early adopters of the “Why-Not-AI?” approach will compound over time through both direct cost advantages and institutional knowledge development.
Organizations that build AI fluency systematically will outperform those that implement it sporadically. For cross-border businesses specifically, AI represents more than incremental improvement. It offers solutions to their operational challenges. By simultaneously addressing language barriers, regulatory differences, and documentation complexity, AI can eliminate multiple layers of traditional inefficiency in a single implementation. The question becomes not whether to implement AI but how comprehensively and systematically to do so.
The “Why-Not-AI?” framework necessitates reimagining educational institutions’ curriculum design and delivery methods. The skills that will prepare students for tomorrow’s workforce transcend traditional technical training to include applied AI fluency across disciplines.
This means introducing AI interaction across university curricula, not just in technical programs but throughout business, logistics, supply chain, international trade, and other professional disciplines. This ensures graduates are prepared for workplaces where reflexive AI usage is expected. The most effective educational approaches will integrate AI tools into existing coursework rather than treating them as separate technical electives, mirroring how they are integrated into workplace processes.
This transition presents an unprecedented opportunity for border economies to overcome disadvantages while creating new competitive foundations. Rather than just improving existing processes, AI can fundamentally redefine cross-border commerce by eliminating many traditional obstacles that have limited regional development.
Companies that effectively implement AI to streamline these processes can gain an advantage in regional trade. The challenge, however, comes from the accelerated pace of change; border communities that traditionally rely on logistics and processing advantages must now evolve toward knowledge-based competitive advantages. Building this regional AI fluency requires coordinated action among businesses, educational institutions, and economic development organizations.
These stakeholder implications suggest that the “Why-Not-AI?” test is more than a corporate decision-making tool. It’s a lens through which to view regional economic evolution in cross-border contexts.
With these stakeholder implications in mind, let’s examine how organizations can implement this approach in practice.
A Practical Path Forward
Implementing the “Why-Not-AI?” test isn’t a binary switch but a gradual process that reshapes organizational thinking and capabilities. I offer a practical three-phase roadmap for cross-border businesses considering this approach, building on lessons from early adopters like Shopify and Duolingo.
Phase 1: Strategic Assessment and Opportunity Mapping
The process begins with a systematic assessment phase. Rather than announcing a broad AI mandate, examine specific workflows where AI can create immediate value. A customs broker might begin by identifying documentation processes where errors frequently occur, or a logistics provider might focus on route optimization challenges.
The key is having department leaders identify their team’s most time-consuming or error-prone processes, evaluating each for AI enhancement potential while preserving areas where human judgment adds unique value. This targeted, data-driven approach creates a solid foundation for implementation priorities and sets realistic expectations about what AI can and cannot accomplish in your specific context.
Phase 2: Cross-Functional Integration Teams
This assessment naturally leads to the second phase: creating cross-functional innovation teams that pair technical expertise with operational knowledge. Duolingo’s success with AI-generated content didn’t come from technical specialists working in isolation but from close collaboration between language experts and AI developers. Similarly, a freight forwarder implementing AI documentation processing needs both technical AI capability and a deep understanding of customs requirements.
The effectiveness of these cross-functional teams comes from their ability to bridge business process expertise with technical implementation skills. When operational specialists who understand business needs collaborate directly with AI implementation experts, they develop solutions that address real-world complexities while leveraging AI capabilities appropriately. This collaborative approach ensures solutions remain grounded in business reality rather than theoretical potential.
Phase 3: AI Fluency Metrics and Incentive Alignment
The final phase involves aligning metrics and incentives with AI-enhanced productivity. Shopify’s approach of incorporating AI usage into performance reviews represents one method, but the broader principle consists of recognizing and rewarding AI fluency development. This might include creating recognition programs for AI implementation successes or incorporating AI fluency development into professional advancement criteria.
Organizations that successfully implement this phase typically observe both accelerated adoption rates and the organic emergence of AI talent from unexpected places in the organization. By explicitly valuing and rewarding AI fluency development, companies create virtuous cycles where improved capabilities lead to better outcomes, which in turn drive further adoption. These reinforcing feedback loops are critical for sustaining momentum beyond initial implementation.
Throughout this process, the focus should remain on enhancing human capabilities rather than replacing them. The most successful implementations from early adopters show that AI works best when it handles routine aspects of work, creating space for human creativity, judgment, and expertise to focus on higher-value activities. This human-centered approach to implementation helps address resistance while delivering measurable business outcomes.
The “Why-Not-AI?” Lens
The “Why-Not-AI?” test I’ve outlined represents more than a tactical approach to resource allocation. It signals a fundamental shift in how businesses conceptualize operations and competitiveness. By reversing the traditional burden of proof, this framework acknowledges that AI capabilities have crossed a threshold where they should be considered the default solution rather than the exception.
This perspective is particularly valuable for cross-border businesses as it addresses many traditional friction points in international operations. From language barriers to regulatory compliance to documentation processing, AI offers solutions that can transform historical challenges into competitive advantages.
The transformation we’re witnessing doesn’t eliminate human expertise. It elevates it. By shifting routine tasks to AI systems, organizations create space for human creativity, judgment, and relationship-building to flourish. The most successful implementations won’t replace people but will redirect human talent toward higher-value activities where creativity and contextual understanding remain essential.
As we move from the experimental to the operational phase of AI integration, the border region faces a decisive moment of transformation. The businesses and professionals who thrive will be those who embrace the “Why-Not-AI?” mindset, systematically evaluate operations through this lens, and build the organizational capabilities to implement AI solutions effectively.
The memos from Lütke and von Ahn may have originated in technology companies far from the border. Still, they signal a transformation that will reshape every industry touched by cross-border trade. The question isn’t whether the “Why-Not-AI?” test will come to your organization. It’s whether you’ll be prepared when it arrives.
Editor’s Note: The above guest column was penned by Dr. Daniel Covarrubias, director of Texas A&M International University’s A.R. Sanchez, Jr. School of Business Texas Center for Economic and Enterprise Development. The column appears in the Rio Grande Guardian International News Service with the permission of the author.
The post Covarrubias: Why‑Not‑AI? A New Litmus Test for Cross-Border Business appeared first on Rio Grande Guardian.
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