Having participated in the July 2026 Community Chat, Information Technology and Project Manager, Andrew Olton, has more thoughts to share on AI and the future of Caribbean competitiveness. In this article, he explores how AI is changing the business landscape and how organisations can better navigate a world that is increasingly becoming AI-driven.

 

For decades, organisations built their strategic position around assets and conditions that competitors could not easily reproduce: scale, knowledge, intellectual property, trusted brands, privileged access to resources, customer information, network effects and advantageous locations. These sources of advantage remain relevant, but AI is changing their economics.

Capabilities that were once expensive are becoming widely available. Small teams can now produce software, research, marketing campaigns, financial models and product concepts that previously required much larger organisations. Knowledge that once resided in specialists can increasingly be accessed through AI systems. Products can be copied faster, services can be personalised more cheaply, and ideas can be tested before an organisation commits substantial capital.

AI therefore creates a paradox. It gives organisations more ways to build advantage, while simultaneously making many advantages easier to imitate.

The strategic question is no longer simply, “Does our organisation use AI?” As AI adoption spreads, access to the technology will become a basic requirement rather than a meaningful differentiator. The more important question is:

What can our organisation do with AI that competitors cannot easily reproduce?

 

The Eight Traditional Sources of Competitive Advantage

One useful framework identifies eight sources of sustainable competitive advantage:

  1. Scale
  2. Intellectual property
  3. Brand loyalty
  4. Locked-up supply
  5. Innovation
  6. Proprietary information
  7. Network effects
  8. Location

AI does not replace this framework. It acts more like a gravitational field, bending each source of advantage into a new shape.

 

1. Scale: From organisational size to scalable intelligence

Historically, scale gave large firms purchasing power, lower unit costs, wider distribution and the ability to spread fixed investments across more customers. A smaller firm can now use cloud infrastructure, generative AI and automated workflows to serve global customers, produce multilingual content and operate continuously with a relatively small workforce. This creates the possibility of scale without size.

The strategic advantage will not necessarily belong to the organisation with the most employees. It may belong to the organisation that can deploy its intelligence across the greatest number of decisions, transactions and customer interactions.

 

2. Intellectual Property: From owning answers to owning methods

Patents, formulas, proprietary processes, software and trade secrets have traditionally protected organisations from imitation. AI complicates this advantage. Generative tools can assist with coding, product design, engineering, research and document production. This reduces the time required to recreate certain features and processes. Intellectual property that is visible through a product may become easier to reverse-engineer or approximate.

The greater opportunity may lie not in owning a single model, but in owning a system for producing superior outcomes. The model itself may be available to everyone. The organisational recipe surrounding it may not be.

 

3. Brand loyalty: Trust becomes more valuable in a synthetic world

As synthetic content becomes cheaper, customers may find it harder to distinguish between credible expertise and polished imitation. This creates a strange new economy: content becomes abundant, while credibility becomes scarce.

A company that uses AI to manipulate customers, obscure decisions or deliver unreliable services may gain a temporary cost advantage while quietly draining its brand equity. Conversely, an organisation that can demonstrate responsible, dependable and human-centred AI may command a trust premium.

The United States National Institute of Standards and Technology1’s AI risk-management guidance similarly emphasises incorporating trustworthiness considerations into the design, deployment and use of AI systems.

 

4. Locked-up supply: New bottlenecks are emerging

In the AI economy, the scarce resource may no longer be a physical commodity. New bottlenecks include:

  • Advanced computing capacity and semiconductors.
  • Reliable and cheap energy.
  • High-quality proprietary data.
  • Specialised technical talent and domain experts.
  • Customer attention and trusted distribution channels.
  • Regulatory approvals and compliance access to sensitive environments.

Organisations may also secure privileged relationships with model providers, cloud platforms, universities, governments or specialist suppliers. The more durable advantage may come from combining scarce inputs with processes and relationships that competitors cannot easily assemble.

 

5. Innovation: Navigating the jagged technological frontier

AI lowers the cost of generating ideas, exploring scenarios, developing prototypes and testing alternatives. It allows organisations to investigate opportunities that were previously too expensive, uncertain or small to justify serious consideration. This is one of AI’s most important strategic effects: it dramatically reduces the cost of being wrong.

A company can test a product concept, simulate customer reactions, develop a basic prototype, analyse a market and compare business models before making a large commitment. Ideas that once required an entire project team and months of work can sometimes be evaluated in days.

Navigating the Frontier: Empirical research (Dell’Acqua et al., 20232) highlights that AI creates a “jagged technological frontier”—vastly boosting performance on tasks within its capability zone while causing silent failures on seemingly simple tasks outside it. Organisations therefore require sharp evaluation mechanisms and human judgment, not merely indiscriminate enthusiasm.

 

6. Proprietary information: Data is not enough

Possessing large quantities of disconnected, outdated or poorly governed data does not automatically create an advantage. The most valuable information may be the proprietary organisational context that general-purpose models do not possess:

  • Why a customer behaved in a particular way.
  • How an experienced employee handles an unusual edge case.
  • Which exceptions occur in an operational process.
  • Why a past project succeeded or failed.
  • Which trade-offs leadership considers acceptable in practice.

In one large customer-support deployment, AI assistance increased average productivity by approximately 15%, with the largest gains among less experienced workers (Brynjolfsson et al., 20253). This illustrates both the value of capturing organisational knowledge and the risk that previously scarce expertise may become widely distributed.

 

7. Network effects: from user networks to learning networks

Traditional network effects arise when a product becomes more valuable as more people use it. AI introduces additional forms of network effects:

  • More users generate more operational feedback.
  • More interactions reveal more edge cases and utility.
  • More developers create broader integration ecosystems.
  • More completed tasks produce richer organisational memory.

The strongest AI-driven businesses develop a flywheel where user interactions yield fine-tuning data, better data improves model outcomes, superior outcomes attract users, and those users trigger further high-value interactions.

 

8. Location: Geography matters differently

AI reduces the importance of location for knowledge work. Organisations can access talent, customers and services across borders. A small enterprise in a developing market can leverage AI to deliver software, research, or advice on a global tier without relocating to a major tech hub.

However, location does not disappear. Its strategic meaning shifts toward physical and regulatory infrastructure:

  • Access to reliable, affordable, and green energy.
  • Data-centre proximity and high-speed digital connectivity.
  • Favourable regulatory frameworks and clear data-sovereignty rules.
  • Proximity to specialised talent, research institutions, and physical supply chains.

AI makes global capabilities accessible while making local context more valuable. The enterprise combining global-tier machine intelligence with deep, culturally grounded local market understanding will routinely outperform both purely local players and disembodied global competitors.

 

The asymmetric shift in organisational risk appetite

AI fundamentally alters the mechanics of risk. Historically, strategic risk was tied to capital commitment—launching a product or entering a market required spending millions upfront before testing demand. AI flips this equation.

  • From downside exposure to exhaustive pre-testing: Low-cost simulation, synthetic testing, and automated prototyping allow organisations to stress-test scenarios before committing capital. The risk appetite for experimentation expands, while the appetite for untested execution vanishes.
  • Inaction as the primary risk: In a landscape driven by compounding learning flywheels, waiting for technology to stabilise carries an existential penalty. Winning firms pivot from risk avoidance (trying to eliminate every error) to risk containment (building guardrails that permit rapid, isolated failures at minimal cost).
  • Governance of autonomous behaviour: As AI moves from generating text to taking actions (e.g., executing trades, adjusting claims, triaging health issues), governance shifts from auditing static inputs to managing model drift, vendor reliance, and algorithmic liability.

 

The new foundation of competitive advantage

AI will make intelligence abundant. It will not make judgment, trust, courage, coordination, empathy, or vision abundant. As organisations buy software from the same vendors, draw from identical model ecosystems, and adopt matching operational playbooks, technological parity will arrive far faster than most executives anticipate. When the underlying tools are equalised, technology ceases to be the differentiator.

The primary engine of competitive advantage shifts directly back to people. The definitive battleground of the AI era will not be technological infrastructure. It will be strategic investment in human capital and modern leadership development.

Commoditised Technology + Unique Human Vision & Leadership = Compounding Moat

 

Vision cannot be outsourced to an algorithm

In this high-velocity landscape, vision is everything. A leader’s ability to see further, faster, and more clearly than the competition determines where the entire enterprise steers its capability. However, leaders who turn to AI to construct their strategy risk falling into a subtle trap: homogenised vision.

Because AI models are trained on historical, publicly available data, an algorithm will naturally suggest statistically probable, consensus-driven strategies. An AI can optimise an existing vector, but it cannot imagine a fundamentally new one. A leader relying on AI for strategic vision will receive a polished, highly logical strategy—that is functionally identical to the vision being generated for their competitors.

The ultimate winners will not be the organisations that spend the most on software or adopt the highest volume of AI tools. They will be the organisations that invest most deeply in their human talent and leadership pipelines—developing leaders who bring genuine, differentiated vision to the table and possess the human judgment required to turn AI’s raw power into an enduring strategic advantage.

 

 

Image credit: Magnific (Magnific)

 

  1. National Institute of Standards and Technology. (2024). Artificial intelligence risk management framework: Generative artificial intelligence profile (NIST AI 600-1). U.S. Department of Commerce ↩︎
  2. Dell’Acqua, F., McFowland, E., III, Mollick, E. R., Lifshitz-Assaf, H., Kellogg, K., Rajendran, S., Krayer, L., Candelon, F., & Lakhani, K. R. (2023). Navigating the jagged technological frontier: Field experimental evidence of the effects of artificial intelligence on knowledge worker productivity and quality. Harvard Business School Working Paper. ↩︎
  3. Brynjolfsson, E., Li, D., & Raymond, L. R. (2025). Generative AI at work. The Quarterly Journal of Economics, 140(2), 889–942 ↩︎