Beyond the Hype: Why AI Alone Won't Secure Competitive Advantage

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Abstract: This article examines the relationship between artificial intelligence and sustainable competitive advantage through an evidence-based strategic lens. While AI technologies promise transformative capabilities, their increasing ubiquity challenges the assumption that AI adoption alone can provide lasting competitive differentiation. Drawing on strategic management theory and emerging market evidence, we analyze why AI is destined to become a competitive necessity rather than advantage as it becomes more accessible and commoditized. The research suggests that sustainable advantage will increasingly derive not from AI technologies themselves, but from the uniquely human capabilities that complement them—creativity, strategic vision, and organizational culture. Organizations seeking lasting differentiation must understand how to integrate AI within a broader strategic framework that leverages distinctly human contributions that resist commoditization.

Artificial intelligence has been heralded as the defining technology of our era, with the potential to reshape entire industries and create unprecedented value. Organizations worldwide are racing to adopt AI capabilities, often driven by the conviction that doing so will deliver sustainable competitive advantage. This perspective has fueled massive investment in AI technologies and talent—global spending on AI is expected to double from 118billionin2022toover118 billion in 2022 to over 118billionin2022toover300 billion by 2026 (International Data Corporation [IDC], 2023). Yet this rush toward AI adoption often overlooks a fundamental principle of competitive strategy: technologies that become universally available cannot, by definition, be a source of sustainable competitive differentiation.

The stakes for organizations are immense. Investments in AI technologies are substantial, requiring not only financial resources but also organizational transformation. The mistaken belief that AI itself will provide lasting competitive advantage risks misallocating these resources while overlooking the true sources of sustainable differentiation in an AI-saturated business landscape. As AI capabilities become increasingly commoditized, understanding what will—and won't—drive long-term competitive advantage becomes an urgent strategic priority.

The AI Competitive Landscape

Defining AI Advantage in Strategic Context

Before examining AI's role in competitive advantage, we must clarify what constitutes sustainable competitive advantage. In strategic management, sustainable competitive advantage refers to a firm's ability to create and capture superior value relative to competitors over an extended period (Barney & Hesterly, 2020). The resource-based view of strategy suggests that to provide sustainable advantage, resources must be valuable, rare, difficult to imitate, and non-substitutable (Barney, 1991). These characteristics define the VRIN framework—Value, Rarity, Inimitability, and Non-substitutability.

AI technologies, despite their transformative potential, increasingly fail the rarity and inimitability tests. As Wingate, Burns, and Barney (2025) argue, "Far from being a source of differentiation, artificial intelligence will be a source of homogenization." While AI will undoubtedly create value, that value will be accessible to all serious market participants, making it impossible for any single organization to derive sustainable advantage from the technology itself.

Prevalence, Drivers, and Distribution of AI Technologies

The trajectory of AI development and adoption reveals three critical trends undermining its potential as a source of sustainable advantage:

1. Algorithmic commoditization: Leading AI models are increasingly available through open-source alternatives or cloud platforms. What began with research papers and proprietary systems has evolved into widely accessible tools. For example, OpenAI's GPT technology, once available only to select partners, can now be accessed by virtually any organization through APIs or open-source alternatives (Zhou et al., 2023).

2. Democratization of talent: The AI talent pool is expanding rapidly as universities worldwide increase their AI education programs. While elite AI researchers remain scarce, the operational skills needed to implement AI solutions are becoming more common. LinkedIn data shows a 74% increase in AI-related job listings filled between 2020-2023 (LinkedIn Economic Graph, 2023).

3. Declining implementation costs: As AI tools mature, implementation costs are decreasing substantially. What once required custom development can now be accomplished with pre-built solutions and low/no-code platforms. According to Gartner (2023), the average cost of enterprise AI implementation declined 32% between 2019 and 2023.

These trends mirror historical patterns seen with other transformative technologies. As Brynjolfsson and McAfee (2022) observe, "Technology that provides competitive advantage today becomes table stakes tomorrow." Personal computers, the internet, and cloud computing all followed similar trajectories—initial advantage for early adopters gave way to universal access and implementation, transforming these technologies from competitive advantages to competitive necessities.

Organizational and Individual Consequences of AI Ubiquity

Organizational Performance Impacts

The widespread adoption of AI technologies will fundamentally reshape organizational performance dynamics, but not in the way many executives expect. Research indicates that AI adoption will drive two primary performance effects:

1. Market-wide productivity gains: As AI becomes ubiquitous, it will drive substantial productivity improvements across entire industries. Accenture Research (2024) estimates that AI technologies could increase labor productivity by up to 40% by 2035, representing a significant economic transformation. However, these gains will be distributed across all serious market participants rather than concentrated among early adopters or technology leaders.

2. Compressed performance distributions: As AI tools standardize certain capabilities, the performance gap between leading and trailing firms may narrow in many operational domains. A study of 3,000 firms across industries found that the performance variance in routine operational processes decreased by 27% as AI automation became widespread (Ransbotham et al., 2022).

The quantified effects of these changes are substantial but largely market-wide rather than firm-specific. McKinsey Global Institute (2023) estimates that AI technologies will add $13 trillion to global economic output by 2030, representing about 1.2% of additional GDP growth annually. However, this value creation will be distributed across economies rather than captured by individual firms through sustainable advantage.

Individual Wellbeing and Stakeholder Impacts

At the individual level, the consequences of AI ubiquity will be profound and mixed:

1. Worker displacement and transition: Routine cognitive tasks will face increasing automation. The World Economic Forum (2023) estimates that 85 million jobs may be displaced by AI by 2025, while 97 million new roles may be created. However, this transition will create significant challenges for workers whose skills become less relevant.

2. Premium on creative and strategic capabilities: As routine tasks become automated, uniquely human capabilities will command increasing value. Deloitte's Human Capital Trends (2023) survey found that 82% of executives believe creative problem-solving and strategic thinking will become more valuable as AI becomes ubiquitous.

3. Psychological impacts of human-machine collaboration: Working alongside increasingly capable AI systems will reshape professional identity and satisfaction. Initial research indicates both positive outcomes (reduced burnout from routine tasks) and challenges (reduced autonomy and sense of accomplishment) (Shestakofsky & Kelkar, 2023).

The distributional impacts will not be uniform across organizations. Those that effectively manage the transition to AI-enhanced work while developing complementary human capabilities will gain advantage not from the AI itself, but from their approach to integrating technology and human contribution.

Evidence-Based Organizational Responses

Focusing on Complementary Organizational Capabilities

The most forward-thinking organizations are focusing not on AI technologies themselves but on building the organizational capabilities that complement and enhance AI's value. These capabilities are more likely to meet the VRIN criteria for sustainable advantage.

Organizations that achieve superior performance in AI-intensive environments typically excel at complementary capabilities rather than technical implementation alone. In a study of 450 AI implementations, firms that paired AI adoption with organizational capability development achieved 3.2 times greater ROI than those focusing primarily on technical excellence (Fountaine et al., 2022).

Effective approaches:

• Develop strong data governance and knowledge management processes

• Cultivate cross-functional collaboration between technical and domain experts

• Create feedback mechanisms to continuously refine AI applications based on frontline insights

• Invest in specialized domain knowledge that contextualizes AI outputs

Goldman Sachs has paired its significant investment in AI trading systems with substantial investments in unique data sources and specialized financial expertise. While competitors have similar algorithmic capabilities, Goldman's advantage comes from its proprietary data relationships and domain-specific knowledge that contextualize AI outputs. This combination has helped the firm maintain a leadership position in automated trading despite widespread access to similar technical capabilities (Economist, 2023).

Cultivating Distinctively Human Capabilities

As AI capabilities expand, organizations are increasingly recognizing that their most sustainable advantages lie in capabilities that remain distinctively human.

Deloitte's study of 2,500 organizations found that those achieving the greatest value from AI focused explicitly on enhancing human capabilities alongside technical implementation. These organizations reported 2.6x greater financial performance compared to those focusing primarily on automation and cost reduction (Schwartz et al., 2022).

Effective approaches:

• Invest in training programs that enhance creative problem-solving and critical thinking

• Develop human-centered design capabilities that ensure AI serves authentic human needs

• Create organizational structures that enable rapid human judgment and decision-making based on AI-generated insights

• Build emotional intelligence and narrative skills to translate AI-generated insights into compelling action plans

Pixar Animation Studios exemplifies this approach by using AI tools to automate technical aspects of animation while focusing their creative talent on storytelling, character development, and emotional resonance. While competitors have access to similar rendering and animation technologies, Pixar's sustainable advantage comes from its uniquely human creative culture and storytelling capabilities. The studio has maintained its leadership position by systematically developing these human capabilities rather than competing primarily on technical innovation (Catmull & Wallace, 2022).

Developing Distinctive Integration Architectures

Leading organizations recognize that sustainable advantage comes not from AI itself but from unique ways of integrating AI into their broader operating models and value propositions.

Research by MIT Sloan Management Review and Boston Consulting Group found that organizations achieving the greatest business impact from AI developed proprietary integration architectures that were difficult for competitors to replicate. These architectures combined technology, processes, and human capabilities in organization-specific ways (Ransbotham et al., 2023).

Effective approaches:

• Create modular AI systems that can be rapidly reconfigured to address emerging opportunities

• Develop organization-specific workflows that combine AI and human inputs in unique ways

• Build integration layers that connect AI capabilities to proprietary data and domain knowledge

• Design human-AI interaction patterns that reflect the organization's unique strategic positioning

Mayo Clinic has developed a distinctive approach to integrating AI into clinical care that reflects its unique organizational capabilities and mission. Rather than simply implementing off-the-shelf AI diagnostic tools, Mayo developed an integration architecture that combines AI insights with their unparalleled repository of clinical data and physician expertise. This architecture ensures that AI augments rather than replaces physician judgment and is embedded within Mayo's distinctive collaborative care model. The result is a sustainable advantage based not on the AI technology itself, which competitors can access, but on Mayo's unique integration approach (Mayo Clinic, 2023).

Building Trust-Based Relationships

As AI becomes ubiquitous, the trust relationships organizations build with stakeholders will become increasingly important sources of sustainable advantage.

Edelman's special report on AI and Trust (2024) found that 68% of consumers are concerned about AI use by organizations, and 73% report that an organization's transparency about AI use significantly influences their trust and loyalty. Organizations that proactively build trust in their AI use report 28% higher customer retention compared to those taking a more opaque approach.

Effective approaches:

• Develop clear communication about how and when AI is used in customer interactions

• Create transparent data use policies that give stakeholders control and visibility

• Establish ethics review processes for AI applications that may impact stakeholder wellbeing

• Build feedback mechanisms that incorporate stakeholder concerns into AI development

Spotify has made its recommendation algorithms a core part of its user experience while building trust through transparency and user control. The company provides clear explanations of how its AI systems generate recommendations, allows users to see and edit the data used in these systems, and maintains human curation alongside algorithmic recommendations. This trust-based approach has helped Spotify maintain higher user retention rates compared to competitors with similar technical capabilities, demonstrating how trust relationships can provide sustainable advantage even as the underlying technologies become commoditized (Spotify, 2023).

Building Long-Term Strategic Resilience

Focusing on Purpose and Values Alignment

As AI capabilities become ubiquitous, organizational purpose and values will become increasingly important sources of differentiation and advantage.

Organizations with clearly articulated purposes that transcend profit maximization are better positioned to attract and retain talent, build stakeholder trust, and make consistent strategic choices about AI implementation. Research indicates that purpose-driven organizations experience 40% higher employee retention and 30% higher levels of innovation (Gartner, 2022).

The most successful organizations view AI not as a strategy in itself but as a tool for advancing their fundamental purpose. IKEA's application of AI reflects its purpose of "creating a better everyday life for the many people." The company uses AI to reduce waste in its supply chain, improve sustainability outcomes, and make home design more accessible to customers with diverse needs and budgets. This purpose-aligned approach guides IKEA's AI investments toward capabilities that reinforce rather than dilute its distinctive market position (IKEA Group, 2023).

Developing Dynamic Human-AI Collaboration Models

The most durable sources of advantage will come from organizations that develop unique models of human-AI collaboration that evolve as technologies mature.

Static approaches to AI implementation—whether focused on automation or augmentation—will become increasingly vulnerable as technologies evolve. Organizations that develop dynamic collaboration models that continuously redefine the boundary between human and machine work will maintain advantage as the technological landscape shifts.

Research by Daugherty and Wilson (2023) found that organizations with flexible human-AI collaboration models achieved 38% higher innovation output and adapted 2.4 times faster to technological changes compared to those with more rigid automation strategies.

This approach requires developing what Brynjolfsson and McAfee (2022) call "recombinant capabilities"—the ability to rapidly reconfigure the relationship between human and machine contributions as both technologies and strategies evolve.

Cultivating Organizational Learning Ecosystems

In a business landscape where AI technologies are universally available, the ability to learn faster than competitors becomes a primary source of sustainable advantage.

Organizations that create effective learning ecosystems can more rapidly translate AI-generated insights into strategic action, more quickly adapt their implementation approaches based on outcomes, and more effectively integrate emerging AI capabilities into their operations.

Research by Edmondson and Reynolds (2022) demonstrates that organizations with strong learning capabilities capture 3.5 times more value from new technologies compared to those with weak learning capabilities, even when both have access to the same technical resources.

Microsoft exemplifies this approach through its integrated learning ecosystem that connects AI research, product development, and customer implementation teams. While Microsoft's AI technologies themselves are available to competitors (either directly or through alternative vendors), the company's advantage comes from its ability to rapidly learn from both successes and failures across its vast ecosystem and translate those learnings into improved products and implementation approaches (Microsoft, 2023).

Conclusion

Artificial intelligence represents a transformative technology that will reshape the competitive landscape across industries. However, as AI capabilities become increasingly accessible and commoditized, they will cease to be sources of sustainable competitive advantage. The pattern follows a well-established trajectory: what begins as a distinctive capability for early adopters or technology leaders inevitably becomes a competitive necessity available to all serious market participants.

Organizations seeking sustainable advantage in an AI-saturated business environment should focus on developing complementary capabilities that are valuable, rare, difficult to imitate, and non-substitutable. These include:

1. Distinctively human capabilities like creativity, emotional intelligence, and ethical judgment that resist automation and commoditization

2. Organizational architectures that uniquely integrate AI capabilities with proprietary data, domain knowledge, and human expertise

3. Trust-based relationships with stakeholders that survive and thrive in an environment of technological parity

4. Organizational learning ecosystems that enable more rapid adaptation as AI capabilities evolve

5. Purpose-driven approaches to AI implementation that reinforce rather than dilute distinctive strategic positioning

The future of competitive advantage lies not in AI technologies themselves but in the uniquely human capabilities that complement them. By focusing on these complementary capabilities, organizations can build sustainable advantage even as the AI technologies themselves become universally available.

References

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Jonathan H. Westover, PhD is Chief Academic & Learning Officer (HCI Academy); Associate Dean and Director of HR Programs (WGU); Professor, Organizational Leadership (UVU); OD/HR/Leadership Consultant (Human Capital Innovations). Read Jonathan Westover's executive profile here.

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Suggested Citation: Westover, J. H. (2025). Beyond the Hype: Why AI Alone Won't Secure Competitive Advantage. Human Capital Leadership Review, 26(2). doi.org/10.70175/hclreview.2020.26.2.7